Original Research

ABSTRACT

Delayed unions and nonunions of the scaphoid are most often treated by open reduction and internal fixation with bone grafting. We sought to evaluate a large consecutive series of nondisplaced or minimally displaced scaphoid nonunions and delayed unions treated by a compression screw without bone grafting by 2 fellowship trained hand surgeons. A total of 23 patients (19 males, 4 females) were identified who had fractures located at the distal third (2), the waist (18), and the proximal third (3). Of the 23 patients, 19 had a complete follow-up (mean follow-up period, 5.2 months) with evidence of radiographic union. There were no radiographic signs of arthrosis, osteonecrosis of the scaphoid, hardware-related complications, or reported revision surgeries. In conclusion, nonunions and delayed unions in nondisplaced or minimally displaced scaphoids without carpal malalignment can be successfully treated using compression screw fixation without bone grafting.

Continued to: Scaphoid nonunions or delayed unions with displacement...

Scaphoid nonunions or delayed unions with displacement, humpback deformities, or dorsal intercalated segmental instability deformities require open exposure with reduction of the fracture and autogenous bone grafting (structural or nonstructural and vascularized or nonvascularized).^1,2 However, in the absence of displacement or deformity, compression and internal fixation without bone grafting may be sufficient to achieve union.

Several reports have described the use of internal fixation alone in the management of scaphoid nonunions with both minimal and extensive bone loss.^3-7 These studies have shown that screw fixation alone affords less morbidity to the patient while allowing high rates of union.

Previous reports of internal fixation alone included limited numbers of patients for review. Therefore, we aim to review a large consecutive series of scaphoid delayed unions and nonunions without osteonecrosis or deformity managed by only internal fixation. Our hypothesis is that drilling combined with compression and rigid stabilization would allow for bony union in these cases

MATERIALS AND METHODS

After Institutional Review Board approval was obtained, a retrospective review of prospectively collected data was performed on consecutive patients with a delayed union or nonunion of the scaphoid. All injuries had failed conservative treatment of casting for at least 12 weeks and ultrasound stimulation, and were subsequently treated by compression screw fixation by 1 of 2 fellowship trained hand surgeons. The database comprised the data of patients who presented to a single, Level 1 trauma center between 2000 and 2012.  

Delayed unions and nonunions were defined as a lack of radiographic trabecular bridging and pain on clinical examination at 3 and 6 months, respectively. All fractures were nondisplaced or minimally displaced (<2 mm), and patients with carpal malalignment or humpback deformity (based on scapholunate angle on plain radiographs) were excluded. Clinical outcome measures included evidence of radiographic union, revision surgery, pain, and reported complications.

Continue to: Inclusion criteria were all patients who sustained...

Inclusion criteria were all patients who sustained a minimally displaced scaphoid fracture and were treated conservatively with casting for at least 12 weeks and ultrasound stimulation, and progressed to delayed unions or nonunions.

Patients younger than age 18 years or with radiographic evidence of arthrosis or humpback deformity were excluded. Any fracture with >2 mm of gapping on original injury radiographs was not considered as minimally displaced and was also excluded. Furthermore, patients with a previous ipsilateral scaphoid injury or hand surgery were also excluded.

Compression screw placement was recorded as being either central or eccentric based on Trumble and colleagues’⁸ criteria. Posteroanterior (PA), lateral, and scaphoid view radiographs were reviewed by the first author (DS) and the treating hand surgeon (AS). Central screw placement was substantiated if the screw was in the middle third of the proximal pole in all 3 views.

The final set of postoperative radiographs was reviewed for unions. Union was defined as bridging trabeculation with near or complete obliteration of the fracture line on PA, lateral, and scaphoid radiographic views. Computerized tomography (CT) was performed at the discretion of the treating surgeon, and its use was not required if there was near obliteration of the fracture line on the 3-view radiographs and in the absence of patient-reported pain. Patients with bone loss or sclerosis were included as long as no deformity existed.

After surgical intervention, a short-arm cast was applied for 6 weeks, followed by a wrist splint for 4 to 8 weeks depending on patient comfort.

Continue to: SURGICAL TECHNIQUE...

SURGICAL TECHNIQUE

Either a 1-cm to 2-cm transverse incision distal to Lister’s tubercle or a longitudinal incision just ulnar was utilized. The extensor pollicis longus was identified and retracted. A longitudinal or an L-shaped capsulotomy was made to identify the proximal pole of the scaphoid. With the wrist flexed, a guide wire was inserted down the central axis of the scaphoid and confirmed by fluoroscopy. The measurement was made off the guidewire and 4 to 6 mm was subtracted. The scaphoid was then drilled, and the variable pitch compression screw (Acutrak Headless Compression Screw, Acumed) was inserted. Compression and position of the screw were confirmed by fluoroscopy before closure.

RESULTS

A total of 23 patients (19 males, 4 females) with acute scaphoid fractures who were treated conservatively with casting for at least 12 weeks and ultrasound stimulation, and progressed to delayed unions or nonunions were identified in this study. The ages of the patients ranged from 19 to 50 years. Of the 23 patients, 6 were smokers. The majority of patients sustained fractures in the scaphoid waist (18 patients) (Figure 1). Two patients had distal third fractures, and 3 had proximal third fractures.

The average time from the sustained injury to the surgical intervention was 8.2 months (range, 3.1-27.6 months). There were no patients with delayed diagnoses. Three fractures were identified as delayed unions with failure of union and pain after 3 months of conservative treatment, whereas the other 20 were identified as nonunions with at least 6 months of failed conservative treatment.

Of the 23 patients, 21 were found to have centrally placed variable compression screws based on Trumble and colleagues’⁸ criteria. Of the 23 patients, 19 had a complete follow-up course with radiographs at 6 months after surgery. All of these 19 patients had evidence of radiographic union defined as bridging trabeculation with near or complete obliteration of the fracture line on PA, lateral, and scaphoid radiographic views (Figure 2). Of the 6 smokers, 5 progressed to radiographic union and 1 patient had <6 months of postoperative return visits and could not be contacted. At the final clinic visit, all of the 19 patients denied wrist pain on direct palpation over the scaphoid tubercle, and no complications were reported. There were no repeat or revision surgical interventions.

Four patients had limited follow-up with <6 months of postoperative return visits. Their final set of radiographs did not demonstrate complete bridging trabeculation. One patient who moved away from the area was lost to follow-up but was contacted. The patient stated that he had a pain-free wrist with no further surgical interventions on his scaphoid. The other 3 patients could not be contacted.

DISCUSSION

The management of scaphoid nonunions and delayed unions has dramatically evolved over the past 20 years.^1,3-8 Historically, semi-rigid stabilization using Kirschner wires and casting afforded a 77% union rate in these cases.⁹ More recently, several authors have reported that stabilization without bone grafting can predictably unite scaphoid nonunions. Treating patients with uncomplicated scaphoid nonunions and delayed unions by internal fixation alone may be all that is required to achieve union.

The definitions of a scaphoid nonunion and delayed union are complex. The exact time when a scaphoid fracture heals varies between patients.^2,5,10 However, the majority of hand surgeons believe that failure to see clear signs of healing (in waist fractures) after 3 months from the injury would suggest a failure to heal and a “delayed” union, whereas failure after 6 months from the injury and without clear signs of healing indicate a nonunion.^5,6,10,11 Any resorption at the fracture site suggests that the fracture will not heal by continued immobilization alone and will require surgery.¹⁰

Continue to: Hand surgeons have several surgical options...

Hand surgeons have several surgical options when managing scaphoid injuries. Mahmoud and Koptan⁴ used a volar approach to percutaneously deliver a headless compression screw into 27 nonunions. Postoperative CT scans demonstrated fracture union in all 27 patients, and no patient underwent revision surgery. Interestingly, 14 of their patients had extensive preoperative resorption (but no deformity) of >5 mm.

Although volar percutaneous approaches for internal fixation have been cited to provide high rates of union and high patient satisfaction in acute scaphoid fracture fixation, this study utilized a dorsal approach. Both Wozasek and Moser¹² and Haddad and Goddard¹³ reported excellent results and high union rates using a volar approach in consecutive acute scaphoid fractures. Despite these results, there are concerns that using a volar approach may damage the scaphotrapezial joint and may be prone to eccentric placement of compression screws.^8,14

Slade and colleagues³ did utilize the dorsal approach with arthroscopic assistance to deliver a compression screw into scaphoid nonunions in 15 consecutive patients without any evidence of deformity, sclerosis, or resorption. Similar to our investigation, they treated patients with both delayed unions and nonunions. CT scans were used to confirm unions in all their patients. Using a dorsal approach, Yassaee and Yang¹⁵ treated 9 consecutive patients using a compression screw without bone grafting for both delayed and nonunion scaphoid injuries. Other authors have used both volar and dorsal approaches in 12 consecutive delayed and nonunion scaphoid injuries and found that 11 of the 12 injuries progressed to unions.⁶

Although these authors and several others advocate the use of CT scans to assess unions, our investigation used bridging trabeculation obliteration of the fracture line on 3 standard radiographic views to confirm unions in addition to the absence of pain clinically.^16,17 CT scans expose the patient to increased radiation that, in our experience, does not alter the postoperative clinical course.¹⁸ If there is clear evidence of bridged callus and no pain on physical examination, a CT scan performed to reconfirm the union affords little benefit to clinical management.¹⁹

Continue to: All these previous studies have demonstrated...

All these previous studies have demonstrated excellent union rates but using a limited series of patients. We reviewed a large number of consecutive patients with scaphoid delayed unions and nonunions treated by screw fixation without bone grafting. Our hospital is a safety net institution for a large urban catchment area and had complete radiographic and clinical data for 19 of our 23 patients. One patient was contacted by telephone and he reported no pain and no revision surgical interventions.

The limitations of this study include not only its retrospective design but also its limited secondary outcome measures. However, our primary outcomes of union, pain, and complications are of utmost importance to clinicians and patients alike. Similar to other authors, we used radiographs to confirm unions. Although bridging trabeculation in radiographs has been demonstrated as soon as 1 month after the injury, there may be problems with interobserver reliability.^{4,13,15,20,21}

Patients being lost to follow-up is not uncommon in the orthopedic trauma literature and can influence results.^22,23 It is speculative to infer that the 3 patients who did not complete a follow-up course did not return because their pain had mitigated.

CONCLUSION 

Like several fractures, the lack of stability and the absence of micro-motion are believed to contribute to fibrous nonunions in scaphoid fractures.¹³ This study provides a large consecutive cohort of patients with minimally displaced scaphoid delayed unions and nonunions that were successfully treated by rigid internal fixation without bone grafting. These results confirm previous reports that bone grafting is not required to provide predictable unions for the majority of scaphoid nonunions.

This paper will be judged for the Resident Writer’s Award.

ABSTRACT

Delayed unions and nonunions of the scaphoid are most often treated by open reduction and internal fixation with bone grafting. We sought to evaluate a large consecutive series of nondisplaced or minimally displaced scaphoid nonunions and delayed unions treated by a compression screw without bone grafting by 2 fellowship trained hand surgeons. A total of 23 patients (19 males, 4 females) were identified who had fractures located at the distal third (2), the waist (18), and the proximal third (3). Of the 23 patients, 19 had a complete follow-up (mean follow-up period, 5.2 months) with evidence of radiographic union. There were no radiographic signs of arthrosis, osteonecrosis of the scaphoid, hardware-related complications, or reported revision surgeries. In conclusion, nonunions and delayed unions in nondisplaced or minimally displaced scaphoids without carpal malalignment can be successfully treated using compression screw fixation without bone grafting.

Continued to: Scaphoid nonunions or delayed unions with displacement...

Scaphoid nonunions or delayed unions with displacement, humpback deformities, or dorsal intercalated segmental instability deformities require open exposure with reduction of the fracture and autogenous bone grafting (structural or nonstructural and vascularized or nonvascularized).^1,2 However, in the absence of displacement or deformity, compression and internal fixation without bone grafting may be sufficient to achieve union.

Several reports have described the use of internal fixation alone in the management of scaphoid nonunions with both minimal and extensive bone loss.^3-7 These studies have shown that screw fixation alone affords less morbidity to the patient while allowing high rates of union.

Previous reports of internal fixation alone included limited numbers of patients for review. Therefore, we aim to review a large consecutive series of scaphoid delayed unions and nonunions without osteonecrosis or deformity managed by only internal fixation. Our hypothesis is that drilling combined with compression and rigid stabilization would allow for bony union in these cases

MATERIALS AND METHODS

After Institutional Review Board approval was obtained, a retrospective review of prospectively collected data was performed on consecutive patients with a delayed union or nonunion of the scaphoid. All injuries had failed conservative treatment of casting for at least 12 weeks and ultrasound stimulation, and were subsequently treated by compression screw fixation by 1 of 2 fellowship trained hand surgeons. The database comprised the data of patients who presented to a single, Level 1 trauma center between 2000 and 2012.  

Delayed unions and nonunions were defined as a lack of radiographic trabecular bridging and pain on clinical examination at 3 and 6 months, respectively. All fractures were nondisplaced or minimally displaced (<2 mm), and patients with carpal malalignment or humpback deformity (based on scapholunate angle on plain radiographs) were excluded. Clinical outcome measures included evidence of radiographic union, revision surgery, pain, and reported complications.

Continue to: Inclusion criteria were all patients who sustained...

Inclusion criteria were all patients who sustained a minimally displaced scaphoid fracture and were treated conservatively with casting for at least 12 weeks and ultrasound stimulation, and progressed to delayed unions or nonunions.

Patients younger than age 18 years or with radiographic evidence of arthrosis or humpback deformity were excluded. Any fracture with >2 mm of gapping on original injury radiographs was not considered as minimally displaced and was also excluded. Furthermore, patients with a previous ipsilateral scaphoid injury or hand surgery were also excluded.

Compression screw placement was recorded as being either central or eccentric based on Trumble and colleagues’⁸ criteria. Posteroanterior (PA), lateral, and scaphoid view radiographs were reviewed by the first author (DS) and the treating hand surgeon (AS). Central screw placement was substantiated if the screw was in the middle third of the proximal pole in all 3 views.

The final set of postoperative radiographs was reviewed for unions. Union was defined as bridging trabeculation with near or complete obliteration of the fracture line on PA, lateral, and scaphoid radiographic views. Computerized tomography (CT) was performed at the discretion of the treating surgeon, and its use was not required if there was near obliteration of the fracture line on the 3-view radiographs and in the absence of patient-reported pain. Patients with bone loss or sclerosis were included as long as no deformity existed.

After surgical intervention, a short-arm cast was applied for 6 weeks, followed by a wrist splint for 4 to 8 weeks depending on patient comfort.

Continue to: SURGICAL TECHNIQUE...

SURGICAL TECHNIQUE

Either a 1-cm to 2-cm transverse incision distal to Lister’s tubercle or a longitudinal incision just ulnar was utilized. The extensor pollicis longus was identified and retracted. A longitudinal or an L-shaped capsulotomy was made to identify the proximal pole of the scaphoid. With the wrist flexed, a guide wire was inserted down the central axis of the scaphoid and confirmed by fluoroscopy. The measurement was made off the guidewire and 4 to 6 mm was subtracted. The scaphoid was then drilled, and the variable pitch compression screw (Acutrak Headless Compression Screw, Acumed) was inserted. Compression and position of the screw were confirmed by fluoroscopy before closure.

RESULTS

A total of 23 patients (19 males, 4 females) with acute scaphoid fractures who were treated conservatively with casting for at least 12 weeks and ultrasound stimulation, and progressed to delayed unions or nonunions were identified in this study. The ages of the patients ranged from 19 to 50 years. Of the 23 patients, 6 were smokers. The majority of patients sustained fractures in the scaphoid waist (18 patients) (Figure 1). Two patients had distal third fractures, and 3 had proximal third fractures.

The average time from the sustained injury to the surgical intervention was 8.2 months (range, 3.1-27.6 months). There were no patients with delayed diagnoses. Three fractures were identified as delayed unions with failure of union and pain after 3 months of conservative treatment, whereas the other 20 were identified as nonunions with at least 6 months of failed conservative treatment.

Of the 23 patients, 21 were found to have centrally placed variable compression screws based on Trumble and colleagues’⁸ criteria. Of the 23 patients, 19 had a complete follow-up course with radiographs at 6 months after surgery. All of these 19 patients had evidence of radiographic union defined as bridging trabeculation with near or complete obliteration of the fracture line on PA, lateral, and scaphoid radiographic views (Figure 2). Of the 6 smokers, 5 progressed to radiographic union and 1 patient had <6 months of postoperative return visits and could not be contacted. At the final clinic visit, all of the 19 patients denied wrist pain on direct palpation over the scaphoid tubercle, and no complications were reported. There were no repeat or revision surgical interventions.

Four patients had limited follow-up with <6 months of postoperative return visits. Their final set of radiographs did not demonstrate complete bridging trabeculation. One patient who moved away from the area was lost to follow-up but was contacted. The patient stated that he had a pain-free wrist with no further surgical interventions on his scaphoid. The other 3 patients could not be contacted.

DISCUSSION

The management of scaphoid nonunions and delayed unions has dramatically evolved over the past 20 years.^1,3-8 Historically, semi-rigid stabilization using Kirschner wires and casting afforded a 77% union rate in these cases.⁹ More recently, several authors have reported that stabilization without bone grafting can predictably unite scaphoid nonunions. Treating patients with uncomplicated scaphoid nonunions and delayed unions by internal fixation alone may be all that is required to achieve union.

The definitions of a scaphoid nonunion and delayed union are complex. The exact time when a scaphoid fracture heals varies between patients.^2,5,10 However, the majority of hand surgeons believe that failure to see clear signs of healing (in waist fractures) after 3 months from the injury would suggest a failure to heal and a “delayed” union, whereas failure after 6 months from the injury and without clear signs of healing indicate a nonunion.^5,6,10,11 Any resorption at the fracture site suggests that the fracture will not heal by continued immobilization alone and will require surgery.¹⁰

Continue to: Hand surgeons have several surgical options...

Hand surgeons have several surgical options when managing scaphoid injuries. Mahmoud and Koptan⁴ used a volar approach to percutaneously deliver a headless compression screw into 27 nonunions. Postoperative CT scans demonstrated fracture union in all 27 patients, and no patient underwent revision surgery. Interestingly, 14 of their patients had extensive preoperative resorption (but no deformity) of >5 mm.

Although volar percutaneous approaches for internal fixation have been cited to provide high rates of union and high patient satisfaction in acute scaphoid fracture fixation, this study utilized a dorsal approach. Both Wozasek and Moser¹² and Haddad and Goddard¹³ reported excellent results and high union rates using a volar approach in consecutive acute scaphoid fractures. Despite these results, there are concerns that using a volar approach may damage the scaphotrapezial joint and may be prone to eccentric placement of compression screws.^8,14

Slade and colleagues³ did utilize the dorsal approach with arthroscopic assistance to deliver a compression screw into scaphoid nonunions in 15 consecutive patients without any evidence of deformity, sclerosis, or resorption. Similar to our investigation, they treated patients with both delayed unions and nonunions. CT scans were used to confirm unions in all their patients. Using a dorsal approach, Yassaee and Yang¹⁵ treated 9 consecutive patients using a compression screw without bone grafting for both delayed and nonunion scaphoid injuries. Other authors have used both volar and dorsal approaches in 12 consecutive delayed and nonunion scaphoid injuries and found that 11 of the 12 injuries progressed to unions.⁶

Although these authors and several others advocate the use of CT scans to assess unions, our investigation used bridging trabeculation obliteration of the fracture line on 3 standard radiographic views to confirm unions in addition to the absence of pain clinically.^16,17 CT scans expose the patient to increased radiation that, in our experience, does not alter the postoperative clinical course.¹⁸ If there is clear evidence of bridged callus and no pain on physical examination, a CT scan performed to reconfirm the union affords little benefit to clinical management.¹⁹

Continue to: All these previous studies have demonstrated...

All these previous studies have demonstrated excellent union rates but using a limited series of patients. We reviewed a large number of consecutive patients with scaphoid delayed unions and nonunions treated by screw fixation without bone grafting. Our hospital is a safety net institution for a large urban catchment area and had complete radiographic and clinical data for 19 of our 23 patients. One patient was contacted by telephone and he reported no pain and no revision surgical interventions.

The limitations of this study include not only its retrospective design but also its limited secondary outcome measures. However, our primary outcomes of union, pain, and complications are of utmost importance to clinicians and patients alike. Similar to other authors, we used radiographs to confirm unions. Although bridging trabeculation in radiographs has been demonstrated as soon as 1 month after the injury, there may be problems with interobserver reliability.^{4,13,15,20,21}

Patients being lost to follow-up is not uncommon in the orthopedic trauma literature and can influence results.^22,23 It is speculative to infer that the 3 patients who did not complete a follow-up course did not return because their pain had mitigated.

CONCLUSION 

Like several fractures, the lack of stability and the absence of micro-motion are believed to contribute to fibrous nonunions in scaphoid fractures.¹³ This study provides a large consecutive cohort of patients with minimally displaced scaphoid delayed unions and nonunions that were successfully treated by rigid internal fixation without bone grafting. These results confirm previous reports that bone grafting is not required to provide predictable unions for the majority of scaphoid nonunions.

This paper will be judged for the Resident Writer’s Award.

ABSTRACT

Delayed unions and nonunions of the scaphoid are most often treated by open reduction and internal fixation with bone grafting. We sought to evaluate a large consecutive series of nondisplaced or minimally displaced scaphoid nonunions and delayed unions treated by a compression screw without bone grafting by 2 fellowship trained hand surgeons. A total of 23 patients (19 males, 4 females) were identified who had fractures located at the distal third (2), the waist (18), and the proximal third (3). Of the 23 patients, 19 had a complete follow-up (mean follow-up period, 5.2 months) with evidence of radiographic union. There were no radiographic signs of arthrosis, osteonecrosis of the scaphoid, hardware-related complications, or reported revision surgeries. In conclusion, nonunions and delayed unions in nondisplaced or minimally displaced scaphoids without carpal malalignment can be successfully treated using compression screw fixation without bone grafting.

Continued to: Scaphoid nonunions or delayed unions with displacement...

Scaphoid nonunions or delayed unions with displacement, humpback deformities, or dorsal intercalated segmental instability deformities require open exposure with reduction of the fracture and autogenous bone grafting (structural or nonstructural and vascularized or nonvascularized).^1,2 However, in the absence of displacement or deformity, compression and internal fixation without bone grafting may be sufficient to achieve union.

Several reports have described the use of internal fixation alone in the management of scaphoid nonunions with both minimal and extensive bone loss.^3-7 These studies have shown that screw fixation alone affords less morbidity to the patient while allowing high rates of union.

Previous reports of internal fixation alone included limited numbers of patients for review. Therefore, we aim to review a large consecutive series of scaphoid delayed unions and nonunions without osteonecrosis or deformity managed by only internal fixation. Our hypothesis is that drilling combined with compression and rigid stabilization would allow for bony union in these cases

MATERIALS AND METHODS

After Institutional Review Board approval was obtained, a retrospective review of prospectively collected data was performed on consecutive patients with a delayed union or nonunion of the scaphoid. All injuries had failed conservative treatment of casting for at least 12 weeks and ultrasound stimulation, and were subsequently treated by compression screw fixation by 1 of 2 fellowship trained hand surgeons. The database comprised the data of patients who presented to a single, Level 1 trauma center between 2000 and 2012.  

Delayed unions and nonunions were defined as a lack of radiographic trabecular bridging and pain on clinical examination at 3 and 6 months, respectively. All fractures were nondisplaced or minimally displaced (<2 mm), and patients with carpal malalignment or humpback deformity (based on scapholunate angle on plain radiographs) were excluded. Clinical outcome measures included evidence of radiographic union, revision surgery, pain, and reported complications.

Continue to: Inclusion criteria were all patients who sustained...

Inclusion criteria were all patients who sustained a minimally displaced scaphoid fracture and were treated conservatively with casting for at least 12 weeks and ultrasound stimulation, and progressed to delayed unions or nonunions.

Patients younger than age 18 years or with radiographic evidence of arthrosis or humpback deformity were excluded. Any fracture with >2 mm of gapping on original injury radiographs was not considered as minimally displaced and was also excluded. Furthermore, patients with a previous ipsilateral scaphoid injury or hand surgery were also excluded.

Compression screw placement was recorded as being either central or eccentric based on Trumble and colleagues’⁸ criteria. Posteroanterior (PA), lateral, and scaphoid view radiographs were reviewed by the first author (DS) and the treating hand surgeon (AS). Central screw placement was substantiated if the screw was in the middle third of the proximal pole in all 3 views.

The final set of postoperative radiographs was reviewed for unions. Union was defined as bridging trabeculation with near or complete obliteration of the fracture line on PA, lateral, and scaphoid radiographic views. Computerized tomography (CT) was performed at the discretion of the treating surgeon, and its use was not required if there was near obliteration of the fracture line on the 3-view radiographs and in the absence of patient-reported pain. Patients with bone loss or sclerosis were included as long as no deformity existed.

After surgical intervention, a short-arm cast was applied for 6 weeks, followed by a wrist splint for 4 to 8 weeks depending on patient comfort.

Continue to: SURGICAL TECHNIQUE...

SURGICAL TECHNIQUE

Either a 1-cm to 2-cm transverse incision distal to Lister’s tubercle or a longitudinal incision just ulnar was utilized. The extensor pollicis longus was identified and retracted. A longitudinal or an L-shaped capsulotomy was made to identify the proximal pole of the scaphoid. With the wrist flexed, a guide wire was inserted down the central axis of the scaphoid and confirmed by fluoroscopy. The measurement was made off the guidewire and 4 to 6 mm was subtracted. The scaphoid was then drilled, and the variable pitch compression screw (Acutrak Headless Compression Screw, Acumed) was inserted. Compression and position of the screw were confirmed by fluoroscopy before closure.

RESULTS

A total of 23 patients (19 males, 4 females) with acute scaphoid fractures who were treated conservatively with casting for at least 12 weeks and ultrasound stimulation, and progressed to delayed unions or nonunions were identified in this study. The ages of the patients ranged from 19 to 50 years. Of the 23 patients, 6 were smokers. The majority of patients sustained fractures in the scaphoid waist (18 patients) (Figure 1). Two patients had distal third fractures, and 3 had proximal third fractures.

The average time from the sustained injury to the surgical intervention was 8.2 months (range, 3.1-27.6 months). There were no patients with delayed diagnoses. Three fractures were identified as delayed unions with failure of union and pain after 3 months of conservative treatment, whereas the other 20 were identified as nonunions with at least 6 months of failed conservative treatment.

Of the 23 patients, 21 were found to have centrally placed variable compression screws based on Trumble and colleagues’⁸ criteria. Of the 23 patients, 19 had a complete follow-up course with radiographs at 6 months after surgery. All of these 19 patients had evidence of radiographic union defined as bridging trabeculation with near or complete obliteration of the fracture line on PA, lateral, and scaphoid radiographic views (Figure 2). Of the 6 smokers, 5 progressed to radiographic union and 1 patient had <6 months of postoperative return visits and could not be contacted. At the final clinic visit, all of the 19 patients denied wrist pain on direct palpation over the scaphoid tubercle, and no complications were reported. There were no repeat or revision surgical interventions.

Four patients had limited follow-up with <6 months of postoperative return visits. Their final set of radiographs did not demonstrate complete bridging trabeculation. One patient who moved away from the area was lost to follow-up but was contacted. The patient stated that he had a pain-free wrist with no further surgical interventions on his scaphoid. The other 3 patients could not be contacted.

DISCUSSION

The management of scaphoid nonunions and delayed unions has dramatically evolved over the past 20 years.^1,3-8 Historically, semi-rigid stabilization using Kirschner wires and casting afforded a 77% union rate in these cases.⁹ More recently, several authors have reported that stabilization without bone grafting can predictably unite scaphoid nonunions. Treating patients with uncomplicated scaphoid nonunions and delayed unions by internal fixation alone may be all that is required to achieve union.

The definitions of a scaphoid nonunion and delayed union are complex. The exact time when a scaphoid fracture heals varies between patients.^2,5,10 However, the majority of hand surgeons believe that failure to see clear signs of healing (in waist fractures) after 3 months from the injury would suggest a failure to heal and a “delayed” union, whereas failure after 6 months from the injury and without clear signs of healing indicate a nonunion.^5,6,10,11 Any resorption at the fracture site suggests that the fracture will not heal by continued immobilization alone and will require surgery.¹⁰

Continue to: Hand surgeons have several surgical options...

Hand surgeons have several surgical options when managing scaphoid injuries. Mahmoud and Koptan⁴ used a volar approach to percutaneously deliver a headless compression screw into 27 nonunions. Postoperative CT scans demonstrated fracture union in all 27 patients, and no patient underwent revision surgery. Interestingly, 14 of their patients had extensive preoperative resorption (but no deformity) of >5 mm.

Although volar percutaneous approaches for internal fixation have been cited to provide high rates of union and high patient satisfaction in acute scaphoid fracture fixation, this study utilized a dorsal approach. Both Wozasek and Moser¹² and Haddad and Goddard¹³ reported excellent results and high union rates using a volar approach in consecutive acute scaphoid fractures. Despite these results, there are concerns that using a volar approach may damage the scaphotrapezial joint and may be prone to eccentric placement of compression screws.^8,14

Slade and colleagues³ did utilize the dorsal approach with arthroscopic assistance to deliver a compression screw into scaphoid nonunions in 15 consecutive patients without any evidence of deformity, sclerosis, or resorption. Similar to our investigation, they treated patients with both delayed unions and nonunions. CT scans were used to confirm unions in all their patients. Using a dorsal approach, Yassaee and Yang¹⁵ treated 9 consecutive patients using a compression screw without bone grafting for both delayed and nonunion scaphoid injuries. Other authors have used both volar and dorsal approaches in 12 consecutive delayed and nonunion scaphoid injuries and found that 11 of the 12 injuries progressed to unions.⁶

Although these authors and several others advocate the use of CT scans to assess unions, our investigation used bridging trabeculation obliteration of the fracture line on 3 standard radiographic views to confirm unions in addition to the absence of pain clinically.^16,17 CT scans expose the patient to increased radiation that, in our experience, does not alter the postoperative clinical course.¹⁸ If there is clear evidence of bridged callus and no pain on physical examination, a CT scan performed to reconfirm the union affords little benefit to clinical management.¹⁹

Continue to: All these previous studies have demonstrated...

All these previous studies have demonstrated excellent union rates but using a limited series of patients. We reviewed a large number of consecutive patients with scaphoid delayed unions and nonunions treated by screw fixation without bone grafting. Our hospital is a safety net institution for a large urban catchment area and had complete radiographic and clinical data for 19 of our 23 patients. One patient was contacted by telephone and he reported no pain and no revision surgical interventions.

The limitations of this study include not only its retrospective design but also its limited secondary outcome measures. However, our primary outcomes of union, pain, and complications are of utmost importance to clinicians and patients alike. Similar to other authors, we used radiographs to confirm unions. Although bridging trabeculation in radiographs has been demonstrated as soon as 1 month after the injury, there may be problems with interobserver reliability.^{4,13,15,20,21}

Patients being lost to follow-up is not uncommon in the orthopedic trauma literature and can influence results.^22,23 It is speculative to infer that the 3 patients who did not complete a follow-up course did not return because their pain had mitigated.

CONCLUSION 

Like several fractures, the lack of stability and the absence of micro-motion are believed to contribute to fibrous nonunions in scaphoid fractures.¹³ This study provides a large consecutive cohort of patients with minimally displaced scaphoid delayed unions and nonunions that were successfully treated by rigid internal fixation without bone grafting. These results confirm previous reports that bone grafting is not required to provide predictable unions for the majority of scaphoid nonunions.

This paper will be judged for the Resident Writer’s Award.

The Veterans Health Administration (VHA) provides health care services to over 9 million eligible and enrolled veterans out of a US veteran population of 18.9 million.¹ In 2014, an Office of Inspector General (OIG) investigation identified timely access to health care within the VHA as a serious concern.² In direct response, Congress enacted the Veterans Access, Choice, and Accountability Act (VACAA) of 2014 to expand access to care options available to veterans through referral to non-VA community care providers when the veteran is waiting longer than 30 days for an outpatient appointment or services, resides a significant distance (≥ 40 miles) from a VA facility, or experiences an undue burden to receive care and services.³ The VHA also responded, implementing several initiatives to improve veteran access to VHA health care generally, including the MyVA transformation and the proliferation of connected health technology; including telehealth capability and the expanded use of secure messaging. ^4-6

This study examined veterans’ access to the VA transplant program (VATP) for fiscal year (FY 2014 to FY 2016). Timeliness of services and outcomes in relationship to the distance from a VA transplant center (VATC) were evaluated.

Methods

The VATP comprises the following VATCs: 5 heart (Madison, Wisconsin; Nashville, Tennessee; Palo Alto, California; Richmond, Virginia; and Salt Lake City, Utah); 7 kidney (Birmingham, Alabama; Bronx, New York; Houston, Texas; Iowa City, Iowa; Nashville, Tennessee; Pittsburgh, Pennsylvania; and Portland, Oregon); 6 liver (Houston, Texas; Madison, Wisconsin; Nashville, Tennessee; Pittsburgh, Pennsylvania; Portland, Oregon; and Richmond, Virginia); and 2 lung (Madison, Wisconsin; and Seattle, Washington).

In 2012, the VHA published a policy to establish timeliness standards for a VATC initial review decision and referral evaluation.⁷ In 2013, the VHA National Surgery Office (NSO) implemented a secure intranet-based application called TRACER to facilitate the referral process and track timeliness of initial review decision, evaluation, United Network of Organ Sharing (UNOS) waitlisting, and transplantation.

The referral process is as follows: The referring VA medical facility submits veteran candidate health information into TRACER, selects a VATC, and then TRACER notifies the VATC. The VATC reviews the information and submits an initial review decision as to whether the clinical information supports further evaluation within 48 hours for an emergency referral and 5 business days for a stable referral. If accepted, the VATC completes an evaluation within 30 calendar days of the referral submission date. On evaluation and acceptance, the VATC accepts handoff for transplant-related care, orders additional testing as needed, and waitlists the veteran with UNOS when the clinical status is deemed appropriate.⁴

The TRACER data from 3 separate cohorts were analyzed from October 1, 2013, to September 30, 2016, with a follow-up event capture through March 31, 2017: (1) the referral cohort, representing all referrals to the VATP; (2) the waitlist cohort, representing those undergoing initial UNOS waitlisting; and (3) the transplant cohort, representing those receiving a solid organ transplant. The straight-line distance between the referring VA medical facility and the VATC was determined for each referral and categorized as follows: less than 100 miles, 100 to 300 miles, 301 to 500 miles, and greater than 500 miles.

Mortality outcomes in the TRACER database were confirmed using the VHA Vital Status file, which combines the Centers for Medicare & Medicaid Services, Social Security Administration, and VHA internal utilization data to determine a best source, including flagging of records that indicate a death date followed by use of VA services.^8,9 Records flagged with VA use after death were not considered deaths in this analysis. The NSO regularly refreshes veteran vital status information in the TRACER database for analysis of long-term outcomes.

The analysis methods for this study included Kruskall-Wallis nonparametric 1-way analysis of variance to compare timeliness metrics by distance group, Fine and Gray competing risks models to compare mortality on the UNOS list by distance group, and log-rank and Wilcoxon-Gehan tests to compare patient survival distributions by distance group.^10-14 Analysis was generated using SAS software, version 9.4 (Cary, North Carolina) as well as the R statistical software application (r-project.org).¹⁵ Publicly available solid organ transplant survival rates were obtained from the Scientific Registry for Transplant Recipients (SRTR).¹⁶

Results

For FY 2014 to FY 2016, the referral cohort identified 6,009 veteran referrals to a VATC for solid organ transplant of which 3,500 underwent an evaluation, and 2,137 were waitlisted for solid organ transplant with UNOS (Table 1). 

For the study period, 6,009 referrals resulted in 188 emergency initial review decisions and 3,551 stable initial review decisions with an eligible declaration (Table 2). 

Three thousand five hundred evaluations were performed in a median time of 27 calendar days (IQR 21-32 d) with 948 (27.1%) performed beyond the policy mandated 30 calendar days. Telehealth was used for 555 evaluations (15.9%), primarily for referrals located greater than 100 miles from the VATC. In FY 2016, 13.1% of the 1,321 completed evaluations were performed beyond 30 calendar days, representing an improvement from prior years; 45.7% beyond 30 calendar days in FY 2014 and 26.2% beyond 30 days in FY 2015.

Of the 6,009 referrals submitted in FY 2014 to FY 2016, 2,137 were waitlisted with UNOS. The median time from referral to waitlisting was 78 calendar days (IQR 43-148 d) for the entire study period, decreasing from 90 calendar days in FY 2014 to 70 calendar days in FY 2016.

