Development of Portable Electronic Health Record Based Algorithms to Identify Individuals with Diabetic Retinopathy.

Objectives: To develop, validate and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHR)s. Methods : We developed and validated EHR-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in three independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet one of three criteria: 1) two or more dates with any DR ICD-9/10 code documented in the EHR, or 2) at least one affirmative health-factor or EPIC code for DR along with an ICD9/10 code for DR on a different day, or 3) at least one ICD-9/10 code for any DR occurring within 24 hours of an ophthalmology exam. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology exam. Results: The algorithms, developed and evaluated in VUMC through manual chart review, resulted in a positive predictive value (PPV) of 0.93 for cases and negative predictive value (NPV) of 0.97 for controls. Implementation of algorithms yielded similar metrics in VANEOHS (PPV=0.94; NPV=0.86) and lower in MGB (PPV=0.84; NPV=0.76). In comparison, use of DR definition as implemented in Phenome-wide association study (PheWAS) in VUMC, yielded similar PPV (0.92) but substantially reduced NPV (0.48). Implementation of the algorithms to the Million Veteran Program identified over 62,000 DR cases with genetic data including 14,549 African Americans and 6,209 Hispanics with DR. Conclusions/Discussion: We demonstrate the robustness of the algorithms at three separate health-care centers, with a minimum PPV of 0.84 and substantially improved NPV than existing high-throughput methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.

Investigation of the relationships between sleep behaviors and risk of healthspan termination: a prospective cohort study based on 323,373 UK-Biobank participants.

OBJECTIVES: To examine the associations between four sleep behaviors and the risk of healthspan termination. METHODS: This study included 323,373 participants, free of terminated healthspan at baseline, from the UK-Biobank (UKB). We applied multivariable-adjusted Cox regression models to estimate the risk of terminated healthspan based on four sleep behaviors (insomnia/sleeplessness, napping, daytime sleepiness, and difficulty getting up from bed), which were self-reported and measured on Likert scales from "usually" to "never/rarely" experiences. In this study, healthspan was defined based on eight events that are strongly associated with longevity (congestive heart failure, myocardial infarction, chronic obstructive pulmonary disease, stroke, dementia, diabetes, cancer, and death). RESULTS: Participants who reported the following unhealthy sleep behaviors had a significantly higher risk of terminated healthspan: "usually experience sleeplessness/insomnia" (HR = 1.05, 95% CI: 1.03-1.07; P < 0.001); "usually nap" (HR = 1.22, 95% CI: 1.18-1.26; P < 0.01); "excessive daytime sleepiness" (HR = 1.25, 95% CI: 1.19-1.32; P < 0.001); and "difficult getting up from bed" (HR = 1.08, 95% CI: 1.05-1.10; P < 0.001). The corresponding population attributable risk percentage (PAR%) indicated that about 7% of healthspan termination in this cohort would have been eliminated if all participants had healthy sleep behaviors. CONCLUSION: Participants who reported "usually experience sleeplessness/insomnia," "usually nap," "excessive daytime sleepiness," and "difficult getting up from bed" had increased risk of shortened healthspan. Therefore, adherence to healthy sleep behavior is significant for the extension of healthspan.