Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models. To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented. Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation. Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts). 130 000 critical care admissions across both health systems. Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness. Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures. At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%. In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated. In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling. National Center for Advancing Translational Sciences.

Diabetes mellitus (DM) is a glucose metabolism disorder characterized by chronic hyperglycemia resulting from a deficit of insulin production and/or action. DM affects more than 1 in 10 adults, and it is associated with an increased risk of cardiovascular morbidity and mortality. Cardiovascular disease (CVD) accounts for two thirds of the overall deaths in diabetic patients, with coronary artery disease (CAD) and ischemic cardiomyopathy as the main contributors. Hyperglycemic damage on vascular endothelial cells leading to endothelial dysfunction represents the main initiating factor in the pathogenesis of diabetic vascular complications; however, the underlying pathophysiological mechanisms are still not entirely understood. This review addresses the current knowledge on the pathophysiological links between DM and CAD with a focus on the role of epigenetic modifications, including DNA methylation, histone modifications and noncoding RNA control. Increased knowledge of epigenetic mechanisms has contributed to the development of new pharmacological treatments (“epidrugs”) with epigenetic targets, although these approaches present several challenges. Specific epigenetic biomarkers may also be used to predict or detect the development and progression of diabetes complications. Further studies on diabetes and CAD epigenetics are needed in order to identify possible new therapeutic targets and advance personalized medicine with the prediction of individual drug responses and minimization of adverse effects.

Objectives: The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient’s entire echocardiogram report history. This approach is intended to streamline the extraction of clinical insights from multiple echocardiogram reports, particularly in patients with complex cardiac diseases, thereby enhancing both patient care and research efficiency. Materials and Methods: Data from over 10 years were collected, comprising echocardiogram reports from patients with more than 10 echocardiograms on file at the Mount Sinai Health System. These reports were converted into a single document per patient for analysis, broken down into snippets and relevant snippets were retrieved using text similarity measures. The LLaMA-2 70B model was employed for analyzing the text using a specially crafted prompt. The model’s performance was evaluated against ground-truth answers created by faculty cardiologists. Results: The study analyzed 432 reports from 37 patients for a total of 100 question-answer pairs. The LLM correctly answered 90% questions, with accuracies of 83% for temporality, 93% for severity assessment, 84% for intervention identification, and 100% for diagnosis retrieval. Errors mainly stemmed from the LLM’s inherent limitations, such as misinterpreting numbers or hallucinations. Conclusion: The study demonstrates the feasibility and effectiveness of using a local, open-source LLM for querying and interpreting echocardiogram report data. This approach offers a significant improvement over traditional keyword-based searches, enabling more contextually relevant and semantically accurate responses; in turn showing promise in enhancing clinical decision-making and research by facilitating more efficient access to complex patient data.