BMC cardiovascular disorders · 2026
Impact of heart rate trajectories on mortality in critically Ill patients with atrial fibrillation and heart failure: a longitudinal cohort study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Ni Qian, Qi Jialin, Wang Qinghe, Tang Chen, Liang Zishun, Cai Jing, Qiao Tong, Wang Baoyan
Аннотация
BACKGROUND: This study investigated the prognostic significance of dynamic heart rate trajectories and heart rate coefficient of variation (CV) in critically ill patients with atrial fibrillation and heart failure (AF-HF).
METHODS: This retrospective multicenter cohort study included 8,356 ICU patients with AF-HF from the MIMIC-IV and eICU databases. We applied a Joint Latent Class Model (JLCM), which simultaneously identifies distinct longitudinal heart rate trajectories and estimates their associations with survival, to classify patients into different heart rate trajectory groups over the first 30 days after ICU admission. Multivariable Cox models were used to evaluate the associations between heart rate patterns and 30-day all-cause mortality. A Bayesian joint model was further developed for dynamic risk prediction.
RESULTS: Compared with a baseline heart rate < 80 bpm, the fully adjusted HRs were 1.13 (95% CI: 1.01-1.26) for 80-110 bpm and 1.28 (95% CI: 1.10-1.49) for > 110 bpm. Three distinct heart rate trajectory groups were identified, with a progressively increasing trajectory associated with higher mortality (HR = 2.96, 95% CI: 2.24-3.92). Similarly, patients with increasing heart rate CV trajectories had higher mortality than those with stable CV patterns (HR = 3.11, 95% CI: 2.40-4.03). Subgroup analyses showed significant interactions by renal function and mechanical ventilation status (P for interaction < 0.05). Dynamic prediction models showed improved discrimination with longer observation windows.
CONCLUSIONS: Heart rate trajectories were associated with short-term mortality in ICU patients with AF-HF, and similar associations were observed for heart rate variability trajectories in exploratory analyses. These findings suggest that longitudinal heart rate monitoring may have value for short-term risk stratification, although further validation is required.
METHODS: This retrospective multicenter cohort study included 8,356 ICU patients with AF-HF from the MIMIC-IV and eICU databases. We applied a Joint Latent Class Model (JLCM), which simultaneously identifies distinct longitudinal heart rate trajectories and estimates their associations with survival, to classify patients into different heart rate trajectory groups over the first 30 days after ICU admission. Multivariable Cox models were used to evaluate the associations between heart rate patterns and 30-day all-cause mortality. A Bayesian joint model was further developed for dynamic risk prediction.
RESULTS: Compared with a baseline heart rate < 80 bpm, the fully adjusted HRs were 1.13 (95% CI: 1.01-1.26) for 80-110 bpm and 1.28 (95% CI: 1.10-1.49) for > 110 bpm. Three distinct heart rate trajectory groups were identified, with a progressively increasing trajectory associated with higher mortality (HR = 2.96, 95% CI: 2.24-3.92). Similarly, patients with increasing heart rate CV trajectories had higher mortality than those with stable CV patterns (HR = 3.11, 95% CI: 2.40-4.03). Subgroup analyses showed significant interactions by renal function and mechanical ventilation status (P for interaction < 0.05). Dynamic prediction models showed improved discrimination with longer observation windows.
CONCLUSIONS: Heart rate trajectories were associated with short-term mortality in ICU patients with AF-HF, and similar associations were observed for heart rate variability trajectories in exploratory analyses. These findings suggest that longitudinal heart rate monitoring may have value for short-term risk stratification, although further validation is required.