For all organs and most organ types, the time from referral to initial review decision, evaluation, and waitlisting was statistically less (P < .005) for referrals received from VA medical facilities located less than 100 miles compared with referrals received from VA medical facilities at least 100 miles from the VATC. No statistical difference was found for emergency initial review decision for heart (P = .72) and lung (P = .14), time to evaluation for lung (P = .14), and time to waitlisting for heart (P = .95).

The waitlist cohort data are shown in Table 3. 

TRACER identified that 339 (15.0%) of the waitlist cohort were removed from the UNOS waitlist of which 212 (62.5%) were removed for failure to meet clinical criteria for transplantation, and 127 (37.5%) were removed for patient choice. Overall, 226 (10.0%) veterans died during the study period without receiving a transplant. Organ-specific mortality rates for veterans waitlisted but not transplanted at a VATC are as follows: heart 6.1%, kidney 5.9%, liver 19.0%, and lung 11.5%. As of March 31, 2017, 1,051 veterans were waitlisted with UNOS of which 876 (83.3%) were waitlisted for a kidney transplant.

The rate of mortality on the UNOS waitlist, the percentage of veterans transplanted, the time from waitlisting to transplantation, and the percentage of patients waitlisted at the end of the study period were not statistically different for referrals less than 100 miles compared with referrals at least 100 miles for all organs or kidney and liver separately (P ≤ .05). The relatively small numbers of veterans waitlisted for heart and lung transplants and nominal mortality events precluded making statements regarding significance for waitlist mortality.

The transplant cohort comprised 947 veterans receiving a solid organ transplant, including 102 (10.8%) heart, 411 (43.4%) kidney, 383 (40.4%) liver, and 51 (5.4%) lung transplants (Table 4). 

The transplant 30-day, 180-day, and 1-year survival rates are shown in Table 5. 

Discussion

This study shows that the VATP delivers timely, high-quality care and services even when the veteran’s referring VA medical facility is located a considerable distance from the VATC. Three separate cohorts of veterans were examined for the FY 2014 to FY 2016 study period: those referred, those waitlisted, and those transplanted. The referral cohort identified 6,009 referral submissions, performed 3,500 evaluations on veterans deemed to be potential candidates for solid organ transplantation, and placed 2,137 of these referrals on the UNOS waitlist. The median time from referral to initial review decision was 5 hours for emergency referrals and 3 business days for stable referrals. The median time from referral to evaluation was 27 calendar days, and the median time from referral to UNOS waitlisting was 78 calendar days. Improvements in timeliness for referral initial review decision, evaluation completion, and waitlisting over the study period were reflective of VHA and NSO efforts to enhance access to services. In FY 2016, 100% of emergency referrals received an initial review decision within 48 hours, 91.4% of stable reviews received an initial review decision within 5 business days, and 86.9% of all referrals underwent evaluation within 30 calendar days.

Distance of less than 100 miles between the referring VA medical facility and the VATC was associated with statistically significant shorter times for initial review decision, evaluation, and UNOS waitlisting. Referrals from less than 100 miles were a minority (9.6%) of referrals and most often represented a direct referral from the VATC to its own program. Timeliness of referral initial review decision, evaluation, or UNOS waitlisting was similar for distance categories greater than 100 miles: 100 to 300 miles, 301 to 500 miles, or greater than 500 miles.

The waitlist cohort identified 2,265 veterans, of which 731 (32.3%) underwent transplantation and 226 (10.0%) died. All-cause mortality for veterans once waitlisted, whether or not maintained on the UNOS waitlist, varied among organs and was found to be 6.1% for heart, 5.9% for kidney, 19.0% for liver, and 11.5% for lung. Waitlist mortality and the time from referral to solid organ transplant was similar for all distance categories.

The transplant cohort identified 947 veterans receiving a solid organ transplant with a median time from referral to transplant that varied considerably by organ type; 301 days (10.0 mo) for heart transplants, 914 days (30.5 mo) for kidney transplants, 236 days (7.9 mo) for liver transplants, and 246 days (8.2 mo) for lung transplants. Time to transplant and posttransplant survival were similar in all distance categories. Moreover, the VATP 1-year survival rates compared favorably with published SRTR data.

Prior studies have shown that distance to a transplant center adversely impacts access to transplant services, mortality on the UNOS waitlist, and transplant outcomes.^17-21 Patients living in small towns and isolated rural regions were 8% to 15% less likely to be waitlisted and 10% to 20% less likely to undergo heart, kidney, and liver transplantation than were patients in urban environments.¹⁷ This study found that a referral to the VATP from a VA medical facility located less than 100 miles from the VATC received an evaluation 5 to 7 days sooner and be placed on the UNOS waitlist 21 to 29 days sooner than a veteran referred to a VATC located at least 100 miles away. Contrary to prior studies, the distance from the VATC did not have an adverse impact on UNOS waitlist mortality, time to transplantation, or survival outcomes posttransplant.

The VHA offers a number of advantages to the veteran in need of transplant care and services. The VHA is the largest integrated health care system in the US designed specifically for veterans and their complex and specific needs with greater than 1,200 points of care and a single electronic health record optimizing coordinated services.²² In addition, the VHA’s use of telehealth to expedite evaluations and follow-up transplant care closer to home thereby obviating the need for travel. The VHA also has an electronic process to facilitate referral and tracking of timeliness of care (TRACER). Finally, VHA has policies that supports travel benefits, including lodging for the veteran, caregiver, and living donor if applicable for evaluations, transplant procedures, and follow-up care.^4,23

The coordination of health care services in a single integrated health care system may be the most significant advantage.²⁴ Multiple studies have examined dual care, representing care and services provided across 2 separate health care systems, showing an association between dual care and an increased risk of hospitalization, duplication of tests, rates for prescribing potentially unsafe medications, and mortality.^25-27 Although no study to date is on point, it is reasonable to imply that dual care imposes unnecessary risks to the veteran receiving complex lifelong transplant care when the VATP is shown to provide timely and high-quality care.

Limitations

The retrospective design and limited study period represent limitations. Specifically, survival outcomes for veterans transplanted were limited to 1 year and do not rule out the possibility that distance to a VATC will impact survival rates at 3 and 5 years posttransplant.

Conclusion

A referral distance of less than 100 miles from the VATC most often represents a direct referral and is a factor in timeliness of transplant initial review decision, evaluation, and placement of the veteran on the UNOS waitlist. Distance between the referring VA medical facility and the VATC, including distances of greater than 500 miles, was not found to impact the rate of mortality on the UNOS waitlist, time to transplantation, or posttransplant survival. Overall, the VHA provides timely solid organ transplant care and services with outcomes comparable to that of nationally reported SRTR estimates. Future studies should examine the timeliness of services, outcomes, and costs associated with those veterans authorized by the VHA for non-VA community care and those veterans who independently elect to receive transplant care and services by a non-VA transplant center and return to the VHA for dual care following transplantation.

The Veterans Health Administration (VHA) provides health care services to over 9 million eligible and enrolled veterans out of a US veteran population of 18.9 million.¹ In 2014, an Office of Inspector General (OIG) investigation identified timely access to health care within the VHA as a serious concern.² In direct response, Congress enacted the Veterans Access, Choice, and Accountability Act (VACAA) of 2014 to expand access to care options available to veterans through referral to non-VA community care providers when the veteran is waiting longer than 30 days for an outpatient appointment or services, resides a significant distance (≥ 40 miles) from a VA facility, or experiences an undue burden to receive care and services.³ The VHA also responded, implementing several initiatives to improve veteran access to VHA health care generally, including the MyVA transformation and the proliferation of connected health technology; including telehealth capability and the expanded use of secure messaging. ^4-6

This study examined veterans’ access to the VA transplant program (VATP) for fiscal year (FY 2014 to FY 2016). Timeliness of services and outcomes in relationship to the distance from a VA transplant center (VATC) were evaluated.

Methods

The VATP comprises the following VATCs: 5 heart (Madison, Wisconsin; Nashville, Tennessee; Palo Alto, California; Richmond, Virginia; and Salt Lake City, Utah); 7 kidney (Birmingham, Alabama; Bronx, New York; Houston, Texas; Iowa City, Iowa; Nashville, Tennessee; Pittsburgh, Pennsylvania; and Portland, Oregon); 6 liver (Houston, Texas; Madison, Wisconsin; Nashville, Tennessee; Pittsburgh, Pennsylvania; Portland, Oregon; and Richmond, Virginia); and 2 lung (Madison, Wisconsin; and Seattle, Washington).

In 2012, the VHA published a policy to establish timeliness standards for a VATC initial review decision and referral evaluation.⁷ In 2013, the VHA National Surgery Office (NSO) implemented a secure intranet-based application called TRACER to facilitate the referral process and track timeliness of initial review decision, evaluation, United Network of Organ Sharing (UNOS) waitlisting, and transplantation.

The referral process is as follows: The referring VA medical facility submits veteran candidate health information into TRACER, selects a VATC, and then TRACER notifies the VATC. The VATC reviews the information and submits an initial review decision as to whether the clinical information supports further evaluation within 48 hours for an emergency referral and 5 business days for a stable referral. If accepted, the VATC completes an evaluation within 30 calendar days of the referral submission date. On evaluation and acceptance, the VATC accepts handoff for transplant-related care, orders additional testing as needed, and waitlists the veteran with UNOS when the clinical status is deemed appropriate.⁴

The TRACER data from 3 separate cohorts were analyzed from October 1, 2013, to September 30, 2016, with a follow-up event capture through March 31, 2017: (1) the referral cohort, representing all referrals to the VATP; (2) the waitlist cohort, representing those undergoing initial UNOS waitlisting; and (3) the transplant cohort, representing those receiving a solid organ transplant. The straight-line distance between the referring VA medical facility and the VATC was determined for each referral and categorized as follows: less than 100 miles, 100 to 300 miles, 301 to 500 miles, and greater than 500 miles.

Mortality outcomes in the TRACER database were confirmed using the VHA Vital Status file, which combines the Centers for Medicare & Medicaid Services, Social Security Administration, and VHA internal utilization data to determine a best source, including flagging of records that indicate a death date followed by use of VA services.^8,9 Records flagged with VA use after death were not considered deaths in this analysis. The NSO regularly refreshes veteran vital status information in the TRACER database for analysis of long-term outcomes.

The analysis methods for this study included Kruskall-Wallis nonparametric 1-way analysis of variance to compare timeliness metrics by distance group, Fine and Gray competing risks models to compare mortality on the UNOS list by distance group, and log-rank and Wilcoxon-Gehan tests to compare patient survival distributions by distance group.^10-14 Analysis was generated using SAS software, version 9.4 (Cary, North Carolina) as well as the R statistical software application (r-project.org).¹⁵ Publicly available solid organ transplant survival rates were obtained from the Scientific Registry for Transplant Recipients (SRTR).¹⁶

Results

For FY 2014 to FY 2016, the referral cohort identified 6,009 veteran referrals to a VATC for solid organ transplant of which 3,500 underwent an evaluation, and 2,137 were waitlisted for solid organ transplant with UNOS (Table 1). 

For the study period, 6,009 referrals resulted in 188 emergency initial review decisions and 3,551 stable initial review decisions with an eligible declaration (Table 2). 

Three thousand five hundred evaluations were performed in a median time of 27 calendar days (IQR 21-32 d) with 948 (27.1%) performed beyond the policy mandated 30 calendar days. Telehealth was used for 555 evaluations (15.9%), primarily for referrals located greater than 100 miles from the VATC. In FY 2016, 13.1% of the 1,321 completed evaluations were performed beyond 30 calendar days, representing an improvement from prior years; 45.7% beyond 30 calendar days in FY 2014 and 26.2% beyond 30 days in FY 2015.

Of the 6,009 referrals submitted in FY 2014 to FY 2016, 2,137 were waitlisted with UNOS. The median time from referral to waitlisting was 78 calendar days (IQR 43-148 d) for the entire study period, decreasing from 90 calendar days in FY 2014 to 70 calendar days in FY 2016.

For all organs and most organ types, the time from referral to initial review decision, evaluation, and waitlisting was statistically less (P < .005) for referrals received from VA medical facilities located less than 100 miles compared with referrals received from VA medical facilities at least 100 miles from the VATC. No statistical difference was found for emergency initial review decision for heart (P = .72) and lung (P = .14), time to evaluation for lung (P = .14), and time to waitlisting for heart (P = .95).

The waitlist cohort data are shown in Table 3. 

TRACER identified that 339 (15.0%) of the waitlist cohort were removed from the UNOS waitlist of which 212 (62.5%) were removed for failure to meet clinical criteria for transplantation, and 127 (37.5%) were removed for patient choice. Overall, 226 (10.0%) veterans died during the study period without receiving a transplant. Organ-specific mortality rates for veterans waitlisted but not transplanted at a VATC are as follows: heart 6.1%, kidney 5.9%, liver 19.0%, and lung 11.5%. As of March 31, 2017, 1,051 veterans were waitlisted with UNOS of which 876 (83.3%) were waitlisted for a kidney transplant.

The rate of mortality on the UNOS waitlist, the percentage of veterans transplanted, the time from waitlisting to transplantation, and the percentage of patients waitlisted at the end of the study period were not statistically different for referrals less than 100 miles compared with referrals at least 100 miles for all organs or kidney and liver separately (P ≤ .05). The relatively small numbers of veterans waitlisted for heart and lung transplants and nominal mortality events precluded making statements regarding significance for waitlist mortality.

The transplant cohort comprised 947 veterans receiving a solid organ transplant, including 102 (10.8%) heart, 411 (43.4%) kidney, 383 (40.4%) liver, and 51 (5.4%) lung transplants (Table 4). 

The transplant 30-day, 180-day, and 1-year survival rates are shown in Table 5. 

Discussion

This study shows that the VATP delivers timely, high-quality care and services even when the veteran’s referring VA medical facility is located a considerable distance from the VATC. Three separate cohorts of veterans were examined for the FY 2014 to FY 2016 study period: those referred, those waitlisted, and those transplanted. The referral cohort identified 6,009 referral submissions, performed 3,500 evaluations on veterans deemed to be potential candidates for solid organ transplantation, and placed 2,137 of these referrals on the UNOS waitlist. The median time from referral to initial review decision was 5 hours for emergency referrals and 3 business days for stable referrals. The median time from referral to evaluation was 27 calendar days, and the median time from referral to UNOS waitlisting was 78 calendar days. Improvements in timeliness for referral initial review decision, evaluation completion, and waitlisting over the study period were reflective of VHA and NSO efforts to enhance access to services. In FY 2016, 100% of emergency referrals received an initial review decision within 48 hours, 91.4% of stable reviews received an initial review decision within 5 business days, and 86.9% of all referrals underwent evaluation within 30 calendar days.

Distance of less than 100 miles between the referring VA medical facility and the VATC was associated with statistically significant shorter times for initial review decision, evaluation, and UNOS waitlisting. Referrals from less than 100 miles were a minority (9.6%) of referrals and most often represented a direct referral from the VATC to its own program. Timeliness of referral initial review decision, evaluation, or UNOS waitlisting was similar for distance categories greater than 100 miles: 100 to 300 miles, 301 to 500 miles, or greater than 500 miles.

The waitlist cohort identified 2,265 veterans, of which 731 (32.3%) underwent transplantation and 226 (10.0%) died. All-cause mortality for veterans once waitlisted, whether or not maintained on the UNOS waitlist, varied among organs and was found to be 6.1% for heart, 5.9% for kidney, 19.0% for liver, and 11.5% for lung. Waitlist mortality and the time from referral to solid organ transplant was similar for all distance categories.

The transplant cohort identified 947 veterans receiving a solid organ transplant with a median time from referral to transplant that varied considerably by organ type; 301 days (10.0 mo) for heart transplants, 914 days (30.5 mo) for kidney transplants, 236 days (7.9 mo) for liver transplants, and 246 days (8.2 mo) for lung transplants. Time to transplant and posttransplant survival were similar in all distance categories. Moreover, the VATP 1-year survival rates compared favorably with published SRTR data.

Prior studies have shown that distance to a transplant center adversely impacts access to transplant services, mortality on the UNOS waitlist, and transplant outcomes.^17-21 Patients living in small towns and isolated rural regions were 8% to 15% less likely to be waitlisted and 10% to 20% less likely to undergo heart, kidney, and liver transplantation than were patients in urban environments.¹⁷ This study found that a referral to the VATP from a VA medical facility located less than 100 miles from the VATC received an evaluation 5 to 7 days sooner and be placed on the UNOS waitlist 21 to 29 days sooner than a veteran referred to a VATC located at least 100 miles away. Contrary to prior studies, the distance from the VATC did not have an adverse impact on UNOS waitlist mortality, time to transplantation, or survival outcomes posttransplant.

The VHA offers a number of advantages to the veteran in need of transplant care and services. The VHA is the largest integrated health care system in the US designed specifically for veterans and their complex and specific needs with greater than 1,200 points of care and a single electronic health record optimizing coordinated services.²² In addition, the VHA’s use of telehealth to expedite evaluations and follow-up transplant care closer to home thereby obviating the need for travel. The VHA also has an electronic process to facilitate referral and tracking of timeliness of care (TRACER). Finally, VHA has policies that supports travel benefits, including lodging for the veteran, caregiver, and living donor if applicable for evaluations, transplant procedures, and follow-up care.^4,23

The coordination of health care services in a single integrated health care system may be the most significant advantage.²⁴ Multiple studies have examined dual care, representing care and services provided across 2 separate health care systems, showing an association between dual care and an increased risk of hospitalization, duplication of tests, rates for prescribing potentially unsafe medications, and mortality.^25-27 Although no study to date is on point, it is reasonable to imply that dual care imposes unnecessary risks to the veteran receiving complex lifelong transplant care when the VATP is shown to provide timely and high-quality care.

Limitations

The retrospective design and limited study period represent limitations. Specifically, survival outcomes for veterans transplanted were limited to 1 year and do not rule out the possibility that distance to a VATC will impact survival rates at 3 and 5 years posttransplant.

Conclusion

A referral distance of less than 100 miles from the VATC most often represents a direct referral and is a factor in timeliness of transplant initial review decision, evaluation, and placement of the veteran on the UNOS waitlist. Distance between the referring VA medical facility and the VATC, including distances of greater than 500 miles, was not found to impact the rate of mortality on the UNOS waitlist, time to transplantation, or posttransplant survival. Overall, the VHA provides timely solid organ transplant care and services with outcomes comparable to that of nationally reported SRTR estimates. Future studies should examine the timeliness of services, outcomes, and costs associated with those veterans authorized by the VHA for non-VA community care and those veterans who independently elect to receive transplant care and services by a non-VA transplant center and return to the VHA for dual care following transplantation.

The Veterans Health Administration (VHA) provides health care services to over 9 million eligible and enrolled veterans out of a US veteran population of 18.9 million.¹ In 2014, an Office of Inspector General (OIG) investigation identified timely access to health care within the VHA as a serious concern.² In direct response, Congress enacted the Veterans Access, Choice, and Accountability Act (VACAA) of 2014 to expand access to care options available to veterans through referral to non-VA community care providers when the veteran is waiting longer than 30 days for an outpatient appointment or services, resides a significant distance (≥ 40 miles) from a VA facility, or experiences an undue burden to receive care and services.³ The VHA also responded, implementing several initiatives to improve veteran access to VHA health care generally, including the MyVA transformation and the proliferation of connected health technology; including telehealth capability and the expanded use of secure messaging. ^4-6

This study examined veterans’ access to the VA transplant program (VATP) for fiscal year (FY 2014 to FY 2016). Timeliness of services and outcomes in relationship to the distance from a VA transplant center (VATC) were evaluated.

Methods

The VATP comprises the following VATCs: 5 heart (Madison, Wisconsin; Nashville, Tennessee; Palo Alto, California; Richmond, Virginia; and Salt Lake City, Utah); 7 kidney (Birmingham, Alabama; Bronx, New York; Houston, Texas; Iowa City, Iowa; Nashville, Tennessee; Pittsburgh, Pennsylvania; and Portland, Oregon); 6 liver (Houston, Texas; Madison, Wisconsin; Nashville, Tennessee; Pittsburgh, Pennsylvania; Portland, Oregon; and Richmond, Virginia); and 2 lung (Madison, Wisconsin; and Seattle, Washington).

In 2012, the VHA published a policy to establish timeliness standards for a VATC initial review decision and referral evaluation.⁷ In 2013, the VHA National Surgery Office (NSO) implemented a secure intranet-based application called TRACER to facilitate the referral process and track timeliness of initial review decision, evaluation, United Network of Organ Sharing (UNOS) waitlisting, and transplantation.

The referral process is as follows: The referring VA medical facility submits veteran candidate health information into TRACER, selects a VATC, and then TRACER notifies the VATC. The VATC reviews the information and submits an initial review decision as to whether the clinical information supports further evaluation within 48 hours for an emergency referral and 5 business days for a stable referral. If accepted, the VATC completes an evaluation within 30 calendar days of the referral submission date. On evaluation and acceptance, the VATC accepts handoff for transplant-related care, orders additional testing as needed, and waitlists the veteran with UNOS when the clinical status is deemed appropriate.⁴

The TRACER data from 3 separate cohorts were analyzed from October 1, 2013, to September 30, 2016, with a follow-up event capture through March 31, 2017: (1) the referral cohort, representing all referrals to the VATP; (2) the waitlist cohort, representing those undergoing initial UNOS waitlisting; and (3) the transplant cohort, representing those receiving a solid organ transplant. The straight-line distance between the referring VA medical facility and the VATC was determined for each referral and categorized as follows: less than 100 miles, 100 to 300 miles, 301 to 500 miles, and greater than 500 miles.

Mortality outcomes in the TRACER database were confirmed using the VHA Vital Status file, which combines the Centers for Medicare & Medicaid Services, Social Security Administration, and VHA internal utilization data to determine a best source, including flagging of records that indicate a death date followed by use of VA services.^8,9 Records flagged with VA use after death were not considered deaths in this analysis. The NSO regularly refreshes veteran vital status information in the TRACER database for analysis of long-term outcomes.

The analysis methods for this study included Kruskall-Wallis nonparametric 1-way analysis of variance to compare timeliness metrics by distance group, Fine and Gray competing risks models to compare mortality on the UNOS list by distance group, and log-rank and Wilcoxon-Gehan tests to compare patient survival distributions by distance group.^10-14 Analysis was generated using SAS software, version 9.4 (Cary, North Carolina) as well as the R statistical software application (r-project.org).¹⁵ Publicly available solid organ transplant survival rates were obtained from the Scientific Registry for Transplant Recipients (SRTR).¹⁶

Results

For FY 2014 to FY 2016, the referral cohort identified 6,009 veteran referrals to a VATC for solid organ transplant of which 3,500 underwent an evaluation, and 2,137 were waitlisted for solid organ transplant with UNOS (Table 1). 

For the study period, 6,009 referrals resulted in 188 emergency initial review decisions and 3,551 stable initial review decisions with an eligible declaration (Table 2). 

Three thousand five hundred evaluations were performed in a median time of 27 calendar days (IQR 21-32 d) with 948 (27.1%) performed beyond the policy mandated 30 calendar days. Telehealth was used for 555 evaluations (15.9%), primarily for referrals located greater than 100 miles from the VATC. In FY 2016, 13.1% of the 1,321 completed evaluations were performed beyond 30 calendar days, representing an improvement from prior years; 45.7% beyond 30 calendar days in FY 2014 and 26.2% beyond 30 days in FY 2015.

Of the 6,009 referrals submitted in FY 2014 to FY 2016, 2,137 were waitlisted with UNOS. The median time from referral to waitlisting was 78 calendar days (IQR 43-148 d) for the entire study period, decreasing from 90 calendar days in FY 2014 to 70 calendar days in FY 2016.

For all organs and most organ types, the time from referral to initial review decision, evaluation, and waitlisting was statistically less (P < .005) for referrals received from VA medical facilities located less than 100 miles compared with referrals received from VA medical facilities at least 100 miles from the VATC. No statistical difference was found for emergency initial review decision for heart (P = .72) and lung (P = .14), time to evaluation for lung (P = .14), and time to waitlisting for heart (P = .95).

The waitlist cohort data are shown in Table 3. 

TRACER identified that 339 (15.0%) of the waitlist cohort were removed from the UNOS waitlist of which 212 (62.5%) were removed for failure to meet clinical criteria for transplantation, and 127 (37.5%) were removed for patient choice. Overall, 226 (10.0%) veterans died during the study period without receiving a transplant. Organ-specific mortality rates for veterans waitlisted but not transplanted at a VATC are as follows: heart 6.1%, kidney 5.9%, liver 19.0%, and lung 11.5%. As of March 31, 2017, 1,051 veterans were waitlisted with UNOS of which 876 (83.3%) were waitlisted for a kidney transplant.

The rate of mortality on the UNOS waitlist, the percentage of veterans transplanted, the time from waitlisting to transplantation, and the percentage of patients waitlisted at the end of the study period were not statistically different for referrals less than 100 miles compared with referrals at least 100 miles for all organs or kidney and liver separately (P ≤ .05). The relatively small numbers of veterans waitlisted for heart and lung transplants and nominal mortality events precluded making statements regarding significance for waitlist mortality.

The transplant cohort comprised 947 veterans receiving a solid organ transplant, including 102 (10.8%) heart, 411 (43.4%) kidney, 383 (40.4%) liver, and 51 (5.4%) lung transplants (Table 4). 

The transplant 30-day, 180-day, and 1-year survival rates are shown in Table 5. 

Discussion

This study shows that the VATP delivers timely, high-quality care and services even when the veteran’s referring VA medical facility is located a considerable distance from the VATC. Three separate cohorts of veterans were examined for the FY 2014 to FY 2016 study period: those referred, those waitlisted, and those transplanted. The referral cohort identified 6,009 referral submissions, performed 3,500 evaluations on veterans deemed to be potential candidates for solid organ transplantation, and placed 2,137 of these referrals on the UNOS waitlist. The median time from referral to initial review decision was 5 hours for emergency referrals and 3 business days for stable referrals. The median time from referral to evaluation was 27 calendar days, and the median time from referral to UNOS waitlisting was 78 calendar days. Improvements in timeliness for referral initial review decision, evaluation completion, and waitlisting over the study period were reflective of VHA and NSO efforts to enhance access to services. In FY 2016, 100% of emergency referrals received an initial review decision within 48 hours, 91.4% of stable reviews received an initial review decision within 5 business days, and 86.9% of all referrals underwent evaluation within 30 calendar days.

Distance of less than 100 miles between the referring VA medical facility and the VATC was associated with statistically significant shorter times for initial review decision, evaluation, and UNOS waitlisting. Referrals from less than 100 miles were a minority (9.6%) of referrals and most often represented a direct referral from the VATC to its own program. Timeliness of referral initial review decision, evaluation, or UNOS waitlisting was similar for distance categories greater than 100 miles: 100 to 300 miles, 301 to 500 miles, or greater than 500 miles.

The waitlist cohort identified 2,265 veterans, of which 731 (32.3%) underwent transplantation and 226 (10.0%) died. All-cause mortality for veterans once waitlisted, whether or not maintained on the UNOS waitlist, varied among organs and was found to be 6.1% for heart, 5.9% for kidney, 19.0% for liver, and 11.5% for lung. Waitlist mortality and the time from referral to solid organ transplant was similar for all distance categories.

The transplant cohort identified 947 veterans receiving a solid organ transplant with a median time from referral to transplant that varied considerably by organ type; 301 days (10.0 mo) for heart transplants, 914 days (30.5 mo) for kidney transplants, 236 days (7.9 mo) for liver transplants, and 246 days (8.2 mo) for lung transplants. Time to transplant and posttransplant survival were similar in all distance categories. Moreover, the VATP 1-year survival rates compared favorably with published SRTR data.

Prior studies have shown that distance to a transplant center adversely impacts access to transplant services, mortality on the UNOS waitlist, and transplant outcomes.^17-21 Patients living in small towns and isolated rural regions were 8% to 15% less likely to be waitlisted and 10% to 20% less likely to undergo heart, kidney, and liver transplantation than were patients in urban environments.¹⁷ This study found that a referral to the VATP from a VA medical facility located less than 100 miles from the VATC received an evaluation 5 to 7 days sooner and be placed on the UNOS waitlist 21 to 29 days sooner than a veteran referred to a VATC located at least 100 miles away. Contrary to prior studies, the distance from the VATC did not have an adverse impact on UNOS waitlist mortality, time to transplantation, or survival outcomes posttransplant.

The VHA offers a number of advantages to the veteran in need of transplant care and services. The VHA is the largest integrated health care system in the US designed specifically for veterans and their complex and specific needs with greater than 1,200 points of care and a single electronic health record optimizing coordinated services.²² In addition, the VHA’s use of telehealth to expedite evaluations and follow-up transplant care closer to home thereby obviating the need for travel. The VHA also has an electronic process to facilitate referral and tracking of timeliness of care (TRACER). Finally, VHA has policies that supports travel benefits, including lodging for the veteran, caregiver, and living donor if applicable for evaluations, transplant procedures, and follow-up care.^4,23

The coordination of health care services in a single integrated health care system may be the most significant advantage.²⁴ Multiple studies have examined dual care, representing care and services provided across 2 separate health care systems, showing an association between dual care and an increased risk of hospitalization, duplication of tests, rates for prescribing potentially unsafe medications, and mortality.^25-27 Although no study to date is on point, it is reasonable to imply that dual care imposes unnecessary risks to the veteran receiving complex lifelong transplant care when the VATP is shown to provide timely and high-quality care.

Limitations

The retrospective design and limited study period represent limitations. Specifically, survival outcomes for veterans transplanted were limited to 1 year and do not rule out the possibility that distance to a VATC will impact survival rates at 3 and 5 years posttransplant.

Conclusion

A referral distance of less than 100 miles from the VATC most often represents a direct referral and is a factor in timeliness of transplant initial review decision, evaluation, and placement of the veteran on the UNOS waitlist. Distance between the referring VA medical facility and the VATC, including distances of greater than 500 miles, was not found to impact the rate of mortality on the UNOS waitlist, time to transplantation, or posttransplant survival. Overall, the VHA provides timely solid organ transplant care and services with outcomes comparable to that of nationally reported SRTR estimates. Future studies should examine the timeliness of services, outcomes, and costs associated with those veterans authorized by the VHA for non-VA community care and those veterans who independently elect to receive transplant care and services by a non-VA transplant center and return to the VHA for dual care following transplantation.

ABSTRACT

Tranexamic acid (TXA) is an effective agent used for reducing perioperative blood loss and decreasing the potential for postoperative hemarthrosis. We hypothesized that patients who had received intraoperative TXA during total ankle arthroplasty (TAA) would have a reduction in postoperative drain output, thereby resulting in a reduced risk of postoperative hemarthrosis and lower wound complication rates.

A retrospective review was conducted on 50 consecutive patients, 25 receiving TXA (TXA-TAA) and 25 not receiving TXA (No TXA-TAA), who underwent an uncemented TAA between September 2011 and December 2015. Demographic characteristics, drain output, preoperative and postoperative hemoglobin levels, operative and postoperative course, and minor and major wound complications of the patients were reviewed.

Drain output was significantly less in the TXA-TAA group compared to that in the No TXA-TAA group (71.6 ± 60.3 vs 200.2 ± 117.0 mL, respectively, P < .0001). The overall wound complication rate in the No TXA-TAA group was higher (20%, 5/25) than that in the TXA-TAA group (8%, 2/25) (P = .114). The mean change in preoperative to postoperative hemoglobin level was significantly less in the TXA-TAA group compared to that in the No TXA-TAA group (1.5 ± 0.6 vs 2.0 ± 0.4 g/dL, respectively, P = .01).

TXA is an effective hemostatic agent when used during TAA. TXA reduces perioperative blood loss, hemarthrosis, and the risk of wound complications.

Continue to: End-stage ankle arthritis...

End-stage ankle arthritis is a disabling condition that may lead to poor quality of life and difficulties with activities of daily living.¹ The associated mental and physical disability has been demonstrated to be as severe as in end-stage hip arthrosis.² Operative treatment for symptomatic end-stage ankle arthritis includes arthrodesis or total ankle arthroplasty (TAA) in those refractory to nonoperative treatment.³ Newer generation implants have made TAA a more attractive option for both the surgeon and the patient.

Over the past decade, the utility of TAA has increased and attention has turned toward the management of perioperative factors that would maximize patient satisfaction and decrease the length of stay and complication rates, as well as hospital costs.⁴ Comprehensive literature on total knee arthroplasty (TKA) and total hip arthroplasty (THA) has demonstrated that the management of perioperative blood loss, specifically postoperative hemarthrosis, is a modifiable factor affecting patient recovery, complication rates, and hospital costs.^5-8 Drain output has been used as a direct measure of intra-articular blood accumulation.⁹ Decreased drain output implies decreased hemarthrosis, which could potentially alleviate the pressure on the wound and decrease wound complications.

One of the major strategies that has been recognized for reducing blood loss and decreasing the potential for postoperative hemarthrosis is the use of intravenous (IV) or topical tranexamic acid (TXA).^10,11 TXA is a synthetic antifibrinolytic medication that has been extensively used throughout the medical field since the 1960s to help control the bleeding cascade. This medication stabilizes clot formation without inducing a pro-coaguable state.¹² Intraoperative administration of TXA has been shown to reduce drain output and decrease transfusion requirements after TKA and THA without an associated increase in patient morbidity and mortality.^6,11,13-15

Currently, there is a lack of studies evaluating the utility of TXA during TAA. We hypothesize that compared with patients who had not received TXA, those who had received intraoperative TXA during TAA would have a reduction in postoperative drain output and therefore decreased hemarthrosis, lower wound complication rate, and a diminished change in preoperative to postoperative hemoglobin levels, reflecting a reduction in perioperative blood loss.

MATERIALS AND METHODS

This study was approved by the Institutional Review Board at the University at Buffalo, State University of New York. A retrospective chart review was conducted on 50 consecutive patients who underwent an uncemented TAA with the Salto Talaris total ankle prosthesis (Tornier, Inc) between September 2011 and December 2015. All surgeries were performed at 1 institution by a single fellowship surgeon trained in foot and ankle surgery through the anterior approach where a midline incision was made over the ankle. The interval between the tibialis anterior tendon and the extensor hallucis longus tendon was used. We had incorporated intraoperative TXA into the TAA surgical protocol at our institution in January 2014. We evaluated the first 25 consecutive patients who underwent TAA after TXA use began (TXA-TAA) and another 25 consecutive patients who underwent TAA before the routine use of TXA (No TXA-TAA). Inclusion criteria were patients who presented with pain, decreased function, and radiographic parameters of end-stage tibiotalar arthritis due to degenerative arthritis, rheumatoid arthritis, or posttraumatic arthritis who subsequently underwent a TAA. Exclusion criteria were patients with a contraindication for IV TXA use, a preexisting coagulopathy, or where drain output was not recorded. Contraindications for IV TXA use included patients with impaired renal clearance, recent cardiac surgery, myocardial infarction, ischemic stroke, or venous thromboembolism (VTE). Seven patients were ultimately excluded from this study based on the inclusion and exclusion criteria, 3 patients from the TXA-TAA group and 4 patients from the No TXA-TAA group.

Continue to: Charts were reviewed for demographics...

Charts were reviewed for demographics, preoperative and postoperative hemoglobin levels, indications for surgery, surgical procedures, length of surgery, postoperative drain output, length of stay, postoperative pain visual analog scale (VAS) score, minor and major wound complications, and postoperative complications. Minor wound complications were defined as the anterior surgical incision that required local wound care in office or oral antibiotics without subsequent consequences. Major wound complications were defined as requiring surgical débridement and/or any additional treatment in the operating room.¹⁶ Postoperative complications other than wound complications were defined as those requiring a subsequent surgical intervention. Patient demographics and clinical and procedural characteristics of patients in both the TXA-TAA and the No TXA-TAA groups are outlined in Table 1. There were 14 males and 11 females in the TXA-TAA group and 16 males and 9 females in the No TXA-TAA group. The mean age was 65.8 ± 10.9 years in the TXA-TAA group and 66.9 ± 8.0 years in the No TXA-TAA group (P = .69). Mean body mass index (BMI) was 31.6 ± 6.3 in the TXA-TAA group and 29.4 ± 4.9 in the No TXA-TAA group (P = .18). The primary indication for TAA was degenerative osteoarthritis in 26 patients, posttraumatic arthritis in 21 patients, and rheumatoid arthritis in 3 patients. The most common associated procedure was Achilles tendon lengthening in both groups. The mean follow-up in the TXA-TAA group was 9.3 ± 5.8 months (range, 2.0-24.0 months). Postoperative complications due to TXA administration as described in previous literature were defined as VTE, myocardial infarction, or ischemic cerebral event. The TXA-TAA group received a standard 1 g dose of IV TXA 20 minutes prior to tourniquet inflation. A tourniquet was used intraoperatively on all patients included in this study. A postoperative 400-mL surgical drain (Hemovac, Zimmer Biomet) was placed in the ankle joint in all patients and subsequently discontinued on postoperative day 1. Recent literature has reported the minor wound complication rate associated with TAA to be as high as 25% and the major wound complication rate to be 8.5%.¹⁶ To assist in reducing the risk for wound complications, our protocol traditionally uses an intra-articular surgical drain to decrease any pressure on the wound from postoperative hemarthrosis.

