Michael Grover, DO

A 51-year-old woman presented to the family medicine clinic for evaluation of a slightly tender skin lesion on her left eyebrow. The lesion had been slowly growing for a year.

The patient’s family history included multiple family members with colon or breast cancer and other relatives with pancreatic and prostate cancer. A colonoscopy performed a year earlier on the patient was negative. The patient’s past medical history included hypertension, major depressive disorder, hyperlipidemia, and venous insufficiency. She also had a colon polyp history.

Physical examination of the eyebrow showed a 3-mm papule that was firm on palpation. Dermoscopy of the lesion revealed a yellow papule with an overlying telangiectasia (FIGURE 1A and 1B). Although the lesion appeared benign, the treatment team and the patient agreed to pursue a consultation. The dermoscopy images were sent to a dermatologist to help identify the lesion.

WHAT IS YOUR DIAGNOSIS?
HOW WOULD YOU TREAT THIS PATIENT?

A rapid teledermatology consultation helped us to determine that this was a sebaceous lesion, but its location and the overlying telangiectasia raised concerns for malignancy. After shared decision-making with the patient, she agreed to proceed with a biopsy. We first made an incision into the lesion, which was hard, demonstrating that it was not cystic. A shave biopsy was then completed. The dermatopathology findings showed clear-cell change consisting of bubbly or foamy cytoplasm, with scalloping of the nuclei, which is characteristic of a sebaceous origin. There were tumor cells that were enlarged with pleomorphism, multiple nucleoli, and scattered mitotic figures. These findings pointed to a diagnosis of sebaceous carcinoma.

Sebaceous carcinomas most commonly manifest on the eyelids. They can originate from the Meibomian glands as well as from pilosebaceous glands at other sites on the body.¹ They are rare, accounting for only 1% to 5% of eyelid malignancies, and occur in approximately 2 per 1 million people.¹ Tumors can invade locally and metastasize, particularly to surrounding lymph nodes. Periocular pathology may sometimes lead to misdiagnosis, which contributes to a mortality rate that has been reported as high as 20%.¹ Suspicion for malignancy may arise due to ulceration, bleeding, pain, or rapid growth.

A lesson in considering the full differential

While sebaceous lesions on the eyelid and eyebrow are often benign, this case underscored the importance of considering the more worrisome elements in the differential. The differential diagnosis for lesions in the area of the eye include the following:

Sebaceous hyperplasia is a common condition (typically among older patients) in which sebaceous glands increase in size and number.² The classic clinical feature is yellow or skin-colored papules. The lesions typically manifest on the face—particularly on the forehead. They are benign and often have a central umbilication.²

Sebaceous adenomas are benign tumors that may manifest as tan, skin-colored, pink, or yellow papules or nodules.² The lesions are usually asymptomatic, small, and slow growing.²

Continue to: Basal and squamous cell carcinomas

Basal and squamous cell carcinomas. Basal cell carcinomas often feature translucent lesions on areas of the skin that are exposed to sunlight. These lesions often have slightly rolled border edges or overlying branching telangiectasia and may be nodular.³ Squamous cell carcinomas often feature scaled, reddened patches that may become tender and ulcerate.⁴

Hordeolums and chalazions. A hordeolum (or stye) is a painful, acute, localized swelling of the eyelid.⁵ These often develop externally at the lid margin from infection of the follicle. A chalazion is characterized by a persistent, nontender mass that results from small, noninfectious obstruction of the Meibomian glands with secondary granulomatous inflammation.⁵

Dermoscopy can (and did) help with the Dx

Dermoscopy can help confirm whether a lesion has a sebaceous origin because it would show yellow globules with “crown vessel” telangiectasias that classically do not cross midline.⁶ Unfortunately, the findings of yellow globules and dermal vessels do not adequately differentiate benign from malignant lesions.⁶ Carcinomas can manifest in an undifferentiated way early in their course.

Sebaceous carcinomas can be associated with the autosomal dominant Muir-Torre syndrome, a subset of the Lynch syndrome.^7,8 Colorectal and genitourinary carcinomas are the most common internal malignancies seen in patients with Muir-Torre syndrome.⁹

Patients benefit from Mohs surgery

Treatment outcomes for sebaceous carcinoma appear to be improved by Mohs surgery. In a recent review of 1265 patients with early-stage sebaceous carcinomas, Su et al found that 234 patients who were treated with Mohs surgery had improved overall survival, compared with 1031 who were treated with surgical excision.¹⁰

Continue to: Our patient

Our patient was referred to a Mohs surgeon who removed the lesion (FIGURES 2 and 3). Given the overall small tumor size, a sentinel lymph node biopsy was not necessary. Because of the patient’s family history, which was suggestive of a genetic predisposition to cancer, she requested a clinical genetics consultation for definitive testing. She went on to pursue genetic testing, which came back negative for Lynch syndrome genes.

The dermatologist recommended yearly skin examination for 5 years for the patient.

A 51-year-old woman presented to the family medicine clinic for evaluation of a slightly tender skin lesion on her left eyebrow. The lesion had been slowly growing for a year.

The patient’s family history included multiple family members with colon or breast cancer and other relatives with pancreatic and prostate cancer. A colonoscopy performed a year earlier on the patient was negative. The patient’s past medical history included hypertension, major depressive disorder, hyperlipidemia, and venous insufficiency. She also had a colon polyp history.

Physical examination of the eyebrow showed a 3-mm papule that was firm on palpation. Dermoscopy of the lesion revealed a yellow papule with an overlying telangiectasia (FIGURE 1A and 1B). Although the lesion appeared benign, the treatment team and the patient agreed to pursue a consultation. The dermoscopy images were sent to a dermatologist to help identify the lesion.

WHAT IS YOUR DIAGNOSIS?
HOW WOULD YOU TREAT THIS PATIENT?

A rapid teledermatology consultation helped us to determine that this was a sebaceous lesion, but its location and the overlying telangiectasia raised concerns for malignancy. After shared decision-making with the patient, she agreed to proceed with a biopsy. We first made an incision into the lesion, which was hard, demonstrating that it was not cystic. A shave biopsy was then completed. The dermatopathology findings showed clear-cell change consisting of bubbly or foamy cytoplasm, with scalloping of the nuclei, which is characteristic of a sebaceous origin. There were tumor cells that were enlarged with pleomorphism, multiple nucleoli, and scattered mitotic figures. These findings pointed to a diagnosis of sebaceous carcinoma.

Sebaceous carcinomas most commonly manifest on the eyelids. They can originate from the Meibomian glands as well as from pilosebaceous glands at other sites on the body.¹ They are rare, accounting for only 1% to 5% of eyelid malignancies, and occur in approximately 2 per 1 million people.¹ Tumors can invade locally and metastasize, particularly to surrounding lymph nodes. Periocular pathology may sometimes lead to misdiagnosis, which contributes to a mortality rate that has been reported as high as 20%.¹ Suspicion for malignancy may arise due to ulceration, bleeding, pain, or rapid growth.

A lesson in considering the full differential

While sebaceous lesions on the eyelid and eyebrow are often benign, this case underscored the importance of considering the more worrisome elements in the differential. The differential diagnosis for lesions in the area of the eye include the following:

Sebaceous hyperplasia is a common condition (typically among older patients) in which sebaceous glands increase in size and number.² The classic clinical feature is yellow or skin-colored papules. The lesions typically manifest on the face—particularly on the forehead. They are benign and often have a central umbilication.²

Sebaceous adenomas are benign tumors that may manifest as tan, skin-colored, pink, or yellow papules or nodules.² The lesions are usually asymptomatic, small, and slow growing.²

Continue to: Basal and squamous cell carcinomas

Basal and squamous cell carcinomas. Basal cell carcinomas often feature translucent lesions on areas of the skin that are exposed to sunlight. These lesions often have slightly rolled border edges or overlying branching telangiectasia and may be nodular.³ Squamous cell carcinomas often feature scaled, reddened patches that may become tender and ulcerate.⁴

Hordeolums and chalazions. A hordeolum (or stye) is a painful, acute, localized swelling of the eyelid.⁵ These often develop externally at the lid margin from infection of the follicle. A chalazion is characterized by a persistent, nontender mass that results from small, noninfectious obstruction of the Meibomian glands with secondary granulomatous inflammation.⁵

Dermoscopy can (and did) help with the Dx

Dermoscopy can help confirm whether a lesion has a sebaceous origin because it would show yellow globules with “crown vessel” telangiectasias that classically do not cross midline.⁶ Unfortunately, the findings of yellow globules and dermal vessels do not adequately differentiate benign from malignant lesions.⁶ Carcinomas can manifest in an undifferentiated way early in their course.

Sebaceous carcinomas can be associated with the autosomal dominant Muir-Torre syndrome, a subset of the Lynch syndrome.^7,8 Colorectal and genitourinary carcinomas are the most common internal malignancies seen in patients with Muir-Torre syndrome.⁹

Patients benefit from Mohs surgery

Treatment outcomes for sebaceous carcinoma appear to be improved by Mohs surgery. In a recent review of 1265 patients with early-stage sebaceous carcinomas, Su et al found that 234 patients who were treated with Mohs surgery had improved overall survival, compared with 1031 who were treated with surgical excision.¹⁰

Continue to: Our patient

Our patient was referred to a Mohs surgeon who removed the lesion (FIGURES 2 and 3). Given the overall small tumor size, a sentinel lymph node biopsy was not necessary. Because of the patient’s family history, which was suggestive of a genetic predisposition to cancer, she requested a clinical genetics consultation for definitive testing. She went on to pursue genetic testing, which came back negative for Lynch syndrome genes.

The dermatologist recommended yearly skin examination for 5 years for the patient.

A 51-year-old woman presented to the family medicine clinic for evaluation of a slightly tender skin lesion on her left eyebrow. The lesion had been slowly growing for a year.

The patient’s family history included multiple family members with colon or breast cancer and other relatives with pancreatic and prostate cancer. A colonoscopy performed a year earlier on the patient was negative. The patient’s past medical history included hypertension, major depressive disorder, hyperlipidemia, and venous insufficiency. She also had a colon polyp history.

Physical examination of the eyebrow showed a 3-mm papule that was firm on palpation. Dermoscopy of the lesion revealed a yellow papule with an overlying telangiectasia (FIGURE 1A and 1B). Although the lesion appeared benign, the treatment team and the patient agreed to pursue a consultation. The dermoscopy images were sent to a dermatologist to help identify the lesion.

WHAT IS YOUR DIAGNOSIS?
HOW WOULD YOU TREAT THIS PATIENT?

A rapid teledermatology consultation helped us to determine that this was a sebaceous lesion, but its location and the overlying telangiectasia raised concerns for malignancy. After shared decision-making with the patient, she agreed to proceed with a biopsy. We first made an incision into the lesion, which was hard, demonstrating that it was not cystic. A shave biopsy was then completed. The dermatopathology findings showed clear-cell change consisting of bubbly or foamy cytoplasm, with scalloping of the nuclei, which is characteristic of a sebaceous origin. There were tumor cells that were enlarged with pleomorphism, multiple nucleoli, and scattered mitotic figures. These findings pointed to a diagnosis of sebaceous carcinoma.

