Faculty Bibliography

BACKGROUND/UNASSIGNED:The relationship between the extent and severity of stress-induced ischemia and the extent and severity of anatomic coronary artery disease (CAD) in patients with obstructive CAD is multifactorial and includes the intensity of stress achieved, type of testing used, presence and extent of prior infarction, collateral blood flow, plaque characteristics, microvascular disease, coronary vasomotor tone, and genetic factors. Among chronic coronary disease participants with site-determined moderate or severe ischemia, we investigated associations between ischemia severity on stress testing and the extent of CAD on coronary computed tomography angiography. METHODS/UNASSIGNED:Clinically indicated stress testing included nuclear imaging, echocardiography, cardiac magnetic resonance imaging, or nonimaging exercise tolerance test. Among those with preserved renal function who underwent coronary computed tomography angiography, we examined relationships between ischemia and CAD by coronary computed tomography angiography, overall, and by stress test modality, regardless of subsequent randomization. Core laboratories categorized ischemia as severe, moderate, mild, or none, while the extent and severity of anatomic CAD were categorized based on the modified Duke prognostic index. RESULTS/UNASSIGNED:Among 3601 participants with interpretable stress tests and coronary computed tomography angiography, ischemia severity was weakly associated with CAD extent/severity (r=0.27), with modest variability in strength of association by modality: nuclear (n=1532; r=0.40), echocardiography (n=827; r=0.15), cardiac magnetic resonance imaging (n=108; r=0.31), and exercise tolerance test (n=1134; r=0.18). The extent of infarction on nuclear imaging and echocardiography was weakly associated with CAD extent/severity. CONCLUSIONS/UNASSIGNED:Overall, ischemia severity on stress testing showed weak to moderate associations with the anatomic extent of CAD in this cohort with moderate or severe ischemia on local interpretation and controlled symptoms. REGISTRATION/UNASSIGNED:URL: https://www.clinicaltrials.gov; Unique identifier: NCT01471522.

Electronic health record (EHR)-embedded tools are known to improve prescribing of guideline-directed medical therapy (GDMT) for patients with heart failure. However, physicians may perceive EHR tools to be unhelpful, and may be therefore hesitant to implement these in their practice. We surveyed cardiologists about two effective EHR-tools to improve heart failure care, and they perceived the EHR tools to be easy to use, helpful, and improve the overall management of their patients with heart failure.

BACKGROUND/UNASSIGNED:Previous studies evaluated the ability of large language models (LLMs) in medical disciplines; however, few have focused on image analysis, and none specifically on cardiovascular imaging or nuclear cardiology. OBJECTIVES/UNASSIGNED:This study assesses four LLMs - GPT-4, GPT-4 Turbo, GPT-4omni (GPT-4o) (Open AI), and Gemini (Google Inc.) - in responding to questions from the 2023 American Society of Nuclear Cardiology Board Preparation Exam, reflecting the scope of the Certification Board of Nuclear Cardiology (CBNC) examination. METHODS/UNASSIGNED:We used 168 questions: 141 text-only and 27 image-based, categorized into four sections mirroring the CBNC exam. Each LLM was presented with the same standardized prompt and applied to each section 30 times to account for stochasticity. Performance over six weeks was assessed for all models except GPT-4o. McNemar's test compared correct response proportions. RESULTS/UNASSIGNED:GPT-4, Gemini, GPT4-Turbo, and GPT-4o correctly answered median percentiles of 56.8% (95% confidence interval 55.4% - 58.0%), 40.5% (39.9% - 42.9%), 60.7% (59.9% - 61.3%) and 63.1% (62.5 - 64.3%) of questions, respectively. GPT4o significantly outperformed other models (p=0.007 vs. GPT-4Turbo, p<0.001 vs. GPT-4 and Gemini). GPT-4o excelled on text-only questions compared to GPT-4, Gemini, and GPT-4 Turbo (p<0.001, p<0.001, and p=0.001), while Gemini performed worse on image-based questions (p<0.001 for all). CONCLUSION/UNASSIGNED:GPT-4o demonstrated superior performance among the four LLMs, achieving scores likely within or just outside the range required to pass a test akin to the CBNC examination. Although improvements in medical image interpretation are needed, GPT-4o shows potential to support physicians in answering text-based clinical questions.