^aP value was calculated from t-test continuous variables and Chi-square test for categorical variables (TXA-TAA vs No TXA-TAA comparison).

Abbreviations: ASA, American Society of Anesthesiologists; BMI, body mass index.

Total drain output was recorded in milliliters (mL) in all patients. The change between the preoperative hemoglobin level and the hemoglobin level on postoperative day 1 was calculated for each patient. The calculated blood loss was determined using Meunier’s equation, which estimates the total blood volume using Nadler’s formula and then uses preoperative hemoglobin and postoperative day 1 hemoglobin values to calculate blood loss.^17,18 VAS scores (scale, 1-10) were obtained every 4 hours on postoperative day 1 according to the nursing protocol. The number 1 on the scale represents the least amount of pain, whereas 10 indicates the worst pain. The VAS scores were then averaged for each patient.

A power analysis using preliminary data determined that 15 patients were needed in each group to detect a 50% reduction in drain output at a power of 80% and a P value of 0.05. Descriptive statistics were used to analyze demographic data. We compared the demographic and clinical characteristics of patients in the TXA-TAA group with those of patients in the No TXA-TAA group using unpaired student t-tests for continuous variables and Chi-square or Fischer’s exact tests for categorical variables. Simple and adjusted linear regression analyses were used to examine the difference in drain output and blood loss between the 2 groups (TXA-TAA vs No TXA-TAA). Multivariate models were adjusted for age, BMI, and length of surgery. A P value <.05 was considered to be statistically significant. We performed all analyses using a statistical software package (SAS version 9.2, SAS Institute).

RESULTS

Drain output was significantly less in the TXA-TAA group compared to that in the No TXA-TAA group (71.6 ± 60.3 vs 200.2 ± 117.0 mL, respectively, P = .0001) (Figure). The clinical characteristics of the patients who underwent TAA with the use of TXA are outlined in Table 2. The mean change in preoperative to postoperative hemoglobin levels was significantly lower in the TXA-TAA group than in the No TXA-TAA group (1.5 ± 0.6 vs 2.0 ± 0.4 g/dL, respectively; P = .01). The calculated blood loss in patients in the TXA-TAA group was significantly lower than that in patients in the No TXA-TAA group (649.9 ± 332.7 vs 906.8 ± 287.4 mL, respectively; P = .01). No patient in either group received a blood transfusion. We did not observe a significant difference in the length of surgery between the TXA-TAA and the No TXA-TAA groups (112.8 ± 24.8 vs 108.6 ± 26.0 min, respectively; P = .57). The average American Society of Anesthesiologists’ (ASA) classification was similar between the groups (2.2 ± 0.6 and 2.2 ± 0.5, respectively; P = 1.00) as was the age-adjusted Charlson Comorbidity Index (2.8 ± 1.7 vs 2.9 ± 1.6, respectively; P = .93). Mean VAS scores on postoperative day 1 in the TXA-TAA and the No TXA-TAA group were 4.9 ± 1.7 and 5.3 ± 1.4, respectively (P = .71). The average length of stay in the TXA-TAA group was 1.6 ± 0.7 days vs 1.3 ± 0.6 days in the No TXA-TAA group (P = .23). Two patients in the TXA-TAA group had an extended hospital length of stay of 5 days due to discharge planning and social issues.

^aP value was calculated from t-test for continuous variables, and Chi-square test for categorical variables (TXA-TAA vs No TXA-TAA comparison).

Abbreviations: LOS, length of stay; VAS, visual analog scale; POD, postoperative day.

^aLinear regression was used to calculate the P value. ^bAdjusted for age, BMI and length of surgery.

Abbreviation: BMI, body mass index.

^aP value was calculated from Fisher’s Exact test (67% cells had count <5) test for categorical variables (TXA-TAA vs No TXA-TAA comparison).

The crude linear regression model revealed a significant difference in drain output between the TXA-TAA and the No TXA-TAA groups (β = −128.6 ± 26.3, P < .0001) (Table 3). Further adjustment for age and length of surgery slightly strengthened the association (β = −129.6 ± 26.6, P < .0001). The nature of regression coefficient β showed that the mean estimate of drain output was 129.6 mL lower in the TXA-TAA group than that in the No TXA-TAA group. There was a significant difference in blood loss between the TXA-TAA and the No TXA-TAA groups in the crude linear regression model (β = −257.0 ± 87.9, P = .005). Additional adjustment for age, BMI, and length of surgery slightly strengthened the association (β = −275.6 ± 90.7, P = .004). The nature of regression coefficient β showed that the mean estimate of blood loss was 275.6 mL lower in the TXA-TAA group than in the No TXA-TAA group (Table 3).

Continue to: There was no statistically significant difference...

There was no statistically significant difference in wound complications between the TXA-TAA and the No TXA-TAA groups in this study population (P = .114). However, our results showed a higher overall wound complication rate in the No TXA-TAA group than in the TXA-TAA group (20% (5/25) vs 8% (2/25), respectively) (Table 4). In the No TXA-TAA group, there were 4 minor and 1 major wound complications. All 5 patients experiencing a postoperative wound complication required oral antibiotics for a minimum of 4 weeks and local wound care. One patient underwent a surgical débridement meeting the criteria for major wound complications. In the TXA-TAA group, there were 2 minor wound complications and no major wound complications. One patient was administered prophylactic oral antibiotics for 7 days with local wound care for blister formation without evidence of infection. The second patient experiencing a minor wound complication required 3 weeks of oral antibiotics and local wound care. No patients in either group had a deep infection requiring implant removal, IV antibiotics, or subsequent hospital admission. The surgical incisions in all patients healed after the aforementioned treatments with no persistent drainage or development of chronic wounds.

In the TXA-TAA group, there was 1 patient who sustained an intraoperative medial malleolus fracture. One patient developed an extensor hallucis longus contracture 5 months postoperatively that subsequently underwent release and lengthening. There was 1 patient in this group who sustained a distal tibia fracture 5 cm proximal to the prosthesis 3 months postoperatively after a mechanical fall. In the No TXA-TAA group, there were 2 patients who sustained intraoperative medial malleolus fractures. One patient underwent a revision of the tibial component 24 months postoperatively due to aseptic loosening. In addition, another patient in this group who sustained an Achilles tendon rupture 5 months postoperatively after a fall subsequently underwent repair with tibialis anterior tendon allograft.

There were no patients in either group who experienced any hospital readmissions in the acute follow-up period as defined by a 90-day period after discharge. There were no complications associated with TXA administration in either group.

DISCUSSION

Recent advances in total ankle prosthetic design coupled with increased survival and improved short- to midterm follow-up results make TAA an effective treatment option for end-stage ankle arthritis. Management of perioperative blood loss and reducing the potential for significant hemarthrosis and subsequent wound complications are important factors to consider for patients undergoing TAA. TXA administration is used in several centers as part of an intraoperative strategy to reduce blood loss and decrease intra-articular blood accumulation. To our knowledge, this is the first study to evaluate the management of blood loss and hemarthrosis using TXA during TAA.

IV and topical administrations of TXA have been demonstrated to be highly effective hemostatic agents in the perioperative period for TKA and THA.¹¹ Recent literature has demonstrated a significant reduction in drain output and mean change in preoperative to postoperative hemoglobin levels in patients who received TXA compared to that in patients who did not receive TXA. The patients who did not receive TXA had more than twice as much drain output.^{5,10,14,19-21}

Continue to: The ankle has a thin...

The ankle has a thin soft tissue envelope that does not have elaborate elastic properties. The soft tissue release and bleeding surfaces of the bone during TAA are not as extensive when compared with TKA and THA, but the intra-articular volume is smaller and the surrounding soft tissues may be less yielding when blood accumulation occurs.²² The vascular supply can be rich surrounding the ankle in the absence of arterial disease and is not as apt to tolerate dislocation and subluxation as in the case of THA or TKA.²³ Shear forces can easily tear the branches of the anterior tibial artery that lie within the fascia that is continuous with the periosteum on the distal tibia.²⁴ Reduction of hemarthrosis within the ankle joint may lead to a decrease in postoperative swelling, decreased pain, and increased range of motion due to the diminished potential for fibrosis. We also believe that there could be a reduced risk for wound complications. The current literature reports the rate of wound complications to be anywhere from 2% to 25%, with diabetes, inflammatory conditions, coronary artery disease, peripheral vascular disease, and smoking history >12-pack-years as risk factors.^16,25,26 In this study, we observed a significant reduction in drain output and an overall reduced percentage of postoperative wound complications in patients who received TXA. These results demonstrate that TXA use decreases postoperative hemarthrosis.

TXA use in TKA and THA has been shown to decrease direct hospital costs and hospital length of stay.^7,14,27 A recent study by Moskal and colleagues⁷ showed that topical TXA use has the potential to significantly decrease hospital man-hours for those patients undergoing TKA and achieve larger cost savings. Although there was no significant difference in the length of stay between the 2 groups, the average length of stay after TAA was shorter in both groups compared to the reported national average (1.49 vs 2.2 days, respectively).⁴ The administration of TXA in the appropriate patient has the potential to decrease hospital costs by controlling postoperative pain and swelling, allowing for earlier discharge. Long-term cost benefits could also include decreased infection rates and wound complications, and improved clinical outcomes because of improved range of motion and function scores.

The limitations of this study include the retrospective nature of its design and the relatively small sample size. The results showed nonstatistically significant differences in wound complications between the TXA-TAA and the No TXA-TAA groups, consistent with an insufficient sample size and thus inadequate power to detect the significant difference. However, this study clearly showed that the wound complication rates were higher in the No TXA-TAA group than in the TXA-TAA group, suggesting the importance of further similar studies using a larger sample size.

CONCLUSION

Current TAA offers a viable alternative to arthrodesis for end-stage ankle arthritis. TXA is an inexpensive and effective hemostatic agent used during TAA. If no major contraindication is present, routine use of TXA is recommended to assist in blood loss management, decrease postoperative hemarthrosis, and help to reduce the risk of postoperative wound complications.

ABSTRACT

Tranexamic acid (TXA) is an effective agent used for reducing perioperative blood loss and decreasing the potential for postoperative hemarthrosis. We hypothesized that patients who had received intraoperative TXA during total ankle arthroplasty (TAA) would have a reduction in postoperative drain output, thereby resulting in a reduced risk of postoperative hemarthrosis and lower wound complication rates.

A retrospective review was conducted on 50 consecutive patients, 25 receiving TXA (TXA-TAA) and 25 not receiving TXA (No TXA-TAA), who underwent an uncemented TAA between September 2011 and December 2015. Demographic characteristics, drain output, preoperative and postoperative hemoglobin levels, operative and postoperative course, and minor and major wound complications of the patients were reviewed.

Drain output was significantly less in the TXA-TAA group compared to that in the No TXA-TAA group (71.6 ± 60.3 vs 200.2 ± 117.0 mL, respectively, P < .0001). The overall wound complication rate in the No TXA-TAA group was higher (20%, 5/25) than that in the TXA-TAA group (8%, 2/25) (P = .114). The mean change in preoperative to postoperative hemoglobin level was significantly less in the TXA-TAA group compared to that in the No TXA-TAA group (1.5 ± 0.6 vs 2.0 ± 0.4 g/dL, respectively, P = .01).

TXA is an effective hemostatic agent when used during TAA. TXA reduces perioperative blood loss, hemarthrosis, and the risk of wound complications.

Continue to: End-stage ankle arthritis...

End-stage ankle arthritis is a disabling condition that may lead to poor quality of life and difficulties with activities of daily living.¹ The associated mental and physical disability has been demonstrated to be as severe as in end-stage hip arthrosis.² Operative treatment for symptomatic end-stage ankle arthritis includes arthrodesis or total ankle arthroplasty (TAA) in those refractory to nonoperative treatment.³ Newer generation implants have made TAA a more attractive option for both the surgeon and the patient.

Over the past decade, the utility of TAA has increased and attention has turned toward the management of perioperative factors that would maximize patient satisfaction and decrease the length of stay and complication rates, as well as hospital costs.⁴ Comprehensive literature on total knee arthroplasty (TKA) and total hip arthroplasty (THA) has demonstrated that the management of perioperative blood loss, specifically postoperative hemarthrosis, is a modifiable factor affecting patient recovery, complication rates, and hospital costs.^5-8 Drain output has been used as a direct measure of intra-articular blood accumulation.⁹ Decreased drain output implies decreased hemarthrosis, which could potentially alleviate the pressure on the wound and decrease wound complications.

One of the major strategies that has been recognized for reducing blood loss and decreasing the potential for postoperative hemarthrosis is the use of intravenous (IV) or topical tranexamic acid (TXA).^10,11 TXA is a synthetic antifibrinolytic medication that has been extensively used throughout the medical field since the 1960s to help control the bleeding cascade. This medication stabilizes clot formation without inducing a pro-coaguable state.¹² Intraoperative administration of TXA has been shown to reduce drain output and decrease transfusion requirements after TKA and THA without an associated increase in patient morbidity and mortality.^6,11,13-15

Currently, there is a lack of studies evaluating the utility of TXA during TAA. We hypothesize that compared with patients who had not received TXA, those who had received intraoperative TXA during TAA would have a reduction in postoperative drain output and therefore decreased hemarthrosis, lower wound complication rate, and a diminished change in preoperative to postoperative hemoglobin levels, reflecting a reduction in perioperative blood loss.

MATERIALS AND METHODS

This study was approved by the Institutional Review Board at the University at Buffalo, State University of New York. A retrospective chart review was conducted on 50 consecutive patients who underwent an uncemented TAA with the Salto Talaris total ankle prosthesis (Tornier, Inc) between September 2011 and December 2015. All surgeries were performed at 1 institution by a single fellowship surgeon trained in foot and ankle surgery through the anterior approach where a midline incision was made over the ankle. The interval between the tibialis anterior tendon and the extensor hallucis longus tendon was used. We had incorporated intraoperative TXA into the TAA surgical protocol at our institution in January 2014. We evaluated the first 25 consecutive patients who underwent TAA after TXA use began (TXA-TAA) and another 25 consecutive patients who underwent TAA before the routine use of TXA (No TXA-TAA). Inclusion criteria were patients who presented with pain, decreased function, and radiographic parameters of end-stage tibiotalar arthritis due to degenerative arthritis, rheumatoid arthritis, or posttraumatic arthritis who subsequently underwent a TAA. Exclusion criteria were patients with a contraindication for IV TXA use, a preexisting coagulopathy, or where drain output was not recorded. Contraindications for IV TXA use included patients with impaired renal clearance, recent cardiac surgery, myocardial infarction, ischemic stroke, or venous thromboembolism (VTE). Seven patients were ultimately excluded from this study based on the inclusion and exclusion criteria, 3 patients from the TXA-TAA group and 4 patients from the No TXA-TAA group.

Continue to: Charts were reviewed for demographics...

Charts were reviewed for demographics, preoperative and postoperative hemoglobin levels, indications for surgery, surgical procedures, length of surgery, postoperative drain output, length of stay, postoperative pain visual analog scale (VAS) score, minor and major wound complications, and postoperative complications. Minor wound complications were defined as the anterior surgical incision that required local wound care in office or oral antibiotics without subsequent consequences. Major wound complications were defined as requiring surgical débridement and/or any additional treatment in the operating room.¹⁶ Postoperative complications other than wound complications were defined as those requiring a subsequent surgical intervention. Patient demographics and clinical and procedural characteristics of patients in both the TXA-TAA and the No TXA-TAA groups are outlined in Table 1. There were 14 males and 11 females in the TXA-TAA group and 16 males and 9 females in the No TXA-TAA group. The mean age was 65.8 ± 10.9 years in the TXA-TAA group and 66.9 ± 8.0 years in the No TXA-TAA group (P = .69). Mean body mass index (BMI) was 31.6 ± 6.3 in the TXA-TAA group and 29.4 ± 4.9 in the No TXA-TAA group (P = .18). The primary indication for TAA was degenerative osteoarthritis in 26 patients, posttraumatic arthritis in 21 patients, and rheumatoid arthritis in 3 patients. The most common associated procedure was Achilles tendon lengthening in both groups. The mean follow-up in the TXA-TAA group was 9.3 ± 5.8 months (range, 2.0-24.0 months). Postoperative complications due to TXA administration as described in previous literature were defined as VTE, myocardial infarction, or ischemic cerebral event. The TXA-TAA group received a standard 1 g dose of IV TXA 20 minutes prior to tourniquet inflation. A tourniquet was used intraoperatively on all patients included in this study. A postoperative 400-mL surgical drain (Hemovac, Zimmer Biomet) was placed in the ankle joint in all patients and subsequently discontinued on postoperative day 1. Recent literature has reported the minor wound complication rate associated with TAA to be as high as 25% and the major wound complication rate to be 8.5%.¹⁶ To assist in reducing the risk for wound complications, our protocol traditionally uses an intra-articular surgical drain to decrease any pressure on the wound from postoperative hemarthrosis.

^aP value was calculated from t-test continuous variables and Chi-square test for categorical variables (TXA-TAA vs No TXA-TAA comparison).

Abbreviations: ASA, American Society of Anesthesiologists; BMI, body mass index.

Total drain output was recorded in milliliters (mL) in all patients. The change between the preoperative hemoglobin level and the hemoglobin level on postoperative day 1 was calculated for each patient. The calculated blood loss was determined using Meunier’s equation, which estimates the total blood volume using Nadler’s formula and then uses preoperative hemoglobin and postoperative day 1 hemoglobin values to calculate blood loss.^17,18 VAS scores (scale, 1-10) were obtained every 4 hours on postoperative day 1 according to the nursing protocol. The number 1 on the scale represents the least amount of pain, whereas 10 indicates the worst pain. The VAS scores were then averaged for each patient.

A power analysis using preliminary data determined that 15 patients were needed in each group to detect a 50% reduction in drain output at a power of 80% and a P value of 0.05. Descriptive statistics were used to analyze demographic data. We compared the demographic and clinical characteristics of patients in the TXA-TAA group with those of patients in the No TXA-TAA group using unpaired student t-tests for continuous variables and Chi-square or Fischer’s exact tests for categorical variables. Simple and adjusted linear regression analyses were used to examine the difference in drain output and blood loss between the 2 groups (TXA-TAA vs No TXA-TAA). Multivariate models were adjusted for age, BMI, and length of surgery. A P value <.05 was considered to be statistically significant. We performed all analyses using a statistical software package (SAS version 9.2, SAS Institute).

RESULTS

Drain output was significantly less in the TXA-TAA group compared to that in the No TXA-TAA group (71.6 ± 60.3 vs 200.2 ± 117.0 mL, respectively, P = .0001) (Figure). The clinical characteristics of the patients who underwent TAA with the use of TXA are outlined in Table 2. The mean change in preoperative to postoperative hemoglobin levels was significantly lower in the TXA-TAA group than in the No TXA-TAA group (1.5 ± 0.6 vs 2.0 ± 0.4 g/dL, respectively; P = .01). The calculated blood loss in patients in the TXA-TAA group was significantly lower than that in patients in the No TXA-TAA group (649.9 ± 332.7 vs 906.8 ± 287.4 mL, respectively; P = .01). No patient in either group received a blood transfusion. We did not observe a significant difference in the length of surgery between the TXA-TAA and the No TXA-TAA groups (112.8 ± 24.8 vs 108.6 ± 26.0 min, respectively; P = .57). The average American Society of Anesthesiologists’ (ASA) classification was similar between the groups (2.2 ± 0.6 and 2.2 ± 0.5, respectively; P = 1.00) as was the age-adjusted Charlson Comorbidity Index (2.8 ± 1.7 vs 2.9 ± 1.6, respectively; P = .93). Mean VAS scores on postoperative day 1 in the TXA-TAA and the No TXA-TAA group were 4.9 ± 1.7 and 5.3 ± 1.4, respectively (P = .71). The average length of stay in the TXA-TAA group was 1.6 ± 0.7 days vs 1.3 ± 0.6 days in the No TXA-TAA group (P = .23). Two patients in the TXA-TAA group had an extended hospital length of stay of 5 days due to discharge planning and social issues.

^aP value was calculated from t-test for continuous variables, and Chi-square test for categorical variables (TXA-TAA vs No TXA-TAA comparison).

Abbreviations: LOS, length of stay; VAS, visual analog scale; POD, postoperative day.

^aLinear regression was used to calculate the P value. ^bAdjusted for age, BMI and length of surgery.

Abbreviation: BMI, body mass index.

^aP value was calculated from Fisher’s Exact test (67% cells had count <5) test for categorical variables (TXA-TAA vs No TXA-TAA comparison).

The crude linear regression model revealed a significant difference in drain output between the TXA-TAA and the No TXA-TAA groups (β = −128.6 ± 26.3, P < .0001) (Table 3). Further adjustment for age and length of surgery slightly strengthened the association (β = −129.6 ± 26.6, P < .0001). The nature of regression coefficient β showed that the mean estimate of drain output was 129.6 mL lower in the TXA-TAA group than that in the No TXA-TAA group. There was a significant difference in blood loss between the TXA-TAA and the No TXA-TAA groups in the crude linear regression model (β = −257.0 ± 87.9, P = .005). Additional adjustment for age, BMI, and length of surgery slightly strengthened the association (β = −275.6 ± 90.7, P = .004). The nature of regression coefficient β showed that the mean estimate of blood loss was 275.6 mL lower in the TXA-TAA group than in the No TXA-TAA group (Table 3).

Continue to: There was no statistically significant difference...

There was no statistically significant difference in wound complications between the TXA-TAA and the No TXA-TAA groups in this study population (P = .114). However, our results showed a higher overall wound complication rate in the No TXA-TAA group than in the TXA-TAA group (20% (5/25) vs 8% (2/25), respectively) (Table 4). In the No TXA-TAA group, there were 4 minor and 1 major wound complications. All 5 patients experiencing a postoperative wound complication required oral antibiotics for a minimum of 4 weeks and local wound care. One patient underwent a surgical débridement meeting the criteria for major wound complications. In the TXA-TAA group, there were 2 minor wound complications and no major wound complications. One patient was administered prophylactic oral antibiotics for 7 days with local wound care for blister formation without evidence of infection. The second patient experiencing a minor wound complication required 3 weeks of oral antibiotics and local wound care. No patients in either group had a deep infection requiring implant removal, IV antibiotics, or subsequent hospital admission. The surgical incisions in all patients healed after the aforementioned treatments with no persistent drainage or development of chronic wounds.

In the TXA-TAA group, there was 1 patient who sustained an intraoperative medial malleolus fracture. One patient developed an extensor hallucis longus contracture 5 months postoperatively that subsequently underwent release and lengthening. There was 1 patient in this group who sustained a distal tibia fracture 5 cm proximal to the prosthesis 3 months postoperatively after a mechanical fall. In the No TXA-TAA group, there were 2 patients who sustained intraoperative medial malleolus fractures. One patient underwent a revision of the tibial component 24 months postoperatively due to aseptic loosening. In addition, another patient in this group who sustained an Achilles tendon rupture 5 months postoperatively after a fall subsequently underwent repair with tibialis anterior tendon allograft.

There were no patients in either group who experienced any hospital readmissions in the acute follow-up period as defined by a 90-day period after discharge. There were no complications associated with TXA administration in either group.

DISCUSSION

Recent advances in total ankle prosthetic design coupled with increased survival and improved short- to midterm follow-up results make TAA an effective treatment option for end-stage ankle arthritis. Management of perioperative blood loss and reducing the potential for significant hemarthrosis and subsequent wound complications are important factors to consider for patients undergoing TAA. TXA administration is used in several centers as part of an intraoperative strategy to reduce blood loss and decrease intra-articular blood accumulation. To our knowledge, this is the first study to evaluate the management of blood loss and hemarthrosis using TXA during TAA.

IV and topical administrations of TXA have been demonstrated to be highly effective hemostatic agents in the perioperative period for TKA and THA.¹¹ Recent literature has demonstrated a significant reduction in drain output and mean change in preoperative to postoperative hemoglobin levels in patients who received TXA compared to that in patients who did not receive TXA. The patients who did not receive TXA had more than twice as much drain output.^{5,10,14,19-21}

Continue to: The ankle has a thin...

The ankle has a thin soft tissue envelope that does not have elaborate elastic properties. The soft tissue release and bleeding surfaces of the bone during TAA are not as extensive when compared with TKA and THA, but the intra-articular volume is smaller and the surrounding soft tissues may be less yielding when blood accumulation occurs.²² The vascular supply can be rich surrounding the ankle in the absence of arterial disease and is not as apt to tolerate dislocation and subluxation as in the case of THA or TKA.²³ Shear forces can easily tear the branches of the anterior tibial artery that lie within the fascia that is continuous with the periosteum on the distal tibia.²⁴ Reduction of hemarthrosis within the ankle joint may lead to a decrease in postoperative swelling, decreased pain, and increased range of motion due to the diminished potential for fibrosis. We also believe that there could be a reduced risk for wound complications. The current literature reports the rate of wound complications to be anywhere from 2% to 25%, with diabetes, inflammatory conditions, coronary artery disease, peripheral vascular disease, and smoking history >12-pack-years as risk factors.^16,25,26 In this study, we observed a significant reduction in drain output and an overall reduced percentage of postoperative wound complications in patients who received TXA. These results demonstrate that TXA use decreases postoperative hemarthrosis.

TXA use in TKA and THA has been shown to decrease direct hospital costs and hospital length of stay.^7,14,27 A recent study by Moskal and colleagues⁷ showed that topical TXA use has the potential to significantly decrease hospital man-hours for those patients undergoing TKA and achieve larger cost savings. Although there was no significant difference in the length of stay between the 2 groups, the average length of stay after TAA was shorter in both groups compared to the reported national average (1.49 vs 2.2 days, respectively).⁴ The administration of TXA in the appropriate patient has the potential to decrease hospital costs by controlling postoperative pain and swelling, allowing for earlier discharge. Long-term cost benefits could also include decreased infection rates and wound complications, and improved clinical outcomes because of improved range of motion and function scores.

The limitations of this study include the retrospective nature of its design and the relatively small sample size. The results showed nonstatistically significant differences in wound complications between the TXA-TAA and the No TXA-TAA groups, consistent with an insufficient sample size and thus inadequate power to detect the significant difference. However, this study clearly showed that the wound complication rates were higher in the No TXA-TAA group than in the TXA-TAA group, suggesting the importance of further similar studies using a larger sample size.

CONCLUSION

Current TAA offers a viable alternative to arthrodesis for end-stage ankle arthritis. TXA is an inexpensive and effective hemostatic agent used during TAA. If no major contraindication is present, routine use of TXA is recommended to assist in blood loss management, decrease postoperative hemarthrosis, and help to reduce the risk of postoperative wound complications.

ABSTRACT

Tranexamic acid (TXA) is an effective agent used for reducing perioperative blood loss and decreasing the potential for postoperative hemarthrosis. We hypothesized that patients who had received intraoperative TXA during total ankle arthroplasty (TAA) would have a reduction in postoperative drain output, thereby resulting in a reduced risk of postoperative hemarthrosis and lower wound complication rates.

A retrospective review was conducted on 50 consecutive patients, 25 receiving TXA (TXA-TAA) and 25 not receiving TXA (No TXA-TAA), who underwent an uncemented TAA between September 2011 and December 2015. Demographic characteristics, drain output, preoperative and postoperative hemoglobin levels, operative and postoperative course, and minor and major wound complications of the patients were reviewed.

Drain output was significantly less in the TXA-TAA group compared to that in the No TXA-TAA group (71.6 ± 60.3 vs 200.2 ± 117.0 mL, respectively, P < .0001). The overall wound complication rate in the No TXA-TAA group was higher (20%, 5/25) than that in the TXA-TAA group (8%, 2/25) (P = .114). The mean change in preoperative to postoperative hemoglobin level was significantly less in the TXA-TAA group compared to that in the No TXA-TAA group (1.5 ± 0.6 vs 2.0 ± 0.4 g/dL, respectively, P = .01).

TXA is an effective hemostatic agent when used during TAA. TXA reduces perioperative blood loss, hemarthrosis, and the risk of wound complications.

Continue to: End-stage ankle arthritis...

End-stage ankle arthritis is a disabling condition that may lead to poor quality of life and difficulties with activities of daily living.¹ The associated mental and physical disability has been demonstrated to be as severe as in end-stage hip arthrosis.² Operative treatment for symptomatic end-stage ankle arthritis includes arthrodesis or total ankle arthroplasty (TAA) in those refractory to nonoperative treatment.³ Newer generation implants have made TAA a more attractive option for both the surgeon and the patient.

Over the past decade, the utility of TAA has increased and attention has turned toward the management of perioperative factors that would maximize patient satisfaction and decrease the length of stay and complication rates, as well as hospital costs.⁴ Comprehensive literature on total knee arthroplasty (TKA) and total hip arthroplasty (THA) has demonstrated that the management of perioperative blood loss, specifically postoperative hemarthrosis, is a modifiable factor affecting patient recovery, complication rates, and hospital costs.^5-8 Drain output has been used as a direct measure of intra-articular blood accumulation.⁹ Decreased drain output implies decreased hemarthrosis, which could potentially alleviate the pressure on the wound and decrease wound complications.

One of the major strategies that has been recognized for reducing blood loss and decreasing the potential for postoperative hemarthrosis is the use of intravenous (IV) or topical tranexamic acid (TXA).^10,11 TXA is a synthetic antifibrinolytic medication that has been extensively used throughout the medical field since the 1960s to help control the bleeding cascade. This medication stabilizes clot formation without inducing a pro-coaguable state.¹² Intraoperative administration of TXA has been shown to reduce drain output and decrease transfusion requirements after TKA and THA without an associated increase in patient morbidity and mortality.^6,11,13-15

Currently, there is a lack of studies evaluating the utility of TXA during TAA. We hypothesize that compared with patients who had not received TXA, those who had received intraoperative TXA during TAA would have a reduction in postoperative drain output and therefore decreased hemarthrosis, lower wound complication rate, and a diminished change in preoperative to postoperative hemoglobin levels, reflecting a reduction in perioperative blood loss.

MATERIALS AND METHODS

This study was approved by the Institutional Review Board at the University at Buffalo, State University of New York. A retrospective chart review was conducted on 50 consecutive patients who underwent an uncemented TAA with the Salto Talaris total ankle prosthesis (Tornier, Inc) between September 2011 and December 2015. All surgeries were performed at 1 institution by a single fellowship surgeon trained in foot and ankle surgery through the anterior approach where a midline incision was made over the ankle. The interval between the tibialis anterior tendon and the extensor hallucis longus tendon was used. We had incorporated intraoperative TXA into the TAA surgical protocol at our institution in January 2014. We evaluated the first 25 consecutive patients who underwent TAA after TXA use began (TXA-TAA) and another 25 consecutive patients who underwent TAA before the routine use of TXA (No TXA-TAA). Inclusion criteria were patients who presented with pain, decreased function, and radiographic parameters of end-stage tibiotalar arthritis due to degenerative arthritis, rheumatoid arthritis, or posttraumatic arthritis who subsequently underwent a TAA. Exclusion criteria were patients with a contraindication for IV TXA use, a preexisting coagulopathy, or where drain output was not recorded. Contraindications for IV TXA use included patients with impaired renal clearance, recent cardiac surgery, myocardial infarction, ischemic stroke, or venous thromboembolism (VTE). Seven patients were ultimately excluded from this study based on the inclusion and exclusion criteria, 3 patients from the TXA-TAA group and 4 patients from the No TXA-TAA group.

Continue to: Charts were reviewed for demographics...

Charts were reviewed for demographics, preoperative and postoperative hemoglobin levels, indications for surgery, surgical procedures, length of surgery, postoperative drain output, length of stay, postoperative pain visual analog scale (VAS) score, minor and major wound complications, and postoperative complications. Minor wound complications were defined as the anterior surgical incision that required local wound care in office or oral antibiotics without subsequent consequences. Major wound complications were defined as requiring surgical débridement and/or any additional treatment in the operating room.¹⁶ Postoperative complications other than wound complications were defined as those requiring a subsequent surgical intervention. Patient demographics and clinical and procedural characteristics of patients in both the TXA-TAA and the No TXA-TAA groups are outlined in Table 1. There were 14 males and 11 females in the TXA-TAA group and 16 males and 9 females in the No TXA-TAA group. The mean age was 65.8 ± 10.9 years in the TXA-TAA group and 66.9 ± 8.0 years in the No TXA-TAA group (P = .69). Mean body mass index (BMI) was 31.6 ± 6.3 in the TXA-TAA group and 29.4 ± 4.9 in the No TXA-TAA group (P = .18). The primary indication for TAA was degenerative osteoarthritis in 26 patients, posttraumatic arthritis in 21 patients, and rheumatoid arthritis in 3 patients. The most common associated procedure was Achilles tendon lengthening in both groups. The mean follow-up in the TXA-TAA group was 9.3 ± 5.8 months (range, 2.0-24.0 months). Postoperative complications due to TXA administration as described in previous literature were defined as VTE, myocardial infarction, or ischemic cerebral event. The TXA-TAA group received a standard 1 g dose of IV TXA 20 minutes prior to tourniquet inflation. A tourniquet was used intraoperatively on all patients included in this study. A postoperative 400-mL surgical drain (Hemovac, Zimmer Biomet) was placed in the ankle joint in all patients and subsequently discontinued on postoperative day 1. Recent literature has reported the minor wound complication rate associated with TAA to be as high as 25% and the major wound complication rate to be 8.5%.¹⁶ To assist in reducing the risk for wound complications, our protocol traditionally uses an intra-articular surgical drain to decrease any pressure on the wound from postoperative hemarthrosis.

^aP value was calculated from t-test continuous variables and Chi-square test for categorical variables (TXA-TAA vs No TXA-TAA comparison).

Abbreviations: ASA, American Society of Anesthesiologists; BMI, body mass index.

Total drain output was recorded in milliliters (mL) in all patients. The change between the preoperative hemoglobin level and the hemoglobin level on postoperative day 1 was calculated for each patient. The calculated blood loss was determined using Meunier’s equation, which estimates the total blood volume using Nadler’s formula and then uses preoperative hemoglobin and postoperative day 1 hemoglobin values to calculate blood loss.^17,18 VAS scores (scale, 1-10) were obtained every 4 hours on postoperative day 1 according to the nursing protocol. The number 1 on the scale represents the least amount of pain, whereas 10 indicates the worst pain. The VAS scores were then averaged for each patient.

A power analysis using preliminary data determined that 15 patients were needed in each group to detect a 50% reduction in drain output at a power of 80% and a P value of 0.05. Descriptive statistics were used to analyze demographic data. We compared the demographic and clinical characteristics of patients in the TXA-TAA group with those of patients in the No TXA-TAA group using unpaired student t-tests for continuous variables and Chi-square or Fischer’s exact tests for categorical variables. Simple and adjusted linear regression analyses were used to examine the difference in drain output and blood loss between the 2 groups (TXA-TAA vs No TXA-TAA). Multivariate models were adjusted for age, BMI, and length of surgery. A P value <.05 was considered to be statistically significant. We performed all analyses using a statistical software package (SAS version 9.2, SAS Institute).

RESULTS

Drain output was significantly less in the TXA-TAA group compared to that in the No TXA-TAA group (71.6 ± 60.3 vs 200.2 ± 117.0 mL, respectively, P = .0001) (Figure). The clinical characteristics of the patients who underwent TAA with the use of TXA are outlined in Table 2. The mean change in preoperative to postoperative hemoglobin levels was significantly lower in the TXA-TAA group than in the No TXA-TAA group (1.5 ± 0.6 vs 2.0 ± 0.4 g/dL, respectively; P = .01). The calculated blood loss in patients in the TXA-TAA group was significantly lower than that in patients in the No TXA-TAA group (649.9 ± 332.7 vs 906.8 ± 287.4 mL, respectively; P = .01). No patient in either group received a blood transfusion. We did not observe a significant difference in the length of surgery between the TXA-TAA and the No TXA-TAA groups (112.8 ± 24.8 vs 108.6 ± 26.0 min, respectively; P = .57). The average American Society of Anesthesiologists’ (ASA) classification was similar between the groups (2.2 ± 0.6 and 2.2 ± 0.5, respectively; P = 1.00) as was the age-adjusted Charlson Comorbidity Index (2.8 ± 1.7 vs 2.9 ± 1.6, respectively; P = .93). Mean VAS scores on postoperative day 1 in the TXA-TAA and the No TXA-TAA group were 4.9 ± 1.7 and 5.3 ± 1.4, respectively (P = .71). The average length of stay in the TXA-TAA group was 1.6 ± 0.7 days vs 1.3 ± 0.6 days in the No TXA-TAA group (P = .23). Two patients in the TXA-TAA group had an extended hospital length of stay of 5 days due to discharge planning and social issues.

