Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.1 In medicine, the convergence of meaningful use of electronic medical records, ICD-10 diagnostic coding, data warehouses, and integrated healthcare systems are bringing such predictive analytics to the bedside and clinics in order to improve the health of the nation. The U.S. is investing significant amount of resources into the informational technology infrastructure with the intent of harnessing such big data to help predict, diagnose, and treat medical conditions and thereby improve population health. We need to strategically bring such resources to sleep medicine. In this issue of the Journal, Ustun and colleagues set us in such a deliberate direction by applying a new machine learning method known as SLIM (Supersparse Linear Integer Models). They tested the hypothesis that a diagnostic screening tool based on routinely available medical information would be superior to one based solely on patient-reported sleep-related symptoms. Their rationale was that the application of such technology—in an automated manner—to data residing in electronic medical records can assist with large-scale screening for obstructive sleep apnea (OSA).
In a recent review of epidemiological studies published over the last two decades, the prevalence of OSA, defined by polysomnography with an apnea hypopnea index greater or equal to 5/hour was estimated to be 22% in men and 17% in women.2 OSA is common, and the prevalence is rising3; however, OSA remains underdiagnosed.4 OSA is associated with a variety of negative health consequences, ranging from increased risk of hypertension, diabetes, cancer, and increased mortality.5–7 Accordingly, the American Academy of Sleep Medicine task force identified improvement in disease detection and categorization as one of many key outcome measures.8 Given these adverse effects, as well as the existence of effective treatments for OSA,9 there is a clear need for appropriate screening methods of patients at risk for OSA. In their article, Ustun et al. describe the use of machine learning techniques to develop screening algorithms for OSA10 in a sleep laboratory-based population. They tested whether the characteristics of their algorithms were comparable or superior to existing screening tools. Specifically, they compared the utility of patient-reported symptoms of sleep apnea (snoring, witnessed apnea, etc.) versus non-sleep specific factors (“extractable features”) that are typically available through an electronic medical record (age, body mass index, gender, and chronic medical problems such as hypertension). The approach of using a mix of reported sleep symptoms as well as non-sleep specific factors has been previously used in other screening tools, such as the Berlin questionnaire11 and the STOP-Bang.12,13 The authors found that the use of extractable features to develop a screening tool was significantly better than the use of symptoms alone. Additionally, the combined model utilizing both extractable features and symptoms was not superior to the use of extractable features alone.
All of the extractable features were intentionally selected to be readily available from the medical record. Although simple screening tools such as the STOP-Bang are available for screening in the primary care setting, an implementation gap exists with regards to routine screening for OSA or any other sleep disorder in the primary care setting.14,15 The lack of recognition of OSA in primary care is likely due to multiple underlying factors, including limited reporting of OSA symptoms by patients, limited visit time, as well as lack of provider knowledge. Research has shown that approximately 30% to 40% of primary care physicians' patients are at high risk for OSA based on the Berlin Questionnaire,16,17 and 90% to 99% may report a sleep related symptom when surveyed.18 Despite this high rate of symptoms when queried, only one in five patients had discussed their sleep concerns with their physician.16 Others have reported that only 7% of primary care physicians asked unsolicited questions regarding sleep.19
Prior research has shown that the use of a chart-based simple reminder can improve screening for sleep disorders.20 Potentially, the tool described by Ustun et al. could be integrated into the electronic medical record, flagging high-risk patients and prompting physicians to further screen for OSA. These high-risk patients could then be referred for diagnostic testing for OSA. The approach to use a screening tool that is not dependent upon patient-reported sleep symptoms sidesteps the barriers for detection of OSA in the busy clinic setting. While management of OSA by sleep-certified physicians may confer an advantage over providers with no prior experience in managing patients with OSA, such automated electronic medical record based systems could assist with case-finding and conceivably be comparable between providers who are not experienced, nor received training, in managing patients with as yet undiagnosed OSA versus those managed by sleep-certified physicians.21–23 Ustun and colleagues should be commended for bringing both big and slim data to our doorsteps. Future research is needed to determine the feasibility, cost-effectiveness, barriers to implementation, and patient-outcomes of integrating such predictive analytics into routine practice.
Dr. Parthasarathy reports grants from NIH/NHLBI (HL095799), grants from Patient Centered Outcomes Research Institute (IHS-1306-2505), grants from US Department of Defense, grants from NIH (National Cancer Institute; R21CA184920), grants from US Department of Army, grants from Johrei Institute, personal fees from American Academy of Sleep Medicine, personal fees from American College of Chest Physicians, non-financial support from National Center for Sleep Disorders Research of the NIH (NHLBI), personal fees from UpToDate Inc., Philips-Respironics, Inc., and Vaopotherm, Inc.; grants from Younes Sleep Technologies, Ltd., Niveus Medical Inc., and Philips-Respironics, Inc. outside the submitted work. In addition, Dr. Parthasarathy has a patent UA 14-018 U.S.S.N. 61/884,654; PTAS 502570970 (Home breathing device) pending. The above-mentioned conflicts including the patent are unrelated to the topic of this paper. The other authors have indicated no financial conflicts of interest.
Combs D, Shetty S, Parthasarathy S. Big-data or slim-data: predictive analytics will rule with world. J Clin Sleep Med 2016;12(2):157–158.
Encyclopedia WTF. Predictive analytics.
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