Citations

Background The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug‐eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. Methods and Results We developed and validated a new AI‐based pipeline using retrospective data of drug‐eluting stent‐treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de‐identified Clinformatics Data Mart Database (n=9978). The 36 months following drug‐eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI‐DAPT model. The AI‐DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%–92%] for ischemia and 84% [95% CI, 82%–87%] for bleeding predictions. Conclusions Our AI‐DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.

Abstract Objective Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a “sick care” system into a “health-promoting” system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and measurable way. Material and Methods Drawing on the content of existing ontologies relevant to certain aspects of SDoH, we used a top-down approach to formally model classes, relationships, and constraints based on multiple SDoH-related resources. Expert review and coverage evaluation, using a bottom-up approach employing clinical notes data and a national survey, were performed. Results We constructed the SDoHO with 708 classes, 106 object properties, and 20 data properties, with 1,561 logical axioms and 976 declaration axioms in the current version. Three experts achieved 0.967 agreement in the semantic evaluation of the ontology. A comparison between the coverage of the ontology and SDOH concepts in 2 sets of clinical notes and a national survey instrument also showed satisfactory results. Discussion SDoHO could potentially play an essential role in providing a foundation for a comprehensive understanding of the associations between SDoH and health outcomes and paving the way for health equity across populations. Conclusion SDoHO has well-designed hierarchies, practical objective properties, and versatile functionalities, and the comprehensive semantic and coverage evaluation achieved promising performance compared to the existing ontologies relevant to SDoH.

Abstract1q gain (+1q) is the most common high‐risk cytogenetic abnormality (HRCA) in patients with multiple myeloma (MM). However, its prognostic value remains unclear in the era of novel agents. Here, we retrospectively analyzed the impact of +1q on the outcomes of 934 patients newly diagnosed with MM. +1q was identified in 53.1% of patients and verified as an independent variate for inferior overall survival (OS) (hazard ratio, 1.400; 95% confidence interval, 1.097–1.787; p = .007). Concurrence of other HRCAs (particularly t(14;16) and del(17p)) further exacerbated the outcomes of patients with +1q, suggesting prognostic heterogeneity. Thus, a risk‐scoring algorithm based on four risk variates (t(14;16), hypercalcemia, ISS III, and high LDH) was developed to estimate the outcomes of patients with +1q. Of the patients, 376 evaluable patients with +1q were re‐stratified into low (31.6%), intermediate (61.7%), and high risk (6.7%) groups, with significantly different progression‐free survival and OS (p < .0001), in association with early relapse of the disease. The prognostic value of this model was validated in the CoMMpass cohort. While attaining undetectable MRD largely circumvented the adverse impact of +1q, it scarcely ameliorated the outcome of the patients with high risk, who likely represent a subset of patients with extremely poor survival. Hence, patients with +1q are a heterogeneous group of high‐risk patients, therefore underlining the necessity for their re‐stratification. The proposed simple risk‐scoring model can estimate the outcomes of patients with +1q, which may help guide risk‐adapted treatment for such patients.