^aP value was calculated from t-test for continuous variables, and Chi-square test for categorical variables (TXA-TAA vs No TXA-TAA comparison).

Abbreviations: LOS, length of stay; VAS, visual analog scale; POD, postoperative day.

^aLinear regression was used to calculate the P value. ^bAdjusted for age, BMI and length of surgery.

Abbreviation: BMI, body mass index.

^aP value was calculated from Fisher’s Exact test (67% cells had count <5) test for categorical variables (TXA-TAA vs No TXA-TAA comparison).

The crude linear regression model revealed a significant difference in drain output between the TXA-TAA and the No TXA-TAA groups (β = −128.6 ± 26.3, P < .0001) (Table 3). Further adjustment for age and length of surgery slightly strengthened the association (β = −129.6 ± 26.6, P < .0001). The nature of regression coefficient β showed that the mean estimate of drain output was 129.6 mL lower in the TXA-TAA group than that in the No TXA-TAA group. There was a significant difference in blood loss between the TXA-TAA and the No TXA-TAA groups in the crude linear regression model (β = −257.0 ± 87.9, P = .005). Additional adjustment for age, BMI, and length of surgery slightly strengthened the association (β = −275.6 ± 90.7, P = .004). The nature of regression coefficient β showed that the mean estimate of blood loss was 275.6 mL lower in the TXA-TAA group than in the No TXA-TAA group (Table 3).

Continue to: There was no statistically significant difference...

There was no statistically significant difference in wound complications between the TXA-TAA and the No TXA-TAA groups in this study population (P = .114). However, our results showed a higher overall wound complication rate in the No TXA-TAA group than in the TXA-TAA group (20% (5/25) vs 8% (2/25), respectively) (Table 4). In the No TXA-TAA group, there were 4 minor and 1 major wound complications. All 5 patients experiencing a postoperative wound complication required oral antibiotics for a minimum of 4 weeks and local wound care. One patient underwent a surgical débridement meeting the criteria for major wound complications. In the TXA-TAA group, there were 2 minor wound complications and no major wound complications. One patient was administered prophylactic oral antibiotics for 7 days with local wound care for blister formation without evidence of infection. The second patient experiencing a minor wound complication required 3 weeks of oral antibiotics and local wound care. No patients in either group had a deep infection requiring implant removal, IV antibiotics, or subsequent hospital admission. The surgical incisions in all patients healed after the aforementioned treatments with no persistent drainage or development of chronic wounds.

In the TXA-TAA group, there was 1 patient who sustained an intraoperative medial malleolus fracture. One patient developed an extensor hallucis longus contracture 5 months postoperatively that subsequently underwent release and lengthening. There was 1 patient in this group who sustained a distal tibia fracture 5 cm proximal to the prosthesis 3 months postoperatively after a mechanical fall. In the No TXA-TAA group, there were 2 patients who sustained intraoperative medial malleolus fractures. One patient underwent a revision of the tibial component 24 months postoperatively due to aseptic loosening. In addition, another patient in this group who sustained an Achilles tendon rupture 5 months postoperatively after a fall subsequently underwent repair with tibialis anterior tendon allograft.

There were no patients in either group who experienced any hospital readmissions in the acute follow-up period as defined by a 90-day period after discharge. There were no complications associated with TXA administration in either group.

DISCUSSION

Recent advances in total ankle prosthetic design coupled with increased survival and improved short- to midterm follow-up results make TAA an effective treatment option for end-stage ankle arthritis. Management of perioperative blood loss and reducing the potential for significant hemarthrosis and subsequent wound complications are important factors to consider for patients undergoing TAA. TXA administration is used in several centers as part of an intraoperative strategy to reduce blood loss and decrease intra-articular blood accumulation. To our knowledge, this is the first study to evaluate the management of blood loss and hemarthrosis using TXA during TAA.

IV and topical administrations of TXA have been demonstrated to be highly effective hemostatic agents in the perioperative period for TKA and THA.¹¹ Recent literature has demonstrated a significant reduction in drain output and mean change in preoperative to postoperative hemoglobin levels in patients who received TXA compared to that in patients who did not receive TXA. The patients who did not receive TXA had more than twice as much drain output.^{5,10,14,19-21}

Continue to: The ankle has a thin...

The ankle has a thin soft tissue envelope that does not have elaborate elastic properties. The soft tissue release and bleeding surfaces of the bone during TAA are not as extensive when compared with TKA and THA, but the intra-articular volume is smaller and the surrounding soft tissues may be less yielding when blood accumulation occurs.²² The vascular supply can be rich surrounding the ankle in the absence of arterial disease and is not as apt to tolerate dislocation and subluxation as in the case of THA or TKA.²³ Shear forces can easily tear the branches of the anterior tibial artery that lie within the fascia that is continuous with the periosteum on the distal tibia.²⁴ Reduction of hemarthrosis within the ankle joint may lead to a decrease in postoperative swelling, decreased pain, and increased range of motion due to the diminished potential for fibrosis. We also believe that there could be a reduced risk for wound complications. The current literature reports the rate of wound complications to be anywhere from 2% to 25%, with diabetes, inflammatory conditions, coronary artery disease, peripheral vascular disease, and smoking history >12-pack-years as risk factors.^16,25,26 In this study, we observed a significant reduction in drain output and an overall reduced percentage of postoperative wound complications in patients who received TXA. These results demonstrate that TXA use decreases postoperative hemarthrosis.

TXA use in TKA and THA has been shown to decrease direct hospital costs and hospital length of stay.^7,14,27 A recent study by Moskal and colleagues⁷ showed that topical TXA use has the potential to significantly decrease hospital man-hours for those patients undergoing TKA and achieve larger cost savings. Although there was no significant difference in the length of stay between the 2 groups, the average length of stay after TAA was shorter in both groups compared to the reported national average (1.49 vs 2.2 days, respectively).⁴ The administration of TXA in the appropriate patient has the potential to decrease hospital costs by controlling postoperative pain and swelling, allowing for earlier discharge. Long-term cost benefits could also include decreased infection rates and wound complications, and improved clinical outcomes because of improved range of motion and function scores.

The limitations of this study include the retrospective nature of its design and the relatively small sample size. The results showed nonstatistically significant differences in wound complications between the TXA-TAA and the No TXA-TAA groups, consistent with an insufficient sample size and thus inadequate power to detect the significant difference. However, this study clearly showed that the wound complication rates were higher in the No TXA-TAA group than in the TXA-TAA group, suggesting the importance of further similar studies using a larger sample size.

CONCLUSION

Current TAA offers a viable alternative to arthrodesis for end-stage ankle arthritis. TXA is an inexpensive and effective hemostatic agent used during TAA. If no major contraindication is present, routine use of TXA is recommended to assist in blood loss management, decrease postoperative hemarthrosis, and help to reduce the risk of postoperative wound complications.

Provider attire has come under scrutiny in the more recent medical literature. Epidemiologic data have shown that lab coats, ties, and other articles of clothing are frequently contaminated with disease-causing pathogens including methicillin-resistant Staphylococcus aureus , vancomycin-resistant enterococci, Acinetobacter species, Enterobacteriaceae, Pseudomona s species, and Clostridium difficile.¹ Clothing may serve as a vector for spread of these bacteria and may contribute to hospital-acquired infections, increased cost of care, and patient morbidity. Prior to February 2015, the dermatology service line at Geisinger Medical Center in Danville, Pennsylvania, had followed a formal dress code that included white lab coats (white coats) along with long-sleeve shirts and ties/bowties for male providers and blouses, skirts, dress pants, and dresses for female providers. After a review of the recent literature on contamination rates of provider attire,² we transitioned away from formal attire to adopt fitted, embroidered, black or navy blue scrubs to be worn in the clinic (Figure). Fitted scrubs differ from traditional unisex operating room scrubs, conferring a more professional appearance.

Limited research has shown that dermatology patients may have a slight preference for formal provider attire.^2,3 In these studies, patients were shown photographs of providers in various dress (ie, professional attire, business attire, casual attire, scrubs). Patients preferred or had more confidence in the photograph of the provider in professional attire^2,3; however, it is unclear if dermatology provider attire has any measurable effect on overall patient satisfaction. Patient satisfaction relies on a myriad of factors, including both spoken and unspoken communication skills. Patient satisfaction has become an integral part of health care, and with an emphasis on value-based care, it will likely be one determining factor in how providers are reimbursed for their services.^4,5 In this study, we investigated if a change from formal attire to fitted scrubs influenced patient satisfaction using a common third-party patient satisfaction survey.

Methods

Patient Satisfaction Survey
We conducted a retrospective cohort study analyzing 10 questions from the care provider section of the Press Ganey third-party patient satisfaction survey regarding providers in our dermatology service line. Only providers with at least 12 months of survey data before (study period 1) and after (study period 2) the change in attire were included in the study. Mohs surgeons were excluded, as they already wore fitted scrubs in the clinic. Residents also were excluded, as they are rapidly developing their patient communication skills and may have a notable change in patient satisfaction over a 2-year period.

The survey data were collected, and provider names were removed and replaced with alphanumeric codes to protect anonymity while still allowing individual provider analysis. Aggregate patient comments from surveys before and after the change in attire were digitally searched using the terms scrub, coat, white, attire, and clothing for pertinent positive or negative comments.

Outcomes
We compared individual and aggregate satisfaction scores for our providers during the 12-month periods before and after the adoption of fitted scrubs. The primary outcome was statistically significant change in patient satisfaction scores before and after the institution of fitted scrubs. Secondary outcomes included summation of patient comments, both positive and negative, regarding provider attire, as recorded on satisfaction surveys.

Statistical Analysis
Overall survey scores and scores on individual survey items were summarized using mean (SD), median and interquartile range, or frequency counts and percentage, as appropriate. The overall satisfaction score and responses to individual survey items were compared using Mantel-Haenszel or Pearson χ² tests, as appropriate.

Assuming an equal number of surveys would be completed during study periods 1 and 2, an average (SD) satisfaction score of 95.4 (15), we calculated that as many as 2136 surveys would be needed to conclude satisfaction scores are the same for equivalence limits of −1.9 and 1.9 (a 1% difference). As few as 352 surveys would be needed to conclude satisfaction scores are the same for equivalence limits of −4.7 and 4.7 (a 5% difference). Sample size calculations assume 80% power and a significance level of 0.05. Comparison of responses for study periods 1 and 2 were made using the Mantel-Haenszel χ² test.

Because more than 80% of respondents selected very good for each question, the responses also were treated as dichotomous variables with a category for very good and a category for responses that were lower than very good (ie, good, fair, poor, very poor). Responses of very good versus less than very good were compared for the study periods 1 and 2 using the Pearson χ² test.

Two versions of an overall score were analyzed. The first version was for patients who responded to at least 1 of 10 survey items. If responses to all the items were very good, the patient was assigned to the category of all very good. If a patient answered any of the questions with a response less than very good, he/she was categorized as at least 1 less than very good. The second version was for patients who responded to all 10 survey items. If all 10 responses were very good, the patient was assigned to a category of all very good. If any of the 10 responses were less than very good, he/she was categorized as at least 1 less than very good. Differences between study periods for both score versions were tested using the Pearson χ² test.

Results

Data for 22 providers in the dermatology service line—13 staff dermatologists, 6 physician assistants, 1 nurse practitioner, and 2 podiatrists—were included in the study, with a total of 7702 patient satisfaction surveys completed between February 1, 2014, and January 31, 2016: 3511 were completed between February 1, 2014, and January 31, 2015 (study period 1), and 4191 were completed between February 1, 2015, and January 31, 2016 (study period 2).

Analysis of the overall distribution of possible responses for each survey item showed significant differences between study periods 1 and 2 for friendliness/courtesy of the care provider (P=.0307), explanations the care provider gave about the problem or condition (P=.0038), concern the care provider showed for questions or worries (P=.0087), care provider’s efforts to include the patient in decisions about treatment (P=.0377), and patient confidence in the care provider (P=.0156). These survey items trended toward more positive responses in study period 2. The full results are provided in eTable 1.

The analysis that looked at responses as binary (very good vs less than very good) showed a greater proportion of very good responses for friendliness/courtesy of the care provider (P=.0438), explanations the care provider gave about the problem or condition (P=.0115), concern the care provider showed for questions or worries (P=.0188), and patient confidence in the care provider (P=.0417). The full results are provided in eTable 2.

There were no significant differences in the overall satisfaction scores between the first and second study periods. The differences were statistically significant when the overall score was calculated if any questions were answered (P=.5177) and when the overall score was calculated if all 10 questions were answered (P=.9959). For patients who responded to all survey items, 75.3% selected all very good responses for both the first and second study periods.

Review of the surveys for comments from both study periods revealed only a single patient comment pertaining to attire. The comment, which was submitted during study period 2, was considered positive, referring to the fitted scrubs as neat and professional. No negative comments were found during either period.

Comment

In this study, we did not find that a change from formal attire to fitted scrubs had a measurable negative impact on patient satisfaction scores. Conversely, we found a small but statistically significant improvement on several survey items after the change to fitted scrubs. The data suggest that changing from formal attire to fitted scrubs in an outpatient dermatology clinic had little impact on overall patient satisfaction. Only 1 positive comment and no negative comments were received regarding providers wearing fitted scrubs.

A prior study in an outpatient gynecology/obstetrics clinic showed similar results.⁶ In that study, providers were randomly assigned to business attire, casual attire, or scrubs. A 10-question patient satisfaction survey was designed that specifically avoided asking about provider attire to reduce any bias. The study found that over a 3-month period, attire had no influence on patient satisfaction.⁶

Our data suggest that factors beyond provider attire have the greatest influence on patient satisfaction scores. Patient satisfaction is likely driven by other factors such as provider communication skills, concern for patient well-being, ability to empathize, and timeliness. Given the biologic plausibility of increased infection rate from contaminated provider attire, we feel that comfortable, washable, fitted scrubs provide a sanitary and acceptable alternative to more traditional formal provider attire in the office setting. Bearman et al¹ suggest consideration of a bare-below-the-elbows policy (with or without scrubs) for inpatient services and lab coats (if worn per facility policy), and other articles of clothing should be laundered frequently or if visibly soiled. We feel these policies also can be applied to outpatient dermatology clinics, as long as the rationale is well communicated to all parties.

Several items on the patient satisfaction survey were statistically improved during the second study period; however, it is impossible to determine if provider attire was an important factor in this change. Improvement in satisfaction scores could be attributed to ongoing departmental and institutional emphasis on patient care and servic

Anecdotally, most providers in our department were enthusiastic and supportive of the change to fitted scrubs. It is possible that provider happiness is reflected in improved patient satisfaction scores. Provider satisfaction has been shown to correlate with patient satisfaction.⁷

Limitations include possible other unmeasured variables that had a more substantial impact on patient satisfaction survey results. We also recognize that the survey used in this study contained no questions that directly asked patients about their satisfaction with provider attire; however, bias or any preconception patients may have had regarding attire may have been avoided in the process. We also were not able to separate patient surveys based on age or other demographics. Finally, our results may not be generalizable to other settings where patient perceptions may be different from those of central Pennsylvania.

Conclusion

Transitioning from formal provider attire to fitted scrubs did not have a strong impact on overall patient satisfaction scores in an outpatient dermatology clinic. Providers and institutions should consider this information when developing dress code policies.

Provider attire has come under scrutiny in the more recent medical literature. Epidemiologic data have shown that lab coats, ties, and other articles of clothing are frequently contaminated with disease-causing pathogens including methicillin-resistant Staphylococcus aureus , vancomycin-resistant enterococci, Acinetobacter species, Enterobacteriaceae, Pseudomona s species, and Clostridium difficile.¹ Clothing may serve as a vector for spread of these bacteria and may contribute to hospital-acquired infections, increased cost of care, and patient morbidity. Prior to February 2015, the dermatology service line at Geisinger Medical Center in Danville, Pennsylvania, had followed a formal dress code that included white lab coats (white coats) along with long-sleeve shirts and ties/bowties for male providers and blouses, skirts, dress pants, and dresses for female providers. After a review of the recent literature on contamination rates of provider attire,² we transitioned away from formal attire to adopt fitted, embroidered, black or navy blue scrubs to be worn in the clinic (Figure). Fitted scrubs differ from traditional unisex operating room scrubs, conferring a more professional appearance.

Limited research has shown that dermatology patients may have a slight preference for formal provider attire.^2,3 In these studies, patients were shown photographs of providers in various dress (ie, professional attire, business attire, casual attire, scrubs). Patients preferred or had more confidence in the photograph of the provider in professional attire^2,3; however, it is unclear if dermatology provider attire has any measurable effect on overall patient satisfaction. Patient satisfaction relies on a myriad of factors, including both spoken and unspoken communication skills. Patient satisfaction has become an integral part of health care, and with an emphasis on value-based care, it will likely be one determining factor in how providers are reimbursed for their services.^4,5 In this study, we investigated if a change from formal attire to fitted scrubs influenced patient satisfaction using a common third-party patient satisfaction survey.

Methods

Patient Satisfaction Survey
We conducted a retrospective cohort study analyzing 10 questions from the care provider section of the Press Ganey third-party patient satisfaction survey regarding providers in our dermatology service line. Only providers with at least 12 months of survey data before (study period 1) and after (study period 2) the change in attire were included in the study. Mohs surgeons were excluded, as they already wore fitted scrubs in the clinic. Residents also were excluded, as they are rapidly developing their patient communication skills and may have a notable change in patient satisfaction over a 2-year period.

The survey data were collected, and provider names were removed and replaced with alphanumeric codes to protect anonymity while still allowing individual provider analysis. Aggregate patient comments from surveys before and after the change in attire were digitally searched using the terms scrub, coat, white, attire, and clothing for pertinent positive or negative comments.

Outcomes
We compared individual and aggregate satisfaction scores for our providers during the 12-month periods before and after the adoption of fitted scrubs. The primary outcome was statistically significant change in patient satisfaction scores before and after the institution of fitted scrubs. Secondary outcomes included summation of patient comments, both positive and negative, regarding provider attire, as recorded on satisfaction surveys.

Statistical Analysis
Overall survey scores and scores on individual survey items were summarized using mean (SD), median and interquartile range, or frequency counts and percentage, as appropriate. The overall satisfaction score and responses to individual survey items were compared using Mantel-Haenszel or Pearson χ² tests, as appropriate.

Assuming an equal number of surveys would be completed during study periods 1 and 2, an average (SD) satisfaction score of 95.4 (15), we calculated that as many as 2136 surveys would be needed to conclude satisfaction scores are the same for equivalence limits of −1.9 and 1.9 (a 1% difference). As few as 352 surveys would be needed to conclude satisfaction scores are the same for equivalence limits of −4.7 and 4.7 (a 5% difference). Sample size calculations assume 80% power and a significance level of 0.05. Comparison of responses for study periods 1 and 2 were made using the Mantel-Haenszel χ² test.

Because more than 80% of respondents selected very good for each question, the responses also were treated as dichotomous variables with a category for very good and a category for responses that were lower than very good (ie, good, fair, poor, very poor). Responses of very good versus less than very good were compared for the study periods 1 and 2 using the Pearson χ² test.

Two versions of an overall score were analyzed. The first version was for patients who responded to at least 1 of 10 survey items. If responses to all the items were very good, the patient was assigned to the category of all very good. If a patient answered any of the questions with a response less than very good, he/she was categorized as at least 1 less than very good. The second version was for patients who responded to all 10 survey items. If all 10 responses were very good, the patient was assigned to a category of all very good. If any of the 10 responses were less than very good, he/she was categorized as at least 1 less than very good. Differences between study periods for both score versions were tested using the Pearson χ² test.

Results

Data for 22 providers in the dermatology service line—13 staff dermatologists, 6 physician assistants, 1 nurse practitioner, and 2 podiatrists—were included in the study, with a total of 7702 patient satisfaction surveys completed between February 1, 2014, and January 31, 2016: 3511 were completed between February 1, 2014, and January 31, 2015 (study period 1), and 4191 were completed between February 1, 2015, and January 31, 2016 (study period 2).

Analysis of the overall distribution of possible responses for each survey item showed significant differences between study periods 1 and 2 for friendliness/courtesy of the care provider (P=.0307), explanations the care provider gave about the problem or condition (P=.0038), concern the care provider showed for questions or worries (P=.0087), care provider’s efforts to include the patient in decisions about treatment (P=.0377), and patient confidence in the care provider (P=.0156). These survey items trended toward more positive responses in study period 2. The full results are provided in eTable 1.

The analysis that looked at responses as binary (very good vs less than very good) showed a greater proportion of very good responses for friendliness/courtesy of the care provider (P=.0438), explanations the care provider gave about the problem or condition (P=.0115), concern the care provider showed for questions or worries (P=.0188), and patient confidence in the care provider (P=.0417). The full results are provided in eTable 2.

There were no significant differences in the overall satisfaction scores between the first and second study periods. The differences were statistically significant when the overall score was calculated if any questions were answered (P=.5177) and when the overall score was calculated if all 10 questions were answered (P=.9959). For patients who responded to all survey items, 75.3% selected all very good responses for both the first and second study periods.

Review of the surveys for comments from both study periods revealed only a single patient comment pertaining to attire. The comment, which was submitted during study period 2, was considered positive, referring to the fitted scrubs as neat and professional. No negative comments were found during either period.

Comment

In this study, we did not find that a change from formal attire to fitted scrubs had a measurable negative impact on patient satisfaction scores. Conversely, we found a small but statistically significant improvement on several survey items after the change to fitted scrubs. The data suggest that changing from formal attire to fitted scrubs in an outpatient dermatology clinic had little impact on overall patient satisfaction. Only 1 positive comment and no negative comments were received regarding providers wearing fitted scrubs.

A prior study in an outpatient gynecology/obstetrics clinic showed similar results.⁶ In that study, providers were randomly assigned to business attire, casual attire, or scrubs. A 10-question patient satisfaction survey was designed that specifically avoided asking about provider attire to reduce any bias. The study found that over a 3-month period, attire had no influence on patient satisfaction.⁶

Our data suggest that factors beyond provider attire have the greatest influence on patient satisfaction scores. Patient satisfaction is likely driven by other factors such as provider communication skills, concern for patient well-being, ability to empathize, and timeliness. Given the biologic plausibility of increased infection rate from contaminated provider attire, we feel that comfortable, washable, fitted scrubs provide a sanitary and acceptable alternative to more traditional formal provider attire in the office setting. Bearman et al¹ suggest consideration of a bare-below-the-elbows policy (with or without scrubs) for inpatient services and lab coats (if worn per facility policy), and other articles of clothing should be laundered frequently or if visibly soiled. We feel these policies also can be applied to outpatient dermatology clinics, as long as the rationale is well communicated to all parties.

Several items on the patient satisfaction survey were statistically improved during the second study period; however, it is impossible to determine if provider attire was an important factor in this change. Improvement in satisfaction scores could be attributed to ongoing departmental and institutional emphasis on patient care and servic

Anecdotally, most providers in our department were enthusiastic and supportive of the change to fitted scrubs. It is possible that provider happiness is reflected in improved patient satisfaction scores. Provider satisfaction has been shown to correlate with patient satisfaction.⁷

Limitations include possible other unmeasured variables that had a more substantial impact on patient satisfaction survey results. We also recognize that the survey used in this study contained no questions that directly asked patients about their satisfaction with provider attire; however, bias or any preconception patients may have had regarding attire may have been avoided in the process. We also were not able to separate patient surveys based on age or other demographics. Finally, our results may not be generalizable to other settings where patient perceptions may be different from those of central Pennsylvania.

Conclusion

Transitioning from formal provider attire to fitted scrubs did not have a strong impact on overall patient satisfaction scores in an outpatient dermatology clinic. Providers and institutions should consider this information when developing dress code policies.

Provider attire has come under scrutiny in the more recent medical literature. Epidemiologic data have shown that lab coats, ties, and other articles of clothing are frequently contaminated with disease-causing pathogens including methicillin-resistant Staphylococcus aureus , vancomycin-resistant enterococci, Acinetobacter species, Enterobacteriaceae, Pseudomona s species, and Clostridium difficile.¹ Clothing may serve as a vector for spread of these bacteria and may contribute to hospital-acquired infections, increased cost of care, and patient morbidity. Prior to February 2015, the dermatology service line at Geisinger Medical Center in Danville, Pennsylvania, had followed a formal dress code that included white lab coats (white coats) along with long-sleeve shirts and ties/bowties for male providers and blouses, skirts, dress pants, and dresses for female providers. After a review of the recent literature on contamination rates of provider attire,² we transitioned away from formal attire to adopt fitted, embroidered, black or navy blue scrubs to be worn in the clinic (Figure). Fitted scrubs differ from traditional unisex operating room scrubs, conferring a more professional appearance.

Limited research has shown that dermatology patients may have a slight preference for formal provider attire.^2,3 In these studies, patients were shown photographs of providers in various dress (ie, professional attire, business attire, casual attire, scrubs). Patients preferred or had more confidence in the photograph of the provider in professional attire^2,3; however, it is unclear if dermatology provider attire has any measurable effect on overall patient satisfaction. Patient satisfaction relies on a myriad of factors, including both spoken and unspoken communication skills. Patient satisfaction has become an integral part of health care, and with an emphasis on value-based care, it will likely be one determining factor in how providers are reimbursed for their services.^4,5 In this study, we investigated if a change from formal attire to fitted scrubs influenced patient satisfaction using a common third-party patient satisfaction survey.

Methods

Patient Satisfaction Survey
We conducted a retrospective cohort study analyzing 10 questions from the care provider section of the Press Ganey third-party patient satisfaction survey regarding providers in our dermatology service line. Only providers with at least 12 months of survey data before (study period 1) and after (study period 2) the change in attire were included in the study. Mohs surgeons were excluded, as they already wore fitted scrubs in the clinic. Residents also were excluded, as they are rapidly developing their patient communication skills and may have a notable change in patient satisfaction over a 2-year period.

The survey data were collected, and provider names were removed and replaced with alphanumeric codes to protect anonymity while still allowing individual provider analysis. Aggregate patient comments from surveys before and after the change in attire were digitally searched using the terms scrub, coat, white, attire, and clothing for pertinent positive or negative comments.

Outcomes
We compared individual and aggregate satisfaction scores for our providers during the 12-month periods before and after the adoption of fitted scrubs. The primary outcome was statistically significant change in patient satisfaction scores before and after the institution of fitted scrubs. Secondary outcomes included summation of patient comments, both positive and negative, regarding provider attire, as recorded on satisfaction surveys.

Statistical Analysis
Overall survey scores and scores on individual survey items were summarized using mean (SD), median and interquartile range, or frequency counts and percentage, as appropriate. The overall satisfaction score and responses to individual survey items were compared using Mantel-Haenszel or Pearson χ² tests, as appropriate.

Assuming an equal number of surveys would be completed during study periods 1 and 2, an average (SD) satisfaction score of 95.4 (15), we calculated that as many as 2136 surveys would be needed to conclude satisfaction scores are the same for equivalence limits of −1.9 and 1.9 (a 1% difference). As few as 352 surveys would be needed to conclude satisfaction scores are the same for equivalence limits of −4.7 and 4.7 (a 5% difference). Sample size calculations assume 80% power and a significance level of 0.05. Comparison of responses for study periods 1 and 2 were made using the Mantel-Haenszel χ² test.

Because more than 80% of respondents selected very good for each question, the responses also were treated as dichotomous variables with a category for very good and a category for responses that were lower than very good (ie, good, fair, poor, very poor). Responses of very good versus less than very good were compared for the study periods 1 and 2 using the Pearson χ² test.

Two versions of an overall score were analyzed. The first version was for patients who responded to at least 1 of 10 survey items. If responses to all the items were very good, the patient was assigned to the category of all very good. If a patient answered any of the questions with a response less than very good, he/she was categorized as at least 1 less than very good. The second version was for patients who responded to all 10 survey items. If all 10 responses were very good, the patient was assigned to a category of all very good. If any of the 10 responses were less than very good, he/she was categorized as at least 1 less than very good. Differences between study periods for both score versions were tested using the Pearson χ² test.

Results

Data for 22 providers in the dermatology service line—13 staff dermatologists, 6 physician assistants, 1 nurse practitioner, and 2 podiatrists—were included in the study, with a total of 7702 patient satisfaction surveys completed between February 1, 2014, and January 31, 2016: 3511 were completed between February 1, 2014, and January 31, 2015 (study period 1), and 4191 were completed between February 1, 2015, and January 31, 2016 (study period 2).

Analysis of the overall distribution of possible responses for each survey item showed significant differences between study periods 1 and 2 for friendliness/courtesy of the care provider (P=.0307), explanations the care provider gave about the problem or condition (P=.0038), concern the care provider showed for questions or worries (P=.0087), care provider’s efforts to include the patient in decisions about treatment (P=.0377), and patient confidence in the care provider (P=.0156). These survey items trended toward more positive responses in study period 2. The full results are provided in eTable 1.

The analysis that looked at responses as binary (very good vs less than very good) showed a greater proportion of very good responses for friendliness/courtesy of the care provider (P=.0438), explanations the care provider gave about the problem or condition (P=.0115), concern the care provider showed for questions or worries (P=.0188), and patient confidence in the care provider (P=.0417). The full results are provided in eTable 2.

There were no significant differences in the overall satisfaction scores between the first and second study periods. The differences were statistically significant when the overall score was calculated if any questions were answered (P=.5177) and when the overall score was calculated if all 10 questions were answered (P=.9959). For patients who responded to all survey items, 75.3% selected all very good responses for both the first and second study periods.

Review of the surveys for comments from both study periods revealed only a single patient comment pertaining to attire. The comment, which was submitted during study period 2, was considered positive, referring to the fitted scrubs as neat and professional. No negative comments were found during either period.

Comment

In this study, we did not find that a change from formal attire to fitted scrubs had a measurable negative impact on patient satisfaction scores. Conversely, we found a small but statistically significant improvement on several survey items after the change to fitted scrubs. The data suggest that changing from formal attire to fitted scrubs in an outpatient dermatology clinic had little impact on overall patient satisfaction. Only 1 positive comment and no negative comments were received regarding providers wearing fitted scrubs.

A prior study in an outpatient gynecology/obstetrics clinic showed similar results.⁶ In that study, providers were randomly assigned to business attire, casual attire, or scrubs. A 10-question patient satisfaction survey was designed that specifically avoided asking about provider attire to reduce any bias. The study found that over a 3-month period, attire had no influence on patient satisfaction.⁶

Our data suggest that factors beyond provider attire have the greatest influence on patient satisfaction scores. Patient satisfaction is likely driven by other factors such as provider communication skills, concern for patient well-being, ability to empathize, and timeliness. Given the biologic plausibility of increased infection rate from contaminated provider attire, we feel that comfortable, washable, fitted scrubs provide a sanitary and acceptable alternative to more traditional formal provider attire in the office setting. Bearman et al¹ suggest consideration of a bare-below-the-elbows policy (with or without scrubs) for inpatient services and lab coats (if worn per facility policy), and other articles of clothing should be laundered frequently or if visibly soiled. We feel these policies also can be applied to outpatient dermatology clinics, as long as the rationale is well communicated to all parties.

Several items on the patient satisfaction survey were statistically improved during the second study period; however, it is impossible to determine if provider attire was an important factor in this change. Improvement in satisfaction scores could be attributed to ongoing departmental and institutional emphasis on patient care and servic

Anecdotally, most providers in our department were enthusiastic and supportive of the change to fitted scrubs. It is possible that provider happiness is reflected in improved patient satisfaction scores. Provider satisfaction has been shown to correlate with patient satisfaction.⁷

Limitations include possible other unmeasured variables that had a more substantial impact on patient satisfaction survey results. We also recognize that the survey used in this study contained no questions that directly asked patients about their satisfaction with provider attire; however, bias or any preconception patients may have had regarding attire may have been avoided in the process. We also were not able to separate patient surveys based on age or other demographics. Finally, our results may not be generalizable to other settings where patient perceptions may be different from those of central Pennsylvania.

Conclusion

Transitioning from formal provider attire to fitted scrubs did not have a strong impact on overall patient satisfaction scores in an outpatient dermatology clinic. Providers and institutions should consider this information when developing dress code policies.

Peripherally inserted central catheters (PICCs) have evolved since their inception in the early 1970s and are used with increasing frequency for pediatric inpatients and outpatients.^1-3 Emerging literature, including a meta-analysis of international observational studies,⁴ reports PICC failure (complications necessitating premature removal) occurs in up to 30% of PICCs, most commonly due to infection, thrombosis, occlusion, and fracture.^4-7 Raffini et al.⁷ report the increasing incidence of pediatric PICC-related thrombosis increases morbidity and mortality⁸ and negatively impacts future vessel health and preservation.⁹

PICCs have progressed from relatively simple, silicone-based catheters with an external clamp to chemically engineered polyurethane with pressure-activated valves placed at the proximal or distal catheter hub with the intent to reduce occlusion.¹⁰ Further modernization of PICC material occurred with the incorporation of antithrombogenic (AT) material (Endexo^®). These PICCs are designed to contain a nonstick polymer, which is designed to reduce the adherence of blood components (platelets and clotting factors) and inhibit thrombus formation (and hence prevent deep vein thrombosis andocclusion, as well as inhibit microbial biofilm attachment [and subsequent infection]).¹¹

In addition to new materials, other aspects of this PICC design have been the addition of a pressure-activated safety valve (PASV^®) built into the proximal hub. Pressure-activated valve technology promises to prevent catheter occlusion by reducing blood reflux into the PICC; the valve opens with pressure during infusion and aspiration and remains closed with normal venous pressure, circumventing the need for clinicians to manually clamp the PICC and reducing human error and the potential for thrombosis, occlusion, and fracture development.¹² Hoffer et al.¹³ reported half as many occlusions of valved PICCs (3.3%) compared with nonvalved or clamped PICCs (7.1%); although not statistically significant (P = .10), perhaps due to the small sample, overall complications, including occlusion and infection, were significantly lessened with the valved PICC (35% vs 79%; P = .02). Comparatively, Pittiruti et al.¹⁴ conducted a trial of 2 types of valved PICCs with an open-ended, nonvalved PICC and found no reduction in PICC occlusion or catheter malfunction.

Today, PICC use is common for patients who require short-to-medium intravenous therapy. PICCs are increasingly recognized for their significant complications, including thrombosis and infection.¹⁵ Novel PICC technology, including the incorporation of AT material such as Endexo^® and PASV^®, may reduce complications; however, the clinical efficacy, cost-effectiveness, and acceptability of these innovations have not been tested through randomized trials in pediatric patients. In accordance with Medical Research Council guidelines¹⁶ for developing interventions, we pilot tested the feasibility of the BioFlo^® PICC, including intervention acceptability, compliance, recruitment, and initial estimates of effect, in anticipation of a subsequent full-scale efficacy randomized controlled trial. Our secondary aim was to compare the effectiveness of the BioFlo^® PICC with Endexo^® and PASV^® technology in reducing PICC complications and failure.

Design

We undertook a pilot randomized controlled trial comparing the standard polyurethane PICC (with external clamp) with the BioFlo^® PICC (with internal valve) in preventing catheter failure in pediatric patients. The study was prospectively registered with the Australian Clinical Trials Registry (ACTRN12615001290583), and the research protocol was published.¹⁷

Study Setting

The study commenced in March 2016 at the Lady Cilento Children’s Hospital in South Brisbane, Australia, a tertiary-level, specialist, pediatric teaching hospital in Queensland, Australia, providing full-spectrum health services to children and young people from birth to 18 years of age. Recruitment, including data collection, was completed in November 2016.

Sample

The target sample size was 110 participants, 50 participants per group plus 10% for potential attrition, as determined by standard pilot trial sample size recommendations.¹⁸ With ethics approval, the sample size was later increased to 150 participants in order to adequately pilot a microbiological substudy method (published separately).¹⁷ Participants were consecutively recruited if they met the inclusion criteria: PICC insertion, age <18 years, predicted hospital stay >24 hours, single-lumen PICC, and written informed consent by an English-speaking, legal parent or guardian. Patients were excluded if they had a current (<48 hours) blood stream infection (BSI), vessel size <2 mm, could not speak English without an interpreter, required a multilumen PICC, or were previously enrolled in the study.

Interventions

Participants were randomized to receive either of the following PICCs: (1) standard care: Cook™ polyurethane, turbo-ject, power-injectable PICC (Cook Medical, Bloomington, IN) or (2) comparison: BioFlo^® polyurethane with Endexo^® technology (AngioDynamics Inc, Queensbury, NY).

