RESUMEN
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.
RESUMEN
BACKGROUND: Fatty infiltration (FI) is one of the most important prognostic factors for outcomes after rotator cuff surgery. Established risk factors include advancing age, larger tear size, and increased tear chronicity. A growing body of evidence suggests that sex and obesity are associated with FI; however, data are limited. METHODS: We recruited 2 well-characterized multicenter cohorts of patients with rotator cuff tears (Multicenter Orthopaedic Outcomes Network [MOON] cohort [n = 80] and Rotator Cuff Outcomes Workgroup [ROW] cohort [n = 158]). We used multivariable logistic regression to evaluate the relationship between body mass index (BMI) and the presence of FI while adjusting for the participant's age at magnetic resonance imaging, sex, and duration of shoulder symptoms, as well as the cross-sectional area of the tear. We analyzed the 2 cohorts separately and performed a meta-analysis to combine estimates. RESULTS: A total of 27 patients (33.8%) in the Multicenter Orthopaedic Outcomes Network (MOON) cohort and 57 patients (36.1%) in the Rotator Cuff Outcomes Workgroup (ROW) cohort had FI. When BMI < 25 kg/m2 was used as the reference category, being overweight was associated with a 2.37-fold (95% confidence interval [CI], 0.77-7.29) increased odds of FI and being obese was associated with a 3.28-fold (95% CI, 1.16-9.25) increased odds of FI. Women were 4.9 times (95% CI, 2.06-11.69) as likely to have FI as men. CONCLUSIONS: Among patients with rotator cuff tears, obese patients had a substantially higher likelihood of FI. Further research is needed to assess whether modifying BMI can alter FI in patients with rotator cuff tears. This may have significant clinical implications for presurgical surgical management of rotator cuff tears. Sex was also significantly associated with FI, with women having higher odds of FI than men. Higher odds of FI in female patients may also explain previously reported early suboptimal outcomes of rotator cuff surgery and higher pain levels in female patients as compared with male patients.
