Abstract

Background: We aim to investigate the use of intra-operative heart rate (HR) trajectories to develop and characterize sub-phenotypes of coronary artery bypass grafting (CABG) patients with distinct risk and outcome profiles. Methods: We used a retrospective cohort including 4194 CABG patients admitted to a large rural healthcare system between 2012 and 2021. Functional data analysis (FDA) was applied to patients’ intra-operative HR trajectories to identify distinct sub-phenotypes. Results: The elbow method suggested that the optimal number of clusters is four. Fig. 1 shows the mean HR trajectory curve (top) and Kaplan-Meier survival curve (bottom) for each of the four identified groups. G1 (Low HR) includes 35.2% of the patients with median age 68 years. Patients in G1 had the lowest ICU after CABG length of stay (LoS) of 76.1 hours and the longest CABG median procedure duration of 4.6 hours. Assignment to G1 was significantly associated with shorter ICU LoS (p= 2.2E-6). G2 (Intermediate HR) includes 35.4% of the patients with median age of 67 years. Patients in G2 had the highest prevalence of urgent admissions, 25.5%. Assignment to G2 was significantly associated with shorter ICU LoS (p=0.016). G3 (Increasing HR) includes 15.6% of the patients with median age 67. Patients in G3 had the shortest median procedure duration of 3.68 hours. Those patients were found to be significantly at risk of prolonged ICU stays (p=0.0002). Finally, G4 (High HR) includes 16.2% with the lowest observed median age of 65 years. Patients in G4 have the highest prevalence of in-hospital mortality (5.2%), 30-day readmission (4.3%), and 1-year mortality (4.8%). Assignment to G4 was significantly associated with the following outcomes: prolonged ICU stays (p=2.62e-9); in-hospital mortality (p=8.03e-5); 30-day readmission (p=0.017); and 1-year mortality (p=0.02). Conclusions: CABG sub-phenotypes based on intra-operative HR trajectories had distinct clinical characteristics and outcomes.

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