Continuous positive airway pressure therapy (CPAP) is the first-line treatment for obstructive sleep apnea (OSA) recommended by the American College of Physicians and the American Academy of Sleep Medicine.^1,2 CPAP reduces the apnea hypopnea index (AHI), improves oxyhemoglobin desaturation, and reduces cortical arousals associated with apneic/hypopneic events.³ Despite being an effective treatment for OSA, a significant limitation of CPAP is treatment adherence. Factors associated with CPAP adherence include disease and patient characteristics, perceived self-efficacy, treatment titration procedure, device technology factors, adverse effects, and psychosocial factors.⁴

Recent studies suggest that insomnia and depression may be associated with OSA. According to a review by Luyster and colleagues, insomnia is present in 39% to 58% of patients with OSA.⁵ Since OSA may disturb sleep by the number of nightly awakenings, OSA may cause or worsen insomnia. Furthermore, insomnia may exacerbate sleep apnea thus impeding the effectiveness of sleep apnea treatment.

In some studies, the presence of insomnia symptoms prior to initiating CPAP treatment has been found to be associated with reduced CPAP adherence. For example, in 2010, Wickwire and colleagues found that there was a negative association with the average nightly minutes of CPAP use for those patients with OSA that reported symptoms of sleep maintenance insomnia.⁶ This was not found for those patients with OSA who reported symptoms of sleep onset insomnia or reported no insomnia at all. In another study by Pieh and colleagues, self-reported insomnia symptoms were predictive of CPAP adherence (defined as < 4 hours use/night) at a 6-month follow-up.⁷ However, results from a separate study indicated that insomnia was not associated with 6-month CPAP adherence.⁸

Depressive symptoms are commonly reported by patients with OSA, and higher rates of depressive symptomatology in patients with OSA have been observed in a number of prevalence studies when compared with the general population.^9,10 Between 15% and 56% of patients with OSA are diagnosed with a depressive disorder compared with 6.6% of the general population.¹¹ OSA may be causally related with depression or coexist as a separate disorder. Apnea severity has been shown to exacerbate depressive symptoms, and treatment with CPAP can improve depressive symptoms.^12,13 Unfortunately, depression has been found to reduce CPAP adherence. For example, Law and colleagues found that depression was independently associated with poorer adherence during home-based auto-PAP titration.¹⁴ Furthermore, in a study by Gurlanick and colleagues, depressive symptoms were independently associated with reduced CPAP adherence in surgical patients with OSA.¹⁵

To the best of our knowledge, the combined impact of both insomnia and depression on CPAP adherence has not been investigated. In military populations this may be especially important as CPAP adherence has been reported to be worse in military patients with posttraumatic stress disorder (PTSD) and other psychiatric disorders, and there are increasing rates of insomnia and OSA in the military.^16,17 We hypothesize that active-duty and retired military patients with self-reported insomnia and depression will have reduced short and long-term CPAP adherence.

Methods

This is a retrospective cohort study that reviewed charts of active-duty and retired military members diagnosed with OSA by the Sleep Medicine Clinic at Naval Medical Center San Diego in California using a home sleep test (HST). The HSTs were interpreted by board-certified physicians in sleep medicine. Prior to the HST, all patients completed a sleep questionnaire that included self-reports of daytime sleepiness, using the Epworth Sleepiness Scale (ESS), depression using the Center for Epidemiologic Studies Depression Scale (CES-D) and insomnia using the Insomnia Severity Index (ISI).

The study population included active-duty and veteran patients diagnosed with OSA who chose treatment with a CPAP and attended the sleep clinic’s OSA educational class, which discussed the diagnosis and treatment of OSA. Inclusion criteria were patients aged > 18 years and diagnosed with OSA at the Naval Medical Center San Diego sleep lab between June 2014 and June 2015.

The study population was stratified into 4 groups: (1) those with OSA but no self-reported depression or insomnia; (2) those with OSA and self-reported depression but no insomnia; (3) those with OSA and insomnia but no depression; and (4) those with OSA and self-reported depression and insomnia. Charts were excluded from the review if there were incomplete data or if the patient selected an alternative treatment for OSA, such as an oral appliance. A total of 120 charts were included in the final review. This study was approved by the Naval Medical Center San Diego Institutional Review Board.

Data Collection

Data collected included the individual’s age, sex, minimum oxygen saturation during sleep, body mass index (BMI), height, weight, ESS score at time of diagnosis, date of HST, and date of attendance at the clinic’s OSA group treatment class. Diagnosis of OSA was based on the patient’s ≥ 5 AHI. OSA severity was divided into mild (AHI 5-14), moderate (AHI 15-29), or severe (AHI ≥ 30). A patient with a CES-D score > 14 was considered to have clinically significant depression, and a patient with an ISI score of > 14 was considered to have clinically significant insomnia. ISI is a reliable and valid instrument to quantify perceived insomnia severity.¹⁸ The CES-D was used only as an indicator of symptoms relating to depression, not to clinically diagnose depression. It also has been used extensively to investigate levels of depression without a psychiatric diagnosis.¹⁹

Follow-up CPAP adherence was collected at 3- and 12-month intervals after the date of the patient’s OSA treatment group class and included AHI, median pressure setting, median days used, average time used per night, and percentage of days used for more than 4 hours for the previous 30 days. Data were obtained through Sleep Data and ResMed websites, which receive patient adherence data directly from the patient’s CPAP device. Patients were considered to be adherent with CPAP usage based on the Medicare definition: Use of the CPAP device > 4 hours per night for at least 70% of nights during a 30-day period). The 3-month time frame was used as a short interval because that is when patients are seen in the pulmonary clinic for their initial follow-up appointment. Patients are seen again at 12 months because durable medical equipment supplies must be reordered after 12 months, which requires a patient visit.

Statistical Analysis

Linear regression methods were used to characterize any potential relationships between the predictor variables and the target outcome variables associated with CPAP adherence at 3 and 12 months. Scatterplots were produced to assess whether linear structure was sufficient to characterize any detectable relationships, or whether there existed more complex, nonlinear relationships. The best-fitting linear regression line was examined in relation to the confidence bands of the corresponding LOESS line to determine whether a more complicated model structure was needed to capture the relationship.

Standard tests of assumptions required for these methods were also carried out: QQ plots of residuals to test for normality, the Durbin-Watson test for independence of residuals, and the nonconstant variance score test for heteroskedasticity (ie, Breusch-Pagan test). The results of these assumptions tests are reported only in cases in which the assumptions were revealed to be untenable. In cases in which suspicious outlying observations may have biased analyses, robust versions of the corresponding models were constructed. In no cases did the resulting conclusions change; only the results of the original analysis are reported. All analyses were carried out in R (R Foundation, r-project.org). Statistical significance was defined as P < .05.

Results

Our study population was predominately male (90%) with a median age of 41 years (range 22-65) and BMI of 29.8 (range 7.7-57.2)(Table 1). 

Predictors of CPAP Adherence

OSA severity, as measured by the AHI, was the only promising predictor of CPAP use at 3 months (b, 2.128; t₈₀, 2.854; P = .005; adjusted R², 0.081). The severity of self-reported daytime sleepiness prior to a diagnosis of OSA, as measured by the ESS, did not predict 3-month CPAP adherence (b, 0.688; t₇₇, 0.300; P = .765; adjusted R², -0.012). Self-reported depression as measured by the CES-D also did not predict CPAP use at 3 months (b, -0.078; t₈₀, -0.014; P = .941; adjusted R², -0.012). Similarly, self-reported insomnia, as measured by the ISI, did not predict 3-month CPAP adherence (b, 1.765; t₈₀, 0.939; P = .350; adjusted R², -0.001). Furthermore, a model that incorporated both depression and insomnia proved no better at accounting for variation in 3-month CPAP use (R², -0.012). Demographic variables, such as age, sex, or BMI did not predict 3-month CPAP adherence (all Ps > .20). Finally, median CPAP pressure approached statistical significance as a predictor of 3-month CPAP adherence (b, 9.493; t₆₆, 1.881; P = .064; adjusted R², 0.037) (Figure 1).

CPAP Use at 12 months

The results for CPAP use at 12 months mirrored the results for 3 months with one main exception: OSA severity, as measured by the AHI, did not predict CPAP use at 12 months (b, 1.158; t₅₂, 1.245; P = .219; adjusted R², 0.010). Neither adding a quadratic predictor nor log transforming the AHI values produced a better model (R², -0.0007 vs R², 0.0089, respectively). The severity of self-reported daytime sleepiness, as measured by the ESS, did not predict 12-month CPAP adherence (b, -2.201; t₅₀, -0.752; P = .456; adjusted R² = -0.0086). Self-reported depression as measured by the CES-D also did not predict CPAP use at 12 months (b, 0.034, t₅₂, 0.022; P = .983; adjusted R², -0.092). Self-reported insomnia, as measured by the ISI, also did not predict 12-month CPAP adherence (b, 1.765; t₈₀, 0.939; P = .350; adjusted R² = -0.001). Furthermore, a model that incorporated both depression and insomnia proved no better at accounting for variation in 12-month CPAP use, (R², -0.0298). 

Discussion

Our study did not provide evidence that self-reported depressive and insomnia symptoms, as measured by the CES-D and ISI, can serve as useful predictors of short and long-term CPAP adherence in a sample of active-duty and retired military. OSA severity, as measured by the AHI, was the only promising predictor of CPAP adherence at 3 months.

Insomnia has been shown to improve with the use of CPAP. In a pilot study, Krakow and colleagues investigated the use of CPAP, oral appliances, or bilateral turbinectomy on patients with OSA and chronic insomnia.²⁰ Objective measures of insomnia improved with 1 night of CPAP titration. Björnsdóttir and colleagues evaluated the long-term effects of positive airway pressure (PAP) treatment on 705 adults with middle insomnia.²¹ They found after 2 years of PAP treatment combined with cognitive behavioral therapy for insomnia, patients had reduced symptoms of middle insomnia. It is possible that persistent insomnia is associated with more severe OSA which was not studied in our population.²²

As reported in other studies, it is possible that patients with depressive symptoms can improve with CPAP use, suggesting that depression and CPAP use are not totally unrelated. Edwards and colleagues studied the impact of CPAP on depressive symptoms in men and woman. They found that depressive symptoms are common in OSA and markedly improve with CPAP.²³ Bopparaju and colleagues found a high prevalence of anxiety and depression in patients with OSA but did not influence CPAP adherence.²⁴

The results of this study differ from some previous findings where depression was found to predict CPAP adherence.¹⁰ This may be due in part to differences in the type of instrument used to assess depression. Wells and colleagues found that baseline depressive symptoms did not correlate with CPAP adherence and that patients with greater CPAP adherence had improvement in OSA and depressive symptoms.²⁵ Furthermore, patients with residual OSA symptoms using CPAP had more depressive symptoms, suggesting that it is the improvement in OSA symptoms that may be correlated with the improvement in depressive symptoms. Although soldiers with PTSD may have reduced CPAP adherence, use of CPAP is associated with improvement in PTSD symptoms.^11,26

Limitations

This study had several limitations, including a small sample size. Study patients were also from a single institution, and the majority of patients had mild-to-moderate OSA. A multicenter prospective study with a larger sample size that included more severe patients with OSA may have shown different results. The participants in this study were limited to members from the active-duty and retired military population. The findings in this population may not be transferrable to the general public. Another study limitation was that the ISI and the CES-D were only administered prior to the initiation of CPAP. If the CES-D and ISI were administered at the 3- and 12-month follow-up visits, we could determine whether short and long-term CPAP improved these symptoms or whether there was no association between CPAP adherence with insomnia and depressive symptoms. Another limitation is that we did not have access to information about potential PTSD symptomatology, which has been associated with reduced CPAP adherence and is more common in a military and veteran population.¹¹

Conclusion

This study found little evidence that symptoms of depression and insomnia are useful predictors of CPAP adherence, in either short- or long-term use, in an active-duty and retired military sample. Although these were not found to be predictors of CPAP adherence, further research will be necessary to determine whether CPAP adherence improves symptoms of depression and insomnia in military and veteran populations. Apnea severity did predict CPAP adherence in the short term, but not for any length of time beyond 3 months. More research is needed to explore strategies to improve CPAP adherence in military populations.

Continuous positive airway pressure therapy (CPAP) is the first-line treatment for obstructive sleep apnea (OSA) recommended by the American College of Physicians and the American Academy of Sleep Medicine.^1,2 CPAP reduces the apnea hypopnea index (AHI), improves oxyhemoglobin desaturation, and reduces cortical arousals associated with apneic/hypopneic events.³ Despite being an effective treatment for OSA, a significant limitation of CPAP is treatment adherence. Factors associated with CPAP adherence include disease and patient characteristics, perceived self-efficacy, treatment titration procedure, device technology factors, adverse effects, and psychosocial factors.⁴

Recent studies suggest that insomnia and depression may be associated with OSA. According to a review by Luyster and colleagues, insomnia is present in 39% to 58% of patients with OSA.⁵ Since OSA may disturb sleep by the number of nightly awakenings, OSA may cause or worsen insomnia. Furthermore, insomnia may exacerbate sleep apnea thus impeding the effectiveness of sleep apnea treatment.

In some studies, the presence of insomnia symptoms prior to initiating CPAP treatment has been found to be associated with reduced CPAP adherence. For example, in 2010, Wickwire and colleagues found that there was a negative association with the average nightly minutes of CPAP use for those patients with OSA that reported symptoms of sleep maintenance insomnia.⁶ This was not found for those patients with OSA who reported symptoms of sleep onset insomnia or reported no insomnia at all. In another study by Pieh and colleagues, self-reported insomnia symptoms were predictive of CPAP adherence (defined as < 4 hours use/night) at a 6-month follow-up.⁷ However, results from a separate study indicated that insomnia was not associated with 6-month CPAP adherence.⁸

Depressive symptoms are commonly reported by patients with OSA, and higher rates of depressive symptomatology in patients with OSA have been observed in a number of prevalence studies when compared with the general population.^9,10 Between 15% and 56% of patients with OSA are diagnosed with a depressive disorder compared with 6.6% of the general population.¹¹ OSA may be causally related with depression or coexist as a separate disorder. Apnea severity has been shown to exacerbate depressive symptoms, and treatment with CPAP can improve depressive symptoms.^12,13 Unfortunately, depression has been found to reduce CPAP adherence. For example, Law and colleagues found that depression was independently associated with poorer adherence during home-based auto-PAP titration.¹⁴ Furthermore, in a study by Gurlanick and colleagues, depressive symptoms were independently associated with reduced CPAP adherence in surgical patients with OSA.¹⁵

To the best of our knowledge, the combined impact of both insomnia and depression on CPAP adherence has not been investigated. In military populations this may be especially important as CPAP adherence has been reported to be worse in military patients with posttraumatic stress disorder (PTSD) and other psychiatric disorders, and there are increasing rates of insomnia and OSA in the military.^16,17 We hypothesize that active-duty and retired military patients with self-reported insomnia and depression will have reduced short and long-term CPAP adherence.

Methods

This is a retrospective cohort study that reviewed charts of active-duty and retired military members diagnosed with OSA by the Sleep Medicine Clinic at Naval Medical Center San Diego in California using a home sleep test (HST). The HSTs were interpreted by board-certified physicians in sleep medicine. Prior to the HST, all patients completed a sleep questionnaire that included self-reports of daytime sleepiness, using the Epworth Sleepiness Scale (ESS), depression using the Center for Epidemiologic Studies Depression Scale (CES-D) and insomnia using the Insomnia Severity Index (ISI).

The study population included active-duty and veteran patients diagnosed with OSA who chose treatment with a CPAP and attended the sleep clinic’s OSA educational class, which discussed the diagnosis and treatment of OSA. Inclusion criteria were patients aged > 18 years and diagnosed with OSA at the Naval Medical Center San Diego sleep lab between June 2014 and June 2015.

The study population was stratified into 4 groups: (1) those with OSA but no self-reported depression or insomnia; (2) those with OSA and self-reported depression but no insomnia; (3) those with OSA and insomnia but no depression; and (4) those with OSA and self-reported depression and insomnia. Charts were excluded from the review if there were incomplete data or if the patient selected an alternative treatment for OSA, such as an oral appliance. A total of 120 charts were included in the final review. This study was approved by the Naval Medical Center San Diego Institutional Review Board.

Data Collection

Data collected included the individual’s age, sex, minimum oxygen saturation during sleep, body mass index (BMI), height, weight, ESS score at time of diagnosis, date of HST, and date of attendance at the clinic’s OSA group treatment class. Diagnosis of OSA was based on the patient’s ≥ 5 AHI. OSA severity was divided into mild (AHI 5-14), moderate (AHI 15-29), or severe (AHI ≥ 30). A patient with a CES-D score > 14 was considered to have clinically significant depression, and a patient with an ISI score of > 14 was considered to have clinically significant insomnia. ISI is a reliable and valid instrument to quantify perceived insomnia severity.¹⁸ The CES-D was used only as an indicator of symptoms relating to depression, not to clinically diagnose depression. It also has been used extensively to investigate levels of depression without a psychiatric diagnosis.¹⁹

Follow-up CPAP adherence was collected at 3- and 12-month intervals after the date of the patient’s OSA treatment group class and included AHI, median pressure setting, median days used, average time used per night, and percentage of days used for more than 4 hours for the previous 30 days. Data were obtained through Sleep Data and ResMed websites, which receive patient adherence data directly from the patient’s CPAP device. Patients were considered to be adherent with CPAP usage based on the Medicare definition: Use of the CPAP device > 4 hours per night for at least 70% of nights during a 30-day period). The 3-month time frame was used as a short interval because that is when patients are seen in the pulmonary clinic for their initial follow-up appointment. Patients are seen again at 12 months because durable medical equipment supplies must be reordered after 12 months, which requires a patient visit.

Statistical Analysis

Linear regression methods were used to characterize any potential relationships between the predictor variables and the target outcome variables associated with CPAP adherence at 3 and 12 months. Scatterplots were produced to assess whether linear structure was sufficient to characterize any detectable relationships, or whether there existed more complex, nonlinear relationships. The best-fitting linear regression line was examined in relation to the confidence bands of the corresponding LOESS line to determine whether a more complicated model structure was needed to capture the relationship.

Standard tests of assumptions required for these methods were also carried out: QQ plots of residuals to test for normality, the Durbin-Watson test for independence of residuals, and the nonconstant variance score test for heteroskedasticity (ie, Breusch-Pagan test). The results of these assumptions tests are reported only in cases in which the assumptions were revealed to be untenable. In cases in which suspicious outlying observations may have biased analyses, robust versions of the corresponding models were constructed. In no cases did the resulting conclusions change; only the results of the original analysis are reported. All analyses were carried out in R (R Foundation, r-project.org). Statistical significance was defined as P < .05.

Results

Our study population was predominately male (90%) with a median age of 41 years (range 22-65) and BMI of 29.8 (range 7.7-57.2)(Table 1). 

Predictors of CPAP Adherence

OSA severity, as measured by the AHI, was the only promising predictor of CPAP use at 3 months (b, 2.128; t₈₀, 2.854; P = .005; adjusted R², 0.081). The severity of self-reported daytime sleepiness prior to a diagnosis of OSA, as measured by the ESS, did not predict 3-month CPAP adherence (b, 0.688; t₇₇, 0.300; P = .765; adjusted R², -0.012). Self-reported depression as measured by the CES-D also did not predict CPAP use at 3 months (b, -0.078; t₈₀, -0.014; P = .941; adjusted R², -0.012). Similarly, self-reported insomnia, as measured by the ISI, did not predict 3-month CPAP adherence (b, 1.765; t₈₀, 0.939; P = .350; adjusted R², -0.001). Furthermore, a model that incorporated both depression and insomnia proved no better at accounting for variation in 3-month CPAP use (R², -0.012). Demographic variables, such as age, sex, or BMI did not predict 3-month CPAP adherence (all Ps > .20). Finally, median CPAP pressure approached statistical significance as a predictor of 3-month CPAP adherence (b, 9.493; t₆₆, 1.881; P = .064; adjusted R², 0.037) (Figure 1).

CPAP Use at 12 months

The results for CPAP use at 12 months mirrored the results for 3 months with one main exception: OSA severity, as measured by the AHI, did not predict CPAP use at 12 months (b, 1.158; t₅₂, 1.245; P = .219; adjusted R², 0.010). Neither adding a quadratic predictor nor log transforming the AHI values produced a better model (R², -0.0007 vs R², 0.0089, respectively). The severity of self-reported daytime sleepiness, as measured by the ESS, did not predict 12-month CPAP adherence (b, -2.201; t₅₀, -0.752; P = .456; adjusted R² = -0.0086). Self-reported depression as measured by the CES-D also did not predict CPAP use at 12 months (b, 0.034, t₅₂, 0.022; P = .983; adjusted R², -0.092). Self-reported insomnia, as measured by the ISI, also did not predict 12-month CPAP adherence (b, 1.765; t₈₀, 0.939; P = .350; adjusted R² = -0.001). Furthermore, a model that incorporated both depression and insomnia proved no better at accounting for variation in 12-month CPAP use, (R², -0.0298). 

Discussion

Our study did not provide evidence that self-reported depressive and insomnia symptoms, as measured by the CES-D and ISI, can serve as useful predictors of short and long-term CPAP adherence in a sample of active-duty and retired military. OSA severity, as measured by the AHI, was the only promising predictor of CPAP adherence at 3 months.