Outcomes

The primary outcome was feasibility of a full-efficacy trial established by composite analysis of the elements of eligibility (>70% of patients will be eligible), recruitment (>70% of patients will agree to enroll), retention and attrition (<15% of participants are lost to follow-up or withdraw from the study), protocol adherence (>80% of participants receive their allocated, randomly assigned study product), missing data (<10% of data are missed during data collection), parent and healthcare staff satisfaction, and PICC failure effect size estimates to allow sample size calculations.^18,19 PICC failure was defined as the following complications associated with PICC removal: (1) catheter-associated BSI,^8,20-22 (2) local site infection,²² (3) venous thrombosis,²³ (4) occlusion,^24,25 (5) PICC fracture, or (6) PICC dislodgement.^25,26 Parents (or caregivers) and healthcare staff were asked to rate their level of confidence with the study product and ease of PICC removal by using a 0 to 100 numeric rating scale (NRS) of increasing confidence and/or ease. These data were collected at the time of PICC removal. Operators were also asked to rate their levels of satisfaction with the insertion equipment and ease of PICC insertion immediately upon completion of the insertion procedure (both 0-100 NRS of increasing satisfaction and/or ease). Secondary outcomes included individual PICC complications (eg, occlusion) occurring at any time point during the PICC dwell (including at removal), adverse events, pain, redness at the insertion site, and overall PICC dwell.

Study Procedures

The research nurse (ReN) screened operating theater lists for patients, obtained written informed consent, and initiated the randomization. Randomization was computer generated, and web based via Griffith University (https://www151.griffith.edu.au/random) to ensure allocation concealment until study entry. Patients were randomly assigned in a 1:1 ratio with computer-generated and randomly varied block sizes of 2 and 4. Data were collected by the ReN on the day of insertion, at day 1 postinsertion, then every 2 to 3 days thereafter so that PICCs were checked at least twice per week until study completion. Participants were included in the trial until 12 weeks post-PICC insertion, study withdrawal or PICC removal (whichever came first), with an additional 48 hours follow-up for infection outcomes. Patient review was face to face during the inpatient stay, with discharged patients’ follow-up occurring via outpatient clinics, hospital-in-the-home service, or telephone.

Data collection was via Research Electronic Data Capture (http://project-redcap.org/). The ReN collected data on primary and secondary outcomes by using the predefined criteria. Demographic and clinical data were collected to assess the success of randomization, describe the participant group, and display characteristics known to increase the risk of PICC complication and thrombosis. A blinded radiologist and infectious disease specialist reviewed and diagnosed thrombosis of deep veins and catheter-associated BSI outcomes, respectively.

PICC Procedures

Extensive prestudy education for 2 months prior to trial commencement was provided to all clinicians involved with the insertion and care of PICCs, including the study products. PICCs were inserted in an operating theater environment by a qualified consultant pediatric anesthetist, a senior anesthetic registrar or fellow in an approved anesthetic training program, or pediatric vascular access nurse practitioner. Ultrasound guidance was used to assess a patient’s vasculature and puncture the vessel. The operator chose the PICC size on the basis of clinical judgment of vessel size and patient needs and then inserted the allocated PICC.²⁷ Preferred PICC tip location was the cavoatrial junction. All PICC tip positions were confirmed with a chest x-ray before use.

Postinsertion, PICCs were managed by local interdisciplinary clinicians in accordance with local practice guidelines.^27-31 PICC care and management includes the use of 2% chlorhexidine gluconate in 70% alcohol for site antisepsis and neutral displacement needleless connectors (TUTA Pulse; Medical Australia Limited, Lidcombe, New South Wales, Australia); normal saline was used to flush after medication administration, and if the device was not in use for 6 hours or longer, heparin is instilled with securement via bordered polyurethane dressing (Tegaderm 1616; 3M, St Paul, Minnesota) and a sutureless securement device (Statlock VPPCSP; Bard, Georgia).

Statistical Analyses

Data were exported to Stata 15³² for cleaning and analysis. Data cleaning of outlying figures and missing and implausible data was undertaken prior to analysis. Missing data were not imputed. The PICC was the unit of measurement, and all randomly assigned patients were analyzed on an intention-to-treat basis.³³ Descriptive statistics (frequencies and percentages) were used to ascertain the primary outcome of feasibility for the larger trial. Incidence rates (per 1,000 catheter days) and rate ratios, including 95% confidence intervals (CIs), were calculated. The comparability of groups at baseline was described across demographic, clinical, and device characteristics. Kaplan-Meier survival curves (with log-rank tests) were used to compare PICC failure between study groups over time. Associations between baseline characteristics and failure were described by calculating hazard ratios (HRs). Univariable Cox regression was performed only due to the relatively low number of outcomes. P values of <.05 were considered statistically significant.

Ethics

The Children’s Health Service District, Queensland (Human Research Ethics Committee/15/QRCH/164), and Griffith University (2016/077) Human Research Ethics Committees provided ethics and governance approval. Informed consent was obtained from parents or legal guardians, with children providing youth assent if they were 7 years or older, dependent upon cognitive ability.

Participant and PICC Characteristics

Feasibility Outcomes

PICC Failure and Complications

DISCUSSION

This is the first pilot feasibility trial of new PICC materials and valve design incorporated in the BioFlo^® PICC in the pediatric population. The trial incorporated best practice for randomized trials, including using a concurrent control group, centralized and concealed randomization, predetermined feasibility criteria, and a registered and published trial protocol.¹⁷ As in other studies,^15,24,34 PICC failure and complication prevalence was unacceptably high for this essential device. Standard care PICCs failed twice as often as the new BioFlo^® PICCs (22% vs 11%), which is a clinically important difference. As researchers in a pilot study, we did not expect to detect statistically significant differences; however, we found that overall complications during the dwell occurred significantly more with the standard care than BioFlo^® PICCs (P = .009).

BioFlo^® PICC material offers a major advancement in PICC material through the incorporation of AT technologies into catheter materials, such as PICCs. Endexo^® is a low molecular–weight, fluoro-oligomeric additive that self-locates to the top few nanometers of the material surface. When added to power-injectable polyurethane, the additive results in a strong but passive, nonstick, fluorinated surface in the base PICC material. This inhibits platelet adhesion, suppresses protein procoagulant conformation, and thereby reduces thrombus formation in medical devices. Additionally, Endexo^® is not a catheter coating; rather, it is incorporated within the polyurethane of the PICC, thereby ensuring these AT properties are present on the internal, external, and cut surfaces of the PICC. If this technology can reduce complication during treatment and reduce failure from infection, thrombosis, occlusion, fracture, and dislodgement, it will improve patient outcomes considerably and lower health system costs. Previous studies investigating valve technology in PICC design to reduce occlusion have been inconclusive.^12-14,35,36 Occlusion (both partial and complete) was less frequent in our study with the BioFlo^® group (n = 3; 4%) compared to the standard care group (n = 6; 8%). The results of this pilot study suggest that either the Endexo^® material or PASV^® technology has a positive association with occlusion reduction during PICC treatment.

Thrombosis was the primary failure type for the standard care PICCs, comprising one-third of failures. All but one patient with radiologically confirmed thrombosis required the removal of the PICC prior to completion of treatment. The decision to remove the PICC or retain and treat conservatively remained with the treating team. Raffini et al.⁷ found thrombosis to increase in patients with one or more coexisting chronic medical condition. Slightly more standard care than BioFlo^® patients were free of such comorbidities (25% vs 16%), yet standard care patients still had the higher number of thromboses (7% vs 3%). Morgenthaler and Rodriguez³⁷ reported vascular access-associated thrombosis in pediatrics to be less common than in adults but higher in medically complex children. Worryingly, Menendez et al.³⁸ reported pediatric thrombosis to be largely asymptomatic, so the true incidence in our study is likely higher because only radiologically confirmed thromboses were recorded.

Occlusion (partial or complete) was the predominant complication across the study, being associated with one-third of all failures. When occlusion complications during the dwell (some of which were resolved with treatment), in addition to those causing failure, were considered, this number was even greater. Occlusion complications are prevalent and costly. Smith et al.²⁴ reported that occlusion was the most common reason for PICC removal and the most likely complication to delay treatment. Both the BioFlo^® and standard care PICCs are pressure rated with good tensile strength; however, fracture occurred in 4% (n = 3) of standard care PICCs compared to no fractures in BioFlo^® PICCs. Although the numbers are small, it may suggest a superior tensile strength of the BioFlo^® material.

This study reinforces previously published results^24,38 that PICC tip position is important and can influence complications, such as occlusion and thrombosis. In addition, we found a significant association with failure when PICCs did not have a continuous infusion. These findings reinforce the need for optimal tip location at insertion and ongoing flushing and maintenance of PICCs not used for infusions.

Limitations of this study include the small sample size, which was not designed to detect statistical differences in the primary outcome between groups. Despite randomization, there were slight imbalances at baseline for inserter type and leukocyte count, although these were not significantly associated with PICC failure in the Cox regression (data not shown), and thus were unlikely to influence findings. Additionally, a difference of <10% was associated with PICC tip position, favoring the BioFlo^® group. PICC tip position outside the cavoatrial junction was positively associated with failure; therefore, the effect of tip positioning on outcomes is difficult to ascertain given the small sample size and feasibility nature of the study. Further study is warranted to further explore this effect. The population sampled was pediatric medical and surgical inpatients with a vessel size >2 mm attending the operating theater suite for PICC insertion, thereby limiting the study’s generalizability to adults and other populations, including neonates and those with PICCs inserted in the pediatric intensive care unit. The study could not be blinded because study products had to be visible to the clinical and research staff. However, it is unlikely that staff would intentionally sabotage PICCs to bias the study. Blinding was possible for the assessment of blood culture and ultrasound reports to diagnose infection and thrombosis. Strengths of this study included 100% protocol adherence, and no patients were lost to follow-up.

These results confirm that PICC failure is unacceptably high and suggest that the innovative BioFlo^® PICC material and design holds promise to improve PICC outcomes by reducing complications and overall PICC failure. Trials of this technology are feasible, safe, and acceptable to healthcare staff and parents. Further trials are required, including in other patient populations, to definitively identify clinical, cost-effective methods to prevent PICC failure and improve reliability during treatment.

Acknowledgments

The authors thank the children and parents of Lady Cilento Children’s Hospital for participating in this important research. A special thank you goes to the nurses within the Vascular Assessment and Management Service and to Karen Turner, Julieta Woosley, and Anna Dean for their efforts in data collecting and ensuring protocol adherence.

Disclosure

Griffith University has received unrestricted, investigator-initiated research or educational grants to support the research of T. K., A. J. U., and C. R. M. from product manufacturers 3M, Adhezion Inc, AngioDynamics, Bard Medical, Baxter, B. Braun Medical Inc, Becton Dickinson, CareFusion, Centurion Medical Products, Cook Medical, Entrotech, FloMedical, ICU Medical Inc, Medical Australia Limited, Medtronic, Smiths Medical, and Teleflex. Griffith University has received consultancy payments on behalf of C. R. M., A. J. U., and T. K. from manufacturers 3M, AngioDynamics, Bard Medical, B. Braun Medical Inc, Becton Dickinson, CareFusion, Mayo Healthcare Inc, ResQDevices, and Smiths Medical. AngioDynamics (the BioFlo^® PICC manufacturer) provided partial funds to undertake this research via an unrestricted donation to Griffith University (but not the study authors). Queensland Health provided in-kind support to fund the remainder of the trial. The funders had no role in the study design, collection, analysis, or interpretation of the data, writing of the report, or decision to submit the article for publication.

Peripherally inserted central catheters (PICCs) have evolved since their inception in the early 1970s and are used with increasing frequency for pediatric inpatients and outpatients.^1-3 Emerging literature, including a meta-analysis of international observational studies,⁴ reports PICC failure (complications necessitating premature removal) occurs in up to 30% of PICCs, most commonly due to infection, thrombosis, occlusion, and fracture.^4-7 Raffini et al.⁷ report the increasing incidence of pediatric PICC-related thrombosis increases morbidity and mortality⁸ and negatively impacts future vessel health and preservation.⁹

PICCs have progressed from relatively simple, silicone-based catheters with an external clamp to chemically engineered polyurethane with pressure-activated valves placed at the proximal or distal catheter hub with the intent to reduce occlusion.¹⁰ Further modernization of PICC material occurred with the incorporation of antithrombogenic (AT) material (Endexo^®). These PICCs are designed to contain a nonstick polymer, which is designed to reduce the adherence of blood components (platelets and clotting factors) and inhibit thrombus formation (and hence prevent deep vein thrombosis andocclusion, as well as inhibit microbial biofilm attachment [and subsequent infection]).¹¹

In addition to new materials, other aspects of this PICC design have been the addition of a pressure-activated safety valve (PASV^®) built into the proximal hub. Pressure-activated valve technology promises to prevent catheter occlusion by reducing blood reflux into the PICC; the valve opens with pressure during infusion and aspiration and remains closed with normal venous pressure, circumventing the need for clinicians to manually clamp the PICC and reducing human error and the potential for thrombosis, occlusion, and fracture development.¹² Hoffer et al.¹³ reported half as many occlusions of valved PICCs (3.3%) compared with nonvalved or clamped PICCs (7.1%); although not statistically significant (P = .10), perhaps due to the small sample, overall complications, including occlusion and infection, were significantly lessened with the valved PICC (35% vs 79%; P = .02). Comparatively, Pittiruti et al.¹⁴ conducted a trial of 2 types of valved PICCs with an open-ended, nonvalved PICC and found no reduction in PICC occlusion or catheter malfunction.

Today, PICC use is common for patients who require short-to-medium intravenous therapy. PICCs are increasingly recognized for their significant complications, including thrombosis and infection.¹⁵ Novel PICC technology, including the incorporation of AT material such as Endexo^® and PASV^®, may reduce complications; however, the clinical efficacy, cost-effectiveness, and acceptability of these innovations have not been tested through randomized trials in pediatric patients. In accordance with Medical Research Council guidelines¹⁶ for developing interventions, we pilot tested the feasibility of the BioFlo^® PICC, including intervention acceptability, compliance, recruitment, and initial estimates of effect, in anticipation of a subsequent full-scale efficacy randomized controlled trial. Our secondary aim was to compare the effectiveness of the BioFlo^® PICC with Endexo^® and PASV^® technology in reducing PICC complications and failure.

Design

We undertook a pilot randomized controlled trial comparing the standard polyurethane PICC (with external clamp) with the BioFlo^® PICC (with internal valve) in preventing catheter failure in pediatric patients. The study was prospectively registered with the Australian Clinical Trials Registry (ACTRN12615001290583), and the research protocol was published.¹⁷

Study Setting

The study commenced in March 2016 at the Lady Cilento Children’s Hospital in South Brisbane, Australia, a tertiary-level, specialist, pediatric teaching hospital in Queensland, Australia, providing full-spectrum health services to children and young people from birth to 18 years of age. Recruitment, including data collection, was completed in November 2016.

Sample

The target sample size was 110 participants, 50 participants per group plus 10% for potential attrition, as determined by standard pilot trial sample size recommendations.¹⁸ With ethics approval, the sample size was later increased to 150 participants in order to adequately pilot a microbiological substudy method (published separately).¹⁷ Participants were consecutively recruited if they met the inclusion criteria: PICC insertion, age <18 years, predicted hospital stay >24 hours, single-lumen PICC, and written informed consent by an English-speaking, legal parent or guardian. Patients were excluded if they had a current (<48 hours) blood stream infection (BSI), vessel size <2 mm, could not speak English without an interpreter, required a multilumen PICC, or were previously enrolled in the study.

Interventions

Participants were randomized to receive either of the following PICCs: (1) standard care: Cook™ polyurethane, turbo-ject, power-injectable PICC (Cook Medical, Bloomington, IN) or (2) comparison: BioFlo^® polyurethane with Endexo^® technology (AngioDynamics Inc, Queensbury, NY).

Outcomes

The primary outcome was feasibility of a full-efficacy trial established by composite analysis of the elements of eligibility (>70% of patients will be eligible), recruitment (>70% of patients will agree to enroll), retention and attrition (<15% of participants are lost to follow-up or withdraw from the study), protocol adherence (>80% of participants receive their allocated, randomly assigned study product), missing data (<10% of data are missed during data collection), parent and healthcare staff satisfaction, and PICC failure effect size estimates to allow sample size calculations.^18,19 PICC failure was defined as the following complications associated with PICC removal: (1) catheter-associated BSI,^8,20-22 (2) local site infection,²² (3) venous thrombosis,²³ (4) occlusion,^24,25 (5) PICC fracture, or (6) PICC dislodgement.^25,26 Parents (or caregivers) and healthcare staff were asked to rate their level of confidence with the study product and ease of PICC removal by using a 0 to 100 numeric rating scale (NRS) of increasing confidence and/or ease. These data were collected at the time of PICC removal. Operators were also asked to rate their levels of satisfaction with the insertion equipment and ease of PICC insertion immediately upon completion of the insertion procedure (both 0-100 NRS of increasing satisfaction and/or ease). Secondary outcomes included individual PICC complications (eg, occlusion) occurring at any time point during the PICC dwell (including at removal), adverse events, pain, redness at the insertion site, and overall PICC dwell.

Study Procedures

The research nurse (ReN) screened operating theater lists for patients, obtained written informed consent, and initiated the randomization. Randomization was computer generated, and web based via Griffith University (https://www151.griffith.edu.au/random) to ensure allocation concealment until study entry. Patients were randomly assigned in a 1:1 ratio with computer-generated and randomly varied block sizes of 2 and 4. Data were collected by the ReN on the day of insertion, at day 1 postinsertion, then every 2 to 3 days thereafter so that PICCs were checked at least twice per week until study completion. Participants were included in the trial until 12 weeks post-PICC insertion, study withdrawal or PICC removal (whichever came first), with an additional 48 hours follow-up for infection outcomes. Patient review was face to face during the inpatient stay, with discharged patients’ follow-up occurring via outpatient clinics, hospital-in-the-home service, or telephone.

Data collection was via Research Electronic Data Capture (http://project-redcap.org/). The ReN collected data on primary and secondary outcomes by using the predefined criteria. Demographic and clinical data were collected to assess the success of randomization, describe the participant group, and display characteristics known to increase the risk of PICC complication and thrombosis. A blinded radiologist and infectious disease specialist reviewed and diagnosed thrombosis of deep veins and catheter-associated BSI outcomes, respectively.

PICC Procedures

Extensive prestudy education for 2 months prior to trial commencement was provided to all clinicians involved with the insertion and care of PICCs, including the study products. PICCs were inserted in an operating theater environment by a qualified consultant pediatric anesthetist, a senior anesthetic registrar or fellow in an approved anesthetic training program, or pediatric vascular access nurse practitioner. Ultrasound guidance was used to assess a patient’s vasculature and puncture the vessel. The operator chose the PICC size on the basis of clinical judgment of vessel size and patient needs and then inserted the allocated PICC.²⁷ Preferred PICC tip location was the cavoatrial junction. All PICC tip positions were confirmed with a chest x-ray before use.

Postinsertion, PICCs were managed by local interdisciplinary clinicians in accordance with local practice guidelines.^27-31 PICC care and management includes the use of 2% chlorhexidine gluconate in 70% alcohol for site antisepsis and neutral displacement needleless connectors (TUTA Pulse; Medical Australia Limited, Lidcombe, New South Wales, Australia); normal saline was used to flush after medication administration, and if the device was not in use for 6 hours or longer, heparin is instilled with securement via bordered polyurethane dressing (Tegaderm 1616; 3M, St Paul, Minnesota) and a sutureless securement device (Statlock VPPCSP; Bard, Georgia).

Statistical Analyses

Data were exported to Stata 15³² for cleaning and analysis. Data cleaning of outlying figures and missing and implausible data was undertaken prior to analysis. Missing data were not imputed. The PICC was the unit of measurement, and all randomly assigned patients were analyzed on an intention-to-treat basis.³³ Descriptive statistics (frequencies and percentages) were used to ascertain the primary outcome of feasibility for the larger trial. Incidence rates (per 1,000 catheter days) and rate ratios, including 95% confidence intervals (CIs), were calculated. The comparability of groups at baseline was described across demographic, clinical, and device characteristics. Kaplan-Meier survival curves (with log-rank tests) were used to compare PICC failure between study groups over time. Associations between baseline characteristics and failure were described by calculating hazard ratios (HRs). Univariable Cox regression was performed only due to the relatively low number of outcomes. P values of <.05 were considered statistically significant.

Ethics

The Children’s Health Service District, Queensland (Human Research Ethics Committee/15/QRCH/164), and Griffith University (2016/077) Human Research Ethics Committees provided ethics and governance approval. Informed consent was obtained from parents or legal guardians, with children providing youth assent if they were 7 years or older, dependent upon cognitive ability.

Participant and PICC Characteristics

Feasibility Outcomes

PICC Failure and Complications

DISCUSSION

This is the first pilot feasibility trial of new PICC materials and valve design incorporated in the BioFlo^® PICC in the pediatric population. The trial incorporated best practice for randomized trials, including using a concurrent control group, centralized and concealed randomization, predetermined feasibility criteria, and a registered and published trial protocol.¹⁷ As in other studies,^15,24,34 PICC failure and complication prevalence was unacceptably high for this essential device. Standard care PICCs failed twice as often as the new BioFlo^® PICCs (22% vs 11%), which is a clinically important difference. As researchers in a pilot study, we did not expect to detect statistically significant differences; however, we found that overall complications during the dwell occurred significantly more with the standard care than BioFlo^® PICCs (P = .009).

BioFlo^® PICC material offers a major advancement in PICC material through the incorporation of AT technologies into catheter materials, such as PICCs. Endexo^® is a low molecular–weight, fluoro-oligomeric additive that self-locates to the top few nanometers of the material surface. When added to power-injectable polyurethane, the additive results in a strong but passive, nonstick, fluorinated surface in the base PICC material. This inhibits platelet adhesion, suppresses protein procoagulant conformation, and thereby reduces thrombus formation in medical devices. Additionally, Endexo^® is not a catheter coating; rather, it is incorporated within the polyurethane of the PICC, thereby ensuring these AT properties are present on the internal, external, and cut surfaces of the PICC. If this technology can reduce complication during treatment and reduce failure from infection, thrombosis, occlusion, fracture, and dislodgement, it will improve patient outcomes considerably and lower health system costs. Previous studies investigating valve technology in PICC design to reduce occlusion have been inconclusive.^12-14,35,36 Occlusion (both partial and complete) was less frequent in our study with the BioFlo^® group (n = 3; 4%) compared to the standard care group (n = 6; 8%). The results of this pilot study suggest that either the Endexo^® material or PASV^® technology has a positive association with occlusion reduction during PICC treatment.

Thrombosis was the primary failure type for the standard care PICCs, comprising one-third of failures. All but one patient with radiologically confirmed thrombosis required the removal of the PICC prior to completion of treatment. The decision to remove the PICC or retain and treat conservatively remained with the treating team. Raffini et al.⁷ found thrombosis to increase in patients with one or more coexisting chronic medical condition. Slightly more standard care than BioFlo^® patients were free of such comorbidities (25% vs 16%), yet standard care patients still had the higher number of thromboses (7% vs 3%). Morgenthaler and Rodriguez³⁷ reported vascular access-associated thrombosis in pediatrics to be less common than in adults but higher in medically complex children. Worryingly, Menendez et al.³⁸ reported pediatric thrombosis to be largely asymptomatic, so the true incidence in our study is likely higher because only radiologically confirmed thromboses were recorded.

Occlusion (partial or complete) was the predominant complication across the study, being associated with one-third of all failures. When occlusion complications during the dwell (some of which were resolved with treatment), in addition to those causing failure, were considered, this number was even greater. Occlusion complications are prevalent and costly. Smith et al.²⁴ reported that occlusion was the most common reason for PICC removal and the most likely complication to delay treatment. Both the BioFlo^® and standard care PICCs are pressure rated with good tensile strength; however, fracture occurred in 4% (n = 3) of standard care PICCs compared to no fractures in BioFlo^® PICCs. Although the numbers are small, it may suggest a superior tensile strength of the BioFlo^® material.

This study reinforces previously published results^24,38 that PICC tip position is important and can influence complications, such as occlusion and thrombosis. In addition, we found a significant association with failure when PICCs did not have a continuous infusion. These findings reinforce the need for optimal tip location at insertion and ongoing flushing and maintenance of PICCs not used for infusions.

Limitations of this study include the small sample size, which was not designed to detect statistical differences in the primary outcome between groups. Despite randomization, there were slight imbalances at baseline for inserter type and leukocyte count, although these were not significantly associated with PICC failure in the Cox regression (data not shown), and thus were unlikely to influence findings. Additionally, a difference of <10% was associated with PICC tip position, favoring the BioFlo^® group. PICC tip position outside the cavoatrial junction was positively associated with failure; therefore, the effect of tip positioning on outcomes is difficult to ascertain given the small sample size and feasibility nature of the study. Further study is warranted to further explore this effect. The population sampled was pediatric medical and surgical inpatients with a vessel size >2 mm attending the operating theater suite for PICC insertion, thereby limiting the study’s generalizability to adults and other populations, including neonates and those with PICCs inserted in the pediatric intensive care unit. The study could not be blinded because study products had to be visible to the clinical and research staff. However, it is unlikely that staff would intentionally sabotage PICCs to bias the study. Blinding was possible for the assessment of blood culture and ultrasound reports to diagnose infection and thrombosis. Strengths of this study included 100% protocol adherence, and no patients were lost to follow-up.

These results confirm that PICC failure is unacceptably high and suggest that the innovative BioFlo^® PICC material and design holds promise to improve PICC outcomes by reducing complications and overall PICC failure. Trials of this technology are feasible, safe, and acceptable to healthcare staff and parents. Further trials are required, including in other patient populations, to definitively identify clinical, cost-effective methods to prevent PICC failure and improve reliability during treatment.

Acknowledgments

The authors thank the children and parents of Lady Cilento Children’s Hospital for participating in this important research. A special thank you goes to the nurses within the Vascular Assessment and Management Service and to Karen Turner, Julieta Woosley, and Anna Dean for their efforts in data collecting and ensuring protocol adherence.

Disclosure

Griffith University has received unrestricted, investigator-initiated research or educational grants to support the research of T. K., A. J. U., and C. R. M. from product manufacturers 3M, Adhezion Inc, AngioDynamics, Bard Medical, Baxter, B. Braun Medical Inc, Becton Dickinson, CareFusion, Centurion Medical Products, Cook Medical, Entrotech, FloMedical, ICU Medical Inc, Medical Australia Limited, Medtronic, Smiths Medical, and Teleflex. Griffith University has received consultancy payments on behalf of C. R. M., A. J. U., and T. K. from manufacturers 3M, AngioDynamics, Bard Medical, B. Braun Medical Inc, Becton Dickinson, CareFusion, Mayo Healthcare Inc, ResQDevices, and Smiths Medical. AngioDynamics (the BioFlo^® PICC manufacturer) provided partial funds to undertake this research via an unrestricted donation to Griffith University (but not the study authors). Queensland Health provided in-kind support to fund the remainder of the trial. The funders had no role in the study design, collection, analysis, or interpretation of the data, writing of the report, or decision to submit the article for publication.

Peripherally inserted central catheters (PICCs) have evolved since their inception in the early 1970s and are used with increasing frequency for pediatric inpatients and outpatients.^1-3 Emerging literature, including a meta-analysis of international observational studies,⁴ reports PICC failure (complications necessitating premature removal) occurs in up to 30% of PICCs, most commonly due to infection, thrombosis, occlusion, and fracture.^4-7 Raffini et al.⁷ report the increasing incidence of pediatric PICC-related thrombosis increases morbidity and mortality⁸ and negatively impacts future vessel health and preservation.⁹

PICCs have progressed from relatively simple, silicone-based catheters with an external clamp to chemically engineered polyurethane with pressure-activated valves placed at the proximal or distal catheter hub with the intent to reduce occlusion.¹⁰ Further modernization of PICC material occurred with the incorporation of antithrombogenic (AT) material (Endexo^®). These PICCs are designed to contain a nonstick polymer, which is designed to reduce the adherence of blood components (platelets and clotting factors) and inhibit thrombus formation (and hence prevent deep vein thrombosis andocclusion, as well as inhibit microbial biofilm attachment [and subsequent infection]).¹¹

In addition to new materials, other aspects of this PICC design have been the addition of a pressure-activated safety valve (PASV^®) built into the proximal hub. Pressure-activated valve technology promises to prevent catheter occlusion by reducing blood reflux into the PICC; the valve opens with pressure during infusion and aspiration and remains closed with normal venous pressure, circumventing the need for clinicians to manually clamp the PICC and reducing human error and the potential for thrombosis, occlusion, and fracture development.¹² Hoffer et al.¹³ reported half as many occlusions of valved PICCs (3.3%) compared with nonvalved or clamped PICCs (7.1%); although not statistically significant (P = .10), perhaps due to the small sample, overall complications, including occlusion and infection, were significantly lessened with the valved PICC (35% vs 79%; P = .02). Comparatively, Pittiruti et al.¹⁴ conducted a trial of 2 types of valved PICCs with an open-ended, nonvalved PICC and found no reduction in PICC occlusion or catheter malfunction.

Today, PICC use is common for patients who require short-to-medium intravenous therapy. PICCs are increasingly recognized for their significant complications, including thrombosis and infection.¹⁵ Novel PICC technology, including the incorporation of AT material such as Endexo^® and PASV^®, may reduce complications; however, the clinical efficacy, cost-effectiveness, and acceptability of these innovations have not been tested through randomized trials in pediatric patients. In accordance with Medical Research Council guidelines¹⁶ for developing interventions, we pilot tested the feasibility of the BioFlo^® PICC, including intervention acceptability, compliance, recruitment, and initial estimates of effect, in anticipation of a subsequent full-scale efficacy randomized controlled trial. Our secondary aim was to compare the effectiveness of the BioFlo^® PICC with Endexo^® and PASV^® technology in reducing PICC complications and failure.

Design

We undertook a pilot randomized controlled trial comparing the standard polyurethane PICC (with external clamp) with the BioFlo^® PICC (with internal valve) in preventing catheter failure in pediatric patients. The study was prospectively registered with the Australian Clinical Trials Registry (ACTRN12615001290583), and the research protocol was published.¹⁷

Study Setting

The study commenced in March 2016 at the Lady Cilento Children’s Hospital in South Brisbane, Australia, a tertiary-level, specialist, pediatric teaching hospital in Queensland, Australia, providing full-spectrum health services to children and young people from birth to 18 years of age. Recruitment, including data collection, was completed in November 2016.

Sample

The target sample size was 110 participants, 50 participants per group plus 10% for potential attrition, as determined by standard pilot trial sample size recommendations.¹⁸ With ethics approval, the sample size was later increased to 150 participants in order to adequately pilot a microbiological substudy method (published separately).¹⁷ Participants were consecutively recruited if they met the inclusion criteria: PICC insertion, age <18 years, predicted hospital stay >24 hours, single-lumen PICC, and written informed consent by an English-speaking, legal parent or guardian. Patients were excluded if they had a current (<48 hours) blood stream infection (BSI), vessel size <2 mm, could not speak English without an interpreter, required a multilumen PICC, or were previously enrolled in the study.

Interventions

Participants were randomized to receive either of the following PICCs: (1) standard care: Cook™ polyurethane, turbo-ject, power-injectable PICC (Cook Medical, Bloomington, IN) or (2) comparison: BioFlo^® polyurethane with Endexo^® technology (AngioDynamics Inc, Queensbury, NY).

Outcomes

The primary outcome was feasibility of a full-efficacy trial established by composite analysis of the elements of eligibility (>70% of patients will be eligible), recruitment (>70% of patients will agree to enroll), retention and attrition (<15% of participants are lost to follow-up or withdraw from the study), protocol adherence (>80% of participants receive their allocated, randomly assigned study product), missing data (<10% of data are missed during data collection), parent and healthcare staff satisfaction, and PICC failure effect size estimates to allow sample size calculations.^18,19 PICC failure was defined as the following complications associated with PICC removal: (1) catheter-associated BSI,^8,20-22 (2) local site infection,²² (3) venous thrombosis,²³ (4) occlusion,^24,25 (5) PICC fracture, or (6) PICC dislodgement.^25,26 Parents (or caregivers) and healthcare staff were asked to rate their level of confidence with the study product and ease of PICC removal by using a 0 to 100 numeric rating scale (NRS) of increasing confidence and/or ease. These data were collected at the time of PICC removal. Operators were also asked to rate their levels of satisfaction with the insertion equipment and ease of PICC insertion immediately upon completion of the insertion procedure (both 0-100 NRS of increasing satisfaction and/or ease). Secondary outcomes included individual PICC complications (eg, occlusion) occurring at any time point during the PICC dwell (including at removal), adverse events, pain, redness at the insertion site, and overall PICC dwell.

Study Procedures

The research nurse (ReN) screened operating theater lists for patients, obtained written informed consent, and initiated the randomization. Randomization was computer generated, and web based via Griffith University (https://www151.griffith.edu.au/random) to ensure allocation concealment until study entry. Patients were randomly assigned in a 1:1 ratio with computer-generated and randomly varied block sizes of 2 and 4. Data were collected by the ReN on the day of insertion, at day 1 postinsertion, then every 2 to 3 days thereafter so that PICCs were checked at least twice per week until study completion. Participants were included in the trial until 12 weeks post-PICC insertion, study withdrawal or PICC removal (whichever came first), with an additional 48 hours follow-up for infection outcomes. Patient review was face to face during the inpatient stay, with discharged patients’ follow-up occurring via outpatient clinics, hospital-in-the-home service, or telephone.

Data collection was via Research Electronic Data Capture (http://project-redcap.org/). The ReN collected data on primary and secondary outcomes by using the predefined criteria. Demographic and clinical data were collected to assess the success of randomization, describe the participant group, and display characteristics known to increase the risk of PICC complication and thrombosis. A blinded radiologist and infectious disease specialist reviewed and diagnosed thrombosis of deep veins and catheter-associated BSI outcomes, respectively.

PICC Procedures

Extensive prestudy education for 2 months prior to trial commencement was provided to all clinicians involved with the insertion and care of PICCs, including the study products. PICCs were inserted in an operating theater environment by a qualified consultant pediatric anesthetist, a senior anesthetic registrar or fellow in an approved anesthetic training program, or pediatric vascular access nurse practitioner. Ultrasound guidance was used to assess a patient’s vasculature and puncture the vessel. The operator chose the PICC size on the basis of clinical judgment of vessel size and patient needs and then inserted the allocated PICC.²⁷ Preferred PICC tip location was the cavoatrial junction. All PICC tip positions were confirmed with a chest x-ray before use.

Postinsertion, PICCs were managed by local interdisciplinary clinicians in accordance with local practice guidelines.^27-31 PICC care and management includes the use of 2% chlorhexidine gluconate in 70% alcohol for site antisepsis and neutral displacement needleless connectors (TUTA Pulse; Medical Australia Limited, Lidcombe, New South Wales, Australia); normal saline was used to flush after medication administration, and if the device was not in use for 6 hours or longer, heparin is instilled with securement via bordered polyurethane dressing (Tegaderm 1616; 3M, St Paul, Minnesota) and a sutureless securement device (Statlock VPPCSP; Bard, Georgia).

Statistical Analyses

Data were exported to Stata 15³² for cleaning and analysis. Data cleaning of outlying figures and missing and implausible data was undertaken prior to analysis. Missing data were not imputed. The PICC was the unit of measurement, and all randomly assigned patients were analyzed on an intention-to-treat basis.³³ Descriptive statistics (frequencies and percentages) were used to ascertain the primary outcome of feasibility for the larger trial. Incidence rates (per 1,000 catheter days) and rate ratios, including 95% confidence intervals (CIs), were calculated. The comparability of groups at baseline was described across demographic, clinical, and device characteristics. Kaplan-Meier survival curves (with log-rank tests) were used to compare PICC failure between study groups over time. Associations between baseline characteristics and failure were described by calculating hazard ratios (HRs). Univariable Cox regression was performed only due to the relatively low number of outcomes. P values of <.05 were considered statistically significant.

Ethics

The Children’s Health Service District, Queensland (Human Research Ethics Committee/15/QRCH/164), and Griffith University (2016/077) Human Research Ethics Committees provided ethics and governance approval. Informed consent was obtained from parents or legal guardians, with children providing youth assent if they were 7 years or older, dependent upon cognitive ability.

Participant and PICC Characteristics

Feasibility Outcomes

PICC Failure and Complications

DISCUSSION

This is the first pilot feasibility trial of new PICC materials and valve design incorporated in the BioFlo^® PICC in the pediatric population. The trial incorporated best practice for randomized trials, including using a concurrent control group, centralized and concealed randomization, predetermined feasibility criteria, and a registered and published trial protocol.¹⁷ As in other studies,^15,24,34 PICC failure and complication prevalence was unacceptably high for this essential device. Standard care PICCs failed twice as often as the new BioFlo^® PICCs (22% vs 11%), which is a clinically important difference. As researchers in a pilot study, we did not expect to detect statistically significant differences; however, we found that overall complications during the dwell occurred significantly more with the standard care than BioFlo^® PICCs (P = .009).