Sebaceous carcinomas most commonly manifest on the eyelids. They can originate from the Meibomian glands as well as from pilosebaceous glands at other sites on the body.¹ They are rare, accounting for only 1% to 5% of eyelid malignancies, and occur in approximately 2 per 1 million people.¹ Tumors can invade locally and metastasize, particularly to surrounding lymph nodes. Periocular pathology may sometimes lead to misdiagnosis, which contributes to a mortality rate that has been reported as high as 20%.¹ Suspicion for malignancy may arise due to ulceration, bleeding, pain, or rapid growth.

A lesson in considering the full differential

While sebaceous lesions on the eyelid and eyebrow are often benign, this case underscored the importance of considering the more worrisome elements in the differential. The differential diagnosis for lesions in the area of the eye include the following:

Sebaceous hyperplasia is a common condition (typically among older patients) in which sebaceous glands increase in size and number.² The classic clinical feature is yellow or skin-colored papules. The lesions typically manifest on the face—particularly on the forehead. They are benign and often have a central umbilication.²

Sebaceous adenomas are benign tumors that may manifest as tan, skin-colored, pink, or yellow papules or nodules.² The lesions are usually asymptomatic, small, and slow growing.²

Continue to: Basal and squamous cell carcinomas

Basal and squamous cell carcinomas. Basal cell carcinomas often feature translucent lesions on areas of the skin that are exposed to sunlight. These lesions often have slightly rolled border edges or overlying branching telangiectasia and may be nodular.³ Squamous cell carcinomas often feature scaled, reddened patches that may become tender and ulcerate.⁴

Hordeolums and chalazions. A hordeolum (or stye) is a painful, acute, localized swelling of the eyelid.⁵ These often develop externally at the lid margin from infection of the follicle. A chalazion is characterized by a persistent, nontender mass that results from small, noninfectious obstruction of the Meibomian glands with secondary granulomatous inflammation.⁵

Dermoscopy can (and did) help with the Dx

Dermoscopy can help confirm whether a lesion has a sebaceous origin because it would show yellow globules with “crown vessel” telangiectasias that classically do not cross midline.⁶ Unfortunately, the findings of yellow globules and dermal vessels do not adequately differentiate benign from malignant lesions.⁶ Carcinomas can manifest in an undifferentiated way early in their course.

Sebaceous carcinomas can be associated with the autosomal dominant Muir-Torre syndrome, a subset of the Lynch syndrome.^7,8 Colorectal and genitourinary carcinomas are the most common internal malignancies seen in patients with Muir-Torre syndrome.⁹

Patients benefit from Mohs surgery

Treatment outcomes for sebaceous carcinoma appear to be improved by Mohs surgery. In a recent review of 1265 patients with early-stage sebaceous carcinomas, Su et al found that 234 patients who were treated with Mohs surgery had improved overall survival, compared with 1031 who were treated with surgical excision.¹⁰

Continue to: Our patient

Our patient was referred to a Mohs surgeon who removed the lesion (FIGURES 2 and 3). Given the overall small tumor size, a sentinel lymph node biopsy was not necessary. Because of the patient’s family history, which was suggestive of a genetic predisposition to cancer, she requested a clinical genetics consultation for definitive testing. She went on to pursue genetic testing, which came back negative for Lynch syndrome genes.

The dermatologist recommended yearly skin examination for 5 years for the patient.

ABSTRACT

Purpose The “opioid epidemic” in the United States has received increasing attention over the past few years. Most drug overdose deaths involve an opioid, and prescription opioid deaths have quadrupled since 1999. We sought to improve patient safety and adhere to clinical guidelines by standardizing opioid prescribing in our practice.

Methods We implemented a standardized approach to opioid prescribing based on Arizona Department of Health Services guidelines. All of our providers received instruction on Arizona’s Controlled Substance Prescription Monitoring Program (AZCSPMP) database and were encouraged to use it online. Our goal was for patients to have quarterly office visits, complete random urine drug screens, and sign a controlled substance agreement (CSA). The CSA acknowledged their understanding of the risks and benefits of opioid therapy as well as our updated prescribing policies.

 Results Three-hundred fifty-eight of our practice’s patients were receiving chronic opioid therapy. All providers enrolled in AZCSPMP and used it for patient care. We increased rates of signed CSAs from 4.5% to 43.6%, and urine drug screening from 0.8% to 20.1%. For 325 patients remaining in the practice after our interventions, a postintervention chart review demonstrated a statistically significant discontinuation of opioid therapy (71/325, 21.8%; 95% confidence interval, 17.4%-26.7%).

 Conclusion Implementation of a standardized opioid prescribing process resulted in discontinuation of therapy for some patients. Rates increased for signed CSAs and completed random urine drug screening. Future process interventions may improve patient and provider adherence. All primary care physicians should examine their prescribing processes to enhance the safety of opioid therapy.

[polldaddy:10370177]

The US opioid epidemic has received increased attention both nationally and at the state level over the past 2 years. This attention is warranted given the significant societal burden of opioid misuse, abuse, and overdose. Most drug overdose deaths (> 6/10) involve an opioid.¹ Deaths from prescription opioids have quadrupled since 1999 in the United States.² Arizona, the state in which we practice, ranked sixth highest in the nation for drug overdose deaths and had the fifth highest opioid prescribing rate in 2011.³ In response to the growing epidemic, the Centers for Disease Control and Prevention (CDC) released guidelines in 2016 for prescribing and monitoring opioids for chronic pain.⁴

Chronic nonterminal pain (CNTP) remains a significant cause of human suffering and is more prevalent in the United States than cancer, diabetes, and heart disease combined.⁵ The increased use of opioids since 1999 to ease CNTP has not reduced Americans’ reports of pain overall.^6,7 Given the growing opioid epidemic and disease burden of CNTP, we embarked on a quality improvement (QI) project to safely prescribe and refill opioid medications in the Department of Family Medicine at the Mayo Clinic Arizona.

Under our new prescription process, patient completion of controlled substance agreements rose from 4.5% at baseline to 43.6% after the intervention.

METHODS

This project received an exemption from internal review board evaluation as a QI intervention. We used a team-based approach to address standardization of opioid prescribing and monitoring within our practice. The team included physicians (MD/DO), nurses (LPN/RN), and allied health staff (MA), operations and administrative personnel, and information technology (IT) support. We did not involve patients in the initial design of our project. With future quality efforts in this area, we plan to involve patients in design processes.

Continue to: We began by identifying...

We began by identifying the scope of the problem, establishing criteria to search the electronic medical record (EMR) and identify appropriate patients. Chronic pain is often defined as pain lasting more than 3 months. Chronic opioid therapy (COT) has been defined as opioid use lasting longer than 3 months.⁸ Working with our IT colleagues, we defined COT patients as those with 3 or more prescriptions for opioids in the past year or those who received ≥ 30 pills a month (ie, patients who received 180 pills with 2 prescriptions written for the year). This definition gave us the ability to query our EMR to determine which patients were on COT, and we prepared lists of patients by primary care provider (FIGURE). Providers reviewed the lists to ensure these individuals were in fact on COT for CNTP. The number of patients identified after EMR query and provider review was 358, comprising 2.6% of 14,000 empaneled patients.

We based our interventions on the Arizona Department of Health Services 2014 opioid prescribing guidelines.³ The Arizona guidelines used existing national and state opioid prescribing guidelines along with clinical practice guidelines. Our study began prior to the 2016 CDC guidelines, so they were not used in this study. Our practice guidelines recommended that all 23 of our providers (MDs, DOs, and NPs) sign up for Arizona’s Controlled Substance Prescription Monitoring Program (AZCSPMP). We asked each patient to sign a controlled substance agreement (CSA), acknowledging their awareness of our proposed processes and the discussion of opioid therapy. Patients were expected to have face-to-face visits with providers at least quarterly and to complete a random urine drug screen at least annually. Patients were not incentivized to complete the process. We placed reminder calls for appointments just as we do for regular appointments.

Providers were asked to complete the Opioid Risk Tool⁹ with the patient at the initial visit, discuss the risks, benefits, and alternatives of long-term use of opioid medication, and review the 6 As (analgesia, activity, aberrant drug related behavior, adverse effects, affect, and adjunctive treatments). On the day before each patient visit, providers were reminded by a note in the EMR schedule to check AZCSPMP. Initial appointment times would be 30 minutes and follow-up appointments would be scheduled for 15 minutes if only addressing COT.

The QI project was introduced at an all-staff meeting in October 2015 that included providers, allied health staff, front desk personnel, and administrative staff, with the goal of beginning our COT process in November. We mailed letters to COT patients describing our new guidelines and asking them to call to schedule an appointment. If patients on COT came into the office for an alternate appointment and had not yet been seen for a COT visit, providers were encouraged to complete the COT process at that time.

We created a standard order set in the EMR for initial and follow-up visits and for the urine drug screen. We also added an interactive form to the EMR allowing providers to electronically complete the Opioid Risk Tool, and to confirm CSA completion and AZCSPMP review. We developed a database that would query the EMR for patient office visit frequency, CSA completion, and urine drug screen collection. We also placed paper copies of forms in exam rooms with a laminated instruction sheet reviewing the process steps and the 6 As.

Continue to: Soft rollout was...

Soft rollout was November 1, 2015, to assist in working through the process before full rollout. We asked providers to complete the full process on at least 1 patient during this period. This run-through would help ensure that allied health staff who room the patients would have the CSA and Opioid Risk Tool already in the chart before the visit. Full rollout was January 2, 2016. Every 2 to 4 weeks after the full rollout, regular email reminders were sent to providers about the project process and allowed for any feedback about issues that arose.

There was a statistically significant reduction in the number of patients using opioids.

We provided regular updates and discussed the process at department meetings monthly. Quarterly data were reviewed and discussed for the first year of implementation. Providers and staff completed a chart review for each COT patient at project completion, to determine whether opioids had been decreased (in dosage) or discontinued, a nonopioid medicine had been initiated to augment pain control, or whether patients had died or left the practice.

Statistical analysis

We summarized binary data as counts and proportions and compared them using the chi square test. We summarized discrete data by their mean and standard deviation. To analyze binary variables measured repeatedly in time, we used the logistic generalized estimating equation (GEE) with an autoregressive (AR-1) correlation structure. We computed 95% confidence intervals (CIs)for odds ratios using the empirical or “sandwich” standard error estimates. For discrete variables representing counts, we used the negative binomial regression model.

For count data, a Poisson model is typically used; in our case the variance was considerably larger than the mean, exceeding the Poisson-model requirement that they not be significantly different if not exactly the same. This implies that the data are “over dispersed” or more variable than a Poisson model is thought to be able to model accurately. We therefore used a negative binomial model, which is regarded as the better model in this situation. The 95% CIs for the estimate resulting from the negative binomial regression model were computed using the profile-likelihood.¹⁰ All GEEs were clustered on patients (n = 358). We used SAS version 9.3 (Cary, NC) for all analyses.

RESULTS

All providers enrolled for AZCSPMP. CSA completion increased from 16 (4.5%) at baseline to 156 (43.6%) after intervention (P < .001). Patients completed a urine drug screen more frequently as well, from 3 (0.8%) to 72 (20.1%) (P < .001) (TABLES 1 and 2). No statistically significant change was noted in the frequency of office visits.

Continue to: We excluded 33 patients...