Insomnia has been shown to improve with the use of CPAP. In a pilot study, Krakow and colleagues investigated the use of CPAP, oral appliances, or bilateral turbinectomy on patients with OSA and chronic insomnia.²⁰ Objective measures of insomnia improved with 1 night of CPAP titration. Björnsdóttir and colleagues evaluated the long-term effects of positive airway pressure (PAP) treatment on 705 adults with middle insomnia.²¹ They found after 2 years of PAP treatment combined with cognitive behavioral therapy for insomnia, patients had reduced symptoms of middle insomnia. It is possible that persistent insomnia is associated with more severe OSA which was not studied in our population.²²

As reported in other studies, it is possible that patients with depressive symptoms can improve with CPAP use, suggesting that depression and CPAP use are not totally unrelated. Edwards and colleagues studied the impact of CPAP on depressive symptoms in men and woman. They found that depressive symptoms are common in OSA and markedly improve with CPAP.²³ Bopparaju and colleagues found a high prevalence of anxiety and depression in patients with OSA but did not influence CPAP adherence.²⁴

The results of this study differ from some previous findings where depression was found to predict CPAP adherence.¹⁰ This may be due in part to differences in the type of instrument used to assess depression. Wells and colleagues found that baseline depressive symptoms did not correlate with CPAP adherence and that patients with greater CPAP adherence had improvement in OSA and depressive symptoms.²⁵ Furthermore, patients with residual OSA symptoms using CPAP had more depressive symptoms, suggesting that it is the improvement in OSA symptoms that may be correlated with the improvement in depressive symptoms. Although soldiers with PTSD may have reduced CPAP adherence, use of CPAP is associated with improvement in PTSD symptoms.^11,26

Limitations

This study had several limitations, including a small sample size. Study patients were also from a single institution, and the majority of patients had mild-to-moderate OSA. A multicenter prospective study with a larger sample size that included more severe patients with OSA may have shown different results. The participants in this study were limited to members from the active-duty and retired military population. The findings in this population may not be transferrable to the general public. Another study limitation was that the ISI and the CES-D were only administered prior to the initiation of CPAP. If the CES-D and ISI were administered at the 3- and 12-month follow-up visits, we could determine whether short and long-term CPAP improved these symptoms or whether there was no association between CPAP adherence with insomnia and depressive symptoms. Another limitation is that we did not have access to information about potential PTSD symptomatology, which has been associated with reduced CPAP adherence and is more common in a military and veteran population.¹¹

Conclusion

This study found little evidence that symptoms of depression and insomnia are useful predictors of CPAP adherence, in either short- or long-term use, in an active-duty and retired military sample. Although these were not found to be predictors of CPAP adherence, further research will be necessary to determine whether CPAP adherence improves symptoms of depression and insomnia in military and veteran populations. Apnea severity did predict CPAP adherence in the short term, but not for any length of time beyond 3 months. More research is needed to explore strategies to improve CPAP adherence in military populations.

Continuous positive airway pressure therapy (CPAP) is the first-line treatment for obstructive sleep apnea (OSA) recommended by the American College of Physicians and the American Academy of Sleep Medicine.^1,2 CPAP reduces the apnea hypopnea index (AHI), improves oxyhemoglobin desaturation, and reduces cortical arousals associated with apneic/hypopneic events.³ Despite being an effective treatment for OSA, a significant limitation of CPAP is treatment adherence. Factors associated with CPAP adherence include disease and patient characteristics, perceived self-efficacy, treatment titration procedure, device technology factors, adverse effects, and psychosocial factors.⁴

Recent studies suggest that insomnia and depression may be associated with OSA. According to a review by Luyster and colleagues, insomnia is present in 39% to 58% of patients with OSA.⁵ Since OSA may disturb sleep by the number of nightly awakenings, OSA may cause or worsen insomnia. Furthermore, insomnia may exacerbate sleep apnea thus impeding the effectiveness of sleep apnea treatment.

In some studies, the presence of insomnia symptoms prior to initiating CPAP treatment has been found to be associated with reduced CPAP adherence. For example, in 2010, Wickwire and colleagues found that there was a negative association with the average nightly minutes of CPAP use for those patients with OSA that reported symptoms of sleep maintenance insomnia.⁶ This was not found for those patients with OSA who reported symptoms of sleep onset insomnia or reported no insomnia at all. In another study by Pieh and colleagues, self-reported insomnia symptoms were predictive of CPAP adherence (defined as < 4 hours use/night) at a 6-month follow-up.⁷ However, results from a separate study indicated that insomnia was not associated with 6-month CPAP adherence.⁸

Depressive symptoms are commonly reported by patients with OSA, and higher rates of depressive symptomatology in patients with OSA have been observed in a number of prevalence studies when compared with the general population.^9,10 Between 15% and 56% of patients with OSA are diagnosed with a depressive disorder compared with 6.6% of the general population.¹¹ OSA may be causally related with depression or coexist as a separate disorder. Apnea severity has been shown to exacerbate depressive symptoms, and treatment with CPAP can improve depressive symptoms.^12,13 Unfortunately, depression has been found to reduce CPAP adherence. For example, Law and colleagues found that depression was independently associated with poorer adherence during home-based auto-PAP titration.¹⁴ Furthermore, in a study by Gurlanick and colleagues, depressive symptoms were independently associated with reduced CPAP adherence in surgical patients with OSA.¹⁵

To the best of our knowledge, the combined impact of both insomnia and depression on CPAP adherence has not been investigated. In military populations this may be especially important as CPAP adherence has been reported to be worse in military patients with posttraumatic stress disorder (PTSD) and other psychiatric disorders, and there are increasing rates of insomnia and OSA in the military.^16,17 We hypothesize that active-duty and retired military patients with self-reported insomnia and depression will have reduced short and long-term CPAP adherence.

Methods

This is a retrospective cohort study that reviewed charts of active-duty and retired military members diagnosed with OSA by the Sleep Medicine Clinic at Naval Medical Center San Diego in California using a home sleep test (HST). The HSTs were interpreted by board-certified physicians in sleep medicine. Prior to the HST, all patients completed a sleep questionnaire that included self-reports of daytime sleepiness, using the Epworth Sleepiness Scale (ESS), depression using the Center for Epidemiologic Studies Depression Scale (CES-D) and insomnia using the Insomnia Severity Index (ISI).

The study population included active-duty and veteran patients diagnosed with OSA who chose treatment with a CPAP and attended the sleep clinic’s OSA educational class, which discussed the diagnosis and treatment of OSA. Inclusion criteria were patients aged > 18 years and diagnosed with OSA at the Naval Medical Center San Diego sleep lab between June 2014 and June 2015.

The study population was stratified into 4 groups: (1) those with OSA but no self-reported depression or insomnia; (2) those with OSA and self-reported depression but no insomnia; (3) those with OSA and insomnia but no depression; and (4) those with OSA and self-reported depression and insomnia. Charts were excluded from the review if there were incomplete data or if the patient selected an alternative treatment for OSA, such as an oral appliance. A total of 120 charts were included in the final review. This study was approved by the Naval Medical Center San Diego Institutional Review Board.

Data Collection

Data collected included the individual’s age, sex, minimum oxygen saturation during sleep, body mass index (BMI), height, weight, ESS score at time of diagnosis, date of HST, and date of attendance at the clinic’s OSA group treatment class. Diagnosis of OSA was based on the patient’s ≥ 5 AHI. OSA severity was divided into mild (AHI 5-14), moderate (AHI 15-29), or severe (AHI ≥ 30). A patient with a CES-D score > 14 was considered to have clinically significant depression, and a patient with an ISI score of > 14 was considered to have clinically significant insomnia. ISI is a reliable and valid instrument to quantify perceived insomnia severity.¹⁸ The CES-D was used only as an indicator of symptoms relating to depression, not to clinically diagnose depression. It also has been used extensively to investigate levels of depression without a psychiatric diagnosis.¹⁹

Follow-up CPAP adherence was collected at 3- and 12-month intervals after the date of the patient’s OSA treatment group class and included AHI, median pressure setting, median days used, average time used per night, and percentage of days used for more than 4 hours for the previous 30 days. Data were obtained through Sleep Data and ResMed websites, which receive patient adherence data directly from the patient’s CPAP device. Patients were considered to be adherent with CPAP usage based on the Medicare definition: Use of the CPAP device > 4 hours per night for at least 70% of nights during a 30-day period). The 3-month time frame was used as a short interval because that is when patients are seen in the pulmonary clinic for their initial follow-up appointment. Patients are seen again at 12 months because durable medical equipment supplies must be reordered after 12 months, which requires a patient visit.

Statistical Analysis

Linear regression methods were used to characterize any potential relationships between the predictor variables and the target outcome variables associated with CPAP adherence at 3 and 12 months. Scatterplots were produced to assess whether linear structure was sufficient to characterize any detectable relationships, or whether there existed more complex, nonlinear relationships. The best-fitting linear regression line was examined in relation to the confidence bands of the corresponding LOESS line to determine whether a more complicated model structure was needed to capture the relationship.

Standard tests of assumptions required for these methods were also carried out: QQ plots of residuals to test for normality, the Durbin-Watson test for independence of residuals, and the nonconstant variance score test for heteroskedasticity (ie, Breusch-Pagan test). The results of these assumptions tests are reported only in cases in which the assumptions were revealed to be untenable. In cases in which suspicious outlying observations may have biased analyses, robust versions of the corresponding models were constructed. In no cases did the resulting conclusions change; only the results of the original analysis are reported. All analyses were carried out in R (R Foundation, r-project.org). Statistical significance was defined as P < .05.

Results

Our study population was predominately male (90%) with a median age of 41 years (range 22-65) and BMI of 29.8 (range 7.7-57.2)(Table 1). 

Predictors of CPAP Adherence

OSA severity, as measured by the AHI, was the only promising predictor of CPAP use at 3 months (b, 2.128; t₈₀, 2.854; P = .005; adjusted R², 0.081). The severity of self-reported daytime sleepiness prior to a diagnosis of OSA, as measured by the ESS, did not predict 3-month CPAP adherence (b, 0.688; t₇₇, 0.300; P = .765; adjusted R², -0.012). Self-reported depression as measured by the CES-D also did not predict CPAP use at 3 months (b, -0.078; t₈₀, -0.014; P = .941; adjusted R², -0.012). Similarly, self-reported insomnia, as measured by the ISI, did not predict 3-month CPAP adherence (b, 1.765; t₈₀, 0.939; P = .350; adjusted R², -0.001). Furthermore, a model that incorporated both depression and insomnia proved no better at accounting for variation in 3-month CPAP use (R², -0.012). Demographic variables, such as age, sex, or BMI did not predict 3-month CPAP adherence (all Ps > .20). Finally, median CPAP pressure approached statistical significance as a predictor of 3-month CPAP adherence (b, 9.493; t₆₆, 1.881; P = .064; adjusted R², 0.037) (Figure 1).

CPAP Use at 12 months

The results for CPAP use at 12 months mirrored the results for 3 months with one main exception: OSA severity, as measured by the AHI, did not predict CPAP use at 12 months (b, 1.158; t₅₂, 1.245; P = .219; adjusted R², 0.010). Neither adding a quadratic predictor nor log transforming the AHI values produced a better model (R², -0.0007 vs R², 0.0089, respectively). The severity of self-reported daytime sleepiness, as measured by the ESS, did not predict 12-month CPAP adherence (b, -2.201; t₅₀, -0.752; P = .456; adjusted R² = -0.0086). Self-reported depression as measured by the CES-D also did not predict CPAP use at 12 months (b, 0.034, t₅₂, 0.022; P = .983; adjusted R², -0.092). Self-reported insomnia, as measured by the ISI, also did not predict 12-month CPAP adherence (b, 1.765; t₈₀, 0.939; P = .350; adjusted R² = -0.001). Furthermore, a model that incorporated both depression and insomnia proved no better at accounting for variation in 12-month CPAP use, (R², -0.0298). 

Discussion

Our study did not provide evidence that self-reported depressive and insomnia symptoms, as measured by the CES-D and ISI, can serve as useful predictors of short and long-term CPAP adherence in a sample of active-duty and retired military. OSA severity, as measured by the AHI, was the only promising predictor of CPAP adherence at 3 months.

Insomnia has been shown to improve with the use of CPAP. In a pilot study, Krakow and colleagues investigated the use of CPAP, oral appliances, or bilateral turbinectomy on patients with OSA and chronic insomnia.²⁰ Objective measures of insomnia improved with 1 night of CPAP titration. Björnsdóttir and colleagues evaluated the long-term effects of positive airway pressure (PAP) treatment on 705 adults with middle insomnia.²¹ They found after 2 years of PAP treatment combined with cognitive behavioral therapy for insomnia, patients had reduced symptoms of middle insomnia. It is possible that persistent insomnia is associated with more severe OSA which was not studied in our population.²²

As reported in other studies, it is possible that patients with depressive symptoms can improve with CPAP use, suggesting that depression and CPAP use are not totally unrelated. Edwards and colleagues studied the impact of CPAP on depressive symptoms in men and woman. They found that depressive symptoms are common in OSA and markedly improve with CPAP.²³ Bopparaju and colleagues found a high prevalence of anxiety and depression in patients with OSA but did not influence CPAP adherence.²⁴

The results of this study differ from some previous findings where depression was found to predict CPAP adherence.¹⁰ This may be due in part to differences in the type of instrument used to assess depression. Wells and colleagues found that baseline depressive symptoms did not correlate with CPAP adherence and that patients with greater CPAP adherence had improvement in OSA and depressive symptoms.²⁵ Furthermore, patients with residual OSA symptoms using CPAP had more depressive symptoms, suggesting that it is the improvement in OSA symptoms that may be correlated with the improvement in depressive symptoms. Although soldiers with PTSD may have reduced CPAP adherence, use of CPAP is associated with improvement in PTSD symptoms.^11,26

Limitations

This study had several limitations, including a small sample size. Study patients were also from a single institution, and the majority of patients had mild-to-moderate OSA. A multicenter prospective study with a larger sample size that included more severe patients with OSA may have shown different results. The participants in this study were limited to members from the active-duty and retired military population. The findings in this population may not be transferrable to the general public. Another study limitation was that the ISI and the CES-D were only administered prior to the initiation of CPAP. If the CES-D and ISI were administered at the 3- and 12-month follow-up visits, we could determine whether short and long-term CPAP improved these symptoms or whether there was no association between CPAP adherence with insomnia and depressive symptoms. Another limitation is that we did not have access to information about potential PTSD symptomatology, which has been associated with reduced CPAP adherence and is more common in a military and veteran population.¹¹

Conclusion

This study found little evidence that symptoms of depression and insomnia are useful predictors of CPAP adherence, in either short- or long-term use, in an active-duty and retired military sample. Although these were not found to be predictors of CPAP adherence, further research will be necessary to determine whether CPAP adherence improves symptoms of depression and insomnia in military and veteran populations. Apnea severity did predict CPAP adherence in the short term, but not for any length of time beyond 3 months. More research is needed to explore strategies to improve CPAP adherence in military populations.

Schwannomas are benign tumors exclusively composed of Schwann cells that arise from the peripheral nerve sheath; these tumors theoretically can present anywhere in the body where nerves reside. They tend to occur in the head and neck region (classically an acoustic neuroma) but also occur in other locations, including the retroperitoneal space and the extremities, particularly flexural surfaces. Patients with cutaneous schwannomas are most likely to present to their primary care provider’s office reporting skin findings or localized pain, and providers should be aware of schwannomas on the differential for painful nodular growths.

Case Presentation

A 70-year-old man with type 2 diabetes mellitus presented to the primary care clinic for intermittent, sharp, localized left lower quadrant abdominal wall pain that was gradually progressive over the previous few months. The patient noticed the development of a small nodule 7 to 8 months prior to the visit, at which time the pain was less frequent and less severe. He reported no postprandial association of the pain, nausea, vomiting, diarrhea, constipation, or other gastrointestinal symptoms.

Ten months prior to the presentation, he was involved in a low-impact motor vehicle collision as a pedestrian in which he fell face-first onto the hood of an oncoming car. At that time, he did not note any abdominal trauma or pain. Evaluation at a local emergency department did not reveal any major injuries. In the interim, he had self-administered insulin in his abdominal region, as he had without incident for the previous 2 years. He reported that he was not injecting near the site of the nodule since it had formed. He could not recall whether the location was a previous insulin administration site.

On examination, the patient’s vital signs were normal as were the cardiac and respiratory examinations. An abdominal exam revealed normal bowel sounds and no overlying skin changes or discoloration. Palpation revealed a 1.5 x 1 cm rubbery-to-firm, well-circumscribed subcutaneous nodule along his mid-left abdomen, about 7 cm lateral to the umbilicus. The nodule was sensitive to both light touch and deep pressure. It was firmer than expected for an abdominal wall lipoma. There was no central puncta or pore to suggest an epidermal inclusion cyst. There was no surrounding erythema or induration to suggest an abscess. 

The patient was referred for surgery and underwent excisional biopsy of the mass. Pathology revealed a well-circumscribed vascular/spindle-cell lesion consistent with a schwannoma. His postoperative course was uncomplicated. At 4-week follow-up the incision had healed completely and the patient was pain free.

Discussion

Soft-tissue nodules are common—about two-thirds of soft-tissue tumors are classified into 7 diagnostic categories: lipoma and lipoma variants (16%), fibrous histiocytoma (13%), nodular fasciitis (11%), hemangioma (8%), fibromatosis (7%), neurofibroma (5%), and schwannoma (5%).¹ Peripheral nerve tumors (schwannomas, neurofibromas) can be associated with pain or paresthesias, and less commonly, neurologic deficits, such as motor weakness. Peripheral nerve tumors have several classifications, such as nonneoplastic vs neoplastic, benign vs malignant, and sheath vs nonsheath origins. Schwannomas are considered part of the neoplastic subset due to their growth; otherwise, they are benign with a sheath origin. In contrast to neurofibromas, benign schwannomas have a slower rate of progression, lower association with pain, and fewer neurologic symptoms.²

The neural sheath is made up of 3 types of cells: the fibroblast, the Schwann cell, and the perineural cell, which lacks a basement membrane. It is the Schwann cell that can give rise to the 3 main types of cutaneous nerve tumors: neuromas, neurofibromas, and schwannomas.³ A nerve that is both entering and exiting a mass is a classic presentation for a peripheral nerve sheath tumor. If the nerve is eccentric to the lesion, then it is consistent with a schwannoma (not a neurofibroma).⁴ Schwannomas are made exclusively of Schwann cells that arise from the nerve sheath, whereas neurofibromas are made up of all the different cell types that constitute a nerve. Bilateral vestibular schwannomas (acoustic neuromas) are virtually pathognomonic of neurofibromatosis 2 (NF-2), which can manifest as hearing loss, tinnitus, and equilibrium problems. In contrast, neurofibromatosis 1 (NF-1) is more common, characterized by multiple café au lait spots, freckling in the axillary and groin regions, increased risk of cancers overall, and development of pedunculated skin growths, brain, or organ-based neurofibromas.

Diagnosis

A workup generally includes a thorough history and examination as well as imaging. In cases of superficial subcutaneous lesions, an ultrasound is often the imaging modality of choice. However, magnetic resonance imaging (MRI) and computed tomography (CT) scans are frequently used for more deep-seated lesions. There can be significant differences between malignant and benign neural lesions on MRI and CT in terms of contrast-uptake and heterogeneity of tissue, but the visual features are not consistent. Best estimates for MRI suggest 61% sensitivity and 90% specificity for the diagnosis of high-grade malignant peripheral nerve sheath tumors based on imaging alone.⁵

Definitive diagnosis requires surgical excision. Fine-needle aspiration can be used to diagnose subcutaneous nodules, but there is a possibility that degenerative changes and nuclear atypia seen on a smaller sample may be confused with a more aggressive sarcoma. For example, long-standing schwannomas are often called ancient, meaning that they break down over time, and the atypia they display is a regressive phenomenon.⁶ Therefore, a small or limited tissue sampling may not be representative of the entire lesion.⁷ As such, patients will likely need referral for surgical removal to determine the exact nature of the growth.

Although schwannomas are uncommon overall, the highest incidence is in the fourth decade of life with a slight predominance in females. They are often incidentally found as a palpable mass but can be symptomatic with paresthesias, pain, or neurologic changes—particularly when identified in the retroperitoneum or along joints. Schwannomas are most commonly found in the retroperitoneum (32%), mediastinum (23%), head and neck (18%), and extremities (16%).⁸ The majority of cases (about 90%) are sporadic; whereas 2% are related to NF-2.⁹ The abdominal wall schwannoma is rare. Our review of English-language literature in PubMed and EMBASE found only 5 other case reports (Table 1).

On physical examination, superficial lesions are freely movable except for a single point of attachment, which is generally along the long axis of the nerve. 

Pathology

On gross pathology examination, schwannomas have a well-circumscribed smooth external surface. On microscopy, schwannomas are truly encapsulated, uninodular, spindle-cell proliferations arranged in a streaming pattern within a background of thick, hyalinized blood vessels. Classic schwannomas typically exhibit a biphasic pattern of alternating areas of high and low cellularity and are named for Swedish neurologist Nils Antoni. The more cellular regions are referred to as Antoni A areas and consist of streaming fascicles of compact spindle cells that often palisade around acellular eosinophilic areas of fibrillary processes known as Verocay bodies.