BioFlo^® PICC material offers a major advancement in PICC material through the incorporation of AT technologies into catheter materials, such as PICCs. Endexo^® is a low molecular–weight, fluoro-oligomeric additive that self-locates to the top few nanometers of the material surface. When added to power-injectable polyurethane, the additive results in a strong but passive, nonstick, fluorinated surface in the base PICC material. This inhibits platelet adhesion, suppresses protein procoagulant conformation, and thereby reduces thrombus formation in medical devices. Additionally, Endexo^® is not a catheter coating; rather, it is incorporated within the polyurethane of the PICC, thereby ensuring these AT properties are present on the internal, external, and cut surfaces of the PICC. If this technology can reduce complication during treatment and reduce failure from infection, thrombosis, occlusion, fracture, and dislodgement, it will improve patient outcomes considerably and lower health system costs. Previous studies investigating valve technology in PICC design to reduce occlusion have been inconclusive.^12-14,35,36 Occlusion (both partial and complete) was less frequent in our study with the BioFlo^® group (n = 3; 4%) compared to the standard care group (n = 6; 8%). The results of this pilot study suggest that either the Endexo^® material or PASV^® technology has a positive association with occlusion reduction during PICC treatment.

Thrombosis was the primary failure type for the standard care PICCs, comprising one-third of failures. All but one patient with radiologically confirmed thrombosis required the removal of the PICC prior to completion of treatment. The decision to remove the PICC or retain and treat conservatively remained with the treating team. Raffini et al.⁷ found thrombosis to increase in patients with one or more coexisting chronic medical condition. Slightly more standard care than BioFlo^® patients were free of such comorbidities (25% vs 16%), yet standard care patients still had the higher number of thromboses (7% vs 3%). Morgenthaler and Rodriguez³⁷ reported vascular access-associated thrombosis in pediatrics to be less common than in adults but higher in medically complex children. Worryingly, Menendez et al.³⁸ reported pediatric thrombosis to be largely asymptomatic, so the true incidence in our study is likely higher because only radiologically confirmed thromboses were recorded.

Occlusion (partial or complete) was the predominant complication across the study, being associated with one-third of all failures. When occlusion complications during the dwell (some of which were resolved with treatment), in addition to those causing failure, were considered, this number was even greater. Occlusion complications are prevalent and costly. Smith et al.²⁴ reported that occlusion was the most common reason for PICC removal and the most likely complication to delay treatment. Both the BioFlo^® and standard care PICCs are pressure rated with good tensile strength; however, fracture occurred in 4% (n = 3) of standard care PICCs compared to no fractures in BioFlo^® PICCs. Although the numbers are small, it may suggest a superior tensile strength of the BioFlo^® material.

This study reinforces previously published results^24,38 that PICC tip position is important and can influence complications, such as occlusion and thrombosis. In addition, we found a significant association with failure when PICCs did not have a continuous infusion. These findings reinforce the need for optimal tip location at insertion and ongoing flushing and maintenance of PICCs not used for infusions.

Limitations of this study include the small sample size, which was not designed to detect statistical differences in the primary outcome between groups. Despite randomization, there were slight imbalances at baseline for inserter type and leukocyte count, although these were not significantly associated with PICC failure in the Cox regression (data not shown), and thus were unlikely to influence findings. Additionally, a difference of <10% was associated with PICC tip position, favoring the BioFlo^® group. PICC tip position outside the cavoatrial junction was positively associated with failure; therefore, the effect of tip positioning on outcomes is difficult to ascertain given the small sample size and feasibility nature of the study. Further study is warranted to further explore this effect. The population sampled was pediatric medical and surgical inpatients with a vessel size >2 mm attending the operating theater suite for PICC insertion, thereby limiting the study’s generalizability to adults and other populations, including neonates and those with PICCs inserted in the pediatric intensive care unit. The study could not be blinded because study products had to be visible to the clinical and research staff. However, it is unlikely that staff would intentionally sabotage PICCs to bias the study. Blinding was possible for the assessment of blood culture and ultrasound reports to diagnose infection and thrombosis. Strengths of this study included 100% protocol adherence, and no patients were lost to follow-up.

These results confirm that PICC failure is unacceptably high and suggest that the innovative BioFlo^® PICC material and design holds promise to improve PICC outcomes by reducing complications and overall PICC failure. Trials of this technology are feasible, safe, and acceptable to healthcare staff and parents. Further trials are required, including in other patient populations, to definitively identify clinical, cost-effective methods to prevent PICC failure and improve reliability during treatment.

Acknowledgments

The authors thank the children and parents of Lady Cilento Children’s Hospital for participating in this important research. A special thank you goes to the nurses within the Vascular Assessment and Management Service and to Karen Turner, Julieta Woosley, and Anna Dean for their efforts in data collecting and ensuring protocol adherence.

Disclosure

Griffith University has received unrestricted, investigator-initiated research or educational grants to support the research of T. K., A. J. U., and C. R. M. from product manufacturers 3M, Adhezion Inc, AngioDynamics, Bard Medical, Baxter, B. Braun Medical Inc, Becton Dickinson, CareFusion, Centurion Medical Products, Cook Medical, Entrotech, FloMedical, ICU Medical Inc, Medical Australia Limited, Medtronic, Smiths Medical, and Teleflex. Griffith University has received consultancy payments on behalf of C. R. M., A. J. U., and T. K. from manufacturers 3M, AngioDynamics, Bard Medical, B. Braun Medical Inc, Becton Dickinson, CareFusion, Mayo Healthcare Inc, ResQDevices, and Smiths Medical. AngioDynamics (the BioFlo^® PICC manufacturer) provided partial funds to undertake this research via an unrestricted donation to Griffith University (but not the study authors). Queensland Health provided in-kind support to fund the remainder of the trial. The funders had no role in the study design, collection, analysis, or interpretation of the data, writing of the report, or decision to submit the article for publication.

Testing for adrenal insufficiency (AI) is common in the hospital setting. The gold standard remains the insulin tolerance test (ITT), in which cortisol concentration is measured after the induction of hypoglycemia to <35 mg/dL.¹ Alternatively, metyrapone testing works by blocking cortisol synthesis. If pretest adrenocorticotropic hormone (ACTH) concentrations are low and ACTH concentrations do not rise after the administration of metyrapone, the patient is given a diagnosis of AI. Both assays pose some risk to patients with AI and are typically only performed as confirmatory tests. Morning random cortisol concentrations can be used to suggest AI if concentrations are <3 mcg/dL, but they often provide indeterminate results if concentrations are between 3 and 15 mcg/dL.² Thus, morning cortisol concentrations in isolation are not diagnostic of AI. For these reasons, most experts recommend a dynamic, high-dose cosyntropin stimulation testing (CST) with 250 mcg of intravenous cosyntropin to screen for AI. The test can be done any time of day.³ Historically, an incremental response to cosyntropin, or “delta,” was also required to indicate a normal response to stimulation.⁴ However, the baseline cortisol concentration is dependent on circadian rhythm and level of stress. For this reason, a delta, whether large or small, has been abandoned as a requisite for the diagnosis of AI.^5-7 A normal CST is widely accepted to be identified by any cortisol concentration >18 mcg/dL during the test (basal or poststimulation).⁸

The seminal studies by Lindholm, Kehlet, and coauthors^9-11 validated the CST against the gold standard ITT and utilized only 0- and 30-minute cortisol concentrations. A later study in patients with pituitary disease demonstrated that 30-minute concentrations had a stronger correlation with the ITT than 60-minute concentrations (false-negative rate: 10% vs 27%).¹² However, in that study, a higher threshold was used for the 60-minute concentration than for what was obtained at 30 minutes (25.4 vs 21.8 mcg/dL, respectively). Multiple studies have shown that the 60-minute concentration is higher than the 30-minute concentration after cosyntropin stimulation.^4,5,13 Subsequent, small studies of patients who were known to have AI have shown that 60-minute concentrations are as useful as 30-minute concentrations.^5,14,15 Because 30-minute cortisol concentrations are often lower than 60-minute concentrations, a single 30-minute result may lead to a falsely abnormal test.^16,17 As such, the use of a single 60-minute test may be more appropriate. Indeed, some authors have suggested that measuring only 30-minute concentrations may lead to overdiagnosis of AI by missing an appropriate response, serum cortisol >18 mcg/dL, at 60 minutes.^17-19 Peak cortisol concentrations after low-dose cosyntropin stimulation (1 mcg) are seen at 60 minutes, and low-dose stimulation has been shown to be more variable than in the high-dose test (250 mg).^19,20

There is a lack of consensus to guide clinicians as to when cortisol concentrations should be measured after stimulation, and standard references lack uniformity. Commonly accessed medical resources—such as UpToDate and Jameson’s Endocrinology—recommend basal, 30-minute, and 60-minute cortisol concentrations, while Williams Textbook of Endocrinology recommends basal and 30-minute concentrations, and the Washington Manual recommends only a single 30-minute concentration.^7,21,22 Goldman-Cecil Medicine⁸ recommends checking a cortisol concentration between 30 and 60 minutes and recommends the same 18 mcg/dL cutoff for any test obtained in this time period. As a result of these variable recommendations, all 3 time points are often obtained. Prominent review articles continue to recommend checking all 3 concentrations while presenting evidence of peak cortisol response at 60 minutes poststimulation.¹³

In this study, we retrospectively examined CSTs in hospitalized, adult patients both in the intensive care unit (ICU) and hospital ward and/or floor settings to evaluate for significant differences in 30- and 60-minute cortisol concentrations and compare the concordance of screening at each time point alone with traditional CST at all 3 time points. By using these results, we discuss the utility of obtaining 3 cortisol samples.

After receiving approval from the institutional review board, we retrospectively reviewed all standard, high-dose CSTs performed on adult inpatients at the Barnes-Jewish Hospital laboratory from January 1, 2012, to August 31, 2013. All patients received the same standard dose (250 mcg cosyntropin, a synthetic ACTH, at a concentration of 1 mcg/mL administered over 2 minutes) regardless of age or weight. We collected patient gender; age; time of baseline cortisol measurement; cortisol results at baseline, 30, and 60 minutes; and patient location (inpatient floor vs ICU status). Tests were included if results from all 3 time points (0, 30, 60 minute) were available.

Cortisol concentrations were assessed by the laboratory according to the manufacturer’s instructions by using the ADVIA Centaur Cortisol assay (Siemens Healthcare Diagnostics Inc, Tarrytown, NY), a competitive chemiluminescent immunoassay. For the traditional CST, a cortisol concentration ≥18 mcg/dL at any time point during the test was used to define normal (negative). Patients with a positive (no results >18 mcg/mL) CST were defined as “screen positives” for the purposes of this analysis. Patient location data were available that allowed for an ICU vs non-ICU comparison.

Statistical analyses were performed in SAS version 9.4 (SAS Institute Inc, Cary, North Carolina). Continuous variables were compared by using a 2-tailed Student t test. Percentiles and proportions were compared by using χ² tests or Fisher’s exact tests when appropriate. The concordance of screening at each time point compared with the traditional CST was calculated. Positive percent agreement (PPA) with the traditional CST in each subgroup (ICU and floor) and combined was also evaluated. A P value of .05 was used to determine significance.

Cortisol concentrations obtained at 30 minutes were significantly higher than baseline cortisol concentrations (baseline: 12.8 mcg/dL; 30 minutes: 23.9 mcg/dL; P < .001) for all patients. The average cortisol concentrations obtained at 60 minutes (27.4 mcg/dL) were significantly higher than those at baseline and 30 minutes (P < .001). This trend was seen in each subgroup of patients in the ICU and on the floor (Figure). The average baseline cortisol concentration was higher for ICU patients compared to floor patients (17.6 mcg/dL vs 10.9 mcg/dL, respectively).

By using the traditional CST, there were 26 (13.1%) positive tests for AI in ICU patients and 84 (16.7%) positive tests in floor patients (Table).

Our investigation of 702 CSTs, the largest retrospective analysis to date, finds that the 60-minute cortisol concentration is significantly higher than the 30-minute concentration in a standard, high-dose CST. Sixty-minute cortisol concentrations are more concordant with traditional CST results than the 30-minute concentrations in both critically ill ICU and noncritically ill floor patients. This suggests that a single 60-minute measurement is sufficient for AI screening. The use of only 30-minute concentrations would lead to a significant increase in false-positive screening tests and significantly lower PPA (98% vs 57%). With peak cortisol concentrations occurring at 60-minutes poststimulation, measuring both 30- and 60-minute poststimulation concentrations does not appear to be of significant clinical benefit. The cost-saving from reduced phlebotomy and laboratory expenses would be significant, especially in locations with limited staff or financial resources. Our findings are similar to other recent results by Chitale et al.,¹⁷ Mansoor et al.,¹⁶ and Zueger et al.¹⁸

Zueger et al.¹⁸ evaluated the results of high-dose CST in 73 patients and found 13.7% of patients with inadequate cortisol response (<18 mcg/dL) at 30 minutes had normal concentrations at 60 minutes (>18 mcg/dL). Their study did not identify a single case of normal cortisol concentration at 30 minutes that would have inappropriately screened positive for AI if cortisol concentrations were only checked at 60 minutes. Similarly, they suggested that the 30-minute test did not add any additional diagnostic value; however, no confirmatory testing was performed.

Higher cortisol concentrations at 60 minutes poststimulation may result in improved specificity for AI without reducing sensitivity, but it may also indicate that the cutoff value may need to be raised from 18 mcg/dL at 60 minutes to maintain an appropriate clinical sensitivity. Continued research should resolve this clinical question with gold-standard confirmatory testing. Furthermore, there is debate about an appropriate screening cortisol concentration threshold for critically ill patients. Researchers have compared concentrations of 25 mcg/dL to the traditional 18 mcg/dL to improve sensitivity for AI, but these studies do not involve comparisons to confirmatory testing and often result in reduced specificity.^23,24

In our study, only a small fraction of testing was performed in the early-morning hours, when basal cortisol results are of value. There may be indications to perform traditional CSTs with a basal concentration, such as for suspected secondary AI, but testing must be performed in the early morning for interpretable results per current recommendations. However, poststimulation cortisol concentrations may be interpreted regardless of the time of day at which the test was initiated.³

Our study is limited by its scope because it is a retrospective analysis. It is also limited by a lack of gold-standard, clinical confirmatory testing or analysis of other clinical data. Our method of testing and interpretation is considered the screening standard and is often used to plan treatment for AI without confirmatory testing, as ITT is not routinely available for hospitalized patients. The validation of the traditional CST to the ITT has been performed extensively, but a randomized trial comparing a single 60-minute concentration to the ITT may be useful. The exact timing of blood draws may have introduced error in the concentration measurements, and this is critical to screening accuracy. Total serum cortisol is 10% bound to albumin,²⁵ and medications such as steroids or opioids and medical conditions such as obesity or liver disease can affect cortisol concentrations.²⁶ Albumin and free cortisol concentrations that may be used to adjust for these variables were not available.

We recommend changes to the standard CST to exclude a basal cortisol concentration unless it is indicated for the evaluation of secondary AI or obtained at the appropriate early-morning hour. A single 60-minute poststimulation cortisol concentration may be an appropriate screening test for AI and demonstrates high concordance with the traditional CST. The use of a 30-minute poststimulation concentration alone may lead to a significantly higher number of false-positive results. Alternatively, the stimulated cortisol threshold used to define a normal test may need to be higher at 60 minutes to maintain the appropriate sensitivity. Further study and comparison with confirmatory testing are needed.

Disclosure

The authors have no relevant conflicts of interest to disclose.

Testing for adrenal insufficiency (AI) is common in the hospital setting. The gold standard remains the insulin tolerance test (ITT), in which cortisol concentration is measured after the induction of hypoglycemia to <35 mg/dL.¹ Alternatively, metyrapone testing works by blocking cortisol synthesis. If pretest adrenocorticotropic hormone (ACTH) concentrations are low and ACTH concentrations do not rise after the administration of metyrapone, the patient is given a diagnosis of AI. Both assays pose some risk to patients with AI and are typically only performed as confirmatory tests. Morning random cortisol concentrations can be used to suggest AI if concentrations are <3 mcg/dL, but they often provide indeterminate results if concentrations are between 3 and 15 mcg/dL.² Thus, morning cortisol concentrations in isolation are not diagnostic of AI. For these reasons, most experts recommend a dynamic, high-dose cosyntropin stimulation testing (CST) with 250 mcg of intravenous cosyntropin to screen for AI. The test can be done any time of day.³ Historically, an incremental response to cosyntropin, or “delta,” was also required to indicate a normal response to stimulation.⁴ However, the baseline cortisol concentration is dependent on circadian rhythm and level of stress. For this reason, a delta, whether large or small, has been abandoned as a requisite for the diagnosis of AI.^5-7 A normal CST is widely accepted to be identified by any cortisol concentration >18 mcg/dL during the test (basal or poststimulation).⁸

The seminal studies by Lindholm, Kehlet, and coauthors^9-11 validated the CST against the gold standard ITT and utilized only 0- and 30-minute cortisol concentrations. A later study in patients with pituitary disease demonstrated that 30-minute concentrations had a stronger correlation with the ITT than 60-minute concentrations (false-negative rate: 10% vs 27%).¹² However, in that study, a higher threshold was used for the 60-minute concentration than for what was obtained at 30 minutes (25.4 vs 21.8 mcg/dL, respectively). Multiple studies have shown that the 60-minute concentration is higher than the 30-minute concentration after cosyntropin stimulation.^4,5,13 Subsequent, small studies of patients who were known to have AI have shown that 60-minute concentrations are as useful as 30-minute concentrations.^5,14,15 Because 30-minute cortisol concentrations are often lower than 60-minute concentrations, a single 30-minute result may lead to a falsely abnormal test.^16,17 As such, the use of a single 60-minute test may be more appropriate. Indeed, some authors have suggested that measuring only 30-minute concentrations may lead to overdiagnosis of AI by missing an appropriate response, serum cortisol >18 mcg/dL, at 60 minutes.^17-19 Peak cortisol concentrations after low-dose cosyntropin stimulation (1 mcg) are seen at 60 minutes, and low-dose stimulation has been shown to be more variable than in the high-dose test (250 mg).^19,20

There is a lack of consensus to guide clinicians as to when cortisol concentrations should be measured after stimulation, and standard references lack uniformity. Commonly accessed medical resources—such as UpToDate and Jameson’s Endocrinology—recommend basal, 30-minute, and 60-minute cortisol concentrations, while Williams Textbook of Endocrinology recommends basal and 30-minute concentrations, and the Washington Manual recommends only a single 30-minute concentration.^7,21,22 Goldman-Cecil Medicine⁸ recommends checking a cortisol concentration between 30 and 60 minutes and recommends the same 18 mcg/dL cutoff for any test obtained in this time period. As a result of these variable recommendations, all 3 time points are often obtained. Prominent review articles continue to recommend checking all 3 concentrations while presenting evidence of peak cortisol response at 60 minutes poststimulation.¹³

In this study, we retrospectively examined CSTs in hospitalized, adult patients both in the intensive care unit (ICU) and hospital ward and/or floor settings to evaluate for significant differences in 30- and 60-minute cortisol concentrations and compare the concordance of screening at each time point alone with traditional CST at all 3 time points. By using these results, we discuss the utility of obtaining 3 cortisol samples.

After receiving approval from the institutional review board, we retrospectively reviewed all standard, high-dose CSTs performed on adult inpatients at the Barnes-Jewish Hospital laboratory from January 1, 2012, to August 31, 2013. All patients received the same standard dose (250 mcg cosyntropin, a synthetic ACTH, at a concentration of 1 mcg/mL administered over 2 minutes) regardless of age or weight. We collected patient gender; age; time of baseline cortisol measurement; cortisol results at baseline, 30, and 60 minutes; and patient location (inpatient floor vs ICU status). Tests were included if results from all 3 time points (0, 30, 60 minute) were available.

Cortisol concentrations were assessed by the laboratory according to the manufacturer’s instructions by using the ADVIA Centaur Cortisol assay (Siemens Healthcare Diagnostics Inc, Tarrytown, NY), a competitive chemiluminescent immunoassay. For the traditional CST, a cortisol concentration ≥18 mcg/dL at any time point during the test was used to define normal (negative). Patients with a positive (no results >18 mcg/mL) CST were defined as “screen positives” for the purposes of this analysis. Patient location data were available that allowed for an ICU vs non-ICU comparison.

Statistical analyses were performed in SAS version 9.4 (SAS Institute Inc, Cary, North Carolina). Continuous variables were compared by using a 2-tailed Student t test. Percentiles and proportions were compared by using χ² tests or Fisher’s exact tests when appropriate. The concordance of screening at each time point compared with the traditional CST was calculated. Positive percent agreement (PPA) with the traditional CST in each subgroup (ICU and floor) and combined was also evaluated. A P value of .05 was used to determine significance.

Cortisol concentrations obtained at 30 minutes were significantly higher than baseline cortisol concentrations (baseline: 12.8 mcg/dL; 30 minutes: 23.9 mcg/dL; P < .001) for all patients. The average cortisol concentrations obtained at 60 minutes (27.4 mcg/dL) were significantly higher than those at baseline and 30 minutes (P < .001). This trend was seen in each subgroup of patients in the ICU and on the floor (Figure). The average baseline cortisol concentration was higher for ICU patients compared to floor patients (17.6 mcg/dL vs 10.9 mcg/dL, respectively).

By using the traditional CST, there were 26 (13.1%) positive tests for AI in ICU patients and 84 (16.7%) positive tests in floor patients (Table).

Our investigation of 702 CSTs, the largest retrospective analysis to date, finds that the 60-minute cortisol concentration is significantly higher than the 30-minute concentration in a standard, high-dose CST. Sixty-minute cortisol concentrations are more concordant with traditional CST results than the 30-minute concentrations in both critically ill ICU and noncritically ill floor patients. This suggests that a single 60-minute measurement is sufficient for AI screening. The use of only 30-minute concentrations would lead to a significant increase in false-positive screening tests and significantly lower PPA (98% vs 57%). With peak cortisol concentrations occurring at 60-minutes poststimulation, measuring both 30- and 60-minute poststimulation concentrations does not appear to be of significant clinical benefit. The cost-saving from reduced phlebotomy and laboratory expenses would be significant, especially in locations with limited staff or financial resources. Our findings are similar to other recent results by Chitale et al.,¹⁷ Mansoor et al.,¹⁶ and Zueger et al.¹⁸

Zueger et al.¹⁸ evaluated the results of high-dose CST in 73 patients and found 13.7% of patients with inadequate cortisol response (<18 mcg/dL) at 30 minutes had normal concentrations at 60 minutes (>18 mcg/dL). Their study did not identify a single case of normal cortisol concentration at 30 minutes that would have inappropriately screened positive for AI if cortisol concentrations were only checked at 60 minutes. Similarly, they suggested that the 30-minute test did not add any additional diagnostic value; however, no confirmatory testing was performed.

Higher cortisol concentrations at 60 minutes poststimulation may result in improved specificity for AI without reducing sensitivity, but it may also indicate that the cutoff value may need to be raised from 18 mcg/dL at 60 minutes to maintain an appropriate clinical sensitivity. Continued research should resolve this clinical question with gold-standard confirmatory testing. Furthermore, there is debate about an appropriate screening cortisol concentration threshold for critically ill patients. Researchers have compared concentrations of 25 mcg/dL to the traditional 18 mcg/dL to improve sensitivity for AI, but these studies do not involve comparisons to confirmatory testing and often result in reduced specificity.^23,24

In our study, only a small fraction of testing was performed in the early-morning hours, when basal cortisol results are of value. There may be indications to perform traditional CSTs with a basal concentration, such as for suspected secondary AI, but testing must be performed in the early morning for interpretable results per current recommendations. However, poststimulation cortisol concentrations may be interpreted regardless of the time of day at which the test was initiated.³

Our study is limited by its scope because it is a retrospective analysis. It is also limited by a lack of gold-standard, clinical confirmatory testing or analysis of other clinical data. Our method of testing and interpretation is considered the screening standard and is often used to plan treatment for AI without confirmatory testing, as ITT is not routinely available for hospitalized patients. The validation of the traditional CST to the ITT has been performed extensively, but a randomized trial comparing a single 60-minute concentration to the ITT may be useful. The exact timing of blood draws may have introduced error in the concentration measurements, and this is critical to screening accuracy. Total serum cortisol is 10% bound to albumin,²⁵ and medications such as steroids or opioids and medical conditions such as obesity or liver disease can affect cortisol concentrations.²⁶ Albumin and free cortisol concentrations that may be used to adjust for these variables were not available.

We recommend changes to the standard CST to exclude a basal cortisol concentration unless it is indicated for the evaluation of secondary AI or obtained at the appropriate early-morning hour. A single 60-minute poststimulation cortisol concentration may be an appropriate screening test for AI and demonstrates high concordance with the traditional CST. The use of a 30-minute poststimulation concentration alone may lead to a significantly higher number of false-positive results. Alternatively, the stimulated cortisol threshold used to define a normal test may need to be higher at 60 minutes to maintain the appropriate sensitivity. Further study and comparison with confirmatory testing are needed.

Disclosure

The authors have no relevant conflicts of interest to disclose.

Testing for adrenal insufficiency (AI) is common in the hospital setting. The gold standard remains the insulin tolerance test (ITT), in which cortisol concentration is measured after the induction of hypoglycemia to <35 mg/dL.¹ Alternatively, metyrapone testing works by blocking cortisol synthesis. If pretest adrenocorticotropic hormone (ACTH) concentrations are low and ACTH concentrations do not rise after the administration of metyrapone, the patient is given a diagnosis of AI. Both assays pose some risk to patients with AI and are typically only performed as confirmatory tests. Morning random cortisol concentrations can be used to suggest AI if concentrations are <3 mcg/dL, but they often provide indeterminate results if concentrations are between 3 and 15 mcg/dL.² Thus, morning cortisol concentrations in isolation are not diagnostic of AI. For these reasons, most experts recommend a dynamic, high-dose cosyntropin stimulation testing (CST) with 250 mcg of intravenous cosyntropin to screen for AI. The test can be done any time of day.³ Historically, an incremental response to cosyntropin, or “delta,” was also required to indicate a normal response to stimulation.⁴ However, the baseline cortisol concentration is dependent on circadian rhythm and level of stress. For this reason, a delta, whether large or small, has been abandoned as a requisite for the diagnosis of AI.^5-7 A normal CST is widely accepted to be identified by any cortisol concentration >18 mcg/dL during the test (basal or poststimulation).⁸

The seminal studies by Lindholm, Kehlet, and coauthors^9-11 validated the CST against the gold standard ITT and utilized only 0- and 30-minute cortisol concentrations. A later study in patients with pituitary disease demonstrated that 30-minute concentrations had a stronger correlation with the ITT than 60-minute concentrations (false-negative rate: 10% vs 27%).¹² However, in that study, a higher threshold was used for the 60-minute concentration than for what was obtained at 30 minutes (25.4 vs 21.8 mcg/dL, respectively). Multiple studies have shown that the 60-minute concentration is higher than the 30-minute concentration after cosyntropin stimulation.^4,5,13 Subsequent, small studies of patients who were known to have AI have shown that 60-minute concentrations are as useful as 30-minute concentrations.^5,14,15 Because 30-minute cortisol concentrations are often lower than 60-minute concentrations, a single 30-minute result may lead to a falsely abnormal test.^16,17 As such, the use of a single 60-minute test may be more appropriate. Indeed, some authors have suggested that measuring only 30-minute concentrations may lead to overdiagnosis of AI by missing an appropriate response, serum cortisol >18 mcg/dL, at 60 minutes.^17-19 Peak cortisol concentrations after low-dose cosyntropin stimulation (1 mcg) are seen at 60 minutes, and low-dose stimulation has been shown to be more variable than in the high-dose test (250 mg).^19,20

There is a lack of consensus to guide clinicians as to when cortisol concentrations should be measured after stimulation, and standard references lack uniformity. Commonly accessed medical resources—such as UpToDate and Jameson’s Endocrinology—recommend basal, 30-minute, and 60-minute cortisol concentrations, while Williams Textbook of Endocrinology recommends basal and 30-minute concentrations, and the Washington Manual recommends only a single 30-minute concentration.^7,21,22 Goldman-Cecil Medicine⁸ recommends checking a cortisol concentration between 30 and 60 minutes and recommends the same 18 mcg/dL cutoff for any test obtained in this time period. As a result of these variable recommendations, all 3 time points are often obtained. Prominent review articles continue to recommend checking all 3 concentrations while presenting evidence of peak cortisol response at 60 minutes poststimulation.¹³

In this study, we retrospectively examined CSTs in hospitalized, adult patients both in the intensive care unit (ICU) and hospital ward and/or floor settings to evaluate for significant differences in 30- and 60-minute cortisol concentrations and compare the concordance of screening at each time point alone with traditional CST at all 3 time points. By using these results, we discuss the utility of obtaining 3 cortisol samples.

After receiving approval from the institutional review board, we retrospectively reviewed all standard, high-dose CSTs performed on adult inpatients at the Barnes-Jewish Hospital laboratory from January 1, 2012, to August 31, 2013. All patients received the same standard dose (250 mcg cosyntropin, a synthetic ACTH, at a concentration of 1 mcg/mL administered over 2 minutes) regardless of age or weight. We collected patient gender; age; time of baseline cortisol measurement; cortisol results at baseline, 30, and 60 minutes; and patient location (inpatient floor vs ICU status). Tests were included if results from all 3 time points (0, 30, 60 minute) were available.

Cortisol concentrations were assessed by the laboratory according to the manufacturer’s instructions by using the ADVIA Centaur Cortisol assay (Siemens Healthcare Diagnostics Inc, Tarrytown, NY), a competitive chemiluminescent immunoassay. For the traditional CST, a cortisol concentration ≥18 mcg/dL at any time point during the test was used to define normal (negative). Patients with a positive (no results >18 mcg/mL) CST were defined as “screen positives” for the purposes of this analysis. Patient location data were available that allowed for an ICU vs non-ICU comparison.

Statistical analyses were performed in SAS version 9.4 (SAS Institute Inc, Cary, North Carolina). Continuous variables were compared by using a 2-tailed Student t test. Percentiles and proportions were compared by using χ² tests or Fisher’s exact tests when appropriate. The concordance of screening at each time point compared with the traditional CST was calculated. Positive percent agreement (PPA) with the traditional CST in each subgroup (ICU and floor) and combined was also evaluated. A P value of .05 was used to determine significance.

Cortisol concentrations obtained at 30 minutes were significantly higher than baseline cortisol concentrations (baseline: 12.8 mcg/dL; 30 minutes: 23.9 mcg/dL; P < .001) for all patients. The average cortisol concentrations obtained at 60 minutes (27.4 mcg/dL) were significantly higher than those at baseline and 30 minutes (P < .001). This trend was seen in each subgroup of patients in the ICU and on the floor (Figure). The average baseline cortisol concentration was higher for ICU patients compared to floor patients (17.6 mcg/dL vs 10.9 mcg/dL, respectively).

By using the traditional CST, there were 26 (13.1%) positive tests for AI in ICU patients and 84 (16.7%) positive tests in floor patients (Table).

Our investigation of 702 CSTs, the largest retrospective analysis to date, finds that the 60-minute cortisol concentration is significantly higher than the 30-minute concentration in a standard, high-dose CST. Sixty-minute cortisol concentrations are more concordant with traditional CST results than the 30-minute concentrations in both critically ill ICU and noncritically ill floor patients. This suggests that a single 60-minute measurement is sufficient for AI screening. The use of only 30-minute concentrations would lead to a significant increase in false-positive screening tests and significantly lower PPA (98% vs 57%). With peak cortisol concentrations occurring at 60-minutes poststimulation, measuring both 30- and 60-minute poststimulation concentrations does not appear to be of significant clinical benefit. The cost-saving from reduced phlebotomy and laboratory expenses would be significant, especially in locations with limited staff or financial resources. Our findings are similar to other recent results by Chitale et al.,¹⁷ Mansoor et al.,¹⁶ and Zueger et al.¹⁸

Zueger et al.¹⁸ evaluated the results of high-dose CST in 73 patients and found 13.7% of patients with inadequate cortisol response (<18 mcg/dL) at 30 minutes had normal concentrations at 60 minutes (>18 mcg/dL). Their study did not identify a single case of normal cortisol concentration at 30 minutes that would have inappropriately screened positive for AI if cortisol concentrations were only checked at 60 minutes. Similarly, they suggested that the 30-minute test did not add any additional diagnostic value; however, no confirmatory testing was performed.

Higher cortisol concentrations at 60 minutes poststimulation may result in improved specificity for AI without reducing sensitivity, but it may also indicate that the cutoff value may need to be raised from 18 mcg/dL at 60 minutes to maintain an appropriate clinical sensitivity. Continued research should resolve this clinical question with gold-standard confirmatory testing. Furthermore, there is debate about an appropriate screening cortisol concentration threshold for critically ill patients. Researchers have compared concentrations of 25 mcg/dL to the traditional 18 mcg/dL to improve sensitivity for AI, but these studies do not involve comparisons to confirmatory testing and often result in reduced specificity.^23,24

In our study, only a small fraction of testing was performed in the early-morning hours, when basal cortisol results are of value. There may be indications to perform traditional CSTs with a basal concentration, such as for suspected secondary AI, but testing must be performed in the early morning for interpretable results per current recommendations. However, poststimulation cortisol concentrations may be interpreted regardless of the time of day at which the test was initiated.³

Our study is limited by its scope because it is a retrospective analysis. It is also limited by a lack of gold-standard, clinical confirmatory testing or analysis of other clinical data. Our method of testing and interpretation is considered the screening standard and is often used to plan treatment for AI without confirmatory testing, as ITT is not routinely available for hospitalized patients. The validation of the traditional CST to the ITT has been performed extensively, but a randomized trial comparing a single 60-minute concentration to the ITT may be useful. The exact timing of blood draws may have introduced error in the concentration measurements, and this is critical to screening accuracy. Total serum cortisol is 10% bound to albumin,²⁵ and medications such as steroids or opioids and medical conditions such as obesity or liver disease can affect cortisol concentrations.²⁶ Albumin and free cortisol concentrations that may be used to adjust for these variables were not available.

We recommend changes to the standard CST to exclude a basal cortisol concentration unless it is indicated for the evaluation of secondary AI or obtained at the appropriate early-morning hour. A single 60-minute poststimulation cortisol concentration may be an appropriate screening test for AI and demonstrates high concordance with the traditional CST. The use of a 30-minute poststimulation concentration alone may lead to a significantly higher number of false-positive results. Alternatively, the stimulated cortisol threshold used to define a normal test may need to be higher at 60 minutes to maintain the appropriate sensitivity. Further study and comparison with confirmatory testing are needed.

Disclosure

The authors have no relevant conflicts of interest to disclose.

Wasteful care may account for between 21% and 34% of the United States’ $3.2 trillion in annual healthcare expenditures, making it a prime target for cost-saving initiatives.^1,2 Telemetry is a target for value improvement strategies because telemetry is overutilized, rarely leads to a change in management, and has associated guidelines on appropriate use.^3-10 Telemetry use has been a focus of the Joint Commission’s National Patient Safety Goals since 2014, and it is also a focus of the Society of Hospital Medicine’s Choosing Wisely^® campaign.^11-13

Previous initiatives have evaluated how changes to telemetry orders or education and feedback affect telemetry use. Few studies have compared a system-wide electronic health record (EHR) approach to a multifaceted intervention. In seeking to address this gap, we adapted published guidelines from the American Heart Association (AHA) and incorporated them into our EHR ordering process.³ Simultaneously, we implemented a multifaceted quality improvement initiative and compared this combined program’s effectiveness to that of the EHR approach alone.

Study Design, Setting, and Population

We performed a 2-group observational pre- to postintervention study at University of Utah Health. Hospital encounters of patients 18 years and older who had at least 1 inpatient acute care, nonintensive care unit (ICU) room charge and an admission date between January 1, 2014, and July 31, 2016, were included. Patient encounters with missing encounter-level covariates, such as case mix index (CMI) or attending provider identification, were excluded. The Institutional Review Board classified this project as quality improvement and did not require review and oversight.

Intervention

On July 6, 2015, our Epic (Epic Systems Corporation, Madison, WI) EHR telemetry order was modified to discourage unnecessary telemetry monitoring. The new order required providers ordering telemetry to choose a clinical indication and select a duration for monitoring, after which the order would expire and require physician renewal or discontinuation. These were the only changes that occurred for nonhospitalist providers. The nonhospitalist group included all admitting providers who were not hospitalists. This group included neurology (6.98%); cardiology (8.13%); other medical specialties such as pulmonology, hematology, and oncology (21.30%); cardiothoracic surgery (3.72%); orthopedic surgery (14.84%); general surgery (11.11%); neurosurgery (11.07%); and other surgical specialties, including urology, transplant, vascular surgery, and plastics (16.68%).