We excluded 33 patients from the post-intervention chart review (TABLE 3). Twenty-seven had left the practice and 6 had died, leaving 325 patients included in the post-intervention chart review. There was a statistically significant reduction in the number of patients who used opioids 71 (21.8%; 95% CI, 17.4%-26.7%). We noted no statistically significant association with a decrease in opioid dosage. Fifty-five patients (16.9%) added an augmenting medication, the most common being gabapentin. Adding an augmenting medication was not associated with either stopping or decreasing opioid dosage.

There was a statistically significant association between patients who discontinued opioids and those who neglected to sign a CSA (P < .001) (TABLE 4). We tested for associations between office visit frequency and process step completion. There was a nonsignificant trend between increased frequency of office visits and opioid dose reduction. Patients who stopped opioids had fewer office visits (TABLE 5), while patients who had initiated a medication to augment pain relief had more frequent office visits (TABLE 6).

DISCUSSION

Our interventions to improve the quality of our COT processes were moderately successful. We achieved statistically significant increases in our rates of CSA completion and in urine drug screening. However, these increases were not as clinically impactful as we had hoped. Improvements in both patient and provider adherence are needed. We plan to engage allied health staff more fully to assist with adherence and thereby improve quality. This study was not intended to obtain patient-oriented outcomes, such as decreased pain and improved function. The study was designed to improve patient safety and to standardize a process for prescribing and monitoring patients on COT. In the future we plan to look at patient outcomes and expand our focus to patients on high-dose opioids and those on combination therapy with benzodiazepines.

The most impactful steps likely were the letters sent to chronic opioid therapy patients describing our standardized prescribing process and the ensuing provider-patient talks.

We believe the most impactful process steps were our letters sent to COT patients describing our updated, standardized prescribing process, and the ensuing provider-patient discussion to review the risks, benefits, and alternatives to opioid therapy. This frank discussion of treatment options resulted in more than 1 in 5 patients electing to discontinue COT.

There was an association between opioid discontinuation and patients not signing the CSA. This may have been due to patients deciding to discontinue opioids at the initiation review with providers after they received their letter. Therefore, signing the agreement was no longer necessary.

Continue to: We noted that some patients...

We noted that some patients elected to begin a new, nonopioid medication intended to augment their pain relief. However, they did not decrease their use of opioid medicines. We did not collect pain rating scale scores to determine whether the addition of augmenting medicines provided a reduction in pain perception.

Close monitoring of COT patients with frequent office visits may have had an impact on their care. We noted an association between more frequent visits and initiation of pain augmentation medicines. There was also a nonsignificant trend between office visit frequency and dose reduction. These are topics we may re-examine in our practice over time. There was no change in office visit frequency with our intervention, likely a result of these patients having frequent office visits for multiple comorbid medical conditions at baseline.

Evidence of similar benefits in primary care practices that standardized their opioid prescribing guidelines for patients on COT¹¹ illustrates the importance of such a process for ensuring patient safety and decreasing opioid dosage and use.

Limitations to our project are that we did not measure functional changes and quality-of-life scores for patients. We also did not note the opioid dosages for individuals who chose to stop using opioids.

Looking forward. Based on our experience, patient notification with discussion of COT risks, benefits, and alternatives, as well as implementation of a process to monitor COT, appear to be related to patients’ decisions to discontinue COT. Our new standard process did show QI in the process steps but remained suboptimal to our expectations of clinical impact. More frequent office visits may impact patient decisions to reduce opioid dose and to add an augmenting pain medication. We plan to increase the involvement and responsibilities of our allied health staff in our processes to improve rates of adherence and the overall quality of how we manage patients on chronic opioid therapy.

CORRESPONDENCE
David Patchett, DO, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ 85259; [email protected]

ABSTRACT

Purpose The “opioid epidemic” in the United States has received increasing attention over the past few years. Most drug overdose deaths involve an opioid, and prescription opioid deaths have quadrupled since 1999. We sought to improve patient safety and adhere to clinical guidelines by standardizing opioid prescribing in our practice.

Methods We implemented a standardized approach to opioid prescribing based on Arizona Department of Health Services guidelines. All of our providers received instruction on Arizona’s Controlled Substance Prescription Monitoring Program (AZCSPMP) database and were encouraged to use it online. Our goal was for patients to have quarterly office visits, complete random urine drug screens, and sign a controlled substance agreement (CSA). The CSA acknowledged their understanding of the risks and benefits of opioid therapy as well as our updated prescribing policies.

 Results Three-hundred fifty-eight of our practice’s patients were receiving chronic opioid therapy. All providers enrolled in AZCSPMP and used it for patient care. We increased rates of signed CSAs from 4.5% to 43.6%, and urine drug screening from 0.8% to 20.1%. For 325 patients remaining in the practice after our interventions, a postintervention chart review demonstrated a statistically significant discontinuation of opioid therapy (71/325, 21.8%; 95% confidence interval, 17.4%-26.7%).

 Conclusion Implementation of a standardized opioid prescribing process resulted in discontinuation of therapy for some patients. Rates increased for signed CSAs and completed random urine drug screening. Future process interventions may improve patient and provider adherence. All primary care physicians should examine their prescribing processes to enhance the safety of opioid therapy.

[polldaddy:10370177]

The US opioid epidemic has received increased attention both nationally and at the state level over the past 2 years. This attention is warranted given the significant societal burden of opioid misuse, abuse, and overdose. Most drug overdose deaths (> 6/10) involve an opioid.¹ Deaths from prescription opioids have quadrupled since 1999 in the United States.² Arizona, the state in which we practice, ranked sixth highest in the nation for drug overdose deaths and had the fifth highest opioid prescribing rate in 2011.³ In response to the growing epidemic, the Centers for Disease Control and Prevention (CDC) released guidelines in 2016 for prescribing and monitoring opioids for chronic pain.⁴

Chronic nonterminal pain (CNTP) remains a significant cause of human suffering and is more prevalent in the United States than cancer, diabetes, and heart disease combined.⁵ The increased use of opioids since 1999 to ease CNTP has not reduced Americans’ reports of pain overall.^6,7 Given the growing opioid epidemic and disease burden of CNTP, we embarked on a quality improvement (QI) project to safely prescribe and refill opioid medications in the Department of Family Medicine at the Mayo Clinic Arizona.

Under our new prescription process, patient completion of controlled substance agreements rose from 4.5% at baseline to 43.6% after the intervention.

METHODS

This project received an exemption from internal review board evaluation as a QI intervention. We used a team-based approach to address standardization of opioid prescribing and monitoring within our practice. The team included physicians (MD/DO), nurses (LPN/RN), and allied health staff (MA), operations and administrative personnel, and information technology (IT) support. We did not involve patients in the initial design of our project. With future quality efforts in this area, we plan to involve patients in design processes.

Continue to: We began by identifying...

We began by identifying the scope of the problem, establishing criteria to search the electronic medical record (EMR) and identify appropriate patients. Chronic pain is often defined as pain lasting more than 3 months. Chronic opioid therapy (COT) has been defined as opioid use lasting longer than 3 months.⁸ Working with our IT colleagues, we defined COT patients as those with 3 or more prescriptions for opioids in the past year or those who received ≥ 30 pills a month (ie, patients who received 180 pills with 2 prescriptions written for the year). This definition gave us the ability to query our EMR to determine which patients were on COT, and we prepared lists of patients by primary care provider (FIGURE). Providers reviewed the lists to ensure these individuals were in fact on COT for CNTP. The number of patients identified after EMR query and provider review was 358, comprising 2.6% of 14,000 empaneled patients.

We based our interventions on the Arizona Department of Health Services 2014 opioid prescribing guidelines.³ The Arizona guidelines used existing national and state opioid prescribing guidelines along with clinical practice guidelines. Our study began prior to the 2016 CDC guidelines, so they were not used in this study. Our practice guidelines recommended that all 23 of our providers (MDs, DOs, and NPs) sign up for Arizona’s Controlled Substance Prescription Monitoring Program (AZCSPMP). We asked each patient to sign a controlled substance agreement (CSA), acknowledging their awareness of our proposed processes and the discussion of opioid therapy. Patients were expected to have face-to-face visits with providers at least quarterly and to complete a random urine drug screen at least annually. Patients were not incentivized to complete the process. We placed reminder calls for appointments just as we do for regular appointments.

Providers were asked to complete the Opioid Risk Tool⁹ with the patient at the initial visit, discuss the risks, benefits, and alternatives of long-term use of opioid medication, and review the 6 As (analgesia, activity, aberrant drug related behavior, adverse effects, affect, and adjunctive treatments). On the day before each patient visit, providers were reminded by a note in the EMR schedule to check AZCSPMP. Initial appointment times would be 30 minutes and follow-up appointments would be scheduled for 15 minutes if only addressing COT.

The QI project was introduced at an all-staff meeting in October 2015 that included providers, allied health staff, front desk personnel, and administrative staff, with the goal of beginning our COT process in November. We mailed letters to COT patients describing our new guidelines and asking them to call to schedule an appointment. If patients on COT came into the office for an alternate appointment and had not yet been seen for a COT visit, providers were encouraged to complete the COT process at that time.

We created a standard order set in the EMR for initial and follow-up visits and for the urine drug screen. We also added an interactive form to the EMR allowing providers to electronically complete the Opioid Risk Tool, and to confirm CSA completion and AZCSPMP review. We developed a database that would query the EMR for patient office visit frequency, CSA completion, and urine drug screen collection. We also placed paper copies of forms in exam rooms with a laminated instruction sheet reviewing the process steps and the 6 As.

Continue to: Soft rollout was...

Soft rollout was November 1, 2015, to assist in working through the process before full rollout. We asked providers to complete the full process on at least 1 patient during this period. This run-through would help ensure that allied health staff who room the patients would have the CSA and Opioid Risk Tool already in the chart before the visit. Full rollout was January 2, 2016. Every 2 to 4 weeks after the full rollout, regular email reminders were sent to providers about the project process and allowed for any feedback about issues that arose.

There was a statistically significant reduction in the number of patients using opioids.

We provided regular updates and discussed the process at department meetings monthly. Quarterly data were reviewed and discussed for the first year of implementation. Providers and staff completed a chart review for each COT patient at project completion, to determine whether opioids had been decreased (in dosage) or discontinued, a nonopioid medicine had been initiated to augment pain control, or whether patients had died or left the practice.

Statistical analysis

We summarized binary data as counts and proportions and compared them using the chi square test. We summarized discrete data by their mean and standard deviation. To analyze binary variables measured repeatedly in time, we used the logistic generalized estimating equation (GEE) with an autoregressive (AR-1) correlation structure. We computed 95% confidence intervals (CIs)for odds ratios using the empirical or “sandwich” standard error estimates. For discrete variables representing counts, we used the negative binomial regression model.

For count data, a Poisson model is typically used; in our case the variance was considerably larger than the mean, exceeding the Poisson-model requirement that they not be significantly different if not exactly the same. This implies that the data are “over dispersed” or more variable than a Poisson model is thought to be able to model accurately. We therefore used a negative binomial model, which is regarded as the better model in this situation. The 95% CIs for the estimate resulting from the negative binomial regression model were computed using the profile-likelihood.¹⁰ All GEEs were clustered on patients (n = 358). We used SAS version 9.3 (Cary, NC) for all analyses.

RESULTS

All providers enrolled for AZCSPMP. CSA completion increased from 16 (4.5%) at baseline to 156 (43.6%) after intervention (P < .001). Patients completed a urine drug screen more frequently as well, from 3 (0.8%) to 72 (20.1%) (P < .001) (TABLES 1 and 2). No statistically significant change was noted in the frequency of office visits.