In contrast, the lower cellularity regions (Antoni B areas) consist of multipolar, loosely textured cells with abundant cytoplasm, haphazardly arranged processes, and an overall myxoid appearance.¹¹ Schwannomas are known to have widely variable proportions of Antoni A and Antoni B areas; in this case, the excised specimen was noted to have predominately Antoni A areas without well-defined Verocay bodies and only scattered foci showing some suggestion of the hypocellular Antoni B architecture (Figure 2).^9,12 

Immunohistochemical stains for S100 and SOX10 (used to identify cells derived from a neural crest lineage) were strongly positive, which is characteristic of schwannomas.¹³ Although there have only been rare reports of extracranial schwannomas undergoing malignant transformation, it is critical to rule out the possibility of a de novo malignant peripheral nerve sheath tumor (MPNST).¹³ In general, MPNSTs tend to be more cellular, have brisk mitotic activity, areas of necrosis, hyperchromatic nuclei, and conspicuous pleomorphism. Mitotic figures, which can be concerning for malignant potential if present in high number, were noted occasionally in our patient; however, occasional mitosis may be seen in classic schwannomas. Clinically, MPNSTs have a poor prognosis. Based on case reports, disease-specific survival at 10 years is 31.6% for localized disease and only 7.5% for metastatic disease.¹⁴ In this case, there was no evidence of any of the high-grade features of a malignant peripheral nerve sheath tumor, thus supporting the diagnosis of schwannoma (neurilemmoma).

Treatment

Schwannomas are exclusively treated by excision. Prognosis is good with low recurrence rates. It is unknown what the recurrence rates are for completely resected abdominal wall schwannomas since there are so few reports in the literature. For other well-known entities, such as vestibular schwannoma (acoustic neuromas), the recurrence rates are generally 2% to 3%.¹⁵ Transformation of schwannomas into MPNSTs are so unusual that they are only described in single case reports.

Conclusion

Soft-tissue masses are a common complaint. Most are benign and do not require excision unless it interferes with the quality of life of the patient or if the diagnosis is uncertain. It is important to be aware of schwannomas in the differential diagnosis of soft-tissue masses. Diagnosis may be achieved through the combination of imaging and biopsy, but the definitive diagnosis is made on complete excision of the mass.

Acknowledgments
Contributors: Michael Lewis, MD, Department of Pathology, VA Greater Los Angeles Healthcare System. Written permission also was obtained from the patient.

Schwannomas are benign tumors exclusively composed of Schwann cells that arise from the peripheral nerve sheath; these tumors theoretically can present anywhere in the body where nerves reside. They tend to occur in the head and neck region (classically an acoustic neuroma) but also occur in other locations, including the retroperitoneal space and the extremities, particularly flexural surfaces. Patients with cutaneous schwannomas are most likely to present to their primary care provider’s office reporting skin findings or localized pain, and providers should be aware of schwannomas on the differential for painful nodular growths.

Case Presentation

A 70-year-old man with type 2 diabetes mellitus presented to the primary care clinic for intermittent, sharp, localized left lower quadrant abdominal wall pain that was gradually progressive over the previous few months. The patient noticed the development of a small nodule 7 to 8 months prior to the visit, at which time the pain was less frequent and less severe. He reported no postprandial association of the pain, nausea, vomiting, diarrhea, constipation, or other gastrointestinal symptoms.

Ten months prior to the presentation, he was involved in a low-impact motor vehicle collision as a pedestrian in which he fell face-first onto the hood of an oncoming car. At that time, he did not note any abdominal trauma or pain. Evaluation at a local emergency department did not reveal any major injuries. In the interim, he had self-administered insulin in his abdominal region, as he had without incident for the previous 2 years. He reported that he was not injecting near the site of the nodule since it had formed. He could not recall whether the location was a previous insulin administration site.

On examination, the patient’s vital signs were normal as were the cardiac and respiratory examinations. An abdominal exam revealed normal bowel sounds and no overlying skin changes or discoloration. Palpation revealed a 1.5 x 1 cm rubbery-to-firm, well-circumscribed subcutaneous nodule along his mid-left abdomen, about 7 cm lateral to the umbilicus. The nodule was sensitive to both light touch and deep pressure. It was firmer than expected for an abdominal wall lipoma. There was no central puncta or pore to suggest an epidermal inclusion cyst. There was no surrounding erythema or induration to suggest an abscess. 

The patient was referred for surgery and underwent excisional biopsy of the mass. Pathology revealed a well-circumscribed vascular/spindle-cell lesion consistent with a schwannoma. His postoperative course was uncomplicated. At 4-week follow-up the incision had healed completely and the patient was pain free.

Discussion

Soft-tissue nodules are common—about two-thirds of soft-tissue tumors are classified into 7 diagnostic categories: lipoma and lipoma variants (16%), fibrous histiocytoma (13%), nodular fasciitis (11%), hemangioma (8%), fibromatosis (7%), neurofibroma (5%), and schwannoma (5%).¹ Peripheral nerve tumors (schwannomas, neurofibromas) can be associated with pain or paresthesias, and less commonly, neurologic deficits, such as motor weakness. Peripheral nerve tumors have several classifications, such as nonneoplastic vs neoplastic, benign vs malignant, and sheath vs nonsheath origins. Schwannomas are considered part of the neoplastic subset due to their growth; otherwise, they are benign with a sheath origin. In contrast to neurofibromas, benign schwannomas have a slower rate of progression, lower association with pain, and fewer neurologic symptoms.²

The neural sheath is made up of 3 types of cells: the fibroblast, the Schwann cell, and the perineural cell, which lacks a basement membrane. It is the Schwann cell that can give rise to the 3 main types of cutaneous nerve tumors: neuromas, neurofibromas, and schwannomas.³ A nerve that is both entering and exiting a mass is a classic presentation for a peripheral nerve sheath tumor. If the nerve is eccentric to the lesion, then it is consistent with a schwannoma (not a neurofibroma).⁴ Schwannomas are made exclusively of Schwann cells that arise from the nerve sheath, whereas neurofibromas are made up of all the different cell types that constitute a nerve. Bilateral vestibular schwannomas (acoustic neuromas) are virtually pathognomonic of neurofibromatosis 2 (NF-2), which can manifest as hearing loss, tinnitus, and equilibrium problems. In contrast, neurofibromatosis 1 (NF-1) is more common, characterized by multiple café au lait spots, freckling in the axillary and groin regions, increased risk of cancers overall, and development of pedunculated skin growths, brain, or organ-based neurofibromas.

Diagnosis

A workup generally includes a thorough history and examination as well as imaging. In cases of superficial subcutaneous lesions, an ultrasound is often the imaging modality of choice. However, magnetic resonance imaging (MRI) and computed tomography (CT) scans are frequently used for more deep-seated lesions. There can be significant differences between malignant and benign neural lesions on MRI and CT in terms of contrast-uptake and heterogeneity of tissue, but the visual features are not consistent. Best estimates for MRI suggest 61% sensitivity and 90% specificity for the diagnosis of high-grade malignant peripheral nerve sheath tumors based on imaging alone.⁵

Definitive diagnosis requires surgical excision. Fine-needle aspiration can be used to diagnose subcutaneous nodules, but there is a possibility that degenerative changes and nuclear atypia seen on a smaller sample may be confused with a more aggressive sarcoma. For example, long-standing schwannomas are often called ancient, meaning that they break down over time, and the atypia they display is a regressive phenomenon.⁶ Therefore, a small or limited tissue sampling may not be representative of the entire lesion.⁷ As such, patients will likely need referral for surgical removal to determine the exact nature of the growth.

Although schwannomas are uncommon overall, the highest incidence is in the fourth decade of life with a slight predominance in females. They are often incidentally found as a palpable mass but can be symptomatic with paresthesias, pain, or neurologic changes—particularly when identified in the retroperitoneum or along joints. Schwannomas are most commonly found in the retroperitoneum (32%), mediastinum (23%), head and neck (18%), and extremities (16%).⁸ The majority of cases (about 90%) are sporadic; whereas 2% are related to NF-2.⁹ The abdominal wall schwannoma is rare. Our review of English-language literature in PubMed and EMBASE found only 5 other case reports (Table 1).

On physical examination, superficial lesions are freely movable except for a single point of attachment, which is generally along the long axis of the nerve. 

Pathology

On gross pathology examination, schwannomas have a well-circumscribed smooth external surface. On microscopy, schwannomas are truly encapsulated, uninodular, spindle-cell proliferations arranged in a streaming pattern within a background of thick, hyalinized blood vessels. Classic schwannomas typically exhibit a biphasic pattern of alternating areas of high and low cellularity and are named for Swedish neurologist Nils Antoni. The more cellular regions are referred to as Antoni A areas and consist of streaming fascicles of compact spindle cells that often palisade around acellular eosinophilic areas of fibrillary processes known as Verocay bodies.

In contrast, the lower cellularity regions (Antoni B areas) consist of multipolar, loosely textured cells with abundant cytoplasm, haphazardly arranged processes, and an overall myxoid appearance.¹¹ Schwannomas are known to have widely variable proportions of Antoni A and Antoni B areas; in this case, the excised specimen was noted to have predominately Antoni A areas without well-defined Verocay bodies and only scattered foci showing some suggestion of the hypocellular Antoni B architecture (Figure 2).^9,12 

Immunohistochemical stains for S100 and SOX10 (used to identify cells derived from a neural crest lineage) were strongly positive, which is characteristic of schwannomas.¹³ Although there have only been rare reports of extracranial schwannomas undergoing malignant transformation, it is critical to rule out the possibility of a de novo malignant peripheral nerve sheath tumor (MPNST).¹³ In general, MPNSTs tend to be more cellular, have brisk mitotic activity, areas of necrosis, hyperchromatic nuclei, and conspicuous pleomorphism. Mitotic figures, which can be concerning for malignant potential if present in high number, were noted occasionally in our patient; however, occasional mitosis may be seen in classic schwannomas. Clinically, MPNSTs have a poor prognosis. Based on case reports, disease-specific survival at 10 years is 31.6% for localized disease and only 7.5% for metastatic disease.¹⁴ In this case, there was no evidence of any of the high-grade features of a malignant peripheral nerve sheath tumor, thus supporting the diagnosis of schwannoma (neurilemmoma).

Treatment

Schwannomas are exclusively treated by excision. Prognosis is good with low recurrence rates. It is unknown what the recurrence rates are for completely resected abdominal wall schwannomas since there are so few reports in the literature. For other well-known entities, such as vestibular schwannoma (acoustic neuromas), the recurrence rates are generally 2% to 3%.¹⁵ Transformation of schwannomas into MPNSTs are so unusual that they are only described in single case reports.

Conclusion

Soft-tissue masses are a common complaint. Most are benign and do not require excision unless it interferes with the quality of life of the patient or if the diagnosis is uncertain. It is important to be aware of schwannomas in the differential diagnosis of soft-tissue masses. Diagnosis may be achieved through the combination of imaging and biopsy, but the definitive diagnosis is made on complete excision of the mass.

Acknowledgments
Contributors: Michael Lewis, MD, Department of Pathology, VA Greater Los Angeles Healthcare System. Written permission also was obtained from the patient.

Schwannomas are benign tumors exclusively composed of Schwann cells that arise from the peripheral nerve sheath; these tumors theoretically can present anywhere in the body where nerves reside. They tend to occur in the head and neck region (classically an acoustic neuroma) but also occur in other locations, including the retroperitoneal space and the extremities, particularly flexural surfaces. Patients with cutaneous schwannomas are most likely to present to their primary care provider’s office reporting skin findings or localized pain, and providers should be aware of schwannomas on the differential for painful nodular growths.

Case Presentation

A 70-year-old man with type 2 diabetes mellitus presented to the primary care clinic for intermittent, sharp, localized left lower quadrant abdominal wall pain that was gradually progressive over the previous few months. The patient noticed the development of a small nodule 7 to 8 months prior to the visit, at which time the pain was less frequent and less severe. He reported no postprandial association of the pain, nausea, vomiting, diarrhea, constipation, or other gastrointestinal symptoms.

Ten months prior to the presentation, he was involved in a low-impact motor vehicle collision as a pedestrian in which he fell face-first onto the hood of an oncoming car. At that time, he did not note any abdominal trauma or pain. Evaluation at a local emergency department did not reveal any major injuries. In the interim, he had self-administered insulin in his abdominal region, as he had without incident for the previous 2 years. He reported that he was not injecting near the site of the nodule since it had formed. He could not recall whether the location was a previous insulin administration site.

On examination, the patient’s vital signs were normal as were the cardiac and respiratory examinations. An abdominal exam revealed normal bowel sounds and no overlying skin changes or discoloration. Palpation revealed a 1.5 x 1 cm rubbery-to-firm, well-circumscribed subcutaneous nodule along his mid-left abdomen, about 7 cm lateral to the umbilicus. The nodule was sensitive to both light touch and deep pressure. It was firmer than expected for an abdominal wall lipoma. There was no central puncta or pore to suggest an epidermal inclusion cyst. There was no surrounding erythema or induration to suggest an abscess. 

The patient was referred for surgery and underwent excisional biopsy of the mass. Pathology revealed a well-circumscribed vascular/spindle-cell lesion consistent with a schwannoma. His postoperative course was uncomplicated. At 4-week follow-up the incision had healed completely and the patient was pain free.

Discussion

Soft-tissue nodules are common—about two-thirds of soft-tissue tumors are classified into 7 diagnostic categories: lipoma and lipoma variants (16%), fibrous histiocytoma (13%), nodular fasciitis (11%), hemangioma (8%), fibromatosis (7%), neurofibroma (5%), and schwannoma (5%).¹ Peripheral nerve tumors (schwannomas, neurofibromas) can be associated with pain or paresthesias, and less commonly, neurologic deficits, such as motor weakness. Peripheral nerve tumors have several classifications, such as nonneoplastic vs neoplastic, benign vs malignant, and sheath vs nonsheath origins. Schwannomas are considered part of the neoplastic subset due to their growth; otherwise, they are benign with a sheath origin. In contrast to neurofibromas, benign schwannomas have a slower rate of progression, lower association with pain, and fewer neurologic symptoms.²

The neural sheath is made up of 3 types of cells: the fibroblast, the Schwann cell, and the perineural cell, which lacks a basement membrane. It is the Schwann cell that can give rise to the 3 main types of cutaneous nerve tumors: neuromas, neurofibromas, and schwannomas.³ A nerve that is both entering and exiting a mass is a classic presentation for a peripheral nerve sheath tumor. If the nerve is eccentric to the lesion, then it is consistent with a schwannoma (not a neurofibroma).⁴ Schwannomas are made exclusively of Schwann cells that arise from the nerve sheath, whereas neurofibromas are made up of all the different cell types that constitute a nerve. Bilateral vestibular schwannomas (acoustic neuromas) are virtually pathognomonic of neurofibromatosis 2 (NF-2), which can manifest as hearing loss, tinnitus, and equilibrium problems. In contrast, neurofibromatosis 1 (NF-1) is more common, characterized by multiple café au lait spots, freckling in the axillary and groin regions, increased risk of cancers overall, and development of pedunculated skin growths, brain, or organ-based neurofibromas.

Diagnosis

A workup generally includes a thorough history and examination as well as imaging. In cases of superficial subcutaneous lesions, an ultrasound is often the imaging modality of choice. However, magnetic resonance imaging (MRI) and computed tomography (CT) scans are frequently used for more deep-seated lesions. There can be significant differences between malignant and benign neural lesions on MRI and CT in terms of contrast-uptake and heterogeneity of tissue, but the visual features are not consistent. Best estimates for MRI suggest 61% sensitivity and 90% specificity for the diagnosis of high-grade malignant peripheral nerve sheath tumors based on imaging alone.⁵

Definitive diagnosis requires surgical excision. Fine-needle aspiration can be used to diagnose subcutaneous nodules, but there is a possibility that degenerative changes and nuclear atypia seen on a smaller sample may be confused with a more aggressive sarcoma. For example, long-standing schwannomas are often called ancient, meaning that they break down over time, and the atypia they display is a regressive phenomenon.⁶ Therefore, a small or limited tissue sampling may not be representative of the entire lesion.⁷ As such, patients will likely need referral for surgical removal to determine the exact nature of the growth.

Although schwannomas are uncommon overall, the highest incidence is in the fourth decade of life with a slight predominance in females. They are often incidentally found as a palpable mass but can be symptomatic with paresthesias, pain, or neurologic changes—particularly when identified in the retroperitoneum or along joints. Schwannomas are most commonly found in the retroperitoneum (32%), mediastinum (23%), head and neck (18%), and extremities (16%).⁸ The majority of cases (about 90%) are sporadic; whereas 2% are related to NF-2.⁹ The abdominal wall schwannoma is rare. Our review of English-language literature in PubMed and EMBASE found only 5 other case reports (Table 1).

On physical examination, superficial lesions are freely movable except for a single point of attachment, which is generally along the long axis of the nerve. 

Pathology

On gross pathology examination, schwannomas have a well-circumscribed smooth external surface. On microscopy, schwannomas are truly encapsulated, uninodular, spindle-cell proliferations arranged in a streaming pattern within a background of thick, hyalinized blood vessels. Classic schwannomas typically exhibit a biphasic pattern of alternating areas of high and low cellularity and are named for Swedish neurologist Nils Antoni. The more cellular regions are referred to as Antoni A areas and consist of streaming fascicles of compact spindle cells that often palisade around acellular eosinophilic areas of fibrillary processes known as Verocay bodies.

In contrast, the lower cellularity regions (Antoni B areas) consist of multipolar, loosely textured cells with abundant cytoplasm, haphazardly arranged processes, and an overall myxoid appearance.¹¹ Schwannomas are known to have widely variable proportions of Antoni A and Antoni B areas; in this case, the excised specimen was noted to have predominately Antoni A areas without well-defined Verocay bodies and only scattered foci showing some suggestion of the hypocellular Antoni B architecture (Figure 2).^9,12 

Immunohistochemical stains for S100 and SOX10 (used to identify cells derived from a neural crest lineage) were strongly positive, which is characteristic of schwannomas.¹³ Although there have only been rare reports of extracranial schwannomas undergoing malignant transformation, it is critical to rule out the possibility of a de novo malignant peripheral nerve sheath tumor (MPNST).¹³ In general, MPNSTs tend to be more cellular, have brisk mitotic activity, areas of necrosis, hyperchromatic nuclei, and conspicuous pleomorphism. Mitotic figures, which can be concerning for malignant potential if present in high number, were noted occasionally in our patient; however, occasional mitosis may be seen in classic schwannomas. Clinically, MPNSTs have a poor prognosis. Based on case reports, disease-specific survival at 10 years is 31.6% for localized disease and only 7.5% for metastatic disease.¹⁴ In this case, there was no evidence of any of the high-grade features of a malignant peripheral nerve sheath tumor, thus supporting the diagnosis of schwannoma (neurilemmoma).

Treatment

Schwannomas are exclusively treated by excision. Prognosis is good with low recurrence rates. It is unknown what the recurrence rates are for completely resected abdominal wall schwannomas since there are so few reports in the literature. For other well-known entities, such as vestibular schwannoma (acoustic neuromas), the recurrence rates are generally 2% to 3%.¹⁵ Transformation of schwannomas into MPNSTs are so unusual that they are only described in single case reports.

Conclusion

Soft-tissue masses are a common complaint. Most are benign and do not require excision unless it interferes with the quality of life of the patient or if the diagnosis is uncertain. It is important to be aware of schwannomas in the differential diagnosis of soft-tissue masses. Diagnosis may be achieved through the combination of imaging and biopsy, but the definitive diagnosis is made on complete excision of the mass.

Acknowledgments
Contributors: Michael Lewis, MD, Department of Pathology, VA Greater Los Angeles Healthcare System. Written permission also was obtained from the patient.

Historically, the Veterans Health Administration (VHA) has excelled at improving veterans’ access to health care and enhancing foundational services, such as prosthetics and other veteran-centric services, and this continues to be the VHA’s top priority.¹ Travel distance and time are often barriers to accessing health care for many veterans.^2-11 For veterans with disabilities who must overcome additional physical, cognitive, and emotional obstacles to access vital rehabilitation services, these geographic obstacles are magnified. Further compounding the challenge is that rehabilitation therapies frequently require multiple encounters. Telerehabilitation is a promising solution for veterans in need of rehabilitation to regain optimal functioning. This alternative mode of service delivery can help veterans overcome geographic access barriers by delivering health care directly to veterans in their homes or nearby community-based outpatient clinics.^12,13

A growing body of evidence supports telerehabilitation. In a 2017 systematic review and meta-analysis, Cottrell and colleagues reviewed and analyzed data from 13 studies that met their inclusion criteria; specifically, their meta-analytic sample comprised adults aged ≥ 18 years presenting with any diagnosed primary musculoskeletal condition; treatment interventions via a real-time telerehabilitation medium, trials that had a comparison group with the same condition; provided clinical outcomes data, and included published randomized and nonrandomized controlled trials.¹⁴ Based on their aggregated results, they concluded that real-time telerehabilitation was effective in improving physical function (standardized mean difference [SMD], 0.63; 95% CI, 0.92-2.33; I², 93%), and reducing pain (SMD, 0.66; 95% CI, −0.27- .60; I², 96%) in patients with any diagnosed primary musculoskeletal condition.¹⁴

Two other systematic reviews conducted by Pietrzak and colleagues and Agostini and colleagues also demonstrated the clinical effectiveness of telerehabilitation.^15,16 Clinical effectiveness was defined as changes in health, functional status, and satisfaction with the telerehabilitation services delivered. The studies examined in the review included those that provided online self-management and education in addition to exercise via teleconferencing in real time.