Between January 2015 and June 2015, we implemented a multicomponent program among our hospitalist service. The hospitalist service is composed of 4 teams with internal medicine residents and 2 teams with advanced practice providers, all staffed by academic hospitalists. Our program was composed of 5 elements, all of which were made before the hospital-wide changes to electronic telemetry orders and maintained throughout the study period, as follows: (1) a single provider education session reviewing available evidence (eg, AHA guidelines, Choosing Wisely^® campaign), (2) removal of the telemetry order from hospitalist admission order set on March 23, 2015, (3) inclusion of telemetry discussion in the hospitalist group’s daily “Rounding Checklist,”¹⁴ (4) monthly feedback provided as part of hospitalist group meetings, and (5) a financial incentive, awarded to the division (no individual provider payment) if performance targets were met. See supplementary Appendix (“Implementation Manual”) for further details.

Data Source

We obtained data on patient age, gender, Medicare Severity-Diagnosis Related Group, Charlson comorbidity index (CCI), CMI, admitting unit, attending physician, admission and discharge dates, length of stay (LOS), 30-day readmission, bed charge (telemetry or nontelemetry), ICU stay, and inpatient mortality from the enterprise data warehouse. Telemetry days were determined through room billing charges, which are assigned based on the presence or absence of an active telemetry order at midnight. Code events came from a log kept by the hospital telephone operator, who is responsible for sending out all calls to the code team. Code event data were available starting July 19, 2014.

Measures

Our primary outcome was the percentage of hospital days that had telemetry charges for individual patients. All billed telemetry days on acute care floors were included regardless of admission status (inpatient vs observation), service, indication, or ordering provider. Secondary outcomes were inpatient mortality, escalation of care, code event rates, and appropriate telemetry utilization rates. Escalation of care was defined as transfer to an ICU after initially being admitted to an acute care floor. The code event rate was defined as the ratio of the number of code team activations to the number of patient days. Appropriate telemetry utilization rates were determined via chart review, as detailed below.

In order to evaluate changes in appropriateness of telemetry monitoring, 4 of the authors who are internal medicine physicians (KE, CC, JC, DG) performed chart reviews of 25 randomly selected patients in each group (hospitalist and nonhospitalist) before and after the intervention who received at least 1 day of telemetry monitoring. Each reviewer was provided a key based on AHA guidelines for monitoring indications and associated maximum allowable durations.³ Chart reviews were performed to determine the indication (if any) for monitoring, as well as the number of days that were indicated. The number of indicated days was compared to the number of telemetry days the patient received to determine the overall proportion of days that were indicated (“Telemetry appropriateness per visit”). Three reviewers (KE, AR, CC) also evaluated 100 patients on the hospitalist service after the intervention who did not receive any telemetry monitoring to evaluate whether patients with indications for telemetry monitoring were not receiving it after the intervention. For patients who had a possible indication, the indication was classified as Class I (“Cardiac monitoring is indicated in most, if not all, patients in this group”) or Class II (“Cardiac monitoring may be of benefit in some patients but is not considered essential for all patients”).³

Adjustment Variables

To account for differences in patient characteristics between hospitalist and nonhospitalist groups, we included age, gender, CMI, and CCI in statistical models. CCI was calculated according to the algorithm specified by Quan et al.¹⁵ using all patient diagnoses from previous visits and the index visit identified from the facility billing system.

Statistical Analysis

We computed descriptive statistics for study outcomes and visit characteristics for hospitalist and nonhospitalist visits for pre- and postintervention periods. Descriptive statistics were expressed as n (%) for categorical patient characteristics and outcome variables. For continuous patient characteristics, we expressed the variability of individual observations as the mean ± the standard deviation. For continuous outcomes, we expressed the precision of the mean estimates using standard error. Telemetry utilization per visit was weighted by the number of total acute care days per visit. Telemetry appropriateness per visit was weighted by the number of telemetry days per visit. Patients who did not receive any telemetry monitoring were included in the analysis and noted to have 0 telemetry days. All patients had at least 1 acute care day. Categorical variables were compared using χ² tests, and continuous variables were compared using t tests. Code event rates were compared using the binomial probability mid-p exact test for person-time data.¹⁶

We fitted generalized linear regression models using generalized estimating equations to evaluate the relative change in outcomes of interest in the postintervention period compared with the preintervention period after adjusting for study covariates. The models included study group (hospitalist and nonhospitalist), time period (pre- and postintervention), an interaction term between study group and time period, and study covariates (age, gender, CMI, and CCI). The models were defined using a binomial distributional assumption and logit link function for mortality, escalation of care, and whether patients had at least 1 telemetry day. A gamma distributional assumption and log link function were used for LOS, telemetry acute care days per visit, and total acute care days per visit. A negative binomial distributional assumption and log link function were used for telemetry utilization and telemetry appropriateness. We used the log of the acute care days as an offset for telemetry utilization and the log of the telemetry days per visit as an offset for telemetry appropriateness. An exchangeable working correlation matrix was used to account for physician-level clustering for all outcomes. Intervention effects, representing the difference in odds for categorical variables and in amount for continuous variables, were calculated as exponentiation of the beta parameters for the covariate minus 1.

P values <.05 were considered significant. We used SAS version 9.4 statistical software (SAS Institute Inc., Cary, NC) for data analysis.

The percent of patients who had any telemetry charges decreased from 36.2% to 15.9% (P < .001) in the hospitalist group and from 31.8% to 28.0% in the nonhospitalist group (P < .001; Table 1). Rates of code events did not change over time (P = .9).

In this study, implementing a change in the EHR telemetry order produced reductions in telemetry days. However, when combined with a multicomponent program including education, audit and feedback, financial incentives, and changes to remove telemetry orders from admission orders sets, an even more marked improvement was seen. Neither intervention reduced LOS, increased code event rates, or increased rates of escalation of care.

Prior studies have evaluated interventions to reduce unnecessary telemetry monitoring with varying degrees of success. The most successful EHR intervention to date, from Dressler et al.,¹⁸ achieved a 70% reduction in overall telemetry use by integrating the AHA guidelines into their EHR and incorporating nursing discontinuation guidelines to ensure that telemetry discontinuation was both safe and timely. Other studies using stewardship approaches and standardized protocols have been less successful.^19,20 One study utilizing a multidisciplinary approach but not including an EHR component showed modest improvements in telemetry.²¹

Although we are unable to differentiate the exact effect of each component of the intervention, we did note an immediate decrease in telemetry orders after removing the telemetry order from our admission order set, a trend that was magnified after the addition of broader EHR changes (Figure 1). Important additional contributors to our success seem to have been the standardization of rounds to include daily discussion of telemetry and the provision of routine feedback. We cannot discern whether other components of our program (such as the financial incentives) contributed more or less to our program, though the sum of these interventions produced an overall program that required substantial buy in and sustained focus from the hospitalist group. The importance of the hospitalist program is highlighted by the relatively large differences in improvement compared with the nonhospitalist group.

Our study has several limitations. First, the study was conducted at a single center, which may limit its generalizability. Second, the intervention was multifaceted, diminishing our ability to discern which aspects beyond the system-wide change in the telemetry order were most responsible for the observed effect among hospitalists. Third, we are unable to fully account for baseline differences in telemetry utilization between hospitalist and nonhospitalist groups. It is likely that different services utilize telemetry monitoring in different ways, and the hospitalist group may have been more aware of the existing guidelines for monitoring prior to the intervention. Furthermore, we had a limited sample size for the chart audits, which reduced the available statistical power for determining changes in the appropriateness of telemetry utilization. Additionally, because internal medicine residents rotate through various services, it is possible that the education they received on their hospitalist rotation as part of our intervention had a spillover effect in the nonhospitalist group. However, any effect should have decreased the difference between the groups. Lastly, although our postintervention time period was 1 year, we do not have data beyond that to monitor for sustainability of the results.

In this single-site study, combining EHR orders prompting physicians to choose a clinical indication and duration for monitoring with a broader program—including upstream changes in ordering as well as education, audit, and feedback—produced reductions in telemetry usage. Whether this reduction improves the appropriateness of telemetry utilization or reduces other effects of telemetry (eg, alert fatigue, calls for benign arrhythmias) cannot be discerned from our study. However, our results support the idea that multipronged approaches to telemetry use are most likely to produce improvements.

Acknowledgments

The authors thank Dr. Frank Thomas for his assistance with process engineering and Mr. Andrew Wood for his routine provision of data. The statistical analysis was supported by the University of Utah Study Design and Biostatistics Center, with funding in part from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 5UL1TR001067-05 (formerly 8UL1TR000105 and UL1RR025764).

Disclosure

The authors have no conflicts of interest to report.

Wasteful care may account for between 21% and 34% of the United States’ $3.2 trillion in annual healthcare expenditures, making it a prime target for cost-saving initiatives.^1,2 Telemetry is a target for value improvement strategies because telemetry is overutilized, rarely leads to a change in management, and has associated guidelines on appropriate use.^3-10 Telemetry use has been a focus of the Joint Commission’s National Patient Safety Goals since 2014, and it is also a focus of the Society of Hospital Medicine’s Choosing Wisely^® campaign.^11-13

Previous initiatives have evaluated how changes to telemetry orders or education and feedback affect telemetry use. Few studies have compared a system-wide electronic health record (EHR) approach to a multifaceted intervention. In seeking to address this gap, we adapted published guidelines from the American Heart Association (AHA) and incorporated them into our EHR ordering process.³ Simultaneously, we implemented a multifaceted quality improvement initiative and compared this combined program’s effectiveness to that of the EHR approach alone.

Study Design, Setting, and Population

We performed a 2-group observational pre- to postintervention study at University of Utah Health. Hospital encounters of patients 18 years and older who had at least 1 inpatient acute care, nonintensive care unit (ICU) room charge and an admission date between January 1, 2014, and July 31, 2016, were included. Patient encounters with missing encounter-level covariates, such as case mix index (CMI) or attending provider identification, were excluded. The Institutional Review Board classified this project as quality improvement and did not require review and oversight.

Intervention

On July 6, 2015, our Epic (Epic Systems Corporation, Madison, WI) EHR telemetry order was modified to discourage unnecessary telemetry monitoring. The new order required providers ordering telemetry to choose a clinical indication and select a duration for monitoring, after which the order would expire and require physician renewal or discontinuation. These were the only changes that occurred for nonhospitalist providers. The nonhospitalist group included all admitting providers who were not hospitalists. This group included neurology (6.98%); cardiology (8.13%); other medical specialties such as pulmonology, hematology, and oncology (21.30%); cardiothoracic surgery (3.72%); orthopedic surgery (14.84%); general surgery (11.11%); neurosurgery (11.07%); and other surgical specialties, including urology, transplant, vascular surgery, and plastics (16.68%).

Between January 2015 and June 2015, we implemented a multicomponent program among our hospitalist service. The hospitalist service is composed of 4 teams with internal medicine residents and 2 teams with advanced practice providers, all staffed by academic hospitalists. Our program was composed of 5 elements, all of which were made before the hospital-wide changes to electronic telemetry orders and maintained throughout the study period, as follows: (1) a single provider education session reviewing available evidence (eg, AHA guidelines, Choosing Wisely^® campaign), (2) removal of the telemetry order from hospitalist admission order set on March 23, 2015, (3) inclusion of telemetry discussion in the hospitalist group’s daily “Rounding Checklist,”¹⁴ (4) monthly feedback provided as part of hospitalist group meetings, and (5) a financial incentive, awarded to the division (no individual provider payment) if performance targets were met. See supplementary Appendix (“Implementation Manual”) for further details.

Data Source

We obtained data on patient age, gender, Medicare Severity-Diagnosis Related Group, Charlson comorbidity index (CCI), CMI, admitting unit, attending physician, admission and discharge dates, length of stay (LOS), 30-day readmission, bed charge (telemetry or nontelemetry), ICU stay, and inpatient mortality from the enterprise data warehouse. Telemetry days were determined through room billing charges, which are assigned based on the presence or absence of an active telemetry order at midnight. Code events came from a log kept by the hospital telephone operator, who is responsible for sending out all calls to the code team. Code event data were available starting July 19, 2014.

Measures

Our primary outcome was the percentage of hospital days that had telemetry charges for individual patients. All billed telemetry days on acute care floors were included regardless of admission status (inpatient vs observation), service, indication, or ordering provider. Secondary outcomes were inpatient mortality, escalation of care, code event rates, and appropriate telemetry utilization rates. Escalation of care was defined as transfer to an ICU after initially being admitted to an acute care floor. The code event rate was defined as the ratio of the number of code team activations to the number of patient days. Appropriate telemetry utilization rates were determined via chart review, as detailed below.

In order to evaluate changes in appropriateness of telemetry monitoring, 4 of the authors who are internal medicine physicians (KE, CC, JC, DG) performed chart reviews of 25 randomly selected patients in each group (hospitalist and nonhospitalist) before and after the intervention who received at least 1 day of telemetry monitoring. Each reviewer was provided a key based on AHA guidelines for monitoring indications and associated maximum allowable durations.³ Chart reviews were performed to determine the indication (if any) for monitoring, as well as the number of days that were indicated. The number of indicated days was compared to the number of telemetry days the patient received to determine the overall proportion of days that were indicated (“Telemetry appropriateness per visit”). Three reviewers (KE, AR, CC) also evaluated 100 patients on the hospitalist service after the intervention who did not receive any telemetry monitoring to evaluate whether patients with indications for telemetry monitoring were not receiving it after the intervention. For patients who had a possible indication, the indication was classified as Class I (“Cardiac monitoring is indicated in most, if not all, patients in this group”) or Class II (“Cardiac monitoring may be of benefit in some patients but is not considered essential for all patients”).³

Adjustment Variables

To account for differences in patient characteristics between hospitalist and nonhospitalist groups, we included age, gender, CMI, and CCI in statistical models. CCI was calculated according to the algorithm specified by Quan et al.¹⁵ using all patient diagnoses from previous visits and the index visit identified from the facility billing system.

Statistical Analysis

We computed descriptive statistics for study outcomes and visit characteristics for hospitalist and nonhospitalist visits for pre- and postintervention periods. Descriptive statistics were expressed as n (%) for categorical patient characteristics and outcome variables. For continuous patient characteristics, we expressed the variability of individual observations as the mean ± the standard deviation. For continuous outcomes, we expressed the precision of the mean estimates using standard error. Telemetry utilization per visit was weighted by the number of total acute care days per visit. Telemetry appropriateness per visit was weighted by the number of telemetry days per visit. Patients who did not receive any telemetry monitoring were included in the analysis and noted to have 0 telemetry days. All patients had at least 1 acute care day. Categorical variables were compared using χ² tests, and continuous variables were compared using t tests. Code event rates were compared using the binomial probability mid-p exact test for person-time data.¹⁶

We fitted generalized linear regression models using generalized estimating equations to evaluate the relative change in outcomes of interest in the postintervention period compared with the preintervention period after adjusting for study covariates. The models included study group (hospitalist and nonhospitalist), time period (pre- and postintervention), an interaction term between study group and time period, and study covariates (age, gender, CMI, and CCI). The models were defined using a binomial distributional assumption and logit link function for mortality, escalation of care, and whether patients had at least 1 telemetry day. A gamma distributional assumption and log link function were used for LOS, telemetry acute care days per visit, and total acute care days per visit. A negative binomial distributional assumption and log link function were used for telemetry utilization and telemetry appropriateness. We used the log of the acute care days as an offset for telemetry utilization and the log of the telemetry days per visit as an offset for telemetry appropriateness. An exchangeable working correlation matrix was used to account for physician-level clustering for all outcomes. Intervention effects, representing the difference in odds for categorical variables and in amount for continuous variables, were calculated as exponentiation of the beta parameters for the covariate minus 1.

P values <.05 were considered significant. We used SAS version 9.4 statistical software (SAS Institute Inc., Cary, NC) for data analysis.

The percent of patients who had any telemetry charges decreased from 36.2% to 15.9% (P < .001) in the hospitalist group and from 31.8% to 28.0% in the nonhospitalist group (P < .001; Table 1). Rates of code events did not change over time (P = .9).

In this study, implementing a change in the EHR telemetry order produced reductions in telemetry days. However, when combined with a multicomponent program including education, audit and feedback, financial incentives, and changes to remove telemetry orders from admission orders sets, an even more marked improvement was seen. Neither intervention reduced LOS, increased code event rates, or increased rates of escalation of care.

Prior studies have evaluated interventions to reduce unnecessary telemetry monitoring with varying degrees of success. The most successful EHR intervention to date, from Dressler et al.,¹⁸ achieved a 70% reduction in overall telemetry use by integrating the AHA guidelines into their EHR and incorporating nursing discontinuation guidelines to ensure that telemetry discontinuation was both safe and timely. Other studies using stewardship approaches and standardized protocols have been less successful.^19,20 One study utilizing a multidisciplinary approach but not including an EHR component showed modest improvements in telemetry.²¹

Although we are unable to differentiate the exact effect of each component of the intervention, we did note an immediate decrease in telemetry orders after removing the telemetry order from our admission order set, a trend that was magnified after the addition of broader EHR changes (Figure 1). Important additional contributors to our success seem to have been the standardization of rounds to include daily discussion of telemetry and the provision of routine feedback. We cannot discern whether other components of our program (such as the financial incentives) contributed more or less to our program, though the sum of these interventions produced an overall program that required substantial buy in and sustained focus from the hospitalist group. The importance of the hospitalist program is highlighted by the relatively large differences in improvement compared with the nonhospitalist group.

Our study has several limitations. First, the study was conducted at a single center, which may limit its generalizability. Second, the intervention was multifaceted, diminishing our ability to discern which aspects beyond the system-wide change in the telemetry order were most responsible for the observed effect among hospitalists. Third, we are unable to fully account for baseline differences in telemetry utilization between hospitalist and nonhospitalist groups. It is likely that different services utilize telemetry monitoring in different ways, and the hospitalist group may have been more aware of the existing guidelines for monitoring prior to the intervention. Furthermore, we had a limited sample size for the chart audits, which reduced the available statistical power for determining changes in the appropriateness of telemetry utilization. Additionally, because internal medicine residents rotate through various services, it is possible that the education they received on their hospitalist rotation as part of our intervention had a spillover effect in the nonhospitalist group. However, any effect should have decreased the difference between the groups. Lastly, although our postintervention time period was 1 year, we do not have data beyond that to monitor for sustainability of the results.

In this single-site study, combining EHR orders prompting physicians to choose a clinical indication and duration for monitoring with a broader program—including upstream changes in ordering as well as education, audit, and feedback—produced reductions in telemetry usage. Whether this reduction improves the appropriateness of telemetry utilization or reduces other effects of telemetry (eg, alert fatigue, calls for benign arrhythmias) cannot be discerned from our study. However, our results support the idea that multipronged approaches to telemetry use are most likely to produce improvements.

Acknowledgments

The authors thank Dr. Frank Thomas for his assistance with process engineering and Mr. Andrew Wood for his routine provision of data. The statistical analysis was supported by the University of Utah Study Design and Biostatistics Center, with funding in part from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 5UL1TR001067-05 (formerly 8UL1TR000105 and UL1RR025764).

Disclosure

The authors have no conflicts of interest to report.

Wasteful care may account for between 21% and 34% of the United States’ $3.2 trillion in annual healthcare expenditures, making it a prime target for cost-saving initiatives.^1,2 Telemetry is a target for value improvement strategies because telemetry is overutilized, rarely leads to a change in management, and has associated guidelines on appropriate use.^3-10 Telemetry use has been a focus of the Joint Commission’s National Patient Safety Goals since 2014, and it is also a focus of the Society of Hospital Medicine’s Choosing Wisely^® campaign.^11-13

Previous initiatives have evaluated how changes to telemetry orders or education and feedback affect telemetry use. Few studies have compared a system-wide electronic health record (EHR) approach to a multifaceted intervention. In seeking to address this gap, we adapted published guidelines from the American Heart Association (AHA) and incorporated them into our EHR ordering process.³ Simultaneously, we implemented a multifaceted quality improvement initiative and compared this combined program’s effectiveness to that of the EHR approach alone.

Study Design, Setting, and Population

We performed a 2-group observational pre- to postintervention study at University of Utah Health. Hospital encounters of patients 18 years and older who had at least 1 inpatient acute care, nonintensive care unit (ICU) room charge and an admission date between January 1, 2014, and July 31, 2016, were included. Patient encounters with missing encounter-level covariates, such as case mix index (CMI) or attending provider identification, were excluded. The Institutional Review Board classified this project as quality improvement and did not require review and oversight.

Intervention

On July 6, 2015, our Epic (Epic Systems Corporation, Madison, WI) EHR telemetry order was modified to discourage unnecessary telemetry monitoring. The new order required providers ordering telemetry to choose a clinical indication and select a duration for monitoring, after which the order would expire and require physician renewal or discontinuation. These were the only changes that occurred for nonhospitalist providers. The nonhospitalist group included all admitting providers who were not hospitalists. This group included neurology (6.98%); cardiology (8.13%); other medical specialties such as pulmonology, hematology, and oncology (21.30%); cardiothoracic surgery (3.72%); orthopedic surgery (14.84%); general surgery (11.11%); neurosurgery (11.07%); and other surgical specialties, including urology, transplant, vascular surgery, and plastics (16.68%).

Between January 2015 and June 2015, we implemented a multicomponent program among our hospitalist service. The hospitalist service is composed of 4 teams with internal medicine residents and 2 teams with advanced practice providers, all staffed by academic hospitalists. Our program was composed of 5 elements, all of which were made before the hospital-wide changes to electronic telemetry orders and maintained throughout the study period, as follows: (1) a single provider education session reviewing available evidence (eg, AHA guidelines, Choosing Wisely^® campaign), (2) removal of the telemetry order from hospitalist admission order set on March 23, 2015, (3) inclusion of telemetry discussion in the hospitalist group’s daily “Rounding Checklist,”¹⁴ (4) monthly feedback provided as part of hospitalist group meetings, and (5) a financial incentive, awarded to the division (no individual provider payment) if performance targets were met. See supplementary Appendix (“Implementation Manual”) for further details.

Data Source

We obtained data on patient age, gender, Medicare Severity-Diagnosis Related Group, Charlson comorbidity index (CCI), CMI, admitting unit, attending physician, admission and discharge dates, length of stay (LOS), 30-day readmission, bed charge (telemetry or nontelemetry), ICU stay, and inpatient mortality from the enterprise data warehouse. Telemetry days were determined through room billing charges, which are assigned based on the presence or absence of an active telemetry order at midnight. Code events came from a log kept by the hospital telephone operator, who is responsible for sending out all calls to the code team. Code event data were available starting July 19, 2014.

Measures

Our primary outcome was the percentage of hospital days that had telemetry charges for individual patients. All billed telemetry days on acute care floors were included regardless of admission status (inpatient vs observation), service, indication, or ordering provider. Secondary outcomes were inpatient mortality, escalation of care, code event rates, and appropriate telemetry utilization rates. Escalation of care was defined as transfer to an ICU after initially being admitted to an acute care floor. The code event rate was defined as the ratio of the number of code team activations to the number of patient days. Appropriate telemetry utilization rates were determined via chart review, as detailed below.

In order to evaluate changes in appropriateness of telemetry monitoring, 4 of the authors who are internal medicine physicians (KE, CC, JC, DG) performed chart reviews of 25 randomly selected patients in each group (hospitalist and nonhospitalist) before and after the intervention who received at least 1 day of telemetry monitoring. Each reviewer was provided a key based on AHA guidelines for monitoring indications and associated maximum allowable durations.³ Chart reviews were performed to determine the indication (if any) for monitoring, as well as the number of days that were indicated. The number of indicated days was compared to the number of telemetry days the patient received to determine the overall proportion of days that were indicated (“Telemetry appropriateness per visit”). Three reviewers (KE, AR, CC) also evaluated 100 patients on the hospitalist service after the intervention who did not receive any telemetry monitoring to evaluate whether patients with indications for telemetry monitoring were not receiving it after the intervention. For patients who had a possible indication, the indication was classified as Class I (“Cardiac monitoring is indicated in most, if not all, patients in this group”) or Class II (“Cardiac monitoring may be of benefit in some patients but is not considered essential for all patients”).³

Adjustment Variables

To account for differences in patient characteristics between hospitalist and nonhospitalist groups, we included age, gender, CMI, and CCI in statistical models. CCI was calculated according to the algorithm specified by Quan et al.¹⁵ using all patient diagnoses from previous visits and the index visit identified from the facility billing system.

Statistical Analysis

We computed descriptive statistics for study outcomes and visit characteristics for hospitalist and nonhospitalist visits for pre- and postintervention periods. Descriptive statistics were expressed as n (%) for categorical patient characteristics and outcome variables. For continuous patient characteristics, we expressed the variability of individual observations as the mean ± the standard deviation. For continuous outcomes, we expressed the precision of the mean estimates using standard error. Telemetry utilization per visit was weighted by the number of total acute care days per visit. Telemetry appropriateness per visit was weighted by the number of telemetry days per visit. Patients who did not receive any telemetry monitoring were included in the analysis and noted to have 0 telemetry days. All patients had at least 1 acute care day. Categorical variables were compared using χ² tests, and continuous variables were compared using t tests. Code event rates were compared using the binomial probability mid-p exact test for person-time data.¹⁶

We fitted generalized linear regression models using generalized estimating equations to evaluate the relative change in outcomes of interest in the postintervention period compared with the preintervention period after adjusting for study covariates. The models included study group (hospitalist and nonhospitalist), time period (pre- and postintervention), an interaction term between study group and time period, and study covariates (age, gender, CMI, and CCI). The models were defined using a binomial distributional assumption and logit link function for mortality, escalation of care, and whether patients had at least 1 telemetry day. A gamma distributional assumption and log link function were used for LOS, telemetry acute care days per visit, and total acute care days per visit. A negative binomial distributional assumption and log link function were used for telemetry utilization and telemetry appropriateness. We used the log of the acute care days as an offset for telemetry utilization and the log of the telemetry days per visit as an offset for telemetry appropriateness. An exchangeable working correlation matrix was used to account for physician-level clustering for all outcomes. Intervention effects, representing the difference in odds for categorical variables and in amount for continuous variables, were calculated as exponentiation of the beta parameters for the covariate minus 1.

P values <.05 were considered significant. We used SAS version 9.4 statistical software (SAS Institute Inc., Cary, NC) for data analysis.

The percent of patients who had any telemetry charges decreased from 36.2% to 15.9% (P < .001) in the hospitalist group and from 31.8% to 28.0% in the nonhospitalist group (P < .001; Table 1). Rates of code events did not change over time (P = .9).

In this study, implementing a change in the EHR telemetry order produced reductions in telemetry days. However, when combined with a multicomponent program including education, audit and feedback, financial incentives, and changes to remove telemetry orders from admission orders sets, an even more marked improvement was seen. Neither intervention reduced LOS, increased code event rates, or increased rates of escalation of care.

Prior studies have evaluated interventions to reduce unnecessary telemetry monitoring with varying degrees of success. The most successful EHR intervention to date, from Dressler et al.,¹⁸ achieved a 70% reduction in overall telemetry use by integrating the AHA guidelines into their EHR and incorporating nursing discontinuation guidelines to ensure that telemetry discontinuation was both safe and timely. Other studies using stewardship approaches and standardized protocols have been less successful.^19,20 One study utilizing a multidisciplinary approach but not including an EHR component showed modest improvements in telemetry.²¹

Although we are unable to differentiate the exact effect of each component of the intervention, we did note an immediate decrease in telemetry orders after removing the telemetry order from our admission order set, a trend that was magnified after the addition of broader EHR changes (Figure 1). Important additional contributors to our success seem to have been the standardization of rounds to include daily discussion of telemetry and the provision of routine feedback. We cannot discern whether other components of our program (such as the financial incentives) contributed more or less to our program, though the sum of these interventions produced an overall program that required substantial buy in and sustained focus from the hospitalist group. The importance of the hospitalist program is highlighted by the relatively large differences in improvement compared with the nonhospitalist group.

Our study has several limitations. First, the study was conducted at a single center, which may limit its generalizability. Second, the intervention was multifaceted, diminishing our ability to discern which aspects beyond the system-wide change in the telemetry order were most responsible for the observed effect among hospitalists. Third, we are unable to fully account for baseline differences in telemetry utilization between hospitalist and nonhospitalist groups. It is likely that different services utilize telemetry monitoring in different ways, and the hospitalist group may have been more aware of the existing guidelines for monitoring prior to the intervention. Furthermore, we had a limited sample size for the chart audits, which reduced the available statistical power for determining changes in the appropriateness of telemetry utilization. Additionally, because internal medicine residents rotate through various services, it is possible that the education they received on their hospitalist rotation as part of our intervention had a spillover effect in the nonhospitalist group. However, any effect should have decreased the difference between the groups. Lastly, although our postintervention time period was 1 year, we do not have data beyond that to monitor for sustainability of the results.

In this single-site study, combining EHR orders prompting physicians to choose a clinical indication and duration for monitoring with a broader program—including upstream changes in ordering as well as education, audit, and feedback—produced reductions in telemetry usage. Whether this reduction improves the appropriateness of telemetry utilization or reduces other effects of telemetry (eg, alert fatigue, calls for benign arrhythmias) cannot be discerned from our study. However, our results support the idea that multipronged approaches to telemetry use are most likely to produce improvements.

Acknowledgments

The authors thank Dr. Frank Thomas for his assistance with process engineering and Mr. Andrew Wood for his routine provision of data. The statistical analysis was supported by the University of Utah Study Design and Biostatistics Center, with funding in part from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 5UL1TR001067-05 (formerly 8UL1TR000105 and UL1RR025764).

Disclosure

The authors have no conflicts of interest to report.

Given the high cost of readmissions to the healthcare system, there has been a substantial push to reduce readmissions by policymakers.¹ Among these is the Hospital Readmissions Reduction Program (HRRP), in which hospitals with higher than expected readmission rates receive reduced payments from Medicare.² Recent evidence has suggested the success of such policy changes, with multiple reports demonstrating a decrease in 30-day readmission rates in the Medicare population starting in 2010.^3-8

Initiatives to reduce readmissions can also have an effect on total number of admissions.^9,10 Indeed, along with the recent reduction in readmission, there has been a reduction in all admissions among Medicare beneficiaries.^11,12 Some studies have found that as admissions have decreased, the burden of comorbidity has increased among hospitalized patients,^3,11 suggesting that hospitals may be increasingly filled with patients at high risk of readmission. However, whether readmission risk among hospitalized patients has changed remains unknown, and understanding changes in risk profile could help inform which patients to target with future interventions to reduce readmissions.

Hospital efforts to reduce readmissions may have differential effects on types of patients by risk. For instance, low-intensity, system-wide interventions such as standardized discharge instructions or medicine reconciliation may have a stronger effect on patients at relatively low risk of readmission who may have a few important drivers of readmission that are easily overcome. Alternatively, the impact of intensive care transitions management might be greatest for high-risk patients, who have the most need for postdischarge medications, follow-up, and self-care.

The purpose of this study was therefore twofold: (1) to observe changes in average monthly risk of readmission among hospitalized Medicare patients and (2) to examine changes in readmission rates for Medicare patients at various risk of readmission. We hypothesized that readmission risk in the Medicare population would increase in recent years, as overall number of admissions and readmissions have fallen.^7,11 Additionally, we hypothesized that standardized readmission rates would decline less in highest risk patients as compared with the lowest risk patients because transitional care interventions may not be able to mitigate the large burden of comorbidity and social issues present in many high-risk patients.^13,14

We performed a retrospective cohort study of hospitalizations to US nonfederal short-term acute care facilities by Medicare beneficiaries between January 2009 and June 2015. The design involved 4 steps. First, we estimated a predictive model for unplanned readmissions within 30 days of discharge. Second, we assigned each hospitalization a predicted risk of readmission based on the model. Third, we studied trends in mean predicted risk of readmission during the study period. Fourth, we examined trends in observed to expected (O/E) readmission for hospitalizations in the lowest, middle, and highest categories of predicted risk of readmission to determine whether reductions in readmissions were more substantial in certain risk groups than in others.

Data were obtained from the Centers for Medicare and Medicaid Services (CMS) Inpatient Standard Analytic File and the Medicare Enrollment Data Base. We included hospitalizations of fee-for-service Medicare beneficiaries age ≥65 with continuous enrollment in Part A Medicare fee-for-service for at least 1 year prior and 30 days after the hospitalization.¹⁵ Hospitalizations with a discharge disposition of death, transfer to another acute hospital, and left against medical advice (AMA) were excluded. We also excluded patients with enrollment in hospice care prior to hospitalization. We excluded hospitalizations in June 2012 because of an irregularity in data availability for that month.

Hospitalizations were categorized into 5 specialty cohorts according to service line. The 5 cohorts were those used for the CMS hospital-wide readmission measure and included surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology.¹⁵ Among the 3 clinical conditions tracked as part of HRRP, heart failure and pneumonia were a subset of the cardiorespiratory cohort, while acute myocardial infarction was a subset of the cardiovascular cohort. Our use of cohorts was threefold: first, the average risk of readmission differs substantially across these cohorts, so pooling them produces heterogeneous risk strata; second, risk variables perform differently in different cohorts, so one single model may not be as accurate for calculating risk; and, third, the use of disease cohorts makes our results comparable to the CMS model and similar to other readmission studies in Medicare.^7,8,15

For development of the risk model, the outcome was 30-day unplanned hospital readmission. Planned readmissions were excluded; these were defined by the CMS algorithm as readmissions in which a typically planned procedure occurred in a hospitalization with a nonacute principal diagnosis.¹⁶ Independent variables included age and comorbidities in the final hospital-wide readmission models for each of the 5 specialty cohorts.¹⁵ In order to produce the best possible individual risk prediction for each patient, we added additional independent variables that CMS avoids for hospital quality measurement purposes but that contribute to risk of readmission: sex, race, dual eligibility status, number of prior AMA discharges, intensive care unit stay during current hospitalization, coronary care unit stay during current hospitalization, and hospitalization in the prior 30, 90, and 180 days. We also included an indicator variable for hospitalizations with more than 9 discharge diagnosis codes on or after January 2011, the time at which Medicare allowed an increase of the number of International Classification of Diseases, 9th Revision-Clinical Modification diagnosis billing codes from 9 to 25.¹⁷ This indicator adjusts for the increased availability of comorbidity codes, which might otherwise inflate the predicted risk relative to hospitalizations prior to that date.

Based on the risk models, each hospitalization was assigned a predicted risk of readmission. For each specialty cohort, we pooled all hospitalizations across all study years and divided them into risk quintiles. We categorized hospitalizations as high risk if in the highest quintile, medium risk if in the middle 3 quintiles, and low risk if in the lowest quintile of predicted risk for all study hospitalizations in a given specialty cohort.

For our time trend analyses, we studied 2 outcomes: monthly mean predicted risk and monthly ratio of observed readmissions to expected readmissions for patients in the lowest, middle, and highest categories of predicted risk of readmission. We studied monthly predicted risk to determine whether the average readmission risk of patients was changing over time as admission and readmission rates were declining. We studied the ratio of O/E readmissions to determine whether the decline in overall readmissions was more substantial in particular risk strata; we used the ratio of O/E readmissions, which measures number of readmissions divided by number of readmissions predicted by the model, rather than crude observed readmissions, as O/E readmissions account for any changes in risk profiles over time within each risk stratum. Independent variables in our trend analyses were year—entered as a continuous variable—and indicators for postintroduction of the Affordable Care Act (ACA, March 2010) and for postintroduction of HRRP (October 2012); these time indicators were included because of prior studies demonstrating that the introduction of ACA was associated with a decrease from baseline in readmission rates, which leveled off after introduction of HRRP.⁷ We also included an indicator for calendar quarter to account for seasonal effects.

Statistical Analysis

We developed generalized estimating equation models to predict 30-day unplanned readmission for each of the 5 specialty cohorts. The 5 models were fit using all patients in each cohort for the included time period and were adjusted for clustering by hospital. We assessed discrimination by calculating area under the receiver operating characteristic curve (AUC) for the 5 models; the AUCs measured the models’ ability to distinguish patients who were readmitted versus those who were not.¹⁸ We also calculated AUCs for each year to examine model performance over time.

Using these models, we calculated predicted risk for each hospitalization and averaged these to obtain mean predicted risk for each specialty cohort for each month. To test for trends in mean risk, we estimated 5 time series models, one for each cohort, with the dependent variable of monthly mean predicted risk. For each cohort, we first estimated a series of 12 empty autoregressive models, each with a different autoregressive term (1, 2...12). For each model, we calculated χ² for the test that the autocorrelation was 0; based on a comparison of chi-squared values, we specified an autocorrelation of 1 month for all models. Accordingly, a 1-month lag was used to estimate one final model for each cohort. Independent variables included year and indicators for post-ACA and post-HRRP; these variables captured the effect of trends over time and the introduction of these policy changes, respectively.¹⁹

To determine whether changes in risk over time were a result of changes in particular risk groups, we categorized hospitalizations into risk strata based on quintiles of predicted risk for each specialty cohort for the entire study period. For each individual year, we calculated the proportion of hospitalizations in the highest, middle, and lowest readmission risk strata for each cohort.

