Correction: Associations between healthcare utilization and access and diabetic retinopathy complications using All of Us nationwide survey data.

[This corrects the article DOI: 10.1371/journal.pone.0269231.].

Social Determinants of Health Data Availability for Patients with Eye Conditions.

Purpose: To quantify and characterize social determinants of health (SDoH) data coverage using single-center electronic health records (EHRs) and the National Institutes of Health All of Us research program. Design: Retrospective cohort study from June 2014 through June 2021. Participants: Adults 18 years of age or older with a diagnosis of diabetic retinopathy, glaucoma, cataracts, or age-related macular degeneration. Methods: For All of Us, research participants completed online survey forms as part of a nationwide prospective cohort study. In local EHRs, patients were selected based on diagnosis codes. Main Outcome Measures: Social determinants of health data coverage, characterized by the proportion of each disease cohort with available data regarding demographics and socioeconomic factors. Results: In All of Us, we identified 23 806 unique adult patients, of whom 2246 had a diagnosis of diabetic retinopathy, 13 448 had a diagnosis of glaucoma, 6634 had a diagnosis of cataracts, and 1478 had a diagnosis of age-related macular degeneration. Survey completion rates were high (99.5%-100%) across all cohorts for demographic information, overall health, income, education, and lifestyle. However, health care access (12.7%-29.4%), housing (0.7%-1.1%), social isolation (0.2%-0.3%), and food security (0-0.1%) showed significantly lower response rates. In local EHRs, we identified 80 548 adult patients, of whom 6616 had a diagnosis of diabetic retinopathy, 26 793 had a diagnosis of glaucoma, 40 427 had a diagnosis of cataracts, and 6712 had a diagnosis of age-related macular degeneration. High data coverage was found across all cohorts for variables related to tobacco use (82.84%-89.07%), alcohol use (77.45%-83.66%), and intravenous drug use (84.76%-93.14%). However, low data coverage (< 50% completion) was found for all other variables, including education, finances, social isolation, stress, physical activity, food insecurity, and transportation. We used chi-square testing to assess whether the data coverage varied across different disease cohorts and found that all fields varied significantly (P < 0.001). Conclusions: The limited and highly variable data coverage in both local EHRs and All of Us highlights the need for researchers and providers to develop SDoH data collection strategies and to assemble complete datasets.

Novel Association between Opioid Use and Increased Risk of Retinal Vein Occlusion Using the National Institutes of Health All of Us Research Program.

Purpose: To assess for risk factors for retinal vein occlusion (RVO) among participants in the National Institutes of Health All of Us database, particularly social risk factors that have not been well studied, including substance use. Design: Retrospective, case-control study. Participants: Data were extracted for 380 adult participants with branch retinal vein occlusion (BRVO), 311 adult participants with central retinal vein occlusion (CRVO), and 1520 controls sampled among 311 640 adult participants in the All of Us database. Methods: Data were extracted regarding demographics, comorbidities, income, housing, insurance, and substance use. Opioid use was defined by relevant diagnosis and prescription codes, with prescription use > 30 days. Controls were sampled at a 4:1 control to case ratio from a pool of individuals aged > 18 years without a diagnosis of RVO and proportionally matched to the demographic distribution of the 2019 U.S. census. Multivariable logistic regression identified medical and social determinants significantly associated with BRVO or CRVO. Statistical significance was defined as P < 0.05. Main Outcome Measure: Development of BRVO or CRVO based on diagnosis codes. Results: Among patients with BRVO, the mean (standard deviation) age was 70.1 (10.5) years. The majority (53.7%) were female. Cases were diverse; 23.7% identified as Black, and 18.4% identified as Hispanic or Latino. Medical risk factors including glaucoma (odds ratio [OR], 3.29; 95% confidence interval [CI], 2.22-4.90; P < 0.001), hypertension (OR, 2.15; 95% CI, 1.49-3.11; P < 0.001), and diabetes mellitus (OR, 1.68; 95% CI, 1.18-2.38; P = 0.004) were re-demonstrated to be associated with BRVO. Black race (OR, 2.64; 95% CI, 1.22-6.05; P = 0.017) was found to be associated with increased risk of BRVO. Past marijuana use (OR, 0.68; 95% CI, 0.50-0.92; P = 0.013) was associated with decreased risk of BRVO; however, opioid use (OR, 1.98; 95% CI, 1.41-2.78; P < 0.001) was associated with a significantly increased risk of BRVO. Similar associations were found for CRVO. Conclusions: Understanding RVO risk factors is important for primary prevention and improvement in visual outcomes. This study capitalizes on the diversity and scale of a novel nationwide database to elucidate a previously uncharacterized association between RVO and opioid use.