Pietrzak and colleagues found that Internet-based osteoarthritis self-management interventions significantly improved 4 of 6 health status measures reviewed (ie, pain, fatigue, activity limitation, health distress, disability, and self‐reported global health).¹⁵ User acceptance and satisfaction were high (≥ 70% satisfied) in all studies meeting the inclusion criteria.

Agostini and colleagues found that telerehabilitation was more effective than other modes of delivering rehabilitation to regain motor function in cardiac (SMD, 0.24; 95% CI, 0.04-0.43) and total knee arthroplasty (Timed Up and Go test: SMD, −5.17; 95% CI, −9.79- −0.55) patients.¹⁶ Some evidence from VHA and non-VHA studies also support the use of telerehabilitation to reduce health care costs,^17-19 improve treatment adherence,^12,20 and enhance patient physical, cognitive and mobility function, as well as patient satisfaction and health-related quality of life.^13,21-24

Since the first recorded use of telehealth in 1959, the application of technology to deliver health care, including rehabilitation services, has increased exponentially.¹⁴ In fiscal year (FY) 2017 alone, the VA provided > 2 million episodes of care for > 700,000 veterans using telehealth services.²⁵

Although the process for accessing telerehabilitation may vary throughout the VA, typically a few common factors make a veteran eligible for this mode of rehabilitation care delivery: Veterans must meet criteria for a specific program (eg, amputation, occupational therapy, and physical therapy) and receive VA care from a VA medical facility or clinic that offers telehealth services. Care providers must believe that the veteran would benefit from telerehabilitation (eg, limited mobility and long-distance travel to the facility) and that they would be able to receive an appropriate consult. The veteran must meet the following requirements: (1) willingness to consent to a visit via telehealth; (2) access to required equipment/e-mail; and (3) a caregiver to assist if they are unable to complete a visit independently.

In this article, we provide an overview of the growth of telerehabilitation in the VHA. Data are presented for specific telerehabilitation programs over time and by rurality.

Methods

The VHA Support Service Center works with VHA program offices and field users to provide field-focused business, clinical, and special topic reports. An online portal provides access to these customizable reports organized as data cubes, which represent data dimensions (ie, clinic type) and measures (ie, number of unique patients). For this study, we used the Connected Care, Telehealth, Call Centers Clinical Video Telehealth/Store and Forward Telehealth data cube clinical stop codes to identify the numbers of telerehabilitation veteran users and encounters across time. The following telerehabilitation clinic-stop codes were selected: 197 (polytrauma/traumatic brain injury [TBI]–individuals), 201 (Physical Medicine and Rehabilitation [PM&R] Service), 205 (physical therapy), 206 (occupational therapy), 211 (PM&R amputation clinic), 418 (amputation clinic), 214 (kinesiotherapy), and 240 (PM&R assistive technology clinic). Data for total unique patients served and the total number of encounters were extracted at the national level and by rurality from FY 2012 to FY 2017, providing the past 5 years of VHA telerehabilitation data.

It is important to note that in FY 2015, the VHA changed its definition of rurality to a rural-urban commuting areas (RUCA)-based system (www.ruralhealth.va.gov/rural-definition.asp). Prior to FY 2015, the VHA used the US Census Bureau (CB) urbanized area definitions. According to CB, an urbanized area contains a central city and surrounding area that totals > 50,000 in population. It also includes places outside of urbanized areas with populations > 2,500. Rural areas are defined as all other areas. VHA added a third category, highly rural, which is defined as areas that had < 7 people per square mile. In the RUCA system, each census tract defined by the CB is given a score. The VHA definitions are as follows:

• Urban (U)—census tracts with RUCA scores of 1.0 or 1.1. These tracts are determined by the CB as being in an urban core and having the majority of their workers commute within that same core (1.0). If 30% to 49% commute to an even larger urban core, then the code is 1.1;
• Rural (R)—all tracts not receiving scores in the urban or highly rural tiers; and
• Highly rural (H)—tracts with a RUCA score of 10.0. These are the most remote occupied land areas. Less than 10% of workers travel to CB-defined urbanized areas or urban clusters.

In addition, VHA recently added an “I” category to complement “U,” “R,” and “H.” The “I” value is assigned to veterans living on the US insular islands (ie, territories): Guam, American Samoa, Northern Marianas, and US Virgin Islands. For the analysis by rurality in this study, we excluded veterans living in the insular islands and those of unknown rurality (< 1.0% of patients and encounters). Further, because the numbers of highly rural veterans were relatively small (< 2% of patients and encounters), the rural and highly rural categories were combined and compared with urban-dwelling veterans.

Results

Overall, the workload for telerehabilitation nearly quadrupled over the 5-year period (Table 1 and Figure 1). 

Interesting trends were seen by clinic type. Some clinics increased substantially, whereas others showed only moderate increases, and in 1 case (PM&R Service), a decrease. For example, there is significant growth in the number of patients and encounters involving physical therapy through telerehabilitation. This telerehabilitation clinic increased its workload from 1,676 patients with 3,016 encounters in FY 2012 to 9,136 patients with 11,834 encounters in FY 2017, accounting for 62.6% of total growth in patients and 56.8% of total growth in encounters.

Other clinics showing substantial growth over time included occupational therapy and polytrauma/TBI-individual secondary evaluation. Kinesiotherapy telerehabilitation was almost nonexistent in the VHA during FY 2012, with only 23 patients having 23 encounters. By FY 2017, there were 563 patients with 624 kinesiotherapy telerehabilitation encounters, equating to staggering increases in 5 years: 2,348% for patients and 2,613% for encounters. Similarly, the Physical Medicine and Rehabilitation Assistive Technology clinics had very low numbers in FY 2012 (patients, 2; encounters, 3) and increased over time; albeit, at a slow rate.

Trends by Rurality

Trends by rural location of patients and encounters must be interpreted with caution because of the changing rural definition between FY 2014 and FY 2015 (Tables 2 and 3; Figures 3 and 4). 

The increased total number of patients seen between FY 2012 and FY 2014 (old definition) was 225% for rural veterans vs 134% for urban veterans. Between FY 2015 and FY 2017 (new definition), the increase was lower for both groups (rural, 13.4%; urban, 7.3%), but rural veterans still increased at a higher rate than did urban dwellers.

Discussion

Our primary aim was to provide data on the growth of telerehabilitation in the VHA over the past 5 years. Our secondary aim was to examine growth in the use of telerehabilitation by rurality. Specifically, we provided an overview of telerehabilitation growth in terms of unique patients and overall encounters in the VHA by rurality from FY 2012 to FY 2014 and FY 2015 to FY 2017 using the following programs: Polytrauma/TBI, PM&R Service, physical therapy, occupational therapy, PM&R amputation clinic, amputation clinic, kinesiotherapy, and PM&R assistive technology clinic. Our findings demonstrated a noteworthy increase in telerehabilitation encounters and unique patients over time for these programs. These findings were consistent with the overall trend of continued growth and expansion of telehealth within the VHA.

Our findings reveal an upward trend in the total number of rural encounters and rural unique patients despite the change in the VA’s definition of rurality in FY 2015. To our knowledge, urban and rural use of telerehabilitation has not been examined previously. Under both definitions of rurality, encounters and unique patients show an important increase over time, and by year-end 2017, more than half of all patients and encounters were attributed to rural patients (53.7% and 53.9%, respectively). Indeed, the upward trend may have been more pronounced if the rural definition had not changed in FY 2015. Our early VHA stroke patients study on the difference between rural-urban patients and taxonomies showed that the RUCA definition was more likely to reduce the number of rural patients by 8.5% than the early definition used by the VHA.²⁶

It is notable that although the use of tele-delivery of rehabilitation has continually increased, the rate of this increase was steeper from FY 2012 to FY 2014 than FY 2015 to FY 2017. For the programs under consideration in this study, the total number of rural patients/encounters increased throughout the observed periods. However, urban patients and encounters increased through FY 2016 and experienced a slight decrease in FY 2017.

The appearance of a slower rate of increase may be due to a rapid initial rate of increase through early adopters and “crossing the diffusion chasm,” a well-documented process of slower diffusion between the time of invention to penetration that often characterizes the spread of successful telehealth innovations.²⁷ Integrating technology into care delivery innovation requires the integration of technical, clinical, and administrative processes and can take time to scale successfully.²⁸

With an emphasis on increasing access to rehabilitation services, the VHA can expect to see a continuing increase in both the number and the percentage of telerehabilitation rural patients and encounters. The VHA has several telerehabilitation initiatives underway through the VHA’s Physical Medicine and Rehabilitation Telerehabilitation Enterprise Wide Initiative (TREWI) and Rural Veterans Telerehabilitation Initiative. These projects demonstrate the feasibility of this delivery approach and facilitate integration of this modality in clinical workflows. However, to sustain these efforts, facilities will need more infrastructure and personnel resources dedicated to the delivery of services.

In an ongoing evaluation of the TREWI, several factors seem to influence the uptake of the VHA Office of Rural Health TREWI programs. These factors are the presence or absence of a local site champion; the quality of hospital leadership support; the quality of past relationships between telerehabilitation sending sites and receiving sites; barriers to getting a telehealth service agreement in place; the availability of space; administrative know-how on setting up clinics appropriately; time involved to bring on staff; contracting issues; equipment availability and installation; cultural issues in embracing technologic innovation; training burden; hassle factors; and limited funds. Although early adopters may be able to negotiate and push through many of the barriers associated with the diffusion of telerehabilitation, the numerous barriers may slow its larger systemwide diffusion.

Telerehabilitation is a promising mode to deliver care to rural veterans who otherwise may not have access to this type of specialty care. Therefore, the identification of elements that foster telerehabilitation growth in future investigations can assist policy makers and key stakeholders in optimally leveraging program resources for maximal productivity. Future studies investigating the drivers of increases in telerehabilitation growth by rurality are warranted. Furthermore, more research is needed to examine telerehabilitation growth quality of care outcomes (eg, patient and provider satisfaction) to ensure that care is not only timely and accessible, but of high quality.

Conclusion

Disparities between rural and urban veterans compel a mode of expanding delivery of care. The VHA has embraced the use of telehealth modalities to extend its reach of rehabilitation services to veterans with disability and rehabilitation needs. Growth in telerehabilitation rural patient encounters increases access to rehabilitative care, reduces patient and caregiver travel burden, and helps ensure treatment adherence. Telerehabilitation utilization (unique patients and total encounters) is growing more rapidly for rural veterans than for their urban counterparts. Overall, telerehabilitation is filling a gap for rural veterans, as well as veterans in general with challenges in accessibility to health care. In order to make full use of the telerehabilitation services across its health care system, VA health care facilities may need to expand their effort in telerehabilitation dissemination and education among providers and veterans, particularly among providers who are less familiar with telerehabilitation services and among veterans who live in rural or highly rural areas and need special rehabilitation care.

Historically, the Veterans Health Administration (VHA) has excelled at improving veterans’ access to health care and enhancing foundational services, such as prosthetics and other veteran-centric services, and this continues to be the VHA’s top priority.¹ Travel distance and time are often barriers to accessing health care for many veterans.^2-11 For veterans with disabilities who must overcome additional physical, cognitive, and emotional obstacles to access vital rehabilitation services, these geographic obstacles are magnified. Further compounding the challenge is that rehabilitation therapies frequently require multiple encounters. Telerehabilitation is a promising solution for veterans in need of rehabilitation to regain optimal functioning. This alternative mode of service delivery can help veterans overcome geographic access barriers by delivering health care directly to veterans in their homes or nearby community-based outpatient clinics.^12,13

A growing body of evidence supports telerehabilitation. In a 2017 systematic review and meta-analysis, Cottrell and colleagues reviewed and analyzed data from 13 studies that met their inclusion criteria; specifically, their meta-analytic sample comprised adults aged ≥ 18 years presenting with any diagnosed primary musculoskeletal condition; treatment interventions via a real-time telerehabilitation medium, trials that had a comparison group with the same condition; provided clinical outcomes data, and included published randomized and nonrandomized controlled trials.¹⁴ Based on their aggregated results, they concluded that real-time telerehabilitation was effective in improving physical function (standardized mean difference [SMD], 0.63; 95% CI, 0.92-2.33; I², 93%), and reducing pain (SMD, 0.66; 95% CI, −0.27- .60; I², 96%) in patients with any diagnosed primary musculoskeletal condition.¹⁴

Two other systematic reviews conducted by Pietrzak and colleagues and Agostini and colleagues also demonstrated the clinical effectiveness of telerehabilitation.^15,16 Clinical effectiveness was defined as changes in health, functional status, and satisfaction with the telerehabilitation services delivered. The studies examined in the review included those that provided online self-management and education in addition to exercise via teleconferencing in real time.

Pietrzak and colleagues found that Internet-based osteoarthritis self-management interventions significantly improved 4 of 6 health status measures reviewed (ie, pain, fatigue, activity limitation, health distress, disability, and self‐reported global health).¹⁵ User acceptance and satisfaction were high (≥ 70% satisfied) in all studies meeting the inclusion criteria.

Agostini and colleagues found that telerehabilitation was more effective than other modes of delivering rehabilitation to regain motor function in cardiac (SMD, 0.24; 95% CI, 0.04-0.43) and total knee arthroplasty (Timed Up and Go test: SMD, −5.17; 95% CI, −9.79- −0.55) patients.¹⁶ Some evidence from VHA and non-VHA studies also support the use of telerehabilitation to reduce health care costs,^17-19 improve treatment adherence,^12,20 and enhance patient physical, cognitive and mobility function, as well as patient satisfaction and health-related quality of life.^13,21-24

Since the first recorded use of telehealth in 1959, the application of technology to deliver health care, including rehabilitation services, has increased exponentially.¹⁴ In fiscal year (FY) 2017 alone, the VA provided > 2 million episodes of care for > 700,000 veterans using telehealth services.²⁵

Although the process for accessing telerehabilitation may vary throughout the VA, typically a few common factors make a veteran eligible for this mode of rehabilitation care delivery: Veterans must meet criteria for a specific program (eg, amputation, occupational therapy, and physical therapy) and receive VA care from a VA medical facility or clinic that offers telehealth services. Care providers must believe that the veteran would benefit from telerehabilitation (eg, limited mobility and long-distance travel to the facility) and that they would be able to receive an appropriate consult. The veteran must meet the following requirements: (1) willingness to consent to a visit via telehealth; (2) access to required equipment/e-mail; and (3) a caregiver to assist if they are unable to complete a visit independently.

In this article, we provide an overview of the growth of telerehabilitation in the VHA. Data are presented for specific telerehabilitation programs over time and by rurality.

Methods

The VHA Support Service Center works with VHA program offices and field users to provide field-focused business, clinical, and special topic reports. An online portal provides access to these customizable reports organized as data cubes, which represent data dimensions (ie, clinic type) and measures (ie, number of unique patients). For this study, we used the Connected Care, Telehealth, Call Centers Clinical Video Telehealth/Store and Forward Telehealth data cube clinical stop codes to identify the numbers of telerehabilitation veteran users and encounters across time. The following telerehabilitation clinic-stop codes were selected: 197 (polytrauma/traumatic brain injury [TBI]–individuals), 201 (Physical Medicine and Rehabilitation [PM&R] Service), 205 (physical therapy), 206 (occupational therapy), 211 (PM&R amputation clinic), 418 (amputation clinic), 214 (kinesiotherapy), and 240 (PM&R assistive technology clinic). Data for total unique patients served and the total number of encounters were extracted at the national level and by rurality from FY 2012 to FY 2017, providing the past 5 years of VHA telerehabilitation data.

It is important to note that in FY 2015, the VHA changed its definition of rurality to a rural-urban commuting areas (RUCA)-based system (www.ruralhealth.va.gov/rural-definition.asp). Prior to FY 2015, the VHA used the US Census Bureau (CB) urbanized area definitions. According to CB, an urbanized area contains a central city and surrounding area that totals > 50,000 in population. It also includes places outside of urbanized areas with populations > 2,500. Rural areas are defined as all other areas. VHA added a third category, highly rural, which is defined as areas that had < 7 people per square mile. In the RUCA system, each census tract defined by the CB is given a score. The VHA definitions are as follows:

• Urban (U)—census tracts with RUCA scores of 1.0 or 1.1. These tracts are determined by the CB as being in an urban core and having the majority of their workers commute within that same core (1.0). If 30% to 49% commute to an even larger urban core, then the code is 1.1;
• Rural (R)—all tracts not receiving scores in the urban or highly rural tiers; and
• Highly rural (H)—tracts with a RUCA score of 10.0. These are the most remote occupied land areas. Less than 10% of workers travel to CB-defined urbanized areas or urban clusters.

In addition, VHA recently added an “I” category to complement “U,” “R,” and “H.” The “I” value is assigned to veterans living on the US insular islands (ie, territories): Guam, American Samoa, Northern Marianas, and US Virgin Islands. For the analysis by rurality in this study, we excluded veterans living in the insular islands and those of unknown rurality (< 1.0% of patients and encounters). Further, because the numbers of highly rural veterans were relatively small (< 2% of patients and encounters), the rural and highly rural categories were combined and compared with urban-dwelling veterans.

Results

Overall, the workload for telerehabilitation nearly quadrupled over the 5-year period (Table 1 and Figure 1). 

Interesting trends were seen by clinic type. Some clinics increased substantially, whereas others showed only moderate increases, and in 1 case (PM&R Service), a decrease. For example, there is significant growth in the number of patients and encounters involving physical therapy through telerehabilitation. This telerehabilitation clinic increased its workload from 1,676 patients with 3,016 encounters in FY 2012 to 9,136 patients with 11,834 encounters in FY 2017, accounting for 62.6% of total growth in patients and 56.8% of total growth in encounters.

Other clinics showing substantial growth over time included occupational therapy and polytrauma/TBI-individual secondary evaluation. Kinesiotherapy telerehabilitation was almost nonexistent in the VHA during FY 2012, with only 23 patients having 23 encounters. By FY 2017, there were 563 patients with 624 kinesiotherapy telerehabilitation encounters, equating to staggering increases in 5 years: 2,348% for patients and 2,613% for encounters. Similarly, the Physical Medicine and Rehabilitation Assistive Technology clinics had very low numbers in FY 2012 (patients, 2; encounters, 3) and increased over time; albeit, at a slow rate.

Trends by Rurality

Trends by rural location of patients and encounters must be interpreted with caution because of the changing rural definition between FY 2014 and FY 2015 (Tables 2 and 3; Figures 3 and 4). 

The increased total number of patients seen between FY 2012 and FY 2014 (old definition) was 225% for rural veterans vs 134% for urban veterans. Between FY 2015 and FY 2017 (new definition), the increase was lower for both groups (rural, 13.4%; urban, 7.3%), but rural veterans still increased at a higher rate than did urban dwellers.

Discussion

Our primary aim was to provide data on the growth of telerehabilitation in the VHA over the past 5 years. Our secondary aim was to examine growth in the use of telerehabilitation by rurality. Specifically, we provided an overview of telerehabilitation growth in terms of unique patients and overall encounters in the VHA by rurality from FY 2012 to FY 2014 and FY 2015 to FY 2017 using the following programs: Polytrauma/TBI, PM&R Service, physical therapy, occupational therapy, PM&R amputation clinic, amputation clinic, kinesiotherapy, and PM&R assistive technology clinic. Our findings demonstrated a noteworthy increase in telerehabilitation encounters and unique patients over time for these programs. These findings were consistent with the overall trend of continued growth and expansion of telehealth within the VHA.

Our findings reveal an upward trend in the total number of rural encounters and rural unique patients despite the change in the VA’s definition of rurality in FY 2015. To our knowledge, urban and rural use of telerehabilitation has not been examined previously. Under both definitions of rurality, encounters and unique patients show an important increase over time, and by year-end 2017, more than half of all patients and encounters were attributed to rural patients (53.7% and 53.9%, respectively). Indeed, the upward trend may have been more pronounced if the rural definition had not changed in FY 2015. Our early VHA stroke patients study on the difference between rural-urban patients and taxonomies showed that the RUCA definition was more likely to reduce the number of rural patients by 8.5% than the early definition used by the VHA.²⁶

It is notable that although the use of tele-delivery of rehabilitation has continually increased, the rate of this increase was steeper from FY 2012 to FY 2014 than FY 2015 to FY 2017. For the programs under consideration in this study, the total number of rural patients/encounters increased throughout the observed periods. However, urban patients and encounters increased through FY 2016 and experienced a slight decrease in FY 2017.

The appearance of a slower rate of increase may be due to a rapid initial rate of increase through early adopters and “crossing the diffusion chasm,” a well-documented process of slower diffusion between the time of invention to penetration that often characterizes the spread of successful telehealth innovations.²⁷ Integrating technology into care delivery innovation requires the integration of technical, clinical, and administrative processes and can take time to scale successfully.²⁸

With an emphasis on increasing access to rehabilitation services, the VHA can expect to see a continuing increase in both the number and the percentage of telerehabilitation rural patients and encounters. The VHA has several telerehabilitation initiatives underway through the VHA’s Physical Medicine and Rehabilitation Telerehabilitation Enterprise Wide Initiative (TREWI) and Rural Veterans Telerehabilitation Initiative. These projects demonstrate the feasibility of this delivery approach and facilitate integration of this modality in clinical workflows. However, to sustain these efforts, facilities will need more infrastructure and personnel resources dedicated to the delivery of services.