Continue to: We excluded 33 patients...

We excluded 33 patients from the post-intervention chart review (TABLE 3). Twenty-seven had left the practice and 6 had died, leaving 325 patients included in the post-intervention chart review. There was a statistically significant reduction in the number of patients who used opioids 71 (21.8%; 95% CI, 17.4%-26.7%). We noted no statistically significant association with a decrease in opioid dosage. Fifty-five patients (16.9%) added an augmenting medication, the most common being gabapentin. Adding an augmenting medication was not associated with either stopping or decreasing opioid dosage.

There was a statistically significant association between patients who discontinued opioids and those who neglected to sign a CSA (P < .001) (TABLE 4). We tested for associations between office visit frequency and process step completion. There was a nonsignificant trend between increased frequency of office visits and opioid dose reduction. Patients who stopped opioids had fewer office visits (TABLE 5), while patients who had initiated a medication to augment pain relief had more frequent office visits (TABLE 6).

DISCUSSION

Our interventions to improve the quality of our COT processes were moderately successful. We achieved statistically significant increases in our rates of CSA completion and in urine drug screening. However, these increases were not as clinically impactful as we had hoped. Improvements in both patient and provider adherence are needed. We plan to engage allied health staff more fully to assist with adherence and thereby improve quality. This study was not intended to obtain patient-oriented outcomes, such as decreased pain and improved function. The study was designed to improve patient safety and to standardize a process for prescribing and monitoring patients on COT. In the future we plan to look at patient outcomes and expand our focus to patients on high-dose opioids and those on combination therapy with benzodiazepines.

The most impactful steps likely were the letters sent to chronic opioid therapy patients describing our standardized prescribing process and the ensuing provider-patient talks.

We believe the most impactful process steps were our letters sent to COT patients describing our updated, standardized prescribing process, and the ensuing provider-patient discussion to review the risks, benefits, and alternatives to opioid therapy. This frank discussion of treatment options resulted in more than 1 in 5 patients electing to discontinue COT.

There was an association between opioid discontinuation and patients not signing the CSA. This may have been due to patients deciding to discontinue opioids at the initiation review with providers after they received their letter. Therefore, signing the agreement was no longer necessary.

Continue to: We noted that some patients...

We noted that some patients elected to begin a new, nonopioid medication intended to augment their pain relief. However, they did not decrease their use of opioid medicines. We did not collect pain rating scale scores to determine whether the addition of augmenting medicines provided a reduction in pain perception.

Close monitoring of COT patients with frequent office visits may have had an impact on their care. We noted an association between more frequent visits and initiation of pain augmentation medicines. There was also a nonsignificant trend between office visit frequency and dose reduction. These are topics we may re-examine in our practice over time. There was no change in office visit frequency with our intervention, likely a result of these patients having frequent office visits for multiple comorbid medical conditions at baseline.

Evidence of similar benefits in primary care practices that standardized their opioid prescribing guidelines for patients on COT¹¹ illustrates the importance of such a process for ensuring patient safety and decreasing opioid dosage and use.

Limitations to our project are that we did not measure functional changes and quality-of-life scores for patients. We also did not note the opioid dosages for individuals who chose to stop using opioids.

Looking forward. Based on our experience, patient notification with discussion of COT risks, benefits, and alternatives, as well as implementation of a process to monitor COT, appear to be related to patients’ decisions to discontinue COT. Our new standard process did show QI in the process steps but remained suboptimal to our expectations of clinical impact. More frequent office visits may impact patient decisions to reduce opioid dose and to add an augmenting pain medication. We plan to increase the involvement and responsibilities of our allied health staff in our processes to improve rates of adherence and the overall quality of how we manage patients on chronic opioid therapy.

CORRESPONDENCE
David Patchett, DO, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ 85259; [email protected]

ABSTRACT

Purpose The “opioid epidemic” in the United States has received increasing attention over the past few years. Most drug overdose deaths involve an opioid, and prescription opioid deaths have quadrupled since 1999. We sought to improve patient safety and adhere to clinical guidelines by standardizing opioid prescribing in our practice.

Methods We implemented a standardized approach to opioid prescribing based on Arizona Department of Health Services guidelines. All of our providers received instruction on Arizona’s Controlled Substance Prescription Monitoring Program (AZCSPMP) database and were encouraged to use it online. Our goal was for patients to have quarterly office visits, complete random urine drug screens, and sign a controlled substance agreement (CSA). The CSA acknowledged their understanding of the risks and benefits of opioid therapy as well as our updated prescribing policies.

 Results Three-hundred fifty-eight of our practice’s patients were receiving chronic opioid therapy. All providers enrolled in AZCSPMP and used it for patient care. We increased rates of signed CSAs from 4.5% to 43.6%, and urine drug screening from 0.8% to 20.1%. For 325 patients remaining in the practice after our interventions, a postintervention chart review demonstrated a statistically significant discontinuation of opioid therapy (71/325, 21.8%; 95% confidence interval, 17.4%-26.7%).

 Conclusion Implementation of a standardized opioid prescribing process resulted in discontinuation of therapy for some patients. Rates increased for signed CSAs and completed random urine drug screening. Future process interventions may improve patient and provider adherence. All primary care physicians should examine their prescribing processes to enhance the safety of opioid therapy.

[polldaddy:10370177]

The US opioid epidemic has received increased attention both nationally and at the state level over the past 2 years. This attention is warranted given the significant societal burden of opioid misuse, abuse, and overdose. Most drug overdose deaths (> 6/10) involve an opioid.¹ Deaths from prescription opioids have quadrupled since 1999 in the United States.² Arizona, the state in which we practice, ranked sixth highest in the nation for drug overdose deaths and had the fifth highest opioid prescribing rate in 2011.³ In response to the growing epidemic, the Centers for Disease Control and Prevention (CDC) released guidelines in 2016 for prescribing and monitoring opioids for chronic pain.⁴

Chronic nonterminal pain (CNTP) remains a significant cause of human suffering and is more prevalent in the United States than cancer, diabetes, and heart disease combined.⁵ The increased use of opioids since 1999 to ease CNTP has not reduced Americans’ reports of pain overall.^6,7 Given the growing opioid epidemic and disease burden of CNTP, we embarked on a quality improvement (QI) project to safely prescribe and refill opioid medications in the Department of Family Medicine at the Mayo Clinic Arizona.

Under our new prescription process, patient completion of controlled substance agreements rose from 4.5% at baseline to 43.6% after the intervention.

METHODS

This project received an exemption from internal review board evaluation as a QI intervention. We used a team-based approach to address standardization of opioid prescribing and monitoring within our practice. The team included physicians (MD/DO), nurses (LPN/RN), and allied health staff (MA), operations and administrative personnel, and information technology (IT) support. We did not involve patients in the initial design of our project. With future quality efforts in this area, we plan to involve patients in design processes.

Continue to: We began by identifying...

We began by identifying the scope of the problem, establishing criteria to search the electronic medical record (EMR) and identify appropriate patients. Chronic pain is often defined as pain lasting more than 3 months. Chronic opioid therapy (COT) has been defined as opioid use lasting longer than 3 months.⁸ Working with our IT colleagues, we defined COT patients as those with 3 or more prescriptions for opioids in the past year or those who received ≥ 30 pills a month (ie, patients who received 180 pills with 2 prescriptions written for the year). This definition gave us the ability to query our EMR to determine which patients were on COT, and we prepared lists of patients by primary care provider (FIGURE). Providers reviewed the lists to ensure these individuals were in fact on COT for CNTP. The number of patients identified after EMR query and provider review was 358, comprising 2.6% of 14,000 empaneled patients.

We based our interventions on the Arizona Department of Health Services 2014 opioid prescribing guidelines.³ The Arizona guidelines used existing national and state opioid prescribing guidelines along with clinical practice guidelines. Our study began prior to the 2016 CDC guidelines, so they were not used in this study. Our practice guidelines recommended that all 23 of our providers (MDs, DOs, and NPs) sign up for Arizona’s Controlled Substance Prescription Monitoring Program (AZCSPMP). We asked each patient to sign a controlled substance agreement (CSA), acknowledging their awareness of our proposed processes and the discussion of opioid therapy. Patients were expected to have face-to-face visits with providers at least quarterly and to complete a random urine drug screen at least annually. Patients were not incentivized to complete the process. We placed reminder calls for appointments just as we do for regular appointments.

Providers were asked to complete the Opioid Risk Tool⁹ with the patient at the initial visit, discuss the risks, benefits, and alternatives of long-term use of opioid medication, and review the 6 As (analgesia, activity, aberrant drug related behavior, adverse effects, affect, and adjunctive treatments). On the day before each patient visit, providers were reminded by a note in the EMR schedule to check AZCSPMP. Initial appointment times would be 30 minutes and follow-up appointments would be scheduled for 15 minutes if only addressing COT.

The QI project was introduced at an all-staff meeting in October 2015 that included providers, allied health staff, front desk personnel, and administrative staff, with the goal of beginning our COT process in November. We mailed letters to COT patients describing our new guidelines and asking them to call to schedule an appointment. If patients on COT came into the office for an alternate appointment and had not yet been seen for a COT visit, providers were encouraged to complete the COT process at that time.

We created a standard order set in the EMR for initial and follow-up visits and for the urine drug screen. We also added an interactive form to the EMR allowing providers to electronically complete the Opioid Risk Tool, and to confirm CSA completion and AZCSPMP review. We developed a database that would query the EMR for patient office visit frequency, CSA completion, and urine drug screen collection. We also placed paper copies of forms in exam rooms with a laminated instruction sheet reviewing the process steps and the 6 As.

Continue to: Soft rollout was...

Soft rollout was November 1, 2015, to assist in working through the process before full rollout. We asked providers to complete the full process on at least 1 patient during this period. This run-through would help ensure that allied health staff who room the patients would have the CSA and Opioid Risk Tool already in the chart before the visit. Full rollout was January 2, 2016. Every 2 to 4 weeks after the full rollout, regular email reminders were sent to providers about the project process and allowed for any feedback about issues that arose.

There was a statistically significant reduction in the number of patients using opioids.

We provided regular updates and discussed the process at department meetings monthly. Quarterly data were reviewed and discussed for the first year of implementation. Providers and staff completed a chart review for each COT patient at project completion, to determine whether opioids had been decreased (in dosage) or discontinued, a nonopioid medicine had been initiated to augment pain control, or whether patients had died or left the practice.

Statistical analysis

We summarized binary data as counts and proportions and compared them using the chi square test. We summarized discrete data by their mean and standard deviation. To analyze binary variables measured repeatedly in time, we used the logistic generalized estimating equation (GEE) with an autoregressive (AR-1) correlation structure. We computed 95% confidence intervals (CIs)for odds ratios using the empirical or “sandwich” standard error estimates. For discrete variables representing counts, we used the negative binomial regression model.

For count data, a Poisson model is typically used; in our case the variance was considerably larger than the mean, exceeding the Poisson-model requirement that they not be significantly different if not exactly the same. This implies that the data are “over dispersed” or more variable than a Poisson model is thought to be able to model accurately. We therefore used a negative binomial model, which is regarded as the better model in this situation. The 95% CIs for the estimate resulting from the negative binomial regression model were computed using the profile-likelihood.¹⁰ All GEEs were clustered on patients (n = 358). We used SAS version 9.3 (Cary, NC) for all analyses.