We calculated the monthly ratio of O/E readmission for hospitalizations in the lowest 20%, middle 60%, and highest 20% of readmission risk by month; O/E reflects the excess or deficit observed events relative to the number predicted by the model. Using this monthly O/E as the dependent variable, we developed autoregressive time series models as above, again with a 1-month lag, for each of these 3 risk strata in each cohort. As before, independent variables were year as a continuous variable, indicator variables for post-ACA and post-HRRP, and a categorical variable for calendar quarter.

All analyses were done in SAS version 9.3 (SAS Institute Inc., Cary, NC) and Stata version 14.2 (StataCorp LLC, College Station, TX).

We included 47,288,961 hospitalizations in the study, of which 11,231,242 (23.8%) were in the surgery/gynecology cohort, 19,548,711 (41.3%) were in the medicine cohort, 5,433,125 (11.5%) were in the cardiovascular cohort, 8,179,691 (17.3%) were in the cardiorespiratory cohort, and 2,896,192 (6.1%) were in the neurology cohort. The readmission rate was 16.2% (n = 7,642,161) overall, with the highest rates observed in the cardiorespiratory (20.5%) and medicine (17.6%) cohorts and the lowest rates observed in the surgery/gynecology (11.8%) and neurology (13.8%) cohorts.

The final predictive models for each cohort ranged in number of parameters from 56 for the cardiorespiratory cohort to 264 for the surgery/gynecology cohort. The models had AUCs of 0.70, 0.65, 0.67, 0.65, and 0.63 for the surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology cohorts, respectively; AUCs remained fairly stable over time for all disease cohorts (Appendix Table 1).

A number of mechanisms may account for the across-the-board improvements in readmission reduction. Many hospitals have instituted system-wide interventions, including patient education, medicine reconciliation, and early postdischarge follow-up,²⁰ which may have reduced readmissions across all patient risk strata. Alternatively, hospitals may have implemented interventions that disproportionally benefited low-risk patients while simultaneously utilizing interventions that only benefited high-risk patients. For instance, increasing threshold for admission⁷ may have the greatest effect on low-risk patients who could be most easily managed at home, while many intensive transitional care interventions have been developed to target only high-risk patients.^21,22

With the introduction of HRRP, there have been a number of concerns about the readmission measure used to penalize hospitals for high readmission rates. One major concern has been that the readmission metric may be flawed in its ability to capture continued improvement related to readmission.²³ Some have suggested that with better population health management, admissions will decrease, patient risk of the remaining patients will increase, and hospitals will be increasingly filled with patients who have high likelihood of readmission. This potential for increased risk with HRRP was suggested by a recent study that found that comorbidities increased in hospitalized Medicare beneficiaries between 2010 and 2013.¹¹ Our results were mixed in supporting this potential phenomenon because we examined global risk of readmission and found that some of the cohorts had increased risk over time while others did not. Others have expressed concern that readmission measure does not account for socioeconomic status, which has been associated with readmission rates.^24-27 Although we did not directly examine socioeconomic status in our study, we found that hospitals have been able to reduce readmission across all levels of risk, which includes markers of socioeconomic status, including race and Medicaid eligibility status.

Although we hypothesized that readmission risk would increase as number of hospitalizations decreased over time, we found no increase in readmission risk among the cohorts with HRRP diagnoses that had the largest decrease in readmission rates.^7,8 Conversely, readmission risk did increase—with a concurrent increase in the proportion of high-risk hospitalizations—in the surgery/gynecology and neurology cohorts that were not subject to HRRP penalties. Nonetheless, rehospitalizations were reduced for all risk categories in these 2 cohorts. Notably, surgery/gynecology and neurology had the lowest readmission rates overall. These findings suggest that initiatives to prevent initial hospitalizations, such as increasing the threshold for postoperative admission, may have had a greater effect on low- versus high-risk patients in low-risk hospitalizations. However, once a patient is hospitalized, multidisciplinary strategies appear to be effective at reducing readmissions for all risk classes in these cohorts.

For the 3 cohorts in which we observed an increase in readmission risk among hospitalized patients, the risk appeared to increase in early 2011. This time was about 10 months after passage of ACA, the timing of which was previously associated with a drop in readmission rates,^7,8 but well before HRRP went into effect in October 2012. The increase in readmission risk coincided with an increase in the number of diagnostic codes that could be included on a hospital claim to Medicare.¹⁷ This increase in allowable codes allowed us to capture more diagnoses for some patients, potentially resulting in an increase in apparent predicted risk of readmissions. While we adjusted for this in our predictive models, we may not have fully accounted for differences in risk related to coding change. As a result, some of the observed differences in risk in our study may be attributable to coding differences. More broadly, studies demonstrating the success of HRRP have typically examined risk-adjusted rates of readmission.^3,7 It is possible that a small portion of the observed reduction in risk-adjusted readmission rates may be related to the increase in predicted risk of readmission observed in our study. Future assessment of trends in readmission during this period should consider accounting for change in the number of allowed billing codes.

Other limitations should be considered in the interpretation of this study. First, like many predictive models for readmission,¹⁴ ours had imperfect discrimination, which could affect our results. Second, our study was based on older Medicare patients, so findings may not be applicable to younger patients. Third, while we accounted for surrogates for socioeconomic status, including dual eligibility and race, our models lacked other socioeconomic and community factors that can influence readmission.^24-26 Nonetheless, 1 study suggested that easily measured socioeconomic factors may not have a strong influence on the readmission metric used by Medicare.²⁸ Fourth, while our study included over 47 million hospitalizations, our time trend analyses used calendar month as the primary independent variable. As our study included 77 months, we may not have had sufficient power to detect small changes in risk over time.

Medicare readmissions have declined steadily in recent years, presumably at least partly in response to policy changes including HRRP. We found that hospitals have been effective at reducing readmissions across a range of patient risk strata and clinical conditions. As a result, the overall risk of readmission for hospitalized patients has remained constant for some but not all conditions. Whether institutions can continue to reduce readmission rates for most types of patients remains to be seen.

Acknowledgments

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grant R01HS022882. Dr. Blecker was supported by the AHRQ grant K08HS23683. The authors would like to thank Shawn Hoke and Jane Padikkala for administrative support.

Disclosure

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grants R01HS022882 and K08HS23683. The authors have no conflicts to report.

Given the high cost of readmissions to the healthcare system, there has been a substantial push to reduce readmissions by policymakers.¹ Among these is the Hospital Readmissions Reduction Program (HRRP), in which hospitals with higher than expected readmission rates receive reduced payments from Medicare.² Recent evidence has suggested the success of such policy changes, with multiple reports demonstrating a decrease in 30-day readmission rates in the Medicare population starting in 2010.^3-8

Initiatives to reduce readmissions can also have an effect on total number of admissions.^9,10 Indeed, along with the recent reduction in readmission, there has been a reduction in all admissions among Medicare beneficiaries.^11,12 Some studies have found that as admissions have decreased, the burden of comorbidity has increased among hospitalized patients,^3,11 suggesting that hospitals may be increasingly filled with patients at high risk of readmission. However, whether readmission risk among hospitalized patients has changed remains unknown, and understanding changes in risk profile could help inform which patients to target with future interventions to reduce readmissions.

Hospital efforts to reduce readmissions may have differential effects on types of patients by risk. For instance, low-intensity, system-wide interventions such as standardized discharge instructions or medicine reconciliation may have a stronger effect on patients at relatively low risk of readmission who may have a few important drivers of readmission that are easily overcome. Alternatively, the impact of intensive care transitions management might be greatest for high-risk patients, who have the most need for postdischarge medications, follow-up, and self-care.

The purpose of this study was therefore twofold: (1) to observe changes in average monthly risk of readmission among hospitalized Medicare patients and (2) to examine changes in readmission rates for Medicare patients at various risk of readmission. We hypothesized that readmission risk in the Medicare population would increase in recent years, as overall number of admissions and readmissions have fallen.^7,11 Additionally, we hypothesized that standardized readmission rates would decline less in highest risk patients as compared with the lowest risk patients because transitional care interventions may not be able to mitigate the large burden of comorbidity and social issues present in many high-risk patients.^13,14

We performed a retrospective cohort study of hospitalizations to US nonfederal short-term acute care facilities by Medicare beneficiaries between January 2009 and June 2015. The design involved 4 steps. First, we estimated a predictive model for unplanned readmissions within 30 days of discharge. Second, we assigned each hospitalization a predicted risk of readmission based on the model. Third, we studied trends in mean predicted risk of readmission during the study period. Fourth, we examined trends in observed to expected (O/E) readmission for hospitalizations in the lowest, middle, and highest categories of predicted risk of readmission to determine whether reductions in readmissions were more substantial in certain risk groups than in others.

Data were obtained from the Centers for Medicare and Medicaid Services (CMS) Inpatient Standard Analytic File and the Medicare Enrollment Data Base. We included hospitalizations of fee-for-service Medicare beneficiaries age ≥65 with continuous enrollment in Part A Medicare fee-for-service for at least 1 year prior and 30 days after the hospitalization.¹⁵ Hospitalizations with a discharge disposition of death, transfer to another acute hospital, and left against medical advice (AMA) were excluded. We also excluded patients with enrollment in hospice care prior to hospitalization. We excluded hospitalizations in June 2012 because of an irregularity in data availability for that month.

Hospitalizations were categorized into 5 specialty cohorts according to service line. The 5 cohorts were those used for the CMS hospital-wide readmission measure and included surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology.¹⁵ Among the 3 clinical conditions tracked as part of HRRP, heart failure and pneumonia were a subset of the cardiorespiratory cohort, while acute myocardial infarction was a subset of the cardiovascular cohort. Our use of cohorts was threefold: first, the average risk of readmission differs substantially across these cohorts, so pooling them produces heterogeneous risk strata; second, risk variables perform differently in different cohorts, so one single model may not be as accurate for calculating risk; and, third, the use of disease cohorts makes our results comparable to the CMS model and similar to other readmission studies in Medicare.^7,8,15

For development of the risk model, the outcome was 30-day unplanned hospital readmission. Planned readmissions were excluded; these were defined by the CMS algorithm as readmissions in which a typically planned procedure occurred in a hospitalization with a nonacute principal diagnosis.¹⁶ Independent variables included age and comorbidities in the final hospital-wide readmission models for each of the 5 specialty cohorts.¹⁵ In order to produce the best possible individual risk prediction for each patient, we added additional independent variables that CMS avoids for hospital quality measurement purposes but that contribute to risk of readmission: sex, race, dual eligibility status, number of prior AMA discharges, intensive care unit stay during current hospitalization, coronary care unit stay during current hospitalization, and hospitalization in the prior 30, 90, and 180 days. We also included an indicator variable for hospitalizations with more than 9 discharge diagnosis codes on or after January 2011, the time at which Medicare allowed an increase of the number of International Classification of Diseases, 9th Revision-Clinical Modification diagnosis billing codes from 9 to 25.¹⁷ This indicator adjusts for the increased availability of comorbidity codes, which might otherwise inflate the predicted risk relative to hospitalizations prior to that date.

Based on the risk models, each hospitalization was assigned a predicted risk of readmission. For each specialty cohort, we pooled all hospitalizations across all study years and divided them into risk quintiles. We categorized hospitalizations as high risk if in the highest quintile, medium risk if in the middle 3 quintiles, and low risk if in the lowest quintile of predicted risk for all study hospitalizations in a given specialty cohort.

For our time trend analyses, we studied 2 outcomes: monthly mean predicted risk and monthly ratio of observed readmissions to expected readmissions for patients in the lowest, middle, and highest categories of predicted risk of readmission. We studied monthly predicted risk to determine whether the average readmission risk of patients was changing over time as admission and readmission rates were declining. We studied the ratio of O/E readmissions to determine whether the decline in overall readmissions was more substantial in particular risk strata; we used the ratio of O/E readmissions, which measures number of readmissions divided by number of readmissions predicted by the model, rather than crude observed readmissions, as O/E readmissions account for any changes in risk profiles over time within each risk stratum. Independent variables in our trend analyses were year—entered as a continuous variable—and indicators for postintroduction of the Affordable Care Act (ACA, March 2010) and for postintroduction of HRRP (October 2012); these time indicators were included because of prior studies demonstrating that the introduction of ACA was associated with a decrease from baseline in readmission rates, which leveled off after introduction of HRRP.⁷ We also included an indicator for calendar quarter to account for seasonal effects.

Statistical Analysis

We developed generalized estimating equation models to predict 30-day unplanned readmission for each of the 5 specialty cohorts. The 5 models were fit using all patients in each cohort for the included time period and were adjusted for clustering by hospital. We assessed discrimination by calculating area under the receiver operating characteristic curve (AUC) for the 5 models; the AUCs measured the models’ ability to distinguish patients who were readmitted versus those who were not.¹⁸ We also calculated AUCs for each year to examine model performance over time.

Using these models, we calculated predicted risk for each hospitalization and averaged these to obtain mean predicted risk for each specialty cohort for each month. To test for trends in mean risk, we estimated 5 time series models, one for each cohort, with the dependent variable of monthly mean predicted risk. For each cohort, we first estimated a series of 12 empty autoregressive models, each with a different autoregressive term (1, 2...12). For each model, we calculated χ² for the test that the autocorrelation was 0; based on a comparison of chi-squared values, we specified an autocorrelation of 1 month for all models. Accordingly, a 1-month lag was used to estimate one final model for each cohort. Independent variables included year and indicators for post-ACA and post-HRRP; these variables captured the effect of trends over time and the introduction of these policy changes, respectively.¹⁹

To determine whether changes in risk over time were a result of changes in particular risk groups, we categorized hospitalizations into risk strata based on quintiles of predicted risk for each specialty cohort for the entire study period. For each individual year, we calculated the proportion of hospitalizations in the highest, middle, and lowest readmission risk strata for each cohort.

We calculated the monthly ratio of O/E readmission for hospitalizations in the lowest 20%, middle 60%, and highest 20% of readmission risk by month; O/E reflects the excess or deficit observed events relative to the number predicted by the model. Using this monthly O/E as the dependent variable, we developed autoregressive time series models as above, again with a 1-month lag, for each of these 3 risk strata in each cohort. As before, independent variables were year as a continuous variable, indicator variables for post-ACA and post-HRRP, and a categorical variable for calendar quarter.

All analyses were done in SAS version 9.3 (SAS Institute Inc., Cary, NC) and Stata version 14.2 (StataCorp LLC, College Station, TX).

We included 47,288,961 hospitalizations in the study, of which 11,231,242 (23.8%) were in the surgery/gynecology cohort, 19,548,711 (41.3%) were in the medicine cohort, 5,433,125 (11.5%) were in the cardiovascular cohort, 8,179,691 (17.3%) were in the cardiorespiratory cohort, and 2,896,192 (6.1%) were in the neurology cohort. The readmission rate was 16.2% (n = 7,642,161) overall, with the highest rates observed in the cardiorespiratory (20.5%) and medicine (17.6%) cohorts and the lowest rates observed in the surgery/gynecology (11.8%) and neurology (13.8%) cohorts.

The final predictive models for each cohort ranged in number of parameters from 56 for the cardiorespiratory cohort to 264 for the surgery/gynecology cohort. The models had AUCs of 0.70, 0.65, 0.67, 0.65, and 0.63 for the surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology cohorts, respectively; AUCs remained fairly stable over time for all disease cohorts (Appendix Table 1).

A number of mechanisms may account for the across-the-board improvements in readmission reduction. Many hospitals have instituted system-wide interventions, including patient education, medicine reconciliation, and early postdischarge follow-up,²⁰ which may have reduced readmissions across all patient risk strata. Alternatively, hospitals may have implemented interventions that disproportionally benefited low-risk patients while simultaneously utilizing interventions that only benefited high-risk patients. For instance, increasing threshold for admission⁷ may have the greatest effect on low-risk patients who could be most easily managed at home, while many intensive transitional care interventions have been developed to target only high-risk patients.^21,22

With the introduction of HRRP, there have been a number of concerns about the readmission measure used to penalize hospitals for high readmission rates. One major concern has been that the readmission metric may be flawed in its ability to capture continued improvement related to readmission.²³ Some have suggested that with better population health management, admissions will decrease, patient risk of the remaining patients will increase, and hospitals will be increasingly filled with patients who have high likelihood of readmission. This potential for increased risk with HRRP was suggested by a recent study that found that comorbidities increased in hospitalized Medicare beneficiaries between 2010 and 2013.¹¹ Our results were mixed in supporting this potential phenomenon because we examined global risk of readmission and found that some of the cohorts had increased risk over time while others did not. Others have expressed concern that readmission measure does not account for socioeconomic status, which has been associated with readmission rates.^24-27 Although we did not directly examine socioeconomic status in our study, we found that hospitals have been able to reduce readmission across all levels of risk, which includes markers of socioeconomic status, including race and Medicaid eligibility status.

Although we hypothesized that readmission risk would increase as number of hospitalizations decreased over time, we found no increase in readmission risk among the cohorts with HRRP diagnoses that had the largest decrease in readmission rates.^7,8 Conversely, readmission risk did increase—with a concurrent increase in the proportion of high-risk hospitalizations—in the surgery/gynecology and neurology cohorts that were not subject to HRRP penalties. Nonetheless, rehospitalizations were reduced for all risk categories in these 2 cohorts. Notably, surgery/gynecology and neurology had the lowest readmission rates overall. These findings suggest that initiatives to prevent initial hospitalizations, such as increasing the threshold for postoperative admission, may have had a greater effect on low- versus high-risk patients in low-risk hospitalizations. However, once a patient is hospitalized, multidisciplinary strategies appear to be effective at reducing readmissions for all risk classes in these cohorts.

For the 3 cohorts in which we observed an increase in readmission risk among hospitalized patients, the risk appeared to increase in early 2011. This time was about 10 months after passage of ACA, the timing of which was previously associated with a drop in readmission rates,^7,8 but well before HRRP went into effect in October 2012. The increase in readmission risk coincided with an increase in the number of diagnostic codes that could be included on a hospital claim to Medicare.¹⁷ This increase in allowable codes allowed us to capture more diagnoses for some patients, potentially resulting in an increase in apparent predicted risk of readmissions. While we adjusted for this in our predictive models, we may not have fully accounted for differences in risk related to coding change. As a result, some of the observed differences in risk in our study may be attributable to coding differences. More broadly, studies demonstrating the success of HRRP have typically examined risk-adjusted rates of readmission.^3,7 It is possible that a small portion of the observed reduction in risk-adjusted readmission rates may be related to the increase in predicted risk of readmission observed in our study. Future assessment of trends in readmission during this period should consider accounting for change in the number of allowed billing codes.

Other limitations should be considered in the interpretation of this study. First, like many predictive models for readmission,¹⁴ ours had imperfect discrimination, which could affect our results. Second, our study was based on older Medicare patients, so findings may not be applicable to younger patients. Third, while we accounted for surrogates for socioeconomic status, including dual eligibility and race, our models lacked other socioeconomic and community factors that can influence readmission.^24-26 Nonetheless, 1 study suggested that easily measured socioeconomic factors may not have a strong influence on the readmission metric used by Medicare.²⁸ Fourth, while our study included over 47 million hospitalizations, our time trend analyses used calendar month as the primary independent variable. As our study included 77 months, we may not have had sufficient power to detect small changes in risk over time.

Medicare readmissions have declined steadily in recent years, presumably at least partly in response to policy changes including HRRP. We found that hospitals have been effective at reducing readmissions across a range of patient risk strata and clinical conditions. As a result, the overall risk of readmission for hospitalized patients has remained constant for some but not all conditions. Whether institutions can continue to reduce readmission rates for most types of patients remains to be seen.

Acknowledgments

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grant R01HS022882. Dr. Blecker was supported by the AHRQ grant K08HS23683. The authors would like to thank Shawn Hoke and Jane Padikkala for administrative support.

Disclosure

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grants R01HS022882 and K08HS23683. The authors have no conflicts to report.

Given the high cost of readmissions to the healthcare system, there has been a substantial push to reduce readmissions by policymakers.¹ Among these is the Hospital Readmissions Reduction Program (HRRP), in which hospitals with higher than expected readmission rates receive reduced payments from Medicare.² Recent evidence has suggested the success of such policy changes, with multiple reports demonstrating a decrease in 30-day readmission rates in the Medicare population starting in 2010.^3-8

Initiatives to reduce readmissions can also have an effect on total number of admissions.^9,10 Indeed, along with the recent reduction in readmission, there has been a reduction in all admissions among Medicare beneficiaries.^11,12 Some studies have found that as admissions have decreased, the burden of comorbidity has increased among hospitalized patients,^3,11 suggesting that hospitals may be increasingly filled with patients at high risk of readmission. However, whether readmission risk among hospitalized patients has changed remains unknown, and understanding changes in risk profile could help inform which patients to target with future interventions to reduce readmissions.

Hospital efforts to reduce readmissions may have differential effects on types of patients by risk. For instance, low-intensity, system-wide interventions such as standardized discharge instructions or medicine reconciliation may have a stronger effect on patients at relatively low risk of readmission who may have a few important drivers of readmission that are easily overcome. Alternatively, the impact of intensive care transitions management might be greatest for high-risk patients, who have the most need for postdischarge medications, follow-up, and self-care.

The purpose of this study was therefore twofold: (1) to observe changes in average monthly risk of readmission among hospitalized Medicare patients and (2) to examine changes in readmission rates for Medicare patients at various risk of readmission. We hypothesized that readmission risk in the Medicare population would increase in recent years, as overall number of admissions and readmissions have fallen.^7,11 Additionally, we hypothesized that standardized readmission rates would decline less in highest risk patients as compared with the lowest risk patients because transitional care interventions may not be able to mitigate the large burden of comorbidity and social issues present in many high-risk patients.^13,14

We performed a retrospective cohort study of hospitalizations to US nonfederal short-term acute care facilities by Medicare beneficiaries between January 2009 and June 2015. The design involved 4 steps. First, we estimated a predictive model for unplanned readmissions within 30 days of discharge. Second, we assigned each hospitalization a predicted risk of readmission based on the model. Third, we studied trends in mean predicted risk of readmission during the study period. Fourth, we examined trends in observed to expected (O/E) readmission for hospitalizations in the lowest, middle, and highest categories of predicted risk of readmission to determine whether reductions in readmissions were more substantial in certain risk groups than in others.

Data were obtained from the Centers for Medicare and Medicaid Services (CMS) Inpatient Standard Analytic File and the Medicare Enrollment Data Base. We included hospitalizations of fee-for-service Medicare beneficiaries age ≥65 with continuous enrollment in Part A Medicare fee-for-service for at least 1 year prior and 30 days after the hospitalization.¹⁵ Hospitalizations with a discharge disposition of death, transfer to another acute hospital, and left against medical advice (AMA) were excluded. We also excluded patients with enrollment in hospice care prior to hospitalization. We excluded hospitalizations in June 2012 because of an irregularity in data availability for that month.

Hospitalizations were categorized into 5 specialty cohorts according to service line. The 5 cohorts were those used for the CMS hospital-wide readmission measure and included surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology.¹⁵ Among the 3 clinical conditions tracked as part of HRRP, heart failure and pneumonia were a subset of the cardiorespiratory cohort, while acute myocardial infarction was a subset of the cardiovascular cohort. Our use of cohorts was threefold: first, the average risk of readmission differs substantially across these cohorts, so pooling them produces heterogeneous risk strata; second, risk variables perform differently in different cohorts, so one single model may not be as accurate for calculating risk; and, third, the use of disease cohorts makes our results comparable to the CMS model and similar to other readmission studies in Medicare.^7,8,15

For development of the risk model, the outcome was 30-day unplanned hospital readmission. Planned readmissions were excluded; these were defined by the CMS algorithm as readmissions in which a typically planned procedure occurred in a hospitalization with a nonacute principal diagnosis.¹⁶ Independent variables included age and comorbidities in the final hospital-wide readmission models for each of the 5 specialty cohorts.¹⁵ In order to produce the best possible individual risk prediction for each patient, we added additional independent variables that CMS avoids for hospital quality measurement purposes but that contribute to risk of readmission: sex, race, dual eligibility status, number of prior AMA discharges, intensive care unit stay during current hospitalization, coronary care unit stay during current hospitalization, and hospitalization in the prior 30, 90, and 180 days. We also included an indicator variable for hospitalizations with more than 9 discharge diagnosis codes on or after January 2011, the time at which Medicare allowed an increase of the number of International Classification of Diseases, 9th Revision-Clinical Modification diagnosis billing codes from 9 to 25.¹⁷ This indicator adjusts for the increased availability of comorbidity codes, which might otherwise inflate the predicted risk relative to hospitalizations prior to that date.

Based on the risk models, each hospitalization was assigned a predicted risk of readmission. For each specialty cohort, we pooled all hospitalizations across all study years and divided them into risk quintiles. We categorized hospitalizations as high risk if in the highest quintile, medium risk if in the middle 3 quintiles, and low risk if in the lowest quintile of predicted risk for all study hospitalizations in a given specialty cohort.

For our time trend analyses, we studied 2 outcomes: monthly mean predicted risk and monthly ratio of observed readmissions to expected readmissions for patients in the lowest, middle, and highest categories of predicted risk of readmission. We studied monthly predicted risk to determine whether the average readmission risk of patients was changing over time as admission and readmission rates were declining. We studied the ratio of O/E readmissions to determine whether the decline in overall readmissions was more substantial in particular risk strata; we used the ratio of O/E readmissions, which measures number of readmissions divided by number of readmissions predicted by the model, rather than crude observed readmissions, as O/E readmissions account for any changes in risk profiles over time within each risk stratum. Independent variables in our trend analyses were year—entered as a continuous variable—and indicators for postintroduction of the Affordable Care Act (ACA, March 2010) and for postintroduction of HRRP (October 2012); these time indicators were included because of prior studies demonstrating that the introduction of ACA was associated with a decrease from baseline in readmission rates, which leveled off after introduction of HRRP.⁷ We also included an indicator for calendar quarter to account for seasonal effects.

Statistical Analysis

We developed generalized estimating equation models to predict 30-day unplanned readmission for each of the 5 specialty cohorts. The 5 models were fit using all patients in each cohort for the included time period and were adjusted for clustering by hospital. We assessed discrimination by calculating area under the receiver operating characteristic curve (AUC) for the 5 models; the AUCs measured the models’ ability to distinguish patients who were readmitted versus those who were not.¹⁸ We also calculated AUCs for each year to examine model performance over time.

Using these models, we calculated predicted risk for each hospitalization and averaged these to obtain mean predicted risk for each specialty cohort for each month. To test for trends in mean risk, we estimated 5 time series models, one for each cohort, with the dependent variable of monthly mean predicted risk. For each cohort, we first estimated a series of 12 empty autoregressive models, each with a different autoregressive term (1, 2...12). For each model, we calculated χ² for the test that the autocorrelation was 0; based on a comparison of chi-squared values, we specified an autocorrelation of 1 month for all models. Accordingly, a 1-month lag was used to estimate one final model for each cohort. Independent variables included year and indicators for post-ACA and post-HRRP; these variables captured the effect of trends over time and the introduction of these policy changes, respectively.¹⁹

To determine whether changes in risk over time were a result of changes in particular risk groups, we categorized hospitalizations into risk strata based on quintiles of predicted risk for each specialty cohort for the entire study period. For each individual year, we calculated the proportion of hospitalizations in the highest, middle, and lowest readmission risk strata for each cohort.

We calculated the monthly ratio of O/E readmission for hospitalizations in the lowest 20%, middle 60%, and highest 20% of readmission risk by month; O/E reflects the excess or deficit observed events relative to the number predicted by the model. Using this monthly O/E as the dependent variable, we developed autoregressive time series models as above, again with a 1-month lag, for each of these 3 risk strata in each cohort. As before, independent variables were year as a continuous variable, indicator variables for post-ACA and post-HRRP, and a categorical variable for calendar quarter.

All analyses were done in SAS version 9.3 (SAS Institute Inc., Cary, NC) and Stata version 14.2 (StataCorp LLC, College Station, TX).

We included 47,288,961 hospitalizations in the study, of which 11,231,242 (23.8%) were in the surgery/gynecology cohort, 19,548,711 (41.3%) were in the medicine cohort, 5,433,125 (11.5%) were in the cardiovascular cohort, 8,179,691 (17.3%) were in the cardiorespiratory cohort, and 2,896,192 (6.1%) were in the neurology cohort. The readmission rate was 16.2% (n = 7,642,161) overall, with the highest rates observed in the cardiorespiratory (20.5%) and medicine (17.6%) cohorts and the lowest rates observed in the surgery/gynecology (11.8%) and neurology (13.8%) cohorts.

The final predictive models for each cohort ranged in number of parameters from 56 for the cardiorespiratory cohort to 264 for the surgery/gynecology cohort. The models had AUCs of 0.70, 0.65, 0.67, 0.65, and 0.63 for the surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology cohorts, respectively; AUCs remained fairly stable over time for all disease cohorts (Appendix Table 1).

A number of mechanisms may account for the across-the-board improvements in readmission reduction. Many hospitals have instituted system-wide interventions, including patient education, medicine reconciliation, and early postdischarge follow-up,²⁰ which may have reduced readmissions across all patient risk strata. Alternatively, hospitals may have implemented interventions that disproportionally benefited low-risk patients while simultaneously utilizing interventions that only benefited high-risk patients. For instance, increasing threshold for admission⁷ may have the greatest effect on low-risk patients who could be most easily managed at home, while many intensive transitional care interventions have been developed to target only high-risk patients.^21,22

With the introduction of HRRP, there have been a number of concerns about the readmission measure used to penalize hospitals for high readmission rates. One major concern has been that the readmission metric may be flawed in its ability to capture continued improvement related to readmission.²³ Some have suggested that with better population health management, admissions will decrease, patient risk of the remaining patients will increase, and hospitals will be increasingly filled with patients who have high likelihood of readmission. This potential for increased risk with HRRP was suggested by a recent study that found that comorbidities increased in hospitalized Medicare beneficiaries between 2010 and 2013.¹¹ Our results were mixed in supporting this potential phenomenon because we examined global risk of readmission and found that some of the cohorts had increased risk over time while others did not. Others have expressed concern that readmission measure does not account for socioeconomic status, which has been associated with readmission rates.^24-27 Although we did not directly examine socioeconomic status in our study, we found that hospitals have been able to reduce readmission across all levels of risk, which includes markers of socioeconomic status, including race and Medicaid eligibility status.

Although we hypothesized that readmission risk would increase as number of hospitalizations decreased over time, we found no increase in readmission risk among the cohorts with HRRP diagnoses that had the largest decrease in readmission rates.^7,8 Conversely, readmission risk did increase—with a concurrent increase in the proportion of high-risk hospitalizations—in the surgery/gynecology and neurology cohorts that were not subject to HRRP penalties. Nonetheless, rehospitalizations were reduced for all risk categories in these 2 cohorts. Notably, surgery/gynecology and neurology had the lowest readmission rates overall. These findings suggest that initiatives to prevent initial hospitalizations, such as increasing the threshold for postoperative admission, may have had a greater effect on low- versus high-risk patients in low-risk hospitalizations. However, once a patient is hospitalized, multidisciplinary strategies appear to be effective at reducing readmissions for all risk classes in these cohorts.

For the 3 cohorts in which we observed an increase in readmission risk among hospitalized patients, the risk appeared to increase in early 2011. This time was about 10 months after passage of ACA, the timing of which was previously associated with a drop in readmission rates,^7,8 but well before HRRP went into effect in October 2012. The increase in readmission risk coincided with an increase in the number of diagnostic codes that could be included on a hospital claim to Medicare.¹⁷ This increase in allowable codes allowed us to capture more diagnoses for some patients, potentially resulting in an increase in apparent predicted risk of readmissions. While we adjusted for this in our predictive models, we may not have fully accounted for differences in risk related to coding change. As a result, some of the observed differences in risk in our study may be attributable to coding differences. More broadly, studies demonstrating the success of HRRP have typically examined risk-adjusted rates of readmission.^3,7 It is possible that a small portion of the observed reduction in risk-adjusted readmission rates may be related to the increase in predicted risk of readmission observed in our study. Future assessment of trends in readmission during this period should consider accounting for change in the number of allowed billing codes.

Other limitations should be considered in the interpretation of this study. First, like many predictive models for readmission,¹⁴ ours had imperfect discrimination, which could affect our results. Second, our study was based on older Medicare patients, so findings may not be applicable to younger patients. Third, while we accounted for surrogates for socioeconomic status, including dual eligibility and race, our models lacked other socioeconomic and community factors that can influence readmission.^24-26 Nonetheless, 1 study suggested that easily measured socioeconomic factors may not have a strong influence on the readmission metric used by Medicare.²⁸ Fourth, while our study included over 47 million hospitalizations, our time trend analyses used calendar month as the primary independent variable. As our study included 77 months, we may not have had sufficient power to detect small changes in risk over time.

Medicare readmissions have declined steadily in recent years, presumably at least partly in response to policy changes including HRRP. We found that hospitals have been effective at reducing readmissions across a range of patient risk strata and clinical conditions. As a result, the overall risk of readmission for hospitalized patients has remained constant for some but not all conditions. Whether institutions can continue to reduce readmission rates for most types of patients remains to be seen.

Acknowledgments

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grant R01HS022882. Dr. Blecker was supported by the AHRQ grant K08HS23683. The authors would like to thank Shawn Hoke and Jane Padikkala for administrative support.

Disclosure

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grants R01HS022882 and K08HS23683. The authors have no conflicts to report.

Point-of-care ultrasound (POCUS) is a valuable tool to assist in the diagnosis and treatment of many common diseases.^1-11 Its use has increased in clinical settings over the years, primarily because of more portable, economical, high-quality devices and training availability.¹² POCUS improves procedural success and guides the diagnostic management of hospitalized patients.^2,9-12 Literature details the training of medical students,^13,14 residents,^15-21 and providers in emergency medicine²² and critical care,^23,24 as well as focused cardiac training with hospitalists.^25-27 However, no literature exists describing a comprehensive longitudinal training program for hospitalists or skills retention.

This document details the hospital medicine department’s ultrasound training program from Regions Hospital, part of HealthPartners in Saint Paul, Minnesota, a large tertiary care medical center. We describe the development and effectiveness of the Comprehensive Hospitalist Assessment and Mentorship with Portfolios (CHAMP) Ultrasound Program. This approach is intended to support the development of POCUS training programs at other organizations.

The aim of the program was to build a comprehensive bedside ultrasound training paradigm for hospitalists. The primary objective of the study was to assess the program’s effect on skills over time. Secondary objectives were confidence ratings in the use of ultrasound and with various patient care realms (volume management, quality of physical exam, and ability to narrow the differential diagnosis). We hypothesized there would be higher retention of ultrasound skills in those who completed portfolios and/or monthly scanning sessions as well as increased confidence through all secondary outcome measures (see below).

This was a retrospective descriptive report of hospitalists who entered the CHAMP Ultrasound Program. Study participants were providers from the 454-bed Regions Hospital in Saint Paul, Minnesota. The study was deemed exempt by the HealthPartners Institutional Review Board. Three discrete 3-day courses and two 1-day in-person courses held at the Regions Hospital Simulation Center (Saint Paul, Minnesota) were studied.

Program Description

In 2014, a working group was developed in the hospital medicine department to support the hospital-wide POCUS committee with a charter to provide standardized training for providers to complete credentialing.²⁸ The goal of the hospital medicine ultrasound program was to establish the use of ultrasound by credentialed hospitalists into well-defined applications integrated into the practice of hospital medicine. Two providers were selected to lead the efforts and completed additional training through the American College of Chest Physicians (CHEST) Certificate of Completion Program.²⁹ An overall director was designated with the responsibilities delineated in supplementary Appendix 1. This director provided leadership on group practice, protocols, and equipment, creating the organizational framework for success with the training program. The hospital medicine training program had a 3-day in-person component built off the CHEST Critical Care Ultrasonography Program.²⁴ The curriculum was adapted from the American College of Chest Physicians/Société de Réanimation de Langue Française Statement on Competence in Critical Care Ultrasonography.³⁰ See Table 1 for the components of the training program.

Online Modules

As a prerequisite to the 3-day introductory course, hospitalists were required to complete modules for precourse knowledge involving a set of focused-topic online reading and videos with quizzes (supplementary Appendix 3).

3-Day In-Person Course with Assessments

The 3-day course provided 6 hours of didactics, 8 hours of image interpretation, and 9 hours of hands-on instruction (supplementary Appendix 4). Hospitalists first attended a large group didactic, followed by divided groups in image interpretation and hands-on scanning.²⁴