In an ongoing evaluation of the TREWI, several factors seem to influence the uptake of the VHA Office of Rural Health TREWI programs. These factors are the presence or absence of a local site champion; the quality of hospital leadership support; the quality of past relationships between telerehabilitation sending sites and receiving sites; barriers to getting a telehealth service agreement in place; the availability of space; administrative know-how on setting up clinics appropriately; time involved to bring on staff; contracting issues; equipment availability and installation; cultural issues in embracing technologic innovation; training burden; hassle factors; and limited funds. Although early adopters may be able to negotiate and push through many of the barriers associated with the diffusion of telerehabilitation, the numerous barriers may slow its larger systemwide diffusion.

Telerehabilitation is a promising mode to deliver care to rural veterans who otherwise may not have access to this type of specialty care. Therefore, the identification of elements that foster telerehabilitation growth in future investigations can assist policy makers and key stakeholders in optimally leveraging program resources for maximal productivity. Future studies investigating the drivers of increases in telerehabilitation growth by rurality are warranted. Furthermore, more research is needed to examine telerehabilitation growth quality of care outcomes (eg, patient and provider satisfaction) to ensure that care is not only timely and accessible, but of high quality.

Conclusion

Disparities between rural and urban veterans compel a mode of expanding delivery of care. The VHA has embraced the use of telehealth modalities to extend its reach of rehabilitation services to veterans with disability and rehabilitation needs. Growth in telerehabilitation rural patient encounters increases access to rehabilitative care, reduces patient and caregiver travel burden, and helps ensure treatment adherence. Telerehabilitation utilization (unique patients and total encounters) is growing more rapidly for rural veterans than for their urban counterparts. Overall, telerehabilitation is filling a gap for rural veterans, as well as veterans in general with challenges in accessibility to health care. In order to make full use of the telerehabilitation services across its health care system, VA health care facilities may need to expand their effort in telerehabilitation dissemination and education among providers and veterans, particularly among providers who are less familiar with telerehabilitation services and among veterans who live in rural or highly rural areas and need special rehabilitation care.

Historically, the Veterans Health Administration (VHA) has excelled at improving veterans’ access to health care and enhancing foundational services, such as prosthetics and other veteran-centric services, and this continues to be the VHA’s top priority.¹ Travel distance and time are often barriers to accessing health care for many veterans.^2-11 For veterans with disabilities who must overcome additional physical, cognitive, and emotional obstacles to access vital rehabilitation services, these geographic obstacles are magnified. Further compounding the challenge is that rehabilitation therapies frequently require multiple encounters. Telerehabilitation is a promising solution for veterans in need of rehabilitation to regain optimal functioning. This alternative mode of service delivery can help veterans overcome geographic access barriers by delivering health care directly to veterans in their homes or nearby community-based outpatient clinics.^12,13

A growing body of evidence supports telerehabilitation. In a 2017 systematic review and meta-analysis, Cottrell and colleagues reviewed and analyzed data from 13 studies that met their inclusion criteria; specifically, their meta-analytic sample comprised adults aged ≥ 18 years presenting with any diagnosed primary musculoskeletal condition; treatment interventions via a real-time telerehabilitation medium, trials that had a comparison group with the same condition; provided clinical outcomes data, and included published randomized and nonrandomized controlled trials.¹⁴ Based on their aggregated results, they concluded that real-time telerehabilitation was effective in improving physical function (standardized mean difference [SMD], 0.63; 95% CI, 0.92-2.33; I², 93%), and reducing pain (SMD, 0.66; 95% CI, −0.27- .60; I², 96%) in patients with any diagnosed primary musculoskeletal condition.¹⁴

Two other systematic reviews conducted by Pietrzak and colleagues and Agostini and colleagues also demonstrated the clinical effectiveness of telerehabilitation.^15,16 Clinical effectiveness was defined as changes in health, functional status, and satisfaction with the telerehabilitation services delivered. The studies examined in the review included those that provided online self-management and education in addition to exercise via teleconferencing in real time.

Pietrzak and colleagues found that Internet-based osteoarthritis self-management interventions significantly improved 4 of 6 health status measures reviewed (ie, pain, fatigue, activity limitation, health distress, disability, and self‐reported global health).¹⁵ User acceptance and satisfaction were high (≥ 70% satisfied) in all studies meeting the inclusion criteria.

Agostini and colleagues found that telerehabilitation was more effective than other modes of delivering rehabilitation to regain motor function in cardiac (SMD, 0.24; 95% CI, 0.04-0.43) and total knee arthroplasty (Timed Up and Go test: SMD, −5.17; 95% CI, −9.79- −0.55) patients.¹⁶ Some evidence from VHA and non-VHA studies also support the use of telerehabilitation to reduce health care costs,^17-19 improve treatment adherence,^12,20 and enhance patient physical, cognitive and mobility function, as well as patient satisfaction and health-related quality of life.^13,21-24

Since the first recorded use of telehealth in 1959, the application of technology to deliver health care, including rehabilitation services, has increased exponentially.¹⁴ In fiscal year (FY) 2017 alone, the VA provided > 2 million episodes of care for > 700,000 veterans using telehealth services.²⁵

Although the process for accessing telerehabilitation may vary throughout the VA, typically a few common factors make a veteran eligible for this mode of rehabilitation care delivery: Veterans must meet criteria for a specific program (eg, amputation, occupational therapy, and physical therapy) and receive VA care from a VA medical facility or clinic that offers telehealth services. Care providers must believe that the veteran would benefit from telerehabilitation (eg, limited mobility and long-distance travel to the facility) and that they would be able to receive an appropriate consult. The veteran must meet the following requirements: (1) willingness to consent to a visit via telehealth; (2) access to required equipment/e-mail; and (3) a caregiver to assist if they are unable to complete a visit independently.

In this article, we provide an overview of the growth of telerehabilitation in the VHA. Data are presented for specific telerehabilitation programs over time and by rurality.

Methods

The VHA Support Service Center works with VHA program offices and field users to provide field-focused business, clinical, and special topic reports. An online portal provides access to these customizable reports organized as data cubes, which represent data dimensions (ie, clinic type) and measures (ie, number of unique patients). For this study, we used the Connected Care, Telehealth, Call Centers Clinical Video Telehealth/Store and Forward Telehealth data cube clinical stop codes to identify the numbers of telerehabilitation veteran users and encounters across time. The following telerehabilitation clinic-stop codes were selected: 197 (polytrauma/traumatic brain injury [TBI]–individuals), 201 (Physical Medicine and Rehabilitation [PM&R] Service), 205 (physical therapy), 206 (occupational therapy), 211 (PM&R amputation clinic), 418 (amputation clinic), 214 (kinesiotherapy), and 240 (PM&R assistive technology clinic). Data for total unique patients served and the total number of encounters were extracted at the national level and by rurality from FY 2012 to FY 2017, providing the past 5 years of VHA telerehabilitation data.

It is important to note that in FY 2015, the VHA changed its definition of rurality to a rural-urban commuting areas (RUCA)-based system (www.ruralhealth.va.gov/rural-definition.asp). Prior to FY 2015, the VHA used the US Census Bureau (CB) urbanized area definitions. According to CB, an urbanized area contains a central city and surrounding area that totals > 50,000 in population. It also includes places outside of urbanized areas with populations > 2,500. Rural areas are defined as all other areas. VHA added a third category, highly rural, which is defined as areas that had < 7 people per square mile. In the RUCA system, each census tract defined by the CB is given a score. The VHA definitions are as follows:

• Urban (U)—census tracts with RUCA scores of 1.0 or 1.1. These tracts are determined by the CB as being in an urban core and having the majority of their workers commute within that same core (1.0). If 30% to 49% commute to an even larger urban core, then the code is 1.1;
• Rural (R)—all tracts not receiving scores in the urban or highly rural tiers; and
• Highly rural (H)—tracts with a RUCA score of 10.0. These are the most remote occupied land areas. Less than 10% of workers travel to CB-defined urbanized areas or urban clusters.

In addition, VHA recently added an “I” category to complement “U,” “R,” and “H.” The “I” value is assigned to veterans living on the US insular islands (ie, territories): Guam, American Samoa, Northern Marianas, and US Virgin Islands. For the analysis by rurality in this study, we excluded veterans living in the insular islands and those of unknown rurality (< 1.0% of patients and encounters). Further, because the numbers of highly rural veterans were relatively small (< 2% of patients and encounters), the rural and highly rural categories were combined and compared with urban-dwelling veterans.

Results

Overall, the workload for telerehabilitation nearly quadrupled over the 5-year period (Table 1 and Figure 1). 

Interesting trends were seen by clinic type. Some clinics increased substantially, whereas others showed only moderate increases, and in 1 case (PM&R Service), a decrease. For example, there is significant growth in the number of patients and encounters involving physical therapy through telerehabilitation. This telerehabilitation clinic increased its workload from 1,676 patients with 3,016 encounters in FY 2012 to 9,136 patients with 11,834 encounters in FY 2017, accounting for 62.6% of total growth in patients and 56.8% of total growth in encounters.

Other clinics showing substantial growth over time included occupational therapy and polytrauma/TBI-individual secondary evaluation. Kinesiotherapy telerehabilitation was almost nonexistent in the VHA during FY 2012, with only 23 patients having 23 encounters. By FY 2017, there were 563 patients with 624 kinesiotherapy telerehabilitation encounters, equating to staggering increases in 5 years: 2,348% for patients and 2,613% for encounters. Similarly, the Physical Medicine and Rehabilitation Assistive Technology clinics had very low numbers in FY 2012 (patients, 2; encounters, 3) and increased over time; albeit, at a slow rate.

Trends by Rurality

Trends by rural location of patients and encounters must be interpreted with caution because of the changing rural definition between FY 2014 and FY 2015 (Tables 2 and 3; Figures 3 and 4). 

The increased total number of patients seen between FY 2012 and FY 2014 (old definition) was 225% for rural veterans vs 134% for urban veterans. Between FY 2015 and FY 2017 (new definition), the increase was lower for both groups (rural, 13.4%; urban, 7.3%), but rural veterans still increased at a higher rate than did urban dwellers.

Discussion

Our primary aim was to provide data on the growth of telerehabilitation in the VHA over the past 5 years. Our secondary aim was to examine growth in the use of telerehabilitation by rurality. Specifically, we provided an overview of telerehabilitation growth in terms of unique patients and overall encounters in the VHA by rurality from FY 2012 to FY 2014 and FY 2015 to FY 2017 using the following programs: Polytrauma/TBI, PM&R Service, physical therapy, occupational therapy, PM&R amputation clinic, amputation clinic, kinesiotherapy, and PM&R assistive technology clinic. Our findings demonstrated a noteworthy increase in telerehabilitation encounters and unique patients over time for these programs. These findings were consistent with the overall trend of continued growth and expansion of telehealth within the VHA.

Our findings reveal an upward trend in the total number of rural encounters and rural unique patients despite the change in the VA’s definition of rurality in FY 2015. To our knowledge, urban and rural use of telerehabilitation has not been examined previously. Under both definitions of rurality, encounters and unique patients show an important increase over time, and by year-end 2017, more than half of all patients and encounters were attributed to rural patients (53.7% and 53.9%, respectively). Indeed, the upward trend may have been more pronounced if the rural definition had not changed in FY 2015. Our early VHA stroke patients study on the difference between rural-urban patients and taxonomies showed that the RUCA definition was more likely to reduce the number of rural patients by 8.5% than the early definition used by the VHA.²⁶

It is notable that although the use of tele-delivery of rehabilitation has continually increased, the rate of this increase was steeper from FY 2012 to FY 2014 than FY 2015 to FY 2017. For the programs under consideration in this study, the total number of rural patients/encounters increased throughout the observed periods. However, urban patients and encounters increased through FY 2016 and experienced a slight decrease in FY 2017.

The appearance of a slower rate of increase may be due to a rapid initial rate of increase through early adopters and “crossing the diffusion chasm,” a well-documented process of slower diffusion between the time of invention to penetration that often characterizes the spread of successful telehealth innovations.²⁷ Integrating technology into care delivery innovation requires the integration of technical, clinical, and administrative processes and can take time to scale successfully.²⁸

With an emphasis on increasing access to rehabilitation services, the VHA can expect to see a continuing increase in both the number and the percentage of telerehabilitation rural patients and encounters. The VHA has several telerehabilitation initiatives underway through the VHA’s Physical Medicine and Rehabilitation Telerehabilitation Enterprise Wide Initiative (TREWI) and Rural Veterans Telerehabilitation Initiative. These projects demonstrate the feasibility of this delivery approach and facilitate integration of this modality in clinical workflows. However, to sustain these efforts, facilities will need more infrastructure and personnel resources dedicated to the delivery of services.

In an ongoing evaluation of the TREWI, several factors seem to influence the uptake of the VHA Office of Rural Health TREWI programs. These factors are the presence or absence of a local site champion; the quality of hospital leadership support; the quality of past relationships between telerehabilitation sending sites and receiving sites; barriers to getting a telehealth service agreement in place; the availability of space; administrative know-how on setting up clinics appropriately; time involved to bring on staff; contracting issues; equipment availability and installation; cultural issues in embracing technologic innovation; training burden; hassle factors; and limited funds. Although early adopters may be able to negotiate and push through many of the barriers associated with the diffusion of telerehabilitation, the numerous barriers may slow its larger systemwide diffusion.

Telerehabilitation is a promising mode to deliver care to rural veterans who otherwise may not have access to this type of specialty care. Therefore, the identification of elements that foster telerehabilitation growth in future investigations can assist policy makers and key stakeholders in optimally leveraging program resources for maximal productivity. Future studies investigating the drivers of increases in telerehabilitation growth by rurality are warranted. Furthermore, more research is needed to examine telerehabilitation growth quality of care outcomes (eg, patient and provider satisfaction) to ensure that care is not only timely and accessible, but of high quality.

Conclusion

Disparities between rural and urban veterans compel a mode of expanding delivery of care. The VHA has embraced the use of telehealth modalities to extend its reach of rehabilitation services to veterans with disability and rehabilitation needs. Growth in telerehabilitation rural patient encounters increases access to rehabilitative care, reduces patient and caregiver travel burden, and helps ensure treatment adherence. Telerehabilitation utilization (unique patients and total encounters) is growing more rapidly for rural veterans than for their urban counterparts. Overall, telerehabilitation is filling a gap for rural veterans, as well as veterans in general with challenges in accessibility to health care. In order to make full use of the telerehabilitation services across its health care system, VA health care facilities may need to expand their effort in telerehabilitation dissemination and education among providers and veterans, particularly among providers who are less familiar with telerehabilitation services and among veterans who live in rural or highly rural areas and need special rehabilitation care.

Knee osteoarthritis (OA) affects almost 9.3 million adults in the US and accounts for $27 billion in annual health care expenses.^1,2 Due to the increasing cost of health care and an aging population, there has been renewed interest in establishing criteria for nonarthroplasty treatment of knee OA.

In 2013, using the RAND/UCLA Appropriateness method, the American Academy of Orthopaedic Surgeons (AAOS) developed an appropriate use criteria (AUC) for nonarthroplasty management of primary OA of the knee, based on orthopaedic literature and expert opinion.³ Interventions such as activity modification, weight loss, prescribed physical therapy, nonsteroidal anti-inflammatory drugs, tramadol, prescribed oral or transcutaneous opioids, acetaminophen, intra-articular corticosteroids, hinged or unloading knee braces, arthroscopic partial menisectomy or loose body removal, and realignment osteotomy were assessed. An algorithm was developed for 576 patients scenarios that incorporated patient-specific, prognostic/predictor variables to assign designations of “appropriate,” “may be appropriate,” or “rarely appropriate,” to treatment interventions.^4,5 An online version of the algorithm (orthoguidelines.org) is available for physicians and surgeons to judge appropriateness of nonarthroplasty treatments; however, it is not intended to mandate candidacy for treatment or intervention.

Clinical evaluation of the AAOS AUC is necessary to determine how treatment recommendations correlate with current practice. A recent examination of the AAOS Appropriateness System for Surgical Management of Knee OA found that prognostic/predictor variables, such as patient age, OA severity, and pattern of knee OA involvement were more heavily weighted when determining arthroplasty appropriateness than was pain severity or functional loss.⁶ Furthermore, non-AAOS AUC prognostic/predictor variables, such as race and gender, have been linked to disparities in utilization of knee OA interventions.^7-9 Such disparities can be costly not just from a patient perceptive, but also employer and societal perspectives.¹⁰

The Department of Veterans Affairs (VA) health care system represents a model of equal-access-to care system in the US that is ideal for examination of issues about health care utilization and any disparities within the AAOS AUC model and has previously been used to assess utilization of total knee arthroplasty.⁹ The aim of this study was to characterize utilization of the AAOS AUC for nonarthroplasty treatment of knee OA in a VA patient population. We asked the following questions: (1) What variables are predictive of receiving a greater number of AAOS AUC evaluated nonarthroplasty treatments? (2) What variables are predictive of receiving “rarely appropriate” AAOS AUC evaluated nonarthroplasty treatment? (3) What factors are predictive of duration of nonarthroplasty care until total knee arthroplasty (TKA)?

Methods

The institutional review board at the Louis Stokes Cleveland VA Medical Center in Ohio approved a retrospective chart review of nonarthroplasty treatments utilized by patients presenting to its orthopaedic section who subsequently underwent knee arthroplasty between 2013 and 2016. Eligibility criteria included patients aged ≥ 30 years with a diagnosis of unilateral or bilateral primary knee OA. Patients with posttraumatic OA, inflammatory arthritis, and a history of infectious arthritis or Charcot arthropathy of the knee were excluded. Patients with a body mass index (BMI) > 40 or a hemoglobin A_1c > 8.0 at presentation were excluded as nonarthroplasty care was the recommended course of treatment above these thresholds.

Data collected included race, gender, duration of nonarthroplasty treatment, BMI, and Kellgren-Lawrence classification of knee OA at time of presentation for symptomatic knee OA.¹¹ All AAOS AUC-evaluated nonarthroplasty treatments utilized prior to arthroplasty intervention also were recorded (Table 1). 

Statistical Analysis

Statistical analysis was completed with GraphPad Software Prism 7.0a (La Jolla, CA) and Mathworks MatLab R2016b software (Natick, MA). Univariate analysis with Student t tests with Welch corrections in the setting of unequal variance, Mann-Whitney nonparametric tests, and Fisher exact test were generated in the appropriate setting. Multivariable analyses also were conducted. For continuous outcomes, stepwise multiple linear regression was used to generate predictive models; for binary outcomes, binomial logistic regression was used.

Factors analyzed in regression modeling for the total number of AAOS AUC evaluated nonarthroplasty treatments utilized and the likelihood of receiving a rarely appropriate treatment included gender, race, function-limiting pain, range of motion (ROM), ligamentous instability, arthritis pattern, limb alignment, mechanical symptoms, BMI, age, and Kellgren-Lawrence grade. Factors analyzed in timing of TKA included the above variables plus the total number of AUC interventions, whether the patient received an inappropriate intervention, and average appropriateness of the interventions received. Residual analysis with Cook’s distance was used to identify outliers in regression. Observations with Cook’s distance > 3 times the mean Cook’s distance were identified as potential outliers, and models were adjusted accordingly. All statistical analyses were 2-tailed. Statistical significance was set to P ≤ .05 for all outputs.

Results

In the study, 97.8% of participants identified as male, and the mean age was 62.8 years (Table 3). 

Appropriate Use Criteria Interventions

Patients received a mean of 5.2 AAOS AUC evaluated interventions before undergoing arthroplasty management at a mean of 32.3 months (range 2-181 months) from initial presentation. The majority of these interventions were classified as either appropriate or may be appropriate, according to the AUC definitions (95.1%). Self-management and physical therapy programs were widely utilized (100% and 90.1%, respectively), with all use of these interventions classified as appropriate.

Hinged or unloader knee braces were utilized in about half the study patients; this intervention was classified as rarely appropriate in 4.4% of these patients. Medical therapy was also widely used, with all use of NSAIDs, acetaminophen, and tramadol classified as appropriate or may be appropriate. Oral or transcutaneous opioid medications were prescribed in 14.3% of patients, with 92.3% of this use classified as rarely appropriate. Although the opioid medication prescribing provider was not specifically evaluated, there were no instances in which the orthopaedic service provided an oral or transcutaneous opioid prescriptions. Procedural interventions, with the exception of corticosteroid injections, were uncommon; no patient received realignment osteotomy, and only 12.1% of patients underwent arthroscopy. The use of arthroscopy was deemed rarely appropriate in 72.7% of these cases.