RESULTS

All providers enrolled for AZCSPMP. CSA completion increased from 16 (4.5%) at baseline to 156 (43.6%) after intervention (P < .001). Patients completed a urine drug screen more frequently as well, from 3 (0.8%) to 72 (20.1%) (P < .001) (TABLES 1 and 2). No statistically significant change was noted in the frequency of office visits.

Continue to: We excluded 33 patients...

We excluded 33 patients from the post-intervention chart review (TABLE 3). Twenty-seven had left the practice and 6 had died, leaving 325 patients included in the post-intervention chart review. There was a statistically significant reduction in the number of patients who used opioids 71 (21.8%; 95% CI, 17.4%-26.7%). We noted no statistically significant association with a decrease in opioid dosage. Fifty-five patients (16.9%) added an augmenting medication, the most common being gabapentin. Adding an augmenting medication was not associated with either stopping or decreasing opioid dosage.

There was a statistically significant association between patients who discontinued opioids and those who neglected to sign a CSA (P < .001) (TABLE 4). We tested for associations between office visit frequency and process step completion. There was a nonsignificant trend between increased frequency of office visits and opioid dose reduction. Patients who stopped opioids had fewer office visits (TABLE 5), while patients who had initiated a medication to augment pain relief had more frequent office visits (TABLE 6).

DISCUSSION

Our interventions to improve the quality of our COT processes were moderately successful. We achieved statistically significant increases in our rates of CSA completion and in urine drug screening. However, these increases were not as clinically impactful as we had hoped. Improvements in both patient and provider adherence are needed. We plan to engage allied health staff more fully to assist with adherence and thereby improve quality. This study was not intended to obtain patient-oriented outcomes, such as decreased pain and improved function. The study was designed to improve patient safety and to standardize a process for prescribing and monitoring patients on COT. In the future we plan to look at patient outcomes and expand our focus to patients on high-dose opioids and those on combination therapy with benzodiazepines.

The most impactful steps likely were the letters sent to chronic opioid therapy patients describing our standardized prescribing process and the ensuing provider-patient talks.

We believe the most impactful process steps were our letters sent to COT patients describing our updated, standardized prescribing process, and the ensuing provider-patient discussion to review the risks, benefits, and alternatives to opioid therapy. This frank discussion of treatment options resulted in more than 1 in 5 patients electing to discontinue COT.

There was an association between opioid discontinuation and patients not signing the CSA. This may have been due to patients deciding to discontinue opioids at the initiation review with providers after they received their letter. Therefore, signing the agreement was no longer necessary.

Continue to: We noted that some patients...

We noted that some patients elected to begin a new, nonopioid medication intended to augment their pain relief. However, they did not decrease their use of opioid medicines. We did not collect pain rating scale scores to determine whether the addition of augmenting medicines provided a reduction in pain perception.

Close monitoring of COT patients with frequent office visits may have had an impact on their care. We noted an association between more frequent visits and initiation of pain augmentation medicines. There was also a nonsignificant trend between office visit frequency and dose reduction. These are topics we may re-examine in our practice over time. There was no change in office visit frequency with our intervention, likely a result of these patients having frequent office visits for multiple comorbid medical conditions at baseline.

Evidence of similar benefits in primary care practices that standardized their opioid prescribing guidelines for patients on COT¹¹ illustrates the importance of such a process for ensuring patient safety and decreasing opioid dosage and use.

Limitations to our project are that we did not measure functional changes and quality-of-life scores for patients. We also did not note the opioid dosages for individuals who chose to stop using opioids.

Looking forward. Based on our experience, patient notification with discussion of COT risks, benefits, and alternatives, as well as implementation of a process to monitor COT, appear to be related to patients’ decisions to discontinue COT. Our new standard process did show QI in the process steps but remained suboptimal to our expectations of clinical impact. More frequent office visits may impact patient decisions to reduce opioid dose and to add an augmenting pain medication. We plan to increase the involvement and responsibilities of our allied health staff in our processes to improve rates of adherence and the overall quality of how we manage patients on chronic opioid therapy.

CORRESPONDENCE
David Patchett, DO, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ 85259; [email protected]

ABSTRACT

Purpose To derive a predictive model for obstructive sleep apnea (OSA) in primary care practice, using home-based overnight oximetry results to refine posttest probability (PTP) of disease after initial risk stratification with the Sleep Apnea Clinical Score (SACS).

Methods We performed secondary analyses on data from a SACS validation cohort, to compare the diagnostic accuracy of 3 overnight oximetry measurements (oxygen desaturation index [ODI], mean saturation, and minimum saturation) in predicting OSA. Receiver operator characteristics (ROC) were computed for each measurement independently and sequentially after risk stratifying with SACS. We examined the implications of oximetry results for OSA PTP for participants categorized as intermediate risk (SACS 6-14; 66/191 participants [35%]; OSA probability 41%). We calculated positive likelihood ratios (LR) for multiple ODI results and determined which ones allowed recalibration to high- or low-risk PTP.

Results Among the 3 oximetry findings, ODI best predicted OSA (area under the curve [AUC], 0.88; 95% confidence interval [CI], 0.83-0.93). An ODI ≥8.4 (likelihood ratio [LR], 4.19; 95% CI, 2.87-6.10) created a PTP of 77%, while an ODI of 0 to <8.4 (LR, 0.19, 95% CI, 0.12-0.33) created a 14% PTP. Sequential application of SACS and ODI results yielded an AUC result of 0.90 (95% CI, 0.85-0.95).

Conclusions SACS risk stratification provides an advantage over clinical gestalt. In those at intermediate risk, ODI results provide a simple and clinically useful way to further refine diagnostic prediction. Sequential use of SACS and selectively employed overnight oximetry may limit unnecessary polysomnography. Oximetry testing should be avoided in patients deemed low or high risk by SACS, as positive results do not substantially recalibrate risk.

Obstructive sleep apnea (OSA) is a prevalent and underdiagnosed condition. The National Sleep Foundation estimates that 18 million Americans have OSA.¹ Primary care practice may be the best setting in which to identify OSA, as many of our patients have conditions frequently associated with apnea (eg, hypertension, obesity, diabetes, arrhythmia, and neurologic illness). Up to a third of patients in primary care practice may be at increased risk.^2,3

Clinical guidelines of the American Academy of Sleep Medicine (AASM) recommend obtaining a sleep history to evaluate for possible OSA in 3 instances: as part of a routine health maintenance examination, during evaluation of specific complaints associated with OSA (eg, snoring, apnea, daytime sleepiness), and during comprehensive evaluations for individuals with high-risk conditions (ie, obesity, congestive heart failure, refractory hypertension, diabetes, stroke history).⁴

Providers can't simply rely on clinical gestalt when obstructive sleep apnea is suspected.

The American College of Physicians (ACP) Clinical Practice Guideline suggests assessing individuals who have unexplained daytime sleepiness.⁵ The ACP considers this assessment “High-Value Care,” as “evidence shows that before diagnosis, patients with OSA have higher rates of health care use, more frequent and longer hospital stays, and higher health care costs than after diagnosis.”⁵

Continue to: We recently validated the diagnostic accuracy...

We recently validated the diagnostic accuracy of the Sleep Apnea Clinical Score (SACS) for use in a primary care patient population suspected of having OSA.⁶ SACS uses historical and clinical data to derive a score that identifies a patient’s risk level.⁷ However, as an alternative to the 2 levels described in Flemons’ SACS,⁷ we propose creating 3 risk strata (FIGURE 1^7,8). We believe that patients at high risk (SACS ≥15) should be encouraged to undergo sleep evaluations as their posttest probability (PTP) of OSA is 75% to 80%. Individuals at low risk (SACS ≤5; PTP <20%) could receive lifestyle advice and simple clinical interventions that decrease symptoms (eg, weight loss, increased physical activity, sleeping on one’s side). For low-risk patients, clinical observation and reevaluation could take place over time with their primary care provider, without additional testing or referral to specialists.

What about patients at intermediate risk? Many patients suspected of having OSA will be assigned to intermediate risk (SACS 6-14), and their PTP of OSA remains at 40% to 45%, the pre-test level most commonly encountered in suspected OSA. As polysomnography is a limited and expensive clinical resource, intermediate-risk patients would benefit from recalibration of their SACS-based risk assessment using an additional surrogate test such as home-based overnight oximetry. Our internal OSA practice guidelines recommend referral for sleep medicine consultation when oximetry results are abnormal—specifically, an oxygen desaturation index (ODI) of ≥5, a mean saturation less than 89%, and a minimum saturation of 75% or less.

Serial application of the Sleep Apnea Clinical Score and overnight oxygen desaturation index yielded the best diagnostic results.

Our objectives in this study were to compare the diagnostic implications of these 3 measurements from home-based overnight oximetry reports and use the most relevant result to derive a predictive model further refining PTP of OSA in a primary care patient population first stratified to intermediate risk by SACS.

METHODS

Subjects

We performed secondary analyses on data obtained from our SACS validation cohort.⁶ In brief, these were patients suspected of having OSA based on the presence of signs, symptoms, or associated risk factors. One hundred ninety-one patients completed all assessments. Sixty-six of 191 patients (35%) were categorized as intermediate risk (SACS 6-14; OSA probability 41% [27/66]).

Data collection and analyses

Participants completed home-based overnight oximetry using Nonin Model 2500 oximeters (Nonin Medical Inc., Plymouth, Minn). We transferred oximetry results from the sleep lab database to a statistical program for analyses of ODI, mean saturation, and minimal saturation. ODI was defined as the number of 4% drops in saturation from baseline divided by the number of hours of recording time. Although the AASM states that a diagnosis of OSA is confirmed if the number of obstructive events is more than 15 per hour or more than 5 per hour in a patient who reports related symptoms,⁴ we defined OSA as an apnea-hypopnea index (AHI) of >10 based on polysomnography (as this was the threshold used in the derivation cohort for SACS).⁷ We demonstrated the predictive ability of SACS at various AHI definitions of OSA in our validation cohort.⁶ The use of SACS in our validation cohort showed a statistically similar ability to predict OSA at both an AHI of 10 and 20, compared with the derivation cohort.

Continue to: We entered additional information...

We entered additional information reported directly by patients and obtained from their sleep studies into a REDCap database and transferred that to our statistical program. We used descriptive statistics to determine ranges and central tendencies of oximetry results. Receiver operator characteristic (ROC) analyses described the predictive abilities for each oximetry result individually and in serial application with prior SACS determinations. For comparison, we used the area under the ROC curve (AUC) from logistic regression to model the probability of OSA.

An oxygen desaturation index result >10 effected an upward recalibration of disease probability.

We calculated positive likelihood ratios (LR) and 95% confidence intervals (CI) to determine the degree of oximetry abnormality that would recalibrate risk either to a high PTP of OSA (>75%) or a low PTP (<25%). We sorted intermediate-risk SACS scores into quintiles based on ODI results to compare the resulting PTPs of OSA. We applied the PTP of OSA from our previous work (using the SACS score to compute the LR) as the new PTP, estimated the LR based on ODI, and computed an updated PTP of OSA. We also used ROC analysis to determine the optimal cutoff value of the ODI.

Finally, in accordance with our internal clinical practice recommendations, we examined the predictive ability of a “positive” ODI result of ≥5 to recalibrate risk prediction for OSA for patients in the low-risk group. We performed analyses using SAS 9.4 (SAS Institute, Cary, NC).

