Clinical course of adults with co-occurring hypertrophic cardiomyopathy and hypertension: A scoping review.

Introduction: Hypertension affects approximately 50 % of patients with hypertrophic cardiomyopathy (HCM) but clinical course in adults with co-occurring HCM and hypertension is underexplored. Management may be challenging as routine anti-hypertensive medications may worsen obstructive HCM, the most common HCM phenotype. In this scoping review, we sought to synthesize the available literature related to clinical course and outcomes in adults with both conditions and to highlight knowledge gaps to inform future research directions. Methods: We searched 5 electronic databases (PubMed, CINAHL, Scopus, Embase, Web of Science) to identify peer-reviewed articles, 2011-2023. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review (PRISMA-ScR) guideline. Results: Eleven articles met eligibility. Adults with both conditions were older and had higher rates of obesity and diabetes than adults with HCM alone. Results related to functional class and arrhythmia were equivocal in cross-sectional studies. Only 1 article investigated changes in medical therapy among adults with both conditions. Hypertension was a predictor of worse functional class, but was not associated with all-cause mortality, heart failure-related mortality, or sudden-death. No data was found that related to common hypertension-related outcomes, including renal disease progression, nor patient-reported outcomes, including quality of life. Conclusions: Our results highlight areas for future research to improve understanding of co-occurring HCM and hypertension. These include a need for tailored approaches to medical management to optimize outcomes, evaluation of symptom burden and quality of life, and investigation of hypertension-related outcomes, like renal disease and ischemic stroke, to inform cardiovascular risk mitigation strategies.

Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores.

Importance: Large language models (LLMs) are crucial for medical tasks. Ensuring their reliability is vital to avoid false results. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Objective: Evaluate ChatGPT and LlaMA-2 performance in extracting MMSE and CDR scores, including their associated dates. Methods: Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA. After applying inclusion criteria 34,465 notes remained, of which 765 underwent ChatGPT (GPT-4) and LlaMA-2, and 22 experts reviewed the responses. ChatGPT successfully extracted MMSE and CDR instances with dates from 742 notes. We used 20 notes for fine-tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 each assigned to two reviewers simultaneously. Inter-rater-agreement (Fleiss' Kappa), precision, recall, true/false negative rates, and accuracy were calculated. Our study follows TRIPOD reporting guidelines for model validation. Results: For MMSE information extraction, ChatGPT (vs. LlaMA-2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true-negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true-negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and LlaMA-2 on double-reviewed notes. LlaMA-2 errors included 27 cases of total hallucination, 19 cases of reporting other scores instead of MMSE, 25 missed scores, and 23 cases of reporting only the wrong date. In comparison, ChatGPT's errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date. Conclusions: In this diagnostic/prognostic study of ChatGPT and LlaMA-2 for extracting cognitive exam dates and scores from clinical notes, ChatGPT exhibited high accuracy, with better performance compared to LlaMA-2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.