Factors Associated With AAOS AUC Intervention Use

There was no difference in the number of AAOS AUC evaluated interventions received based on BMI (mean [SD] BMI < 35, 5.2 [1.0] vs BMI ≥ 35, 5.3 [1.1], P = .49), age (mean [SD] aged < 60 years, 5.4 [1.0] vs aged ≥ 60 years, 5.1 [1.2], P = .23), or Kellgren-Lawrence arthritic grade (mean [SD] grade ≤ 2, 5.5 [1.0] vs grade > 2, 5.1 [1.1], P = .06). These variables also were not associated with receiving a rarely appropriate intervention (mean [SD] BMI < 35, 0.27 [0.5] vs BMI > 35, 0.2 [0.4], P = .81; aged > 60 years, 0.3 [0.5] vs aged < 60 years, 0.2 [0.4], P = .26; Kellgren-Lawrence grade < 2, 0.4 [0.6] vs grade > 2, 0.2 [0.4], P = .1).

Regression modeling to predict total number of AAOS AUC evaluated interventions received produced a significant model (R² = 0.111, P = .006). The presence of ligamentous instability (β coefficient, -1.61) and the absence of mechanical symptoms (β coefficient, -0.67) were negative predictors of number of AUC interventions received. Variance inflation factors were 1.014 and 1.012, respectively. Likewise, regression modeling to identify factors predictive of receiving a rarely appropriate intervention also produced a significant model (pseudo R²= 0.06, P = .025), with lower Kellgren-Lawrence grade the only significant predictor of receiving a rarely appropriate intervention (odds ratio [OR] 0.54; 95% CI, 0.42 -0.72, per unit increase).

Timing from presentation to arthroplasty intervention was also evaluated. Age was a negative predictor (β coefficient -1.61), while positive predictors were reduced ROM (β coefficient 15.72) and having more AUC interventions (β coefficient 7.31) (model R²= 0.29, P = < .001). Age was the most significant predictor. Variance inflations factors were 1.02, 1.01, and 1.03, respectively. Receiving a rarely appropriate intervention was not associated with TKA timing.

Discussion

This single-center retrospective study examined the utilization of AAOS AUC-evaluated nonarthroplasty interventions for symptomatic knee OA prior to TKA. The aims of this study were to validate the AAOS AUC in a clinical setting and identify predictors of AAOS AUC utilization. In particular, this study focused on the number of interventions utilized prior to knee arthroplasty, whether interventions receiving a designation of rarely appropriate were used, and the duration of nonarthroplasty treatment.

Patients with knee instability used fewer total AAOS AUC evaluated interventions prior to TKA. Subjective instability has been reported as high as 27% in patients with OA and has been associated with fear of falling, poor balance confidence, activity limitations, and lower Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) physical function scores.¹² However, it has not been found to correlate with knee laxity.¹³ Nevertheless, significant functional impairment with the risk of falling may reduce the number of nonarthroplasty interventions attempted. On the other hand, the presence of mechanical symptoms resulted in greater utilization of nonarthroplasty interventions. This is likely due to the greater utilization of arthroscopic partial menisectomy or loose body removal in this group of patients. Despite its inclusion as an AAOS AUC evaluated intervention, arthroscopy remains a contentious treatment for symptomatic knee pain in the setting of OA.^14,15

For every unit decrease in Kellgren-Lawrence OA grade, patients were 54% more likely to receive a rarely appropriate intervention prior to knee arthroplasty. This is supported by the recent literature examining the AAOS AUC for surgical management of knee OA. Riddle and colleagues developed a classification tree to determine the contributions of various prognostic variables in final classifications of the 864 clinical vignettes used to develop the appropriateness algorithm and found that OA severity was strongly favored, with only 4 of the 432 vignettes with severe knee OA judged as rarely appropriate for surgical intervention.⁶

Our findings, too, may be explained by an AAOS AUC system that too heavily weighs radiographic severity of knee OA, resulting in more frequent rarely appropriate interventions in patients with less severe arthritis, including nonarthroplasty treatments. It is likely that rarely appropriate interventions were attempted in this subset of our study cohort based on patient’s subjective symptoms and functional status, both of which have been shown to be discordant with radiographic severity of knee OA.¹⁶

Oral or transcutaneous prescribed opioid medications were the most frequent intervention that received a rarely appropriate designation. Patients with preoperative opioid use undergoing TKA have been shown to have a greater risk for postoperative complications and longer hospital stay, particularly those patients aged < 75 years. Younger age, use of more interventions, and decreased knee ROM at presentation were predictive of longer duration of nonarthroplasty treatment. The use of more AAOS AUC evaluated interventions in these patients suggests that the AAOS AUC model may effectively be used to manage symptomatic OA, increasing the time from presentation to knee arthroplasty.

Interestingly, the use of rarely appropriate interventions did not affect TKA timing, as would be expected in a clinically effective nonarthroplasty treatment model. The reasons for rarely appropriate nonsurgical interventions are complex and require further investigation. One possible explanation is that decreased ROM was a marker for mechanical symptoms that necessitated additional intervention in the form of knee arthroscopy, delaying time to TKA.

Limitations

There are several limitations of this study. First, the small sample size (N = 90) requires acknowledgment; however, this limitation reflects the difficulty in following patients for years prior to an operative intervention. Second, the study population consists of veterans using the VA system and may not be reflective of the general population, differing with respect to gender, racial, and socioeconomic factors. Nevertheless, studies examining TKA utilization found, aside from racial and ethnic variability, patient gender and age do not affect arthroplasty utilization rate in the VA system.¹⁷

Additional limitations stem from the retrospective nature of this study. While the Computerized Patient Record System and centralized care of the VA system allows for review of all physical therapy consultations, orthotic consultations, and medications within the VA system, any treatments and intervention delivered by non-VA providers were not captured. Furthermore, the ability to assess for confounding variables limiting the prescription of certain medications, such as chronic kidney disease with NSAIDs or liver disease with acetaminophen, was limited by our study design.

Although our study suffers from selection bias with respect to examination of nonarthroplasty treatment in patients who have ultimately undergone TKA, we feel that this subset of patients with symptomatic knee OA represents the majority of patients evaluated for knee OA by orthopaedic surgeons in the clinic setting. It should be noted that although realignment osteotomies were sometimes indicated as appropriate by AAOS AUC model in our study population, this intervention was never performed due to patient and surgeon preference. Additionally, although it is not an AAOS AUC evaluated intervention, viscosupplementation was sporadically used during the study period; however, it is now off formulary at the investigation institution.

Conclusion

Our study suggests that patients without knee instability use more nonarthroplasty treatments over a longer period before TKA, and those patients with less severe knee OA are at risk of receiving an intervention judged to be rarely appropriate by the AAOS AUC. Such interventions do not affect timing of TKA. Nonarthroplasty care should be individualized to patients’ needs, and the decision to proceed with arthroplasty should be considered only after exhausting appropriate conservative measures. We recommend that providers use the AAOS AUC, especially when treating younger patients with less severe knee OA, particularly if considering opiate therapy or knee arthroscopy.

Acknowledgments
The authors would like to acknowledge Patrick Getty, MD, for his surgical care of some of the study patients. This material is the result of work supported with resources and the use of facilities at the Louis Stokes Cleveland VA Medical Center in Ohio.

Knee osteoarthritis (OA) affects almost 9.3 million adults in the US and accounts for $27 billion in annual health care expenses.^1,2 Due to the increasing cost of health care and an aging population, there has been renewed interest in establishing criteria for nonarthroplasty treatment of knee OA.

In 2013, using the RAND/UCLA Appropriateness method, the American Academy of Orthopaedic Surgeons (AAOS) developed an appropriate use criteria (AUC) for nonarthroplasty management of primary OA of the knee, based on orthopaedic literature and expert opinion.³ Interventions such as activity modification, weight loss, prescribed physical therapy, nonsteroidal anti-inflammatory drugs, tramadol, prescribed oral or transcutaneous opioids, acetaminophen, intra-articular corticosteroids, hinged or unloading knee braces, arthroscopic partial menisectomy or loose body removal, and realignment osteotomy were assessed. An algorithm was developed for 576 patients scenarios that incorporated patient-specific, prognostic/predictor variables to assign designations of “appropriate,” “may be appropriate,” or “rarely appropriate,” to treatment interventions.^4,5 An online version of the algorithm (orthoguidelines.org) is available for physicians and surgeons to judge appropriateness of nonarthroplasty treatments; however, it is not intended to mandate candidacy for treatment or intervention.

Clinical evaluation of the AAOS AUC is necessary to determine how treatment recommendations correlate with current practice. A recent examination of the AAOS Appropriateness System for Surgical Management of Knee OA found that prognostic/predictor variables, such as patient age, OA severity, and pattern of knee OA involvement were more heavily weighted when determining arthroplasty appropriateness than was pain severity or functional loss.⁶ Furthermore, non-AAOS AUC prognostic/predictor variables, such as race and gender, have been linked to disparities in utilization of knee OA interventions.^7-9 Such disparities can be costly not just from a patient perceptive, but also employer and societal perspectives.¹⁰

The Department of Veterans Affairs (VA) health care system represents a model of equal-access-to care system in the US that is ideal for examination of issues about health care utilization and any disparities within the AAOS AUC model and has previously been used to assess utilization of total knee arthroplasty.⁹ The aim of this study was to characterize utilization of the AAOS AUC for nonarthroplasty treatment of knee OA in a VA patient population. We asked the following questions: (1) What variables are predictive of receiving a greater number of AAOS AUC evaluated nonarthroplasty treatments? (2) What variables are predictive of receiving “rarely appropriate” AAOS AUC evaluated nonarthroplasty treatment? (3) What factors are predictive of duration of nonarthroplasty care until total knee arthroplasty (TKA)?

Methods

The institutional review board at the Louis Stokes Cleveland VA Medical Center in Ohio approved a retrospective chart review of nonarthroplasty treatments utilized by patients presenting to its orthopaedic section who subsequently underwent knee arthroplasty between 2013 and 2016. Eligibility criteria included patients aged ≥ 30 years with a diagnosis of unilateral or bilateral primary knee OA. Patients with posttraumatic OA, inflammatory arthritis, and a history of infectious arthritis or Charcot arthropathy of the knee were excluded. Patients with a body mass index (BMI) > 40 or a hemoglobin A_1c > 8.0 at presentation were excluded as nonarthroplasty care was the recommended course of treatment above these thresholds.

Data collected included race, gender, duration of nonarthroplasty treatment, BMI, and Kellgren-Lawrence classification of knee OA at time of presentation for symptomatic knee OA.¹¹ All AAOS AUC-evaluated nonarthroplasty treatments utilized prior to arthroplasty intervention also were recorded (Table 1). 

Statistical Analysis

Statistical analysis was completed with GraphPad Software Prism 7.0a (La Jolla, CA) and Mathworks MatLab R2016b software (Natick, MA). Univariate analysis with Student t tests with Welch corrections in the setting of unequal variance, Mann-Whitney nonparametric tests, and Fisher exact test were generated in the appropriate setting. Multivariable analyses also were conducted. For continuous outcomes, stepwise multiple linear regression was used to generate predictive models; for binary outcomes, binomial logistic regression was used.

Factors analyzed in regression modeling for the total number of AAOS AUC evaluated nonarthroplasty treatments utilized and the likelihood of receiving a rarely appropriate treatment included gender, race, function-limiting pain, range of motion (ROM), ligamentous instability, arthritis pattern, limb alignment, mechanical symptoms, BMI, age, and Kellgren-Lawrence grade. Factors analyzed in timing of TKA included the above variables plus the total number of AUC interventions, whether the patient received an inappropriate intervention, and average appropriateness of the interventions received. Residual analysis with Cook’s distance was used to identify outliers in regression. Observations with Cook’s distance > 3 times the mean Cook’s distance were identified as potential outliers, and models were adjusted accordingly. All statistical analyses were 2-tailed. Statistical significance was set to P ≤ .05 for all outputs.

Results

In the study, 97.8% of participants identified as male, and the mean age was 62.8 years (Table 3). 

Appropriate Use Criteria Interventions

Patients received a mean of 5.2 AAOS AUC evaluated interventions before undergoing arthroplasty management at a mean of 32.3 months (range 2-181 months) from initial presentation. The majority of these interventions were classified as either appropriate or may be appropriate, according to the AUC definitions (95.1%). Self-management and physical therapy programs were widely utilized (100% and 90.1%, respectively), with all use of these interventions classified as appropriate.

Hinged or unloader knee braces were utilized in about half the study patients; this intervention was classified as rarely appropriate in 4.4% of these patients. Medical therapy was also widely used, with all use of NSAIDs, acetaminophen, and tramadol classified as appropriate or may be appropriate. Oral or transcutaneous opioid medications were prescribed in 14.3% of patients, with 92.3% of this use classified as rarely appropriate. Although the opioid medication prescribing provider was not specifically evaluated, there were no instances in which the orthopaedic service provided an oral or transcutaneous opioid prescriptions. Procedural interventions, with the exception of corticosteroid injections, were uncommon; no patient received realignment osteotomy, and only 12.1% of patients underwent arthroscopy. The use of arthroscopy was deemed rarely appropriate in 72.7% of these cases.

Factors Associated With AAOS AUC Intervention Use

There was no difference in the number of AAOS AUC evaluated interventions received based on BMI (mean [SD] BMI < 35, 5.2 [1.0] vs BMI ≥ 35, 5.3 [1.1], P = .49), age (mean [SD] aged < 60 years, 5.4 [1.0] vs aged ≥ 60 years, 5.1 [1.2], P = .23), or Kellgren-Lawrence arthritic grade (mean [SD] grade ≤ 2, 5.5 [1.0] vs grade > 2, 5.1 [1.1], P = .06). These variables also were not associated with receiving a rarely appropriate intervention (mean [SD] BMI < 35, 0.27 [0.5] vs BMI > 35, 0.2 [0.4], P = .81; aged > 60 years, 0.3 [0.5] vs aged < 60 years, 0.2 [0.4], P = .26; Kellgren-Lawrence grade < 2, 0.4 [0.6] vs grade > 2, 0.2 [0.4], P = .1).

Regression modeling to predict total number of AAOS AUC evaluated interventions received produced a significant model (R² = 0.111, P = .006). The presence of ligamentous instability (β coefficient, -1.61) and the absence of mechanical symptoms (β coefficient, -0.67) were negative predictors of number of AUC interventions received. Variance inflation factors were 1.014 and 1.012, respectively. Likewise, regression modeling to identify factors predictive of receiving a rarely appropriate intervention also produced a significant model (pseudo R²= 0.06, P = .025), with lower Kellgren-Lawrence grade the only significant predictor of receiving a rarely appropriate intervention (odds ratio [OR] 0.54; 95% CI, 0.42 -0.72, per unit increase).

Timing from presentation to arthroplasty intervention was also evaluated. Age was a negative predictor (β coefficient -1.61), while positive predictors were reduced ROM (β coefficient 15.72) and having more AUC interventions (β coefficient 7.31) (model R²= 0.29, P = < .001). Age was the most significant predictor. Variance inflations factors were 1.02, 1.01, and 1.03, respectively. Receiving a rarely appropriate intervention was not associated with TKA timing.

Discussion

This single-center retrospective study examined the utilization of AAOS AUC-evaluated nonarthroplasty interventions for symptomatic knee OA prior to TKA. The aims of this study were to validate the AAOS AUC in a clinical setting and identify predictors of AAOS AUC utilization. In particular, this study focused on the number of interventions utilized prior to knee arthroplasty, whether interventions receiving a designation of rarely appropriate were used, and the duration of nonarthroplasty treatment.

Patients with knee instability used fewer total AAOS AUC evaluated interventions prior to TKA. Subjective instability has been reported as high as 27% in patients with OA and has been associated with fear of falling, poor balance confidence, activity limitations, and lower Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) physical function scores.¹² However, it has not been found to correlate with knee laxity.¹³ Nevertheless, significant functional impairment with the risk of falling may reduce the number of nonarthroplasty interventions attempted. On the other hand, the presence of mechanical symptoms resulted in greater utilization of nonarthroplasty interventions. This is likely due to the greater utilization of arthroscopic partial menisectomy or loose body removal in this group of patients. Despite its inclusion as an AAOS AUC evaluated intervention, arthroscopy remains a contentious treatment for symptomatic knee pain in the setting of OA.^14,15

For every unit decrease in Kellgren-Lawrence OA grade, patients were 54% more likely to receive a rarely appropriate intervention prior to knee arthroplasty. This is supported by the recent literature examining the AAOS AUC for surgical management of knee OA. Riddle and colleagues developed a classification tree to determine the contributions of various prognostic variables in final classifications of the 864 clinical vignettes used to develop the appropriateness algorithm and found that OA severity was strongly favored, with only 4 of the 432 vignettes with severe knee OA judged as rarely appropriate for surgical intervention.⁶

Our findings, too, may be explained by an AAOS AUC system that too heavily weighs radiographic severity of knee OA, resulting in more frequent rarely appropriate interventions in patients with less severe arthritis, including nonarthroplasty treatments. It is likely that rarely appropriate interventions were attempted in this subset of our study cohort based on patient’s subjective symptoms and functional status, both of which have been shown to be discordant with radiographic severity of knee OA.¹⁶

Oral or transcutaneous prescribed opioid medications were the most frequent intervention that received a rarely appropriate designation. Patients with preoperative opioid use undergoing TKA have been shown to have a greater risk for postoperative complications and longer hospital stay, particularly those patients aged < 75 years. Younger age, use of more interventions, and decreased knee ROM at presentation were predictive of longer duration of nonarthroplasty treatment. The use of more AAOS AUC evaluated interventions in these patients suggests that the AAOS AUC model may effectively be used to manage symptomatic OA, increasing the time from presentation to knee arthroplasty.

Interestingly, the use of rarely appropriate interventions did not affect TKA timing, as would be expected in a clinically effective nonarthroplasty treatment model. The reasons for rarely appropriate nonsurgical interventions are complex and require further investigation. One possible explanation is that decreased ROM was a marker for mechanical symptoms that necessitated additional intervention in the form of knee arthroscopy, delaying time to TKA.

Limitations

There are several limitations of this study. First, the small sample size (N = 90) requires acknowledgment; however, this limitation reflects the difficulty in following patients for years prior to an operative intervention. Second, the study population consists of veterans using the VA system and may not be reflective of the general population, differing with respect to gender, racial, and socioeconomic factors. Nevertheless, studies examining TKA utilization found, aside from racial and ethnic variability, patient gender and age do not affect arthroplasty utilization rate in the VA system.¹⁷

Additional limitations stem from the retrospective nature of this study. While the Computerized Patient Record System and centralized care of the VA system allows for review of all physical therapy consultations, orthotic consultations, and medications within the VA system, any treatments and intervention delivered by non-VA providers were not captured. Furthermore, the ability to assess for confounding variables limiting the prescription of certain medications, such as chronic kidney disease with NSAIDs or liver disease with acetaminophen, was limited by our study design.

Although our study suffers from selection bias with respect to examination of nonarthroplasty treatment in patients who have ultimately undergone TKA, we feel that this subset of patients with symptomatic knee OA represents the majority of patients evaluated for knee OA by orthopaedic surgeons in the clinic setting. It should be noted that although realignment osteotomies were sometimes indicated as appropriate by AAOS AUC model in our study population, this intervention was never performed due to patient and surgeon preference. Additionally, although it is not an AAOS AUC evaluated intervention, viscosupplementation was sporadically used during the study period; however, it is now off formulary at the investigation institution.

Conclusion

Our study suggests that patients without knee instability use more nonarthroplasty treatments over a longer period before TKA, and those patients with less severe knee OA are at risk of receiving an intervention judged to be rarely appropriate by the AAOS AUC. Such interventions do not affect timing of TKA. Nonarthroplasty care should be individualized to patients’ needs, and the decision to proceed with arthroplasty should be considered only after exhausting appropriate conservative measures. We recommend that providers use the AAOS AUC, especially when treating younger patients with less severe knee OA, particularly if considering opiate therapy or knee arthroscopy.

Acknowledgments
The authors would like to acknowledge Patrick Getty, MD, for his surgical care of some of the study patients. This material is the result of work supported with resources and the use of facilities at the Louis Stokes Cleveland VA Medical Center in Ohio.

Knee osteoarthritis (OA) affects almost 9.3 million adults in the US and accounts for $27 billion in annual health care expenses.^1,2 Due to the increasing cost of health care and an aging population, there has been renewed interest in establishing criteria for nonarthroplasty treatment of knee OA.

In 2013, using the RAND/UCLA Appropriateness method, the American Academy of Orthopaedic Surgeons (AAOS) developed an appropriate use criteria (AUC) for nonarthroplasty management of primary OA of the knee, based on orthopaedic literature and expert opinion.³ Interventions such as activity modification, weight loss, prescribed physical therapy, nonsteroidal anti-inflammatory drugs, tramadol, prescribed oral or transcutaneous opioids, acetaminophen, intra-articular corticosteroids, hinged or unloading knee braces, arthroscopic partial menisectomy or loose body removal, and realignment osteotomy were assessed. An algorithm was developed for 576 patients scenarios that incorporated patient-specific, prognostic/predictor variables to assign designations of “appropriate,” “may be appropriate,” or “rarely appropriate,” to treatment interventions.^4,5 An online version of the algorithm (orthoguidelines.org) is available for physicians and surgeons to judge appropriateness of nonarthroplasty treatments; however, it is not intended to mandate candidacy for treatment or intervention.