RESULTS

One hundred ninety-one subjects completed assessments. The median and quartile results for ODI, mean saturation, and minimum saturation are found in TABLE 1. TABLE 2 shows the distribution of patients with positive oximetry results. An ODI of 5 or greater was the most frequent abnormal result (135/191; 70.7%).

We used the AUC to measure the comparative abilities of SACS and the 3 overnight oximetry results in predicting OSA (TABLE 3). ODI results demonstrated the best ability to predict OSA, compared with polysomnography as the relative gold standard (AUC, 0.88; 95% confidence interval [CI], 0.83-0.93). Serial application of SACS and ODI yielded even better diagnostic results (AUC, 0.90; 95% CI, 0.85-0.95).

Continue to: As ODI was found to be the strongest predictor of OSA...

As ODI was found to be the strongest predictor of OSA, we grouped these results in quintiles and calculated positive LRs. TABLE 4 shows their effect on PTP of disease among patients with intermediate risk. An ODI result >10 effected an upward recalibration of disease probability (LR, 2.33; 95% CI, 1.27-4.26). The optimal cutoff of ODI to discriminate between those with and without OSA was determined by ROC analysis. An ODI greater than 8.4 created a PTP of disease of approximately 73% to 77%.

Our internal clinical guidelines recommend referring patients with an ODI of 5 or greater for sleep medicine consultation. We examined the ability of this ODI result to recalibrate disease suspicion for a patient at low risk (SACS ≤5). The LR for ODI of 5 or greater is 2.1, but this only results in a recalibration of risk from 24% pretest probability in our validation cohort to 41% PTP (95% CI, 33-49). This low cutoff for a positive test creates false-positive results more than 40% of the time due to low specificity (0.58). This is insufficient to change the suspicion of disease, resulting only in a shift to intermediate OSA risk.

DISCUSSION

Among 3 different oximetry measurements, an ODI ≥10 best predicts OSA, both independently and when used sequentially after the SACS. ODI was by far the most frequent abnormality on oximetry in our cohort, thereby increasing its utility in clinical decision making. For those subjects at intermediate risk, a cutoff of 10 for the ODI result may be a simple and clinically effective way to recalibrate risk and aid in making referral decisions. (This may also be simpler and more easily remembered by clinicians than the 8.4 ODI results from the ROC analyses.)

Assessment is inadequate without a clinical prediction rule. Unfortunately, providers cannot simply rely on clinical gestalt in diagnosing OSA. In their derivation cohort, Flemens et al examined the LRs created by SACS and by clinician prediction based on history and physical exam.⁷ The SACS LRs ranged from 5.17 to 0.25, a 20-fold range. This reflected superior diagnostic information compared with subjective physician impression, where LRs ranged from 3.7 to 0.52, a seven-fold range. Myers et al prepared a meta-analysis of 4 different trials that examined physicians’ ability to predict OSA.⁹ Despite the researchers’ use of experienced sleep medicine doctors, the overall diagnostic accuracy of clinical impression was modest (summary positive LR, 1.7; 95% CI, 1.5-2; I² = 0%; summary negative LR, 0.67; 95% CI, 0.60-0.74; I² = 10%; sensitivity, 58%; specificity, 67%). This is similar to reliance on a single clinical sign or symptom to predict OSA.

Wise use of oximetry augments SACS calculation. To limit unnecessary oximetry testing in low- and high-risk groups and to avoid polysomnography in cases of a low PTP of disease, we advocate limiting oximetry testing to individuals in the SACS intermediate-risk group (FIGURE 2) wherein ODI results can potentially recalibrate risk assessment up or down. (Those in the high- risk group should be referred to a sleep medicine specialist.) Our institutional recommendation of using an ODI result of ≥5 as a threshold to increase suspicion of disease requires a caveat for the low-risk group. “Positive” results at that low diagnostic threshold are frequently false.

Continue to: Multiple benefits of SACS

Multiple benefits of SACS. We believe using the SACS calculation during clinical encounters with patients potentially at risk for OSA would increase diagnostic accuracy. Performing risk stratification with SACS should not be an undue burden on providers, and the increased time spent with patients has its own benefits, including helping them better understand their risk. Using this standardized process—augmented, as needed, with overnight ODI assessment—might also encourage more patients to follow through on subsequent recommendations, as their risk is further quantified objectively. Lastly, unnecessary testing with polysomnography could be avoided.

Limitations of our study. This study’s findings were derived from a patient population in a single institution. Replication of the findings from other settings would be helpful.

Looking forward. It is yet unclear if clinicians will embrace these strategies in real-world primary care practice. We have designed an implementation-and-dissemination trial to assess whether family physicians will use the SACS clinical predication rule in everyday practice and whether our evidence-based recommendations about overnight oximetry will be followed. Underlying our suggested clinical evaluation pathway (FIGURE 2) is the belief that there is value gained from sharing the decision-making process with patients. Although we provide new evidence that informs these conversations, the patient’s values and preferences are important when determining the best direction to proceed in the evaluation for suspected OSA. These recommendations are intended to aid, not replace, good clinical judgment.

Home-based sleep testing has become more widely available, is convenient for patients, and is less expensive than lab-based polysomnography. Our study did not directly address the appropriate circumstances for home studies in clinical evaluation. We rely on the expertise of our sleep medicine colleagues to determine which patients are appropriate candidates for home-based studies.

The AASM states that “portable monitors (PM) for the diagnosis of OSA should be [used] only in conjunction with a comprehensive sleep evaluation. Clinical sleep evaluations using PM must be supervised by a practitioner with board certification in sleep medicine or an individual who fulfills the eligibility criteria for the sleep medicine certification examination.”⁴ Additionally, the group recommends that PM “may be used in the unattended setting as an alternative to polysomnography for the diagnosis of OSA in patients with a high pretest probability of moderate to severe OSA and no comorbid sleep disorder or major comorbid medical disorders.”⁴

Continue to: GRANT SUPPORT

GRANT SUPPORT
The use of the REDCap database is supported by grant UL1 TR000135. This work was supported by a Mayo Foundation CR-20 grant awarded to Dr. Mookadam as Principal investigator and Dr. Grover as Coinvestigator.

Statistical analyses were supported, in part, by the Department of Family Medicine, Mayo Clinic, Scottsdale, Ariz.

CORRESPONDENCE
Michael Grover, DO, Mayo Clinic Thunderbird Primary Care Center-Family Medicine, 13737 N 92nd Street, Scottsdale, AZ 85260; [email protected]

ABSTRACT

Purpose To derive a predictive model for obstructive sleep apnea (OSA) in primary care practice, using home-based overnight oximetry results to refine posttest probability (PTP) of disease after initial risk stratification with the Sleep Apnea Clinical Score (SACS).

Methods We performed secondary analyses on data from a SACS validation cohort, to compare the diagnostic accuracy of 3 overnight oximetry measurements (oxygen desaturation index [ODI], mean saturation, and minimum saturation) in predicting OSA. Receiver operator characteristics (ROC) were computed for each measurement independently and sequentially after risk stratifying with SACS. We examined the implications of oximetry results for OSA PTP for participants categorized as intermediate risk (SACS 6-14; 66/191 participants [35%]; OSA probability 41%). We calculated positive likelihood ratios (LR) for multiple ODI results and determined which ones allowed recalibration to high- or low-risk PTP.

Results Among the 3 oximetry findings, ODI best predicted OSA (area under the curve [AUC], 0.88; 95% confidence interval [CI], 0.83-0.93). An ODI ≥8.4 (likelihood ratio [LR], 4.19; 95% CI, 2.87-6.10) created a PTP of 77%, while an ODI of 0 to <8.4 (LR, 0.19, 95% CI, 0.12-0.33) created a 14% PTP. Sequential application of SACS and ODI results yielded an AUC result of 0.90 (95% CI, 0.85-0.95).

Conclusions SACS risk stratification provides an advantage over clinical gestalt. In those at intermediate risk, ODI results provide a simple and clinically useful way to further refine diagnostic prediction. Sequential use of SACS and selectively employed overnight oximetry may limit unnecessary polysomnography. Oximetry testing should be avoided in patients deemed low or high risk by SACS, as positive results do not substantially recalibrate risk.

Obstructive sleep apnea (OSA) is a prevalent and underdiagnosed condition. The National Sleep Foundation estimates that 18 million Americans have OSA.¹ Primary care practice may be the best setting in which to identify OSA, as many of our patients have conditions frequently associated with apnea (eg, hypertension, obesity, diabetes, arrhythmia, and neurologic illness). Up to a third of patients in primary care practice may be at increased risk.^2,3

Clinical guidelines of the American Academy of Sleep Medicine (AASM) recommend obtaining a sleep history to evaluate for possible OSA in 3 instances: as part of a routine health maintenance examination, during evaluation of specific complaints associated with OSA (eg, snoring, apnea, daytime sleepiness), and during comprehensive evaluations for individuals with high-risk conditions (ie, obesity, congestive heart failure, refractory hypertension, diabetes, stroke history).⁴

Providers can't simply rely on clinical gestalt when obstructive sleep apnea is suspected.

The American College of Physicians (ACP) Clinical Practice Guideline suggests assessing individuals who have unexplained daytime sleepiness.⁵ The ACP considers this assessment “High-Value Care,” as “evidence shows that before diagnosis, patients with OSA have higher rates of health care use, more frequent and longer hospital stays, and higher health care costs than after diagnosis.”⁵

Continue to: We recently validated the diagnostic accuracy...

We recently validated the diagnostic accuracy of the Sleep Apnea Clinical Score (SACS) for use in a primary care patient population suspected of having OSA.⁶ SACS uses historical and clinical data to derive a score that identifies a patient’s risk level.⁷ However, as an alternative to the 2 levels described in Flemons’ SACS,⁷ we propose creating 3 risk strata (FIGURE 1^7,8). We believe that patients at high risk (SACS ≥15) should be encouraged to undergo sleep evaluations as their posttest probability (PTP) of OSA is 75% to 80%. Individuals at low risk (SACS ≤5; PTP <20%) could receive lifestyle advice and simple clinical interventions that decrease symptoms (eg, weight loss, increased physical activity, sleeping on one’s side). For low-risk patients, clinical observation and reevaluation could take place over time with their primary care provider, without additional testing or referral to specialists.

What about patients at intermediate risk? Many patients suspected of having OSA will be assigned to intermediate risk (SACS 6-14), and their PTP of OSA remains at 40% to 45%, the pre-test level most commonly encountered in suspected OSA. As polysomnography is a limited and expensive clinical resource, intermediate-risk patients would benefit from recalibration of their SACS-based risk assessment using an additional surrogate test such as home-based overnight oximetry. Our internal OSA practice guidelines recommend referral for sleep medicine consultation when oximetry results are abnormal—specifically, an oxygen desaturation index (ODI) of ≥5, a mean saturation less than 89%, and a minimum saturation of 75% or less.

Serial application of the Sleep Apnea Clinical Score and overnight oxygen desaturation index yielded the best diagnostic results.

Our objectives in this study were to compare the diagnostic implications of these 3 measurements from home-based overnight oximetry reports and use the most relevant result to derive a predictive model further refining PTP of OSA in a primary care patient population first stratified to intermediate risk by SACS.