BACKGROUND:Electronic health record (EHR) tools can improve prescribing of guideline-recommended therapies for heart failure with reduced ejection fraction (HFrEF), but their effectiveness may vary by physician workload. OBJECTIVES/OBJECTIVE:This paper aims to assess whether physician workload modifies the effectiveness of EHR tools for HFrEF. METHODS:This was a prespecified subgroup analysis of the BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure) cluster-randomized trial, which compared effectiveness of an alert vs message vs usual care on prescribing of mineralocorticoid antagonists (MRAs). The trial included adults with HFrEF seen in cardiology offices who were eligible for and not prescribed MRAs. Visit volume was defined at the cardiologist-level as number of visits per 6-month study period (high = upper tertile vs non-high = remaining). Analysis at the patient-level used likelihood ratio test for interaction with log-binomial models. RESULTS:Among 2,211 patients seen by 174 cardiologists, 932 (42.2%) were seen by high-volume cardiologists (median: 1,853; Q1-Q3: 1,637-2,225 visits/6 mo; and median: 10; Q1-Q3: 9-12 visits/half-day). MRA was prescribed to 5.5% in the high-volume vs 14.8% in the non-high-volume groups in the usual care arm, 10.3% vs 19.6% in the message arm, and 31.2% vs 28.2% in the alert arm, respectively. Visit volume modified treatment effect (P for interaction = 0.02) such that the alert was more effective in the high-volume group (relative risk: 5.16; 95% CI: 2.57-10.4) than the non-high-volume group (relative risk: 1.93; 95% CI: 1.29-2.90). CONCLUSIONS:An EHR-embedded alert increased prescribing by >5-fold among patients seen by high-volume cardiologists. Our findings support use of EHR alerts, especially in busy practice settings. (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure [BETTER CARE-HF]; NCT05275920).

BACKGROUND:Mineralocorticoid receptor antagonists (MRA) are under-prescribed for patients with heart failure with reduced ejection fraction (HFrEF). OBJECTIVE:To compare effectiveness of two automated, electronic health record (EHR)-embedded tools vs. usual care on MRA prescribing in eligible patients with HFrEF. METHODS:BETTER CARE-HF (Building Electronic Tools To Enhance and Reinforce CArdiovascular REcommendations for Heart Failure) was a three-arm, pragmatic, cluster-randomized trial comparing the effectiveness of an alert during individual patient encounters vs. a message about multiple patients between encounters vs. usual care on MRA prescribing. We included adult patients with HFrEF, no active MRA prescription, no contraindication to MRA, and an outpatient cardiologist in a large health system. Patients were cluster-randomized by cardiologist (60 per arm). RESULTS:The study included 2,211 patients (alert: 755, message: 812, usual care [control]: 644), with average age 72.2 years, average EF 33%, who were predominantly male (71.4%) and White (68.9%). New MRA prescribing occurred in 29.6% of patients in the alert arm, 15.6% in the message arm, and 11.7% in the control arm. The alert more than doubled MRA prescribing compared to control (RR: 2.53, 95% CI: 1.77-3.62, p<0.0001), and improved MRA prescribing compared to the message (RR: 1.67, 95% CI: 1.21-2.29, p=0.002). The number of patients with alert needed to result in an additional MRA prescription was 5.6. CONCLUSIONS:An automated, patient-specific, EHR-embedded alert increased MRA prescribing compared to both a message and usual care. Our findings highlight the potential for EHR-embedded tools to substantially increase prescription of life-saving therapies for HFrEF. (NCT05275920).