Clinical evaluation of the AAOS AUC is necessary to determine how treatment recommendations correlate with current practice. A recent examination of the AAOS Appropriateness System for Surgical Management of Knee OA found that prognostic/predictor variables, such as patient age, OA severity, and pattern of knee OA involvement were more heavily weighted when determining arthroplasty appropriateness than was pain severity or functional loss.⁶ Furthermore, non-AAOS AUC prognostic/predictor variables, such as race and gender, have been linked to disparities in utilization of knee OA interventions.^7-9 Such disparities can be costly not just from a patient perceptive, but also employer and societal perspectives.¹⁰

The Department of Veterans Affairs (VA) health care system represents a model of equal-access-to care system in the US that is ideal for examination of issues about health care utilization and any disparities within the AAOS AUC model and has previously been used to assess utilization of total knee arthroplasty.⁹ The aim of this study was to characterize utilization of the AAOS AUC for nonarthroplasty treatment of knee OA in a VA patient population. We asked the following questions: (1) What variables are predictive of receiving a greater number of AAOS AUC evaluated nonarthroplasty treatments? (2) What variables are predictive of receiving “rarely appropriate” AAOS AUC evaluated nonarthroplasty treatment? (3) What factors are predictive of duration of nonarthroplasty care until total knee arthroplasty (TKA)?

Methods

The institutional review board at the Louis Stokes Cleveland VA Medical Center in Ohio approved a retrospective chart review of nonarthroplasty treatments utilized by patients presenting to its orthopaedic section who subsequently underwent knee arthroplasty between 2013 and 2016. Eligibility criteria included patients aged ≥ 30 years with a diagnosis of unilateral or bilateral primary knee OA. Patients with posttraumatic OA, inflammatory arthritis, and a history of infectious arthritis or Charcot arthropathy of the knee were excluded. Patients with a body mass index (BMI) > 40 or a hemoglobin A_1c > 8.0 at presentation were excluded as nonarthroplasty care was the recommended course of treatment above these thresholds.

Data collected included race, gender, duration of nonarthroplasty treatment, BMI, and Kellgren-Lawrence classification of knee OA at time of presentation for symptomatic knee OA.¹¹ All AAOS AUC-evaluated nonarthroplasty treatments utilized prior to arthroplasty intervention also were recorded (Table 1). 

Statistical Analysis

Statistical analysis was completed with GraphPad Software Prism 7.0a (La Jolla, CA) and Mathworks MatLab R2016b software (Natick, MA). Univariate analysis with Student t tests with Welch corrections in the setting of unequal variance, Mann-Whitney nonparametric tests, and Fisher exact test were generated in the appropriate setting. Multivariable analyses also were conducted. For continuous outcomes, stepwise multiple linear regression was used to generate predictive models; for binary outcomes, binomial logistic regression was used.

Factors analyzed in regression modeling for the total number of AAOS AUC evaluated nonarthroplasty treatments utilized and the likelihood of receiving a rarely appropriate treatment included gender, race, function-limiting pain, range of motion (ROM), ligamentous instability, arthritis pattern, limb alignment, mechanical symptoms, BMI, age, and Kellgren-Lawrence grade. Factors analyzed in timing of TKA included the above variables plus the total number of AUC interventions, whether the patient received an inappropriate intervention, and average appropriateness of the interventions received. Residual analysis with Cook’s distance was used to identify outliers in regression. Observations with Cook’s distance > 3 times the mean Cook’s distance were identified as potential outliers, and models were adjusted accordingly. All statistical analyses were 2-tailed. Statistical significance was set to P ≤ .05 for all outputs.

Results

In the study, 97.8% of participants identified as male, and the mean age was 62.8 years (Table 3). 

Appropriate Use Criteria Interventions

Patients received a mean of 5.2 AAOS AUC evaluated interventions before undergoing arthroplasty management at a mean of 32.3 months (range 2-181 months) from initial presentation. The majority of these interventions were classified as either appropriate or may be appropriate, according to the AUC definitions (95.1%). Self-management and physical therapy programs were widely utilized (100% and 90.1%, respectively), with all use of these interventions classified as appropriate.

Hinged or unloader knee braces were utilized in about half the study patients; this intervention was classified as rarely appropriate in 4.4% of these patients. Medical therapy was also widely used, with all use of NSAIDs, acetaminophen, and tramadol classified as appropriate or may be appropriate. Oral or transcutaneous opioid medications were prescribed in 14.3% of patients, with 92.3% of this use classified as rarely appropriate. Although the opioid medication prescribing provider was not specifically evaluated, there were no instances in which the orthopaedic service provided an oral or transcutaneous opioid prescriptions. Procedural interventions, with the exception of corticosteroid injections, were uncommon; no patient received realignment osteotomy, and only 12.1% of patients underwent arthroscopy. The use of arthroscopy was deemed rarely appropriate in 72.7% of these cases.

Factors Associated With AAOS AUC Intervention Use

There was no difference in the number of AAOS AUC evaluated interventions received based on BMI (mean [SD] BMI < 35, 5.2 [1.0] vs BMI ≥ 35, 5.3 [1.1], P = .49), age (mean [SD] aged < 60 years, 5.4 [1.0] vs aged ≥ 60 years, 5.1 [1.2], P = .23), or Kellgren-Lawrence arthritic grade (mean [SD] grade ≤ 2, 5.5 [1.0] vs grade > 2, 5.1 [1.1], P = .06). These variables also were not associated with receiving a rarely appropriate intervention (mean [SD] BMI < 35, 0.27 [0.5] vs BMI > 35, 0.2 [0.4], P = .81; aged > 60 years, 0.3 [0.5] vs aged < 60 years, 0.2 [0.4], P = .26; Kellgren-Lawrence grade < 2, 0.4 [0.6] vs grade > 2, 0.2 [0.4], P = .1).

Regression modeling to predict total number of AAOS AUC evaluated interventions received produced a significant model (R² = 0.111, P = .006). The presence of ligamentous instability (β coefficient, -1.61) and the absence of mechanical symptoms (β coefficient, -0.67) were negative predictors of number of AUC interventions received. Variance inflation factors were 1.014 and 1.012, respectively. Likewise, regression modeling to identify factors predictive of receiving a rarely appropriate intervention also produced a significant model (pseudo R²= 0.06, P = .025), with lower Kellgren-Lawrence grade the only significant predictor of receiving a rarely appropriate intervention (odds ratio [OR] 0.54; 95% CI, 0.42 -0.72, per unit increase).

Timing from presentation to arthroplasty intervention was also evaluated. Age was a negative predictor (β coefficient -1.61), while positive predictors were reduced ROM (β coefficient 15.72) and having more AUC interventions (β coefficient 7.31) (model R²= 0.29, P = < .001). Age was the most significant predictor. Variance inflations factors were 1.02, 1.01, and 1.03, respectively. Receiving a rarely appropriate intervention was not associated with TKA timing.

Discussion

This single-center retrospective study examined the utilization of AAOS AUC-evaluated nonarthroplasty interventions for symptomatic knee OA prior to TKA. The aims of this study were to validate the AAOS AUC in a clinical setting and identify predictors of AAOS AUC utilization. In particular, this study focused on the number of interventions utilized prior to knee arthroplasty, whether interventions receiving a designation of rarely appropriate were used, and the duration of nonarthroplasty treatment.

Patients with knee instability used fewer total AAOS AUC evaluated interventions prior to TKA. Subjective instability has been reported as high as 27% in patients with OA and has been associated with fear of falling, poor balance confidence, activity limitations, and lower Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) physical function scores.¹² However, it has not been found to correlate with knee laxity.¹³ Nevertheless, significant functional impairment with the risk of falling may reduce the number of nonarthroplasty interventions attempted. On the other hand, the presence of mechanical symptoms resulted in greater utilization of nonarthroplasty interventions. This is likely due to the greater utilization of arthroscopic partial menisectomy or loose body removal in this group of patients. Despite its inclusion as an AAOS AUC evaluated intervention, arthroscopy remains a contentious treatment for symptomatic knee pain in the setting of OA.^14,15

For every unit decrease in Kellgren-Lawrence OA grade, patients were 54% more likely to receive a rarely appropriate intervention prior to knee arthroplasty. This is supported by the recent literature examining the AAOS AUC for surgical management of knee OA. Riddle and colleagues developed a classification tree to determine the contributions of various prognostic variables in final classifications of the 864 clinical vignettes used to develop the appropriateness algorithm and found that OA severity was strongly favored, with only 4 of the 432 vignettes with severe knee OA judged as rarely appropriate for surgical intervention.⁶

Our findings, too, may be explained by an AAOS AUC system that too heavily weighs radiographic severity of knee OA, resulting in more frequent rarely appropriate interventions in patients with less severe arthritis, including nonarthroplasty treatments. It is likely that rarely appropriate interventions were attempted in this subset of our study cohort based on patient’s subjective symptoms and functional status, both of which have been shown to be discordant with radiographic severity of knee OA.¹⁶

Oral or transcutaneous prescribed opioid medications were the most frequent intervention that received a rarely appropriate designation. Patients with preoperative opioid use undergoing TKA have been shown to have a greater risk for postoperative complications and longer hospital stay, particularly those patients aged < 75 years. Younger age, use of more interventions, and decreased knee ROM at presentation were predictive of longer duration of nonarthroplasty treatment. The use of more AAOS AUC evaluated interventions in these patients suggests that the AAOS AUC model may effectively be used to manage symptomatic OA, increasing the time from presentation to knee arthroplasty.

Interestingly, the use of rarely appropriate interventions did not affect TKA timing, as would be expected in a clinically effective nonarthroplasty treatment model. The reasons for rarely appropriate nonsurgical interventions are complex and require further investigation. One possible explanation is that decreased ROM was a marker for mechanical symptoms that necessitated additional intervention in the form of knee arthroscopy, delaying time to TKA.

Limitations

There are several limitations of this study. First, the small sample size (N = 90) requires acknowledgment; however, this limitation reflects the difficulty in following patients for years prior to an operative intervention. Second, the study population consists of veterans using the VA system and may not be reflective of the general population, differing with respect to gender, racial, and socioeconomic factors. Nevertheless, studies examining TKA utilization found, aside from racial and ethnic variability, patient gender and age do not affect arthroplasty utilization rate in the VA system.¹⁷

Additional limitations stem from the retrospective nature of this study. While the Computerized Patient Record System and centralized care of the VA system allows for review of all physical therapy consultations, orthotic consultations, and medications within the VA system, any treatments and intervention delivered by non-VA providers were not captured. Furthermore, the ability to assess for confounding variables limiting the prescription of certain medications, such as chronic kidney disease with NSAIDs or liver disease with acetaminophen, was limited by our study design.

Although our study suffers from selection bias with respect to examination of nonarthroplasty treatment in patients who have ultimately undergone TKA, we feel that this subset of patients with symptomatic knee OA represents the majority of patients evaluated for knee OA by orthopaedic surgeons in the clinic setting. It should be noted that although realignment osteotomies were sometimes indicated as appropriate by AAOS AUC model in our study population, this intervention was never performed due to patient and surgeon preference. Additionally, although it is not an AAOS AUC evaluated intervention, viscosupplementation was sporadically used during the study period; however, it is now off formulary at the investigation institution.

Conclusion

Our study suggests that patients without knee instability use more nonarthroplasty treatments over a longer period before TKA, and those patients with less severe knee OA are at risk of receiving an intervention judged to be rarely appropriate by the AAOS AUC. Such interventions do not affect timing of TKA. Nonarthroplasty care should be individualized to patients’ needs, and the decision to proceed with arthroplasty should be considered only after exhausting appropriate conservative measures. We recommend that providers use the AAOS AUC, especially when treating younger patients with less severe knee OA, particularly if considering opiate therapy or knee arthroscopy.

Acknowledgments
The authors would like to acknowledge Patrick Getty, MD, for his surgical care of some of the study patients. This material is the result of work supported with resources and the use of facilities at the Louis Stokes Cleveland VA Medical Center in Ohio.

Clinical trials represent future hope for patients seeking better care, and there is no disease more in need of better care than Alzheimer’s disease. While death rates among most cancers, as well as heart disease, HIV-related illness, and other categories, have declined in the past decade, there has been no progress for Alzheimer’s disease. Better health and wellness overall may be having a beneficial effect that has produced a reduction in age-adjusted dementia rates, but with the aging of the population there are a greater absolute number of dementia cases than ever before, and that number is expected to continue rising. Finding a disease-modifying therapy seems to be the best hope for changing this dim outlook. Clinical trials intend to do just that but are hampered by patient enrollment rates that remain low. Far fewer eligible patients enroll than are needed, causing studies to take longer to complete, driving up their costs and essentially slowing progress. There is a need to increase patient enrollment, and there has been a variety of efforts intended to address this, not the least of which has been an explosion of media coverage of Alzheimer’s disease.

The Global Alzheimer’s Platform (GAP) Foundation, a nonprofit, self-described patient-centric entity dedicated to reducing the time and cost of Alzheimer’s disease clinical trials, recently announced an initiative to increase participation in Alzheimer’s clinical trials by supporting and collaborating with “memory fitness programs” through select Medicare Advantage plans. At worst, this seems a harmless way to increase attention and hopefully interest in clinical trial participation. At best, this may be a cost-effective way to increase enrollment and even improve dementia care. Dementia is notoriously underdiagnosed, especially by overworked, busy primary care providers who simply lack the time to perform the time-consuming testing that is typically required to diagnose and follow such patients.

There are some caveats to consider. First, memory fitness programs are of dubious benefit. They generally fit the description of being harmless, but there is little compelling evidence that they preserve or improve memory.

Second, enrollment in a clinical trial, for a patient, is not always a winning proposition. To date, there has been little success and in the absence of benefit, any downside – even if simply an inconvenience – is a net negative. Recently at the 2018 Clinical Trials on Alzheimer’s Disease meeting, Merck reported that patients with mild cognitive impairment receiving active treatment in the BACE1 inhibitor verubecestat trial actually declined at a more rapid rate than did those on placebo. While the absolute difference was small, and one could argue whether it was clinically significant or simply a random occurrence, it was a reminder that intervention with an experimental agent is not necessarily benign.

Third, Medicare Advantage plans, while popular in some circles, are not considered advantageous to providers so that the proliferation of inadequate reimbursement will potentially fuel the accelerating number of providers who opt out of insurance plans altogether. This is not necessarily an issue for the GAP Foundation specifically but is nonetheless an issue for anything that promotes MA plans).

Finally, it remains important to help patients and families maintain a positive outlook, especially when we have nothing better to offer. Alzheimer’s disease is not a death sentence for every patient affected. While many have difficult and heartbreaking courses, some have slowly progressive courses with relatively little impairment for an extended period of time. There are also the dementia-phobic, cognitively unimpaired individuals (or who simply have normal age-associated cognitive changes) in whom the continued drumbeat of dementia awareness and memory testing raises their paranoia ever higher. We treat deficits (or try to), but we have to live based on our preserved skills. The challenge clinicians must face with patients and families is how to maximize function while compensating for deficits and making sure that patients and families maintain their hope.

Dr. Caselli is professor of neurology at the Mayo Clinic Arizona in Scottsdale and associate director and clinical core director of the Arizona Alzheimer’s Disease Center.

Clinical trials represent future hope for patients seeking better care, and there is no disease more in need of better care than Alzheimer’s disease. While death rates among most cancers, as well as heart disease, HIV-related illness, and other categories, have declined in the past decade, there has been no progress for Alzheimer’s disease. Better health and wellness overall may be having a beneficial effect that has produced a reduction in age-adjusted dementia rates, but with the aging of the population there are a greater absolute number of dementia cases than ever before, and that number is expected to continue rising. Finding a disease-modifying therapy seems to be the best hope for changing this dim outlook. Clinical trials intend to do just that but are hampered by patient enrollment rates that remain low. Far fewer eligible patients enroll than are needed, causing studies to take longer to complete, driving up their costs and essentially slowing progress. There is a need to increase patient enrollment, and there has been a variety of efforts intended to address this, not the least of which has been an explosion of media coverage of Alzheimer’s disease.

The Global Alzheimer’s Platform (GAP) Foundation, a nonprofit, self-described patient-centric entity dedicated to reducing the time and cost of Alzheimer’s disease clinical trials, recently announced an initiative to increase participation in Alzheimer’s clinical trials by supporting and collaborating with “memory fitness programs” through select Medicare Advantage plans. At worst, this seems a harmless way to increase attention and hopefully interest in clinical trial participation. At best, this may be a cost-effective way to increase enrollment and even improve dementia care. Dementia is notoriously underdiagnosed, especially by overworked, busy primary care providers who simply lack the time to perform the time-consuming testing that is typically required to diagnose and follow such patients.

There are some caveats to consider. First, memory fitness programs are of dubious benefit. They generally fit the description of being harmless, but there is little compelling evidence that they preserve or improve memory.

Second, enrollment in a clinical trial, for a patient, is not always a winning proposition. To date, there has been little success and in the absence of benefit, any downside – even if simply an inconvenience – is a net negative. Recently at the 2018 Clinical Trials on Alzheimer’s Disease meeting, Merck reported that patients with mild cognitive impairment receiving active treatment in the BACE1 inhibitor verubecestat trial actually declined at a more rapid rate than did those on placebo. While the absolute difference was small, and one could argue whether it was clinically significant or simply a random occurrence, it was a reminder that intervention with an experimental agent is not necessarily benign.

Third, Medicare Advantage plans, while popular in some circles, are not considered advantageous to providers so that the proliferation of inadequate reimbursement will potentially fuel the accelerating number of providers who opt out of insurance plans altogether. This is not necessarily an issue for the GAP Foundation specifically but is nonetheless an issue for anything that promotes MA plans).

Finally, it remains important to help patients and families maintain a positive outlook, especially when we have nothing better to offer. Alzheimer’s disease is not a death sentence for every patient affected. While many have difficult and heartbreaking courses, some have slowly progressive courses with relatively little impairment for an extended period of time. There are also the dementia-phobic, cognitively unimpaired individuals (or who simply have normal age-associated cognitive changes) in whom the continued drumbeat of dementia awareness and memory testing raises their paranoia ever higher. We treat deficits (or try to), but we have to live based on our preserved skills. The challenge clinicians must face with patients and families is how to maximize function while compensating for deficits and making sure that patients and families maintain their hope.

Dr. Caselli is professor of neurology at the Mayo Clinic Arizona in Scottsdale and associate director and clinical core director of the Arizona Alzheimer’s Disease Center.

Clinical trials represent future hope for patients seeking better care, and there is no disease more in need of better care than Alzheimer’s disease. While death rates among most cancers, as well as heart disease, HIV-related illness, and other categories, have declined in the past decade, there has been no progress for Alzheimer’s disease. Better health and wellness overall may be having a beneficial effect that has produced a reduction in age-adjusted dementia rates, but with the aging of the population there are a greater absolute number of dementia cases than ever before, and that number is expected to continue rising. Finding a disease-modifying therapy seems to be the best hope for changing this dim outlook. Clinical trials intend to do just that but are hampered by patient enrollment rates that remain low. Far fewer eligible patients enroll than are needed, causing studies to take longer to complete, driving up their costs and essentially slowing progress. There is a need to increase patient enrollment, and there has been a variety of efforts intended to address this, not the least of which has been an explosion of media coverage of Alzheimer’s disease.

The Global Alzheimer’s Platform (GAP) Foundation, a nonprofit, self-described patient-centric entity dedicated to reducing the time and cost of Alzheimer’s disease clinical trials, recently announced an initiative to increase participation in Alzheimer’s clinical trials by supporting and collaborating with “memory fitness programs” through select Medicare Advantage plans. At worst, this seems a harmless way to increase attention and hopefully interest in clinical trial participation. At best, this may be a cost-effective way to increase enrollment and even improve dementia care. Dementia is notoriously underdiagnosed, especially by overworked, busy primary care providers who simply lack the time to perform the time-consuming testing that is typically required to diagnose and follow such patients.

There are some caveats to consider. First, memory fitness programs are of dubious benefit. They generally fit the description of being harmless, but there is little compelling evidence that they preserve or improve memory.

Second, enrollment in a clinical trial, for a patient, is not always a winning proposition. To date, there has been little success and in the absence of benefit, any downside – even if simply an inconvenience – is a net negative. Recently at the 2018 Clinical Trials on Alzheimer’s Disease meeting, Merck reported that patients with mild cognitive impairment receiving active treatment in the BACE1 inhibitor verubecestat trial actually declined at a more rapid rate than did those on placebo. While the absolute difference was small, and one could argue whether it was clinically significant or simply a random occurrence, it was a reminder that intervention with an experimental agent is not necessarily benign.

Third, Medicare Advantage plans, while popular in some circles, are not considered advantageous to providers so that the proliferation of inadequate reimbursement will potentially fuel the accelerating number of providers who opt out of insurance plans altogether. This is not necessarily an issue for the GAP Foundation specifically but is nonetheless an issue for anything that promotes MA plans).

Finally, it remains important to help patients and families maintain a positive outlook, especially when we have nothing better to offer. Alzheimer’s disease is not a death sentence for every patient affected. While many have difficult and heartbreaking courses, some have slowly progressive courses with relatively little impairment for an extended period of time. There are also the dementia-phobic, cognitively unimpaired individuals (or who simply have normal age-associated cognitive changes) in whom the continued drumbeat of dementia awareness and memory testing raises their paranoia ever higher. We treat deficits (or try to), but we have to live based on our preserved skills. The challenge clinicians must face with patients and families is how to maximize function while compensating for deficits and making sure that patients and families maintain their hope.

Dr. Caselli is professor of neurology at the Mayo Clinic Arizona in Scottsdale and associate director and clinical core director of the Arizona Alzheimer’s Disease Center.

