Comparison of Heart Team vs Interventional Cardiologist Recommendations for the Treatment of Patients With Multivessel Coronary Artery Disease.

Importance: Although the heart team approach is recommended in revascularization guidelines, the frequency with which heart team decisions differ from those of the original treating interventional cardiologist is unknown. Objective: To examine the difference in decisions between the heart team and the original treating interventional cardiologist for the treatment of patients with multivessel coronary artery disease. Design, Setting, and Participants: In this cross-sectional study, 245 consecutive patients with multivessel coronary artery disease were recruited from 1 high-volume tertiary care referral center (185 patients were enrolled through a screening process, and 60 patients were retrospectively enrolled from the center's database). A total of 237 patients were included in the final virtual heart team analysis. Treatment decisions (which comprised coronary artery bypass grafting, percutaneous coronary intervention, and medication therapy) were made by the original treating interventional cardiologists between March 15, 2012, and October 20, 2014. These decisions were then compared with pooled-majority treatment decisions made by 8 blinded heart teams using structured online case presentations between October 1, 2017, and October 15, 2018. The randomized members of the heart teams comprised experts from 3 domains, with each team containing 1 noninvasive cardiologist, 1 interventional cardiologist, and 1 cardiovascular surgeon. Cases in which all 3 of the heart team members disagreed and cases in which procedural discordance occurred (eg, 2 members chose coronary artery bypass grafting and 1 member chose percutaneous coronary intervention) were discussed in a face-to-face heart team review in October 2018 to obtain pooled-majority decisions. Data were analyzed from May 6, 2019, to April 22, 2020. Main Outcomes and Measures: The Cohen κ coefficient between the treatment recommendation from the heart team and the treatment recommendation from the original treating interventional cardiologist. Results: Among 234 of 237 patients (98.7%) in the analysis for whom complete data were available, the mean (SD) age was 67.8 (10.9) years; 176 patients (75.2%) were male, and 191 patients (81.4%) had stenosis in 3 epicardial coronary vessels. A total of 71 differences (30.3%; 95% CI, 24.5%-36.7%) in treatment decisions between the heart team and the original treating interventional cardiologist occurred, with a Cohen κ of 0.478 (95% CI, 0.336-0.540; P = .006). The heart team decision was more frequently unanimous when it was concordant with the decision of the original treating interventional cardiologist (109 of 163 cases [66.9%]) compared with when it was discordant (28 of 71 cases [39.4%]; P < .001). When the heart team agreed with the original treatment decision, there was more agreement between the heart team interventional cardiologist and the original treating interventional cardiologist (138 of 163 cases [84.7%]) compared with when the heart team disagreed with the original treatment decision (14 of 71 cases [19.7%]); P < .001). Those with an original treatment of coronary artery bypass grafting, percutaneous coronary intervention, and medication therapy, 32 of 148 patients [22.3%], 32 of 71 patients [45.1%], and 6 of 15 patients [40.0%], respectively, received a different treatment recommendation from the heart team than the original treating interventional cardiologist; the difference across the 3 groups was statistically significant (P = .002). Conclusions and Relevance: The heart team's recommended treatment for patients with multivessel coronary artery disease differed from that of the original treating interventional cardiologist in up to 30% of cases. This subset of cases was associated with a lower frequency of unanimous decisions within the heart team and less concordance between the interventional cardiologists; discordance was more frequent when percutaneous coronary intervention or medication therapy were considered. Further research is needed to evaluate whether heart team decisions are associated with improvements in outcomes and, if so, how to identify patients for whom the heart team approach would be beneficial.

Ensembling Electrical and Proteogenomics Biomarkers for Improved Prediction of Cardiac-Related 3-Month Hospitalizations: A Pilot Study.

BACKGROUND: Many risk models for predicting mortality, hospitalizations, or both in patients with heart failure have been developed but do not have sufficient discriminatory ability. The purpose of this study was to identify predictive biomarkers of hospitalizations in heart failure patients using omics-based technologies applied to blood and electrical monitoring of the heart. METHODS: Blood samples were collected from 58 heart failure patients during enrollment into this study. Each patient wore a 48-hour Holter monitor that recorded the electrical activity of their heart. The blood samples were profiled for gene expression using microarrays and protein levels using multiple reaction monitoring. Statistical deconvolution was used to estimate cellular frequencies of common blood cells. Classification models were developed using clinical variables, Holter variables, cell types, gene transcripts, and proteins to predict hospitalization status. RESULTS: Of the 58 patients recruited, 13 were hospitalized within 3 months after enrollment. These patients had lower diastolic and systolic blood pressures, higher brain natriuretic peptide levels, most had higher blood creatinine levels, and had been diagnosed with heart failure for a longer time period. The best-performing clinical model had an area under the receiver operating characteristic curve of 0.76. An ensemble biomarker panel consisting of Holter variables, cell types, gene transcripts, and proteins had an area under the receiver operating characteristic curve of 0.88. CONCLUSIONS: Molecular-based analyses as well as sensory data might provide sensitive biomarkers for the prediction of hospitalizations in heart failure patients. These approaches may be combined with traditional clinical models for the development of improved risk prediction models for heart failure.