Associations between healthcare utilization and access and diabetic retinopathy complications using All of Us nationwide survey data.

PURPOSE: Inadequacies in healthcare access and utilization substantially impact outcomes for diabetic patients. The All of Us database offers extensive survey data pertaining to social determinants that is not routinely available in electronic health records. This study assesses whether social determinants were associated with an increased risk of developing proliferative diabetic retinopathy or related complications (e.g. related diagnoses or procedures). METHODS: We identified 729 adult participants in the National Institutes of Health All of Us Research Program data repository with diabetic retinopathy (DR) who answered survey questions pertaining to healthcare access and utilization. Electronic health record data regarding co-morbidities, laboratory values, and procedures were extracted. Multivariable logistic regression with bi-directional stepwise variable selection was performed from a wide range of predictors. Statistical significance was defined as p<0.05. RESULTS: The mean (standard deviation) age of our cohort was 64.9 (11.4) years. 15.2% identified as Hispanic or Latino, 20.4% identified as Black, 60.6% identified as White, and 19.3% identified as Other. 10-20% of patients endorsed several reasons for avoiding or delaying care, including financial concerns and lack of access to transportation. Additional significant social determinants included race and religion discordance between healthcare provider and patient (odds ratio [OR] 1.20, 95% confidence interval [CI] 1.02-1.41, p = 0.03) and caregiver responsibilities toward others (OR 3.14, 95% CI 1.01-9.50, p = 0.04). CONCLUSIONS: Nationwide data demonstrate substantial barriers to healthcare access among DR patients. In addition to financial and social determinants, race and religion discordance between providers and patients may increase the likelihood of PDR and related complications.

Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review.

Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic review of articles describing predictive models integrated into EHR systems and implemented in clinical practice. We conducted an exhaustive database search and extracted data encompassing multiple facets of implementation. We assessed study quality and level of evidence. We obtained an initial 3393 articles for screening, from which a final set of 44 articles was included for data extraction and analysis. The most common clinical domains of implemented predictive models were related to thrombotic disorders/anticoagulation (25%) and sepsis (16%). The majority of studies were conducted in inpatient academic settings. Implementation challenges included alert fatigue, lack of training, and increased work burden on the care team. Of 32 studies that reported effects on clinical outcomes, 22 (69%) demonstrated improvement after model implementation. Overall, EHR-based predictive models offer promising results for improving clinical outcomes, although several gaps in the literature remain, and most study designs were observational. Future studies using randomized controlled trials may help improve the generalizability of findings.

Endoscopic Mucosal Healing Predicts Favorable Clinical Outcomes in Inflammatory Bowel Disease: A Meta-analysis.

BACKGROUND: Mucosal healing (MH) in inflammatory bowel disease has been associated with improved long-term clinical outcomes. Uncertainty remains as to the magnitude of this effect and to how this association changes with time and degree of healing. METHODS: PubMed, EMBASE, and Web of Science searches identified 1570 citations. Screening of abstracts identified 155 articles for full-text review, of which 19 met inclusion criteria. For 3 outcomes of interest (surgeries, hospitalizations, remission), weighted random-effects meta-analysis was performed. RESULTS: In pooled analysis, MH predicted fewer major abdominal surgeries (relative risk [RR], 0.34; 95% confidence interval [CI], 0.26-0.46), increased remission (RR, 1.84; 95% CI, 1.43-2.36), and fewer hospitalizations (RR, 0.58; 95% CI, 0.42-0.78). Complete MH and partial MH both showed significantly higher rates of favorable outcomes. Separate analyses for Crohn's disease and ulcerative colitis showed identical patterns for surgeries and remission. When subjects with no healing were excluded, and complete versus partial healing was compared, rates of surgery were not significantly different (RR, 0.82; 95% CI, 0.46-1.44). However, complete healing was superior in predicting corticosteroid-free remission (RR, 1.71; 95% CI, 1.24-2.34). Meta-regression found that the predictive power of this complete versus partial healing distinction was strongly associated with the duration of follow-up after endoscopy. CONCLUSIONS: MH is a strong predictor of fewer surgeries, long-term clinical remission, and fewer hospitalizations. Complete healing is not significantly more favorable than partial healing for predicting surgeries or hospitalizations, but it did predict higher rates of clinical remission. This benefit of complete MH over partial healing increases with follow-up time.