EVIDENCE SUMMARY

PRP vs placebo. Three RCTs compared PRP with saline placebo injections and 2 found that PRP improved the Western Ontario and McMaster Universities Arthritis Index (WOMAC, a standardized scale assessing knee pain, function, and stiffness) by 40% to 70%; the third found 24% to 32% improvements in the EuroQol visual analog scale (EQ-VAS) scores at 6 months^1-3 (TABLE^1-12).

The first 2 studies enrolled patients (mean age early 60s, approximately 50% women) with clinically and radiographically evaluated knee OA of mostly moderate severity (baseline WOMAC scores about 50).^1,2 Investigators in the first RCT injected PRP once in one subgroup and twice in another subgroup, compared with a single injection of saline in a third subgroup.¹ They gave 3 weekly injections of PRP or saline in the second RCT.²

The third study enrolled mainly patients with early osteoarthritis (mean age early 50s, slightly more women). Investigators injected PRP 3 times in one subgroup and once (plus 2 saline injections) in another, compared with 3 saline injections, and evaluated patients at baseline and 6 months.³

PRP vs HA. Nine RCTs compared PRP with HA injections. Six studies (673 patients) found no significant difference; 3 studies (376 patients) found that PRP improved standardized knee assessment scores by 35% to 40% at 24-48 weeks.^7,8,10 All studies enrolled patients (mean age early 60s, approximately 50% women) with clinically and radiographically evaluated knee OA of mostly moderate severity. In 7 RCTs, ^4-6,9-12 investigators injected PRP or HA weekly for 3 weeks, in one RCT⁸ they gave 4 weekly injections, and in one⁷they gave 2 PRP injections separated by 4 weeks.

Three RCTs used the International Knee Documentation Committee (IKDC) score, considered the most reliable standardized scoring system, which quantifies subjective symptoms (pain, stiffness, swelling, giving way), activity (climbing stairs, rising from a chair, squatting, jumping), and function pre- and postintervention.^5,9,12 All 3 studies using the IKDC found no difference between PRP and HA injections. Most RCTs used the WOMAC standardized scale, scoring 5 items for pain, 2 for stiffness, and 17 for function.^1,2,4,6-8.10

Risk for bias

A systematic review¹³ that evaluated methodologic quality of the 3 studies comparing PRP with placebo rated 2^1,3 at high risk of bias and one² at moderate risk. Another meta-analysis¹⁴ performed a quality assessment including 4 of the 9 RCTs^,8-10,12 comparing PRP with HA and concluded that 3 had a high risk of bias; the fourth RCT had a moderate risk. No independent quality assessments of the other RCTs were available.^4-7,11

RECOMMENDATIONS

The American Academy of Orthopaedic Surgeons doesn’t recommend for or against PRP injections because of insufficient evidence and strongly recommends against HA injections based on multiple RCTs of moderate quality that found no difference between HA and placebo.¹⁵

EVIDENCE SUMMARY

PRP vs placebo. Three RCTs compared PRP with saline placebo injections and 2 found that PRP improved the Western Ontario and McMaster Universities Arthritis Index (WOMAC, a standardized scale assessing knee pain, function, and stiffness) by 40% to 70%; the third found 24% to 32% improvements in the EuroQol visual analog scale (EQ-VAS) scores at 6 months^1-3 (TABLE^1-12).

The first 2 studies enrolled patients (mean age early 60s, approximately 50% women) with clinically and radiographically evaluated knee OA of mostly moderate severity (baseline WOMAC scores about 50).^1,2 Investigators in the first RCT injected PRP once in one subgroup and twice in another subgroup, compared with a single injection of saline in a third subgroup.¹ They gave 3 weekly injections of PRP or saline in the second RCT.²

The third study enrolled mainly patients with early osteoarthritis (mean age early 50s, slightly more women). Investigators injected PRP 3 times in one subgroup and once (plus 2 saline injections) in another, compared with 3 saline injections, and evaluated patients at baseline and 6 months.³

PRP vs HA. Nine RCTs compared PRP with HA injections. Six studies (673 patients) found no significant difference; 3 studies (376 patients) found that PRP improved standardized knee assessment scores by 35% to 40% at 24-48 weeks.^7,8,10 All studies enrolled patients (mean age early 60s, approximately 50% women) with clinically and radiographically evaluated knee OA of mostly moderate severity. In 7 RCTs, ^4-6,9-12 investigators injected PRP or HA weekly for 3 weeks, in one RCT⁸ they gave 4 weekly injections, and in one⁷they gave 2 PRP injections separated by 4 weeks.

Three RCTs used the International Knee Documentation Committee (IKDC) score, considered the most reliable standardized scoring system, which quantifies subjective symptoms (pain, stiffness, swelling, giving way), activity (climbing stairs, rising from a chair, squatting, jumping), and function pre- and postintervention.^5,9,12 All 3 studies using the IKDC found no difference between PRP and HA injections. Most RCTs used the WOMAC standardized scale, scoring 5 items for pain, 2 for stiffness, and 17 for function.^1,2,4,6-8.10

Risk for bias

A systematic review¹³ that evaluated methodologic quality of the 3 studies comparing PRP with placebo rated 2^1,3 at high risk of bias and one² at moderate risk. Another meta-analysis¹⁴ performed a quality assessment including 4 of the 9 RCTs^,8-10,12 comparing PRP with HA and concluded that 3 had a high risk of bias; the fourth RCT had a moderate risk. No independent quality assessments of the other RCTs were available.^4-7,11

RECOMMENDATIONS

The American Academy of Orthopaedic Surgeons doesn’t recommend for or against PRP injections because of insufficient evidence and strongly recommends against HA injections based on multiple RCTs of moderate quality that found no difference between HA and placebo.¹⁵

EVIDENCE SUMMARY

PRP vs placebo. Three RCTs compared PRP with saline placebo injections and 2 found that PRP improved the Western Ontario and McMaster Universities Arthritis Index (WOMAC, a standardized scale assessing knee pain, function, and stiffness) by 40% to 70%; the third found 24% to 32% improvements in the EuroQol visual analog scale (EQ-VAS) scores at 6 months^1-3 (TABLE^1-12).

The first 2 studies enrolled patients (mean age early 60s, approximately 50% women) with clinically and radiographically evaluated knee OA of mostly moderate severity (baseline WOMAC scores about 50).^1,2 Investigators in the first RCT injected PRP once in one subgroup and twice in another subgroup, compared with a single injection of saline in a third subgroup.¹ They gave 3 weekly injections of PRP or saline in the second RCT.²

The third study enrolled mainly patients with early osteoarthritis (mean age early 50s, slightly more women). Investigators injected PRP 3 times in one subgroup and once (plus 2 saline injections) in another, compared with 3 saline injections, and evaluated patients at baseline and 6 months.³

PRP vs HA. Nine RCTs compared PRP with HA injections. Six studies (673 patients) found no significant difference; 3 studies (376 patients) found that PRP improved standardized knee assessment scores by 35% to 40% at 24-48 weeks.^7,8,10 All studies enrolled patients (mean age early 60s, approximately 50% women) with clinically and radiographically evaluated knee OA of mostly moderate severity. In 7 RCTs, ^4-6,9-12 investigators injected PRP or HA weekly for 3 weeks, in one RCT⁸ they gave 4 weekly injections, and in one⁷they gave 2 PRP injections separated by 4 weeks.

Three RCTs used the International Knee Documentation Committee (IKDC) score, considered the most reliable standardized scoring system, which quantifies subjective symptoms (pain, stiffness, swelling, giving way), activity (climbing stairs, rising from a chair, squatting, jumping), and function pre- and postintervention.^5,9,12 All 3 studies using the IKDC found no difference between PRP and HA injections. Most RCTs used the WOMAC standardized scale, scoring 5 items for pain, 2 for stiffness, and 17 for function.^1,2,4,6-8.10

Risk for bias

A systematic review¹³ that evaluated methodologic quality of the 3 studies comparing PRP with placebo rated 2^1,3 at high risk of bias and one² at moderate risk. Another meta-analysis¹⁴ performed a quality assessment including 4 of the 9 RCTs^,8-10,12 comparing PRP with HA and concluded that 3 had a high risk of bias; the fourth RCT had a moderate risk. No independent quality assessments of the other RCTs were available.^4-7,11

RECOMMENDATIONS

The American Academy of Orthopaedic Surgeons doesn’t recommend for or against PRP injections because of insufficient evidence and strongly recommends against HA injections based on multiple RCTs of moderate quality that found no difference between HA and placebo.¹⁵

SVS is soliciting abstracts for a late-breaking session at the 2019 Vascular Annual Meeting. Preference will be given to prospective, multi-institutional trials and investigational device exemption studies. As the intent is to provide attendees with true “late-breaking” trial data, retrospective analyses will not be considered for this session. Abstracts must be submitted here by 3 p.m. Central Daylight Time, Wednesday, March 27. For more information, contact the SVS Education Department by email at [email protected].

SVS is soliciting abstracts for a late-breaking session at the 2019 Vascular Annual Meeting. Preference will be given to prospective, multi-institutional trials and investigational device exemption studies. As the intent is to provide attendees with true “late-breaking” trial data, retrospective analyses will not be considered for this session. Abstracts must be submitted here by 3 p.m. Central Daylight Time, Wednesday, March 27. For more information, contact the SVS Education Department by email at [email protected].

SVS is soliciting abstracts for a late-breaking session at the 2019 Vascular Annual Meeting. Preference will be given to prospective, multi-institutional trials and investigational device exemption studies. As the intent is to provide attendees with true “late-breaking” trial data, retrospective analyses will not be considered for this session. Abstracts must be submitted here by 3 p.m. Central Daylight Time, Wednesday, March 27. For more information, contact the SVS Education Department by email at [email protected].

Registration and housing have officially opened for the 2019 Vascular Annual Meeting! This year’s event will be held on June 12-15 in National Harbor, Md., (near Washington, D.C.) As always, attendees will learn about cutting-edge vascular research, attend innovative education sessions, have the opportunity to network with other thought leaders in the field and do much more. You can find the link to register for VAM here.

Registration and housing have officially opened for the 2019 Vascular Annual Meeting! This year’s event will be held on June 12-15 in National Harbor, Md., (near Washington, D.C.) As always, attendees will learn about cutting-edge vascular research, attend innovative education sessions, have the opportunity to network with other thought leaders in the field and do much more. You can find the link to register for VAM here.

Registration and housing have officially opened for the 2019 Vascular Annual Meeting! This year’s event will be held on June 12-15 in National Harbor, Md., (near Washington, D.C.) As always, attendees will learn about cutting-edge vascular research, attend innovative education sessions, have the opportunity to network with other thought leaders in the field and do much more. You can find the link to register for VAM here.

John Krais, PhD, a researcher at Fox Chase Cancer Center in Philadelphia, has received a $75,000 grant from the Ovarian Cancer Research Alliance.

Dr. Krais received the 2-year grant to fund his investigation into DNA repair processes in BRCA1-mutant cancers, which will focus on the RNF168 protein.

Seven other early career researchers have joined the Parker Institute for Cancer Immunotherapy as part of the Parker Scholars, Parker Bridge Scholars, and Parker Fellows programs.

[email protected]

John Krais, PhD, a researcher at Fox Chase Cancer Center in Philadelphia, has received a $75,000 grant from the Ovarian Cancer Research Alliance.

Dr. Krais received the 2-year grant to fund his investigation into DNA repair processes in BRCA1-mutant cancers, which will focus on the RNF168 protein.

Seven other early career researchers have joined the Parker Institute for Cancer Immunotherapy as part of the Parker Scholars, Parker Bridge Scholars, and Parker Fellows programs.

[email protected]

John Krais, PhD, a researcher at Fox Chase Cancer Center in Philadelphia, has received a $75,000 grant from the Ovarian Cancer Research Alliance.

Dr. Krais received the 2-year grant to fund his investigation into DNA repair processes in BRCA1-mutant cancers, which will focus on the RNF168 protein.

Seven other early career researchers have joined the Parker Institute for Cancer Immunotherapy as part of the Parker Scholars, Parker Bridge Scholars, and Parker Fellows programs.

[email protected]

CHEST 2019 will be in New Orleans, Louisiana, this year, October 19-23. Here are a few ways to be engaged leading up to the meeting.

Submit Abstracts and Case Reports

Do you have original investigative research to share? There’s still some time to submit your abstracts and case reports for presentation at CHEST 2019 through Friday, March 15. If accepted, all abstracts and case reports will be published as submitted in an online CHEST® journal abstract supplement. No corrections will be made once submission is complete.

View submission details (https://chestmeeting.chestnet.org/abstracts-and-case-reports/)
 

Call for Moderators

CHEST is currently requesting moderators to facilitate discussions, questions, and answers within assigned sessions on-site at CHEST 2019 in New Orleans. Moderators will be notified June to September of their acceptance as a moderator.

View complete details (https://docs.google.com/forms/d/e/1FAIpQLSdSWFSyKAeIjfyYgGRF6km_95znba63bx6iM9TWl08gpdqzEQ/viewform)
 

CHEST Challenge 2019

US-based CHEST fellows-in-training - does your fellowship have what it takes to win CHEST Challenge 2019? CHEST Challenge is a fun and exciting competition in which CHEST fellows-in-training compete against programs around the country for honor and prizes! The first round of the competition consists of two parts: social media challenges and online quiz. The aggregate score for both of these components will be used to identify the top three highest scoring teams. These top three teams will then be invited to send three fellows each to the CHEST Challenge Championship, a Jeopardy-style game show that takes place live during the CHEST Annual Meeting.

See the rules and how to participate. (chestchallenge.org)
 

Apply for CHEST Foundation Grants

The CHEST Foundation has awarded more than $10 million in grant funding to nearly 800 recipients worldwide for clinical research and community service. Each year, the CHEST Foundation offers grants to worthy research candidates, generous community service volunteers, and distinguished scholars in a field of expertise.

The CHEST Foundation is accepting grant applications now through April 8, 2019, in the following areas:

• CHEST Foundation Community Service Grant Honoring D. Robert McCaffree, MD, Master FCCP – Up to $15,000 (multiple recipients selected)*

• The GlaxoSmithKline Distinguished Scholar in Respiratory Health – $150,000*

• CHEST Foundation Research Grant in Asthma – $15,000 – $30,000*

• CHEST Foundation Research Grant in Chronic Obstructive Pulmonary Disease – $25,000 – $50,000*

• CHEST Foundation Research Grant in Cystic Fibrosis – $15,000 – $30,000*

• CHEST Foundation Research Grant in Lung Cancer – $50,000 – $100,000*

• CHEST Foundation Research Grant in Nontuberculous Mycobacteria Diseases – $30,000 – $60,000*

• CHEST Foundation Research Grant in Pulmonary Arterial Hypertension – $25,000 – $50,000*

• CHEST Foundation Research Grant in Pulmonary Fibrosis – $25,000 – $50,000*

• CHEST Foundation Research Grant in Venous Thromboembolism – $15,000 – $30,000*

• CHEST Foundation Research Grant in Women’s Lung Health – $10,000*

*Amount contingent on funding.


Learn more on how to apply now. (https://foundation.chestnet.org/grants/apply-for-a-grant/)


 

CHEST 2019 will be in New Orleans, Louisiana, this year, October 19-23. Here are a few ways to be engaged leading up to the meeting.

Submit Abstracts and Case Reports

Do you have original investigative research to share? There’s still some time to submit your abstracts and case reports for presentation at CHEST 2019 through Friday, March 15. If accepted, all abstracts and case reports will be published as submitted in an online CHEST® journal abstract supplement. No corrections will be made once submission is complete.

View submission details (https://chestmeeting.chestnet.org/abstracts-and-case-reports/)
 

Call for Moderators

CHEST is currently requesting moderators to facilitate discussions, questions, and answers within assigned sessions on-site at CHEST 2019 in New Orleans. Moderators will be notified June to September of their acceptance as a moderator.

View complete details (https://docs.google.com/forms/d/e/1FAIpQLSdSWFSyKAeIjfyYgGRF6km_95znba63bx6iM9TWl08gpdqzEQ/viewform)
 

CHEST Challenge 2019

US-based CHEST fellows-in-training - does your fellowship have what it takes to win CHEST Challenge 2019? CHEST Challenge is a fun and exciting competition in which CHEST fellows-in-training compete against programs around the country for honor and prizes! The first round of the competition consists of two parts: social media challenges and online quiz. The aggregate score for both of these components will be used to identify the top three highest scoring teams. These top three teams will then be invited to send three fellows each to the CHEST Challenge Championship, a Jeopardy-style game show that takes place live during the CHEST Annual Meeting.

See the rules and how to participate. (chestchallenge.org)
 

Apply for CHEST Foundation Grants

The CHEST Foundation has awarded more than $10 million in grant funding to nearly 800 recipients worldwide for clinical research and community service. Each year, the CHEST Foundation offers grants to worthy research candidates, generous community service volunteers, and distinguished scholars in a field of expertise.

The CHEST Foundation is accepting grant applications now through April 8, 2019, in the following areas:

• CHEST Foundation Community Service Grant Honoring D. Robert McCaffree, MD, Master FCCP – Up to $15,000 (multiple recipients selected)*

• The GlaxoSmithKline Distinguished Scholar in Respiratory Health – $150,000*

• CHEST Foundation Research Grant in Asthma – $15,000 – $30,000*

• CHEST Foundation Research Grant in Chronic Obstructive Pulmonary Disease – $25,000 – $50,000*

• CHEST Foundation Research Grant in Cystic Fibrosis – $15,000 – $30,000*

• CHEST Foundation Research Grant in Lung Cancer – $50,000 – $100,000*

• CHEST Foundation Research Grant in Nontuberculous Mycobacteria Diseases – $30,000 – $60,000*

• CHEST Foundation Research Grant in Pulmonary Arterial Hypertension – $25,000 – $50,000*

• CHEST Foundation Research Grant in Pulmonary Fibrosis – $25,000 – $50,000*

• CHEST Foundation Research Grant in Venous Thromboembolism – $15,000 – $30,000*

• CHEST Foundation Research Grant in Women’s Lung Health – $10,000*

*Amount contingent on funding.


Learn more on how to apply now. (https://foundation.chestnet.org/grants/apply-for-a-grant/)


 

CHEST 2019 will be in New Orleans, Louisiana, this year, October 19-23. Here are a few ways to be engaged leading up to the meeting.

Submit Abstracts and Case Reports

Do you have original investigative research to share? There’s still some time to submit your abstracts and case reports for presentation at CHEST 2019 through Friday, March 15. If accepted, all abstracts and case reports will be published as submitted in an online CHEST® journal abstract supplement. No corrections will be made once submission is complete.

View submission details (https://chestmeeting.chestnet.org/abstracts-and-case-reports/)
 

Call for Moderators

CHEST is currently requesting moderators to facilitate discussions, questions, and answers within assigned sessions on-site at CHEST 2019 in New Orleans. Moderators will be notified June to September of their acceptance as a moderator.

View complete details (https://docs.google.com/forms/d/e/1FAIpQLSdSWFSyKAeIjfyYgGRF6km_95znba63bx6iM9TWl08gpdqzEQ/viewform)
 

CHEST Challenge 2019

US-based CHEST fellows-in-training - does your fellowship have what it takes to win CHEST Challenge 2019? CHEST Challenge is a fun and exciting competition in which CHEST fellows-in-training compete against programs around the country for honor and prizes! The first round of the competition consists of two parts: social media challenges and online quiz. The aggregate score for both of these components will be used to identify the top three highest scoring teams. These top three teams will then be invited to send three fellows each to the CHEST Challenge Championship, a Jeopardy-style game show that takes place live during the CHEST Annual Meeting.

See the rules and how to participate. (chestchallenge.org)
 

Apply for CHEST Foundation Grants

The CHEST Foundation has awarded more than $10 million in grant funding to nearly 800 recipients worldwide for clinical research and community service. Each year, the CHEST Foundation offers grants to worthy research candidates, generous community service volunteers, and distinguished scholars in a field of expertise.

The CHEST Foundation is accepting grant applications now through April 8, 2019, in the following areas:

• CHEST Foundation Community Service Grant Honoring D. Robert McCaffree, MD, Master FCCP – Up to $15,000 (multiple recipients selected)*

• The GlaxoSmithKline Distinguished Scholar in Respiratory Health – $150,000*

• CHEST Foundation Research Grant in Asthma – $15,000 – $30,000*

• CHEST Foundation Research Grant in Chronic Obstructive Pulmonary Disease – $25,000 – $50,000*

• CHEST Foundation Research Grant in Cystic Fibrosis – $15,000 – $30,000*

• CHEST Foundation Research Grant in Lung Cancer – $50,000 – $100,000*

• CHEST Foundation Research Grant in Nontuberculous Mycobacteria Diseases – $30,000 – $60,000*

• CHEST Foundation Research Grant in Pulmonary Arterial Hypertension – $25,000 – $50,000*

• CHEST Foundation Research Grant in Pulmonary Fibrosis – $25,000 – $50,000*

• CHEST Foundation Research Grant in Venous Thromboembolism – $15,000 – $30,000*

• CHEST Foundation Research Grant in Women’s Lung Health – $10,000*

*Amount contingent on funding.


Learn more on how to apply now. (https://foundation.chestnet.org/grants/apply-for-a-grant/)