The Role of Hospital Characteristics in Clinical and Quality Outcomes for Gastrointestinal Bleeding in a National Cohort.

INTRODUCTION: There is substantial variability in patient outcomes for gastrointestinal bleeding (GIB) across hospitals. This study aimed to identify hospital factors associated with GIB outcomes. METHODS: This was a retrospective cohort study of Medicare fee-for-service beneficiaries hospitalized for GIB from 2016 to 2018. These data were merged with the American Hospital Association Annual Survey data to incorporate hospital characteristics. We used generalized linear mixed-effect models to estimate the effect of hospital-level characteristics on patient outcomes after adjusting for patient risk factors including anticoagulant and antiplatelet use, recent GIB, and comorbidities. The primary outcome was 30-day mortality, and secondary outcomes included length of stay and a composite outcome of 30-day readmission or mortality. RESULTS: Factors associated with improved GIB 30-day mortality included large hospital size (defined as beds >400, odds ratio [OR] 0.93, 95% confidence interval [CI] 0.90-0.97), greater case volume (OR 0.97, 95% CI 0.96-0.98), increased resident and nurse staffing (OR 0.88, 95% CI 0.83-0.94), and blood donor center designation (OR 0.93, 95% CI 0.88-0.99). Patients treated at a hospital with multiple advanced capabilities, such as availability of advanced endoscopy, advanced intensive care unit (ICU) capabilities (both a medical-surgical ICU and cardiac ICU), blood donor center, and liver transplant center, had a 22% reduction in 30-day mortality risk, compared with those hospitalized in a hospital with none of these services (OR 0.78, 95% CI 0.68-0.91). However, length of stay increased with additional services. DISCUSSION: Patients hospitalized for GIB at hospitals with multiple advanced specialized capabilities have lower mortality but longer lengths of stay. Further research should examine the processes of care linked to these services that contribute to improved mortality in GIB.

An Improved Clinical and Genetics-Based Prediction Model for Diabetic Foot Ulcer Healing.

Objective: The goal of this investigation was to use comprehensive prediction modeling tools and available genetic information to try to improve upon the performance of simple clinical models in predicting whether a diabetic foot ulcer (DFU) will heal. Approach: We utilized a cohort study (n = 206) that included clinical factors, measurements of circulating endothelial precursor cells (CEPCs), and fine sequencing of the NOS1AP gene. We derived and selected relevant predictive features from this patient-level information using statistical and machine learning techniques. We then developed prognostic models using machine learning approaches and assessed predictive performance. The presentation is consistent with TRIPOD requirements. Results: Models using baseline clinical and CEPC data had an area under the receiver operating characteristic curve (AUC) of 0.73 (0.66-0.80). Models using only single nucleotide polymorphisms (SNPs) of the NOS1AP gene had an AUC of 0.67 (95% confidence interval, CI: [0.59-0.75]). However, models incorporating baseline and SNP information resulted in improved AUC (0.80, 95% CI [0.73-0.87]). Innovation: We provide a rigorous analysis demonstrating the predictive potential of genetic information in DFU healing. In this process, we present a framework for using advanced statistical and bioinformatics techniques for creating superior prognostic models and identify potentially predictive SNPs for future research. Conclusion: We have developed a new benchmark for which future predictive models can be compared against. Such models will enable wound care experts to more accurately predict whether a patient will heal and aid clinical trialists in designing studies to evaluate therapies for subjects likely or unlikely to heal.

Changes in Dental Outcomes After Implementation of the Philadelphia Beverage Tax.

INTRODUCTION: Beverage taxes are associated with declines in sugar-sweetened beverage sales and consumption, but few studies have evaluated the associations of these taxes with health outcomes. This study analyzed changes in dental decay after the implementation of the Philadelphia sweetened beverage tax. METHODS: Electronic dental record data were obtained on 83,260 patients living in Philadelphia and control areas from 2014 to 2019. Difference-in-differences analyses compared the number of new Decayed, Missing, and Filled Teeth with that of new Decayed, Missing, and Filled Surfaces before (January 2014-December 2016) and after (January 2019-December 2019) tax implementation in Philadelphia and control patients. Analyses were conducted in older children/adults (aged ≥15 years) and younger children (aged <15 years). Subgroup analyses stratified by Medicaid status. Analyses were conducted in 2022. RESULTS: The number of new Decayed, Missing, and Filled Teeth did not change after tax implementation in Philadelphia in panel analyses of older children/adults (difference-in-differences= -0.02, 95% CI= -0.08, 0.03) or younger children (difference-in-differences=0.07, 95% CI= -0.08, 0.23). There were similarly no post-tax changes in the number of new Decayed, Missing, and Filled Surfaces. However, in cross-sectional samples of patients on Medicaid, the number of new Decayed, Missing, and Filled Teeth was lower after tax implementation in older children/adults (difference-in-differences= -0.18, 95% CI= -0.34, -0.03; -22% decline) and younger children (difference-in-differences= -0.22, 95% CI= -0.46, 0.01; -30% decline), with similar results for number of new Decayed, Missing, and Filled Surfaces. CONCLUSIONS: The Philadelphia beverage tax was not associated with reduced tooth decay in the general population, but it was associated with reduced tooth decay in adults and children on Medicaid, suggesting potential health benefits for low-income populations.