METHODS

Subjects

We performed secondary analyses on data obtained from our SACS validation cohort.⁶ In brief, these were patients suspected of having OSA based on the presence of signs, symptoms, or associated risk factors. One hundred ninety-one patients completed all assessments. Sixty-six of 191 patients (35%) were categorized as intermediate risk (SACS 6-14; OSA probability 41% [27/66]).

Data collection and analyses

Participants completed home-based overnight oximetry using Nonin Model 2500 oximeters (Nonin Medical Inc., Plymouth, Minn). We transferred oximetry results from the sleep lab database to a statistical program for analyses of ODI, mean saturation, and minimal saturation. ODI was defined as the number of 4% drops in saturation from baseline divided by the number of hours of recording time. Although the AASM states that a diagnosis of OSA is confirmed if the number of obstructive events is more than 15 per hour or more than 5 per hour in a patient who reports related symptoms,⁴ we defined OSA as an apnea-hypopnea index (AHI) of >10 based on polysomnography (as this was the threshold used in the derivation cohort for SACS).⁷ We demonstrated the predictive ability of SACS at various AHI definitions of OSA in our validation cohort.⁶ The use of SACS in our validation cohort showed a statistically similar ability to predict OSA at both an AHI of 10 and 20, compared with the derivation cohort.

Continue to: We entered additional information...

We entered additional information reported directly by patients and obtained from their sleep studies into a REDCap database and transferred that to our statistical program. We used descriptive statistics to determine ranges and central tendencies of oximetry results. Receiver operator characteristic (ROC) analyses described the predictive abilities for each oximetry result individually and in serial application with prior SACS determinations. For comparison, we used the area under the ROC curve (AUC) from logistic regression to model the probability of OSA.

An oxygen desaturation index result >10 effected an upward recalibration of disease probability.

We calculated positive likelihood ratios (LR) and 95% confidence intervals (CI) to determine the degree of oximetry abnormality that would recalibrate risk either to a high PTP of OSA (>75%) or a low PTP (<25%). We sorted intermediate-risk SACS scores into quintiles based on ODI results to compare the resulting PTPs of OSA. We applied the PTP of OSA from our previous work (using the SACS score to compute the LR) as the new PTP, estimated the LR based on ODI, and computed an updated PTP of OSA. We also used ROC analysis to determine the optimal cutoff value of the ODI.

Finally, in accordance with our internal clinical practice recommendations, we examined the predictive ability of a “positive” ODI result of ≥5 to recalibrate risk prediction for OSA for patients in the low-risk group. We performed analyses using SAS 9.4 (SAS Institute, Cary, NC).

RESULTS

One hundred ninety-one subjects completed assessments. The median and quartile results for ODI, mean saturation, and minimum saturation are found in TABLE 1. TABLE 2 shows the distribution of patients with positive oximetry results. An ODI of 5 or greater was the most frequent abnormal result (135/191; 70.7%).

We used the AUC to measure the comparative abilities of SACS and the 3 overnight oximetry results in predicting OSA (TABLE 3). ODI results demonstrated the best ability to predict OSA, compared with polysomnography as the relative gold standard (AUC, 0.88; 95% confidence interval [CI], 0.83-0.93). Serial application of SACS and ODI yielded even better diagnostic results (AUC, 0.90; 95% CI, 0.85-0.95).

Continue to: As ODI was found to be the strongest predictor of OSA...

As ODI was found to be the strongest predictor of OSA, we grouped these results in quintiles and calculated positive LRs. TABLE 4 shows their effect on PTP of disease among patients with intermediate risk. An ODI result >10 effected an upward recalibration of disease probability (LR, 2.33; 95% CI, 1.27-4.26). The optimal cutoff of ODI to discriminate between those with and without OSA was determined by ROC analysis. An ODI greater than 8.4 created a PTP of disease of approximately 73% to 77%.

Our internal clinical guidelines recommend referring patients with an ODI of 5 or greater for sleep medicine consultation. We examined the ability of this ODI result to recalibrate disease suspicion for a patient at low risk (SACS ≤5). The LR for ODI of 5 or greater is 2.1, but this only results in a recalibration of risk from 24% pretest probability in our validation cohort to 41% PTP (95% CI, 33-49). This low cutoff for a positive test creates false-positive results more than 40% of the time due to low specificity (0.58). This is insufficient to change the suspicion of disease, resulting only in a shift to intermediate OSA risk.

DISCUSSION

Among 3 different oximetry measurements, an ODI ≥10 best predicts OSA, both independently and when used sequentially after the SACS. ODI was by far the most frequent abnormality on oximetry in our cohort, thereby increasing its utility in clinical decision making. For those subjects at intermediate risk, a cutoff of 10 for the ODI result may be a simple and clinically effective way to recalibrate risk and aid in making referral decisions. (This may also be simpler and more easily remembered by clinicians than the 8.4 ODI results from the ROC analyses.)

Assessment is inadequate without a clinical prediction rule. Unfortunately, providers cannot simply rely on clinical gestalt in diagnosing OSA. In their derivation cohort, Flemens et al examined the LRs created by SACS and by clinician prediction based on history and physical exam.⁷ The SACS LRs ranged from 5.17 to 0.25, a 20-fold range. This reflected superior diagnostic information compared with subjective physician impression, where LRs ranged from 3.7 to 0.52, a seven-fold range. Myers et al prepared a meta-analysis of 4 different trials that examined physicians’ ability to predict OSA.⁹ Despite the researchers’ use of experienced sleep medicine doctors, the overall diagnostic accuracy of clinical impression was modest (summary positive LR, 1.7; 95% CI, 1.5-2; I² = 0%; summary negative LR, 0.67; 95% CI, 0.60-0.74; I² = 10%; sensitivity, 58%; specificity, 67%). This is similar to reliance on a single clinical sign or symptom to predict OSA.

Wise use of oximetry augments SACS calculation. To limit unnecessary oximetry testing in low- and high-risk groups and to avoid polysomnography in cases of a low PTP of disease, we advocate limiting oximetry testing to individuals in the SACS intermediate-risk group (FIGURE 2) wherein ODI results can potentially recalibrate risk assessment up or down. (Those in the high- risk group should be referred to a sleep medicine specialist.) Our institutional recommendation of using an ODI result of ≥5 as a threshold to increase suspicion of disease requires a caveat for the low-risk group. “Positive” results at that low diagnostic threshold are frequently false.

Continue to: Multiple benefits of SACS

Multiple benefits of SACS. We believe using the SACS calculation during clinical encounters with patients potentially at risk for OSA would increase diagnostic accuracy. Performing risk stratification with SACS should not be an undue burden on providers, and the increased time spent with patients has its own benefits, including helping them better understand their risk. Using this standardized process—augmented, as needed, with overnight ODI assessment—might also encourage more patients to follow through on subsequent recommendations, as their risk is further quantified objectively. Lastly, unnecessary testing with polysomnography could be avoided.

Limitations of our study. This study’s findings were derived from a patient population in a single institution. Replication of the findings from other settings would be helpful.

Looking forward. It is yet unclear if clinicians will embrace these strategies in real-world primary care practice. We have designed an implementation-and-dissemination trial to assess whether family physicians will use the SACS clinical predication rule in everyday practice and whether our evidence-based recommendations about overnight oximetry will be followed. Underlying our suggested clinical evaluation pathway (FIGURE 2) is the belief that there is value gained from sharing the decision-making process with patients. Although we provide new evidence that informs these conversations, the patient’s values and preferences are important when determining the best direction to proceed in the evaluation for suspected OSA. These recommendations are intended to aid, not replace, good clinical judgment.

Home-based sleep testing has become more widely available, is convenient for patients, and is less expensive than lab-based polysomnography. Our study did not directly address the appropriate circumstances for home studies in clinical evaluation. We rely on the expertise of our sleep medicine colleagues to determine which patients are appropriate candidates for home-based studies.

The AASM states that “portable monitors (PM) for the diagnosis of OSA should be [used] only in conjunction with a comprehensive sleep evaluation. Clinical sleep evaluations using PM must be supervised by a practitioner with board certification in sleep medicine or an individual who fulfills the eligibility criteria for the sleep medicine certification examination.”⁴ Additionally, the group recommends that PM “may be used in the unattended setting as an alternative to polysomnography for the diagnosis of OSA in patients with a high pretest probability of moderate to severe OSA and no comorbid sleep disorder or major comorbid medical disorders.”⁴

Continue to: GRANT SUPPORT

GRANT SUPPORT
The use of the REDCap database is supported by grant UL1 TR000135. This work was supported by a Mayo Foundation CR-20 grant awarded to Dr. Mookadam as Principal investigator and Dr. Grover as Coinvestigator.

Statistical analyses were supported, in part, by the Department of Family Medicine, Mayo Clinic, Scottsdale, Ariz.

CORRESPONDENCE
Michael Grover, DO, Mayo Clinic Thunderbird Primary Care Center-Family Medicine, 13737 N 92nd Street, Scottsdale, AZ 85260; [email protected]

ABSTRACT

Purpose To derive a predictive model for obstructive sleep apnea (OSA) in primary care practice, using home-based overnight oximetry results to refine posttest probability (PTP) of disease after initial risk stratification with the Sleep Apnea Clinical Score (SACS).

Methods We performed secondary analyses on data from a SACS validation cohort, to compare the diagnostic accuracy of 3 overnight oximetry measurements (oxygen desaturation index [ODI], mean saturation, and minimum saturation) in predicting OSA. Receiver operator characteristics (ROC) were computed for each measurement independently and sequentially after risk stratifying with SACS. We examined the implications of oximetry results for OSA PTP for participants categorized as intermediate risk (SACS 6-14; 66/191 participants [35%]; OSA probability 41%). We calculated positive likelihood ratios (LR) for multiple ODI results and determined which ones allowed recalibration to high- or low-risk PTP.

Results Among the 3 oximetry findings, ODI best predicted OSA (area under the curve [AUC], 0.88; 95% confidence interval [CI], 0.83-0.93). An ODI ≥8.4 (likelihood ratio [LR], 4.19; 95% CI, 2.87-6.10) created a PTP of 77%, while an ODI of 0 to <8.4 (LR, 0.19, 95% CI, 0.12-0.33) created a 14% PTP. Sequential application of SACS and ODI results yielded an AUC result of 0.90 (95% CI, 0.85-0.95).

Conclusions SACS risk stratification provides an advantage over clinical gestalt. In those at intermediate risk, ODI results provide a simple and clinically useful way to further refine diagnostic prediction. Sequential use of SACS and selectively employed overnight oximetry may limit unnecessary polysomnography. Oximetry testing should be avoided in patients deemed low or high risk by SACS, as positive results do not substantially recalibrate risk.

Obstructive sleep apnea (OSA) is a prevalent and underdiagnosed condition. The National Sleep Foundation estimates that 18 million Americans have OSA.¹ Primary care practice may be the best setting in which to identify OSA, as many of our patients have conditions frequently associated with apnea (eg, hypertension, obesity, diabetes, arrhythmia, and neurologic illness). Up to a third of patients in primary care practice may be at increased risk.^2,3

Clinical guidelines of the American Academy of Sleep Medicine (AASM) recommend obtaining a sleep history to evaluate for possible OSA in 3 instances: as part of a routine health maintenance examination, during evaluation of specific complaints associated with OSA (eg, snoring, apnea, daytime sleepiness), and during comprehensive evaluations for individuals with high-risk conditions (ie, obesity, congestive heart failure, refractory hypertension, diabetes, stroke history).⁴

Providers can't simply rely on clinical gestalt when obstructive sleep apnea is suspected.

The American College of Physicians (ACP) Clinical Practice Guideline suggests assessing individuals who have unexplained daytime sleepiness.⁵ The ACP considers this assessment “High-Value Care,” as “evidence shows that before diagnosis, patients with OSA have higher rates of health care use, more frequent and longer hospital stays, and higher health care costs than after diagnosis.”⁵

Continue to: We recently validated the diagnostic accuracy...

We recently validated the diagnostic accuracy of the Sleep Apnea Clinical Score (SACS) for use in a primary care patient population suspected of having OSA.⁶ SACS uses historical and clinical data to derive a score that identifies a patient’s risk level.⁷ However, as an alternative to the 2 levels described in Flemons’ SACS,⁷ we propose creating 3 risk strata (FIGURE 1^7,8). We believe that patients at high risk (SACS ≥15) should be encouraged to undergo sleep evaluations as their posttest probability (PTP) of OSA is 75% to 80%. Individuals at low risk (SACS ≤5; PTP <20%) could receive lifestyle advice and simple clinical interventions that decrease symptoms (eg, weight loss, increased physical activity, sleeping on one’s side). For low-risk patients, clinical observation and reevaluation could take place over time with their primary care provider, without additional testing or referral to specialists.

What about patients at intermediate risk? Many patients suspected of having OSA will be assigned to intermediate risk (SACS 6-14), and their PTP of OSA remains at 40% to 45%, the pre-test level most commonly encountered in suspected OSA. As polysomnography is a limited and expensive clinical resource, intermediate-risk patients would benefit from recalibration of their SACS-based risk assessment using an additional surrogate test such as home-based overnight oximetry. Our internal OSA practice guidelines recommend referral for sleep medicine consultation when oximetry results are abnormal—specifically, an oxygen desaturation index (ODI) of ≥5, a mean saturation less than 89%, and a minimum saturation of 75% or less.

Serial application of the Sleep Apnea Clinical Score and overnight oxygen desaturation index yielded the best diagnostic results.

Our objectives in this study were to compare the diagnostic implications of these 3 measurements from home-based overnight oximetry reports and use the most relevant result to derive a predictive model further refining PTP of OSA in a primary care patient population first stratified to intermediate risk by SACS.

METHODS

Subjects

We performed secondary analyses on data obtained from our SACS validation cohort.⁶ In brief, these were patients suspected of having OSA based on the presence of signs, symptoms, or associated risk factors. One hundred ninety-one patients completed all assessments. Sixty-six of 191 patients (35%) were categorized as intermediate risk (SACS 6-14; OSA probability 41% [27/66]).

Data collection and analyses

Participants completed home-based overnight oximetry using Nonin Model 2500 oximeters (Nonin Medical Inc., Plymouth, Minn). We transferred oximetry results from the sleep lab database to a statistical program for analyses of ODI, mean saturation, and minimal saturation. ODI was defined as the number of 4% drops in saturation from baseline divided by the number of hours of recording time. Although the AASM states that a diagnosis of OSA is confirmed if the number of obstructive events is more than 15 per hour or more than 5 per hour in a patient who reports related symptoms,⁴ we defined OSA as an apnea-hypopnea index (AHI) of >10 based on polysomnography (as this was the threshold used in the derivation cohort for SACS).⁷ We demonstrated the predictive ability of SACS at various AHI definitions of OSA in our validation cohort.⁶ The use of SACS in our validation cohort showed a statistically similar ability to predict OSA at both an AHI of 10 and 20, compared with the derivation cohort.

Continue to: We entered additional information...

We entered additional information reported directly by patients and obtained from their sleep studies into a REDCap database and transferred that to our statistical program. We used descriptive statistics to determine ranges and central tendencies of oximetry results. Receiver operator characteristic (ROC) analyses described the predictive abilities for each oximetry result individually and in serial application with prior SACS determinations. For comparison, we used the area under the ROC curve (AUC) from logistic regression to model the probability of OSA.

An oxygen desaturation index result >10 effected an upward recalibration of disease probability.

We calculated positive likelihood ratios (LR) and 95% confidence intervals (CI) to determine the degree of oximetry abnormality that would recalibrate risk either to a high PTP of OSA (>75%) or a low PTP (<25%). We sorted intermediate-risk SACS scores into quintiles based on ODI results to compare the resulting PTPs of OSA. We applied the PTP of OSA from our previous work (using the SACS score to compute the LR) as the new PTP, estimated the LR based on ODI, and computed an updated PTP of OSA. We also used ROC analysis to determine the optimal cutoff value of the ODI.

Finally, in accordance with our internal clinical practice recommendations, we examined the predictive ability of a “positive” ODI result of ≥5 to recalibrate risk prediction for OSA for patients in the low-risk group. We performed analyses using SAS 9.4 (SAS Institute, Cary, NC).

RESULTS

One hundred ninety-one subjects completed assessments. The median and quartile results for ODI, mean saturation, and minimum saturation are found in TABLE 1. TABLE 2 shows the distribution of patients with positive oximetry results. An ODI of 5 or greater was the most frequent abnormal result (135/191; 70.7%).

We used the AUC to measure the comparative abilities of SACS and the 3 overnight oximetry results in predicting OSA (TABLE 3). ODI results demonstrated the best ability to predict OSA, compared with polysomnography as the relative gold standard (AUC, 0.88; 95% confidence interval [CI], 0.83-0.93). Serial application of SACS and ODI yielded even better diagnostic results (AUC, 0.90; 95% CI, 0.85-0.95).

Continue to: As ODI was found to be the strongest predictor of OSA...

As ODI was found to be the strongest predictor of OSA, we grouped these results in quintiles and calculated positive LRs. TABLE 4 shows their effect on PTP of disease among patients with intermediate risk. An ODI result >10 effected an upward recalibration of disease probability (LR, 2.33; 95% CI, 1.27-4.26). The optimal cutoff of ODI to discriminate between those with and without OSA was determined by ROC analysis. An ODI greater than 8.4 created a PTP of disease of approximately 73% to 77%.

Our internal clinical guidelines recommend referring patients with an ODI of 5 or greater for sleep medicine consultation. We examined the ability of this ODI result to recalibrate disease suspicion for a patient at low risk (SACS ≤5). The LR for ODI of 5 or greater is 2.1, but this only results in a recalibration of risk from 24% pretest probability in our validation cohort to 41% PTP (95% CI, 33-49). This low cutoff for a positive test creates false-positive results more than 40% of the time due to low specificity (0.58). This is insufficient to change the suspicion of disease, resulting only in a shift to intermediate OSA risk.

DISCUSSION

Among 3 different oximetry measurements, an ODI ≥10 best predicts OSA, both independently and when used sequentially after the SACS. ODI was by far the most frequent abnormality on oximetry in our cohort, thereby increasing its utility in clinical decision making. For those subjects at intermediate risk, a cutoff of 10 for the ODI result may be a simple and clinically effective way to recalibrate risk and aid in making referral decisions. (This may also be simpler and more easily remembered by clinicians than the 8.4 ODI results from the ROC analyses.)

Assessment is inadequate without a clinical prediction rule. Unfortunately, providers cannot simply rely on clinical gestalt in diagnosing OSA. In their derivation cohort, Flemens et al examined the LRs created by SACS and by clinician prediction based on history and physical exam.⁷ The SACS LRs ranged from 5.17 to 0.25, a 20-fold range. This reflected superior diagnostic information compared with subjective physician impression, where LRs ranged from 3.7 to 0.52, a seven-fold range. Myers et al prepared a meta-analysis of 4 different trials that examined physicians’ ability to predict OSA.⁹ Despite the researchers’ use of experienced sleep medicine doctors, the overall diagnostic accuracy of clinical impression was modest (summary positive LR, 1.7; 95% CI, 1.5-2; I² = 0%; summary negative LR, 0.67; 95% CI, 0.60-0.74; I² = 10%; sensitivity, 58%; specificity, 67%). This is similar to reliance on a single clinical sign or symptom to predict OSA.

Wise use of oximetry augments SACS calculation. To limit unnecessary oximetry testing in low- and high-risk groups and to avoid polysomnography in cases of a low PTP of disease, we advocate limiting oximetry testing to individuals in the SACS intermediate-risk group (FIGURE 2) wherein ODI results can potentially recalibrate risk assessment up or down. (Those in the high- risk group should be referred to a sleep medicine specialist.) Our institutional recommendation of using an ODI result of ≥5 as a threshold to increase suspicion of disease requires a caveat for the low-risk group. “Positive” results at that low diagnostic threshold are frequently false.

Continue to: Multiple benefits of SACS

Multiple benefits of SACS. We believe using the SACS calculation during clinical encounters with patients potentially at risk for OSA would increase diagnostic accuracy. Performing risk stratification with SACS should not be an undue burden on providers, and the increased time spent with patients has its own benefits, including helping them better understand their risk. Using this standardized process—augmented, as needed, with overnight ODI assessment—might also encourage more patients to follow through on subsequent recommendations, as their risk is further quantified objectively. Lastly, unnecessary testing with polysomnography could be avoided.

Limitations of our study. This study’s findings were derived from a patient population in a single institution. Replication of the findings from other settings would be helpful.

Looking forward. It is yet unclear if clinicians will embrace these strategies in real-world primary care practice. We have designed an implementation-and-dissemination trial to assess whether family physicians will use the SACS clinical predication rule in everyday practice and whether our evidence-based recommendations about overnight oximetry will be followed. Underlying our suggested clinical evaluation pathway (FIGURE 2) is the belief that there is value gained from sharing the decision-making process with patients. Although we provide new evidence that informs these conversations, the patient’s values and preferences are important when determining the best direction to proceed in the evaluation for suspected OSA. These recommendations are intended to aid, not replace, good clinical judgment.

Home-based sleep testing has become more widely available, is convenient for patients, and is less expensive than lab-based polysomnography. Our study did not directly address the appropriate circumstances for home studies in clinical evaluation. We rely on the expertise of our sleep medicine colleagues to determine which patients are appropriate candidates for home-based studies.

The AASM states that “portable monitors (PM) for the diagnosis of OSA should be [used] only in conjunction with a comprehensive sleep evaluation. Clinical sleep evaluations using PM must be supervised by a practitioner with board certification in sleep medicine or an individual who fulfills the eligibility criteria for the sleep medicine certification examination.”⁴ Additionally, the group recommends that PM “may be used in the unattended setting as an alternative to polysomnography for the diagnosis of OSA in patients with a high pretest probability of moderate to severe OSA and no comorbid sleep disorder or major comorbid medical disorders.”⁴

Continue to: GRANT SUPPORT

GRANT SUPPORT
The use of the REDCap database is supported by grant UL1 TR000135. This work was supported by a Mayo Foundation CR-20 grant awarded to Dr. Mookadam as Principal investigator and Dr. Grover as Coinvestigator.

Statistical analyses were supported, in part, by the Department of Family Medicine, Mayo Clinic, Scottsdale, Ariz.

CORRESPONDENCE
Michael Grover, DO, Mayo Clinic Thunderbird Primary Care Center-Family Medicine, 13737 N 92nd Street, Scottsdale, AZ 85260; [email protected]