Early heart-rate trajectory phenotypes predict short-term mortality in critically ill patients: a dynamic time-warping cluster analysis

Heart rate (HR) reflects illness severity in critically ill patients, but the prognostic significance of early HR changes is unclear. We aimed to identify HR trajectory phenotypes during the first 24 h of ICU admission and assess their association with short-term mortality. Adults admitted to the intensive care unit (ICU) between January 2018 and December 2024 with ≥ 12 h of HR data were eligible; patients with early discharge/death, mechanical support, or sustained bradycardia were excluded. Hourly HR means from the first 24 h were imputed and clustered via Dynamic Time Warping-based TimeSeriesKMeans; five trajectories were selected using internal validation. Thirty- and 90-day mortality were assessed by multivariable Cox models. A total of 4491 patients were analyzed after applying exclusion criteria. TimeSeriesKMeans identified five early HR trajectory phenotypes: Cluster A (persistently tachycardic), Cluster B (early HR normalization), Cluster C (rising HR), Cluster D (mild tachycardic-normal), and Cluster E (normal HR). Cluster A was associated with a markedly increased risk of 30-day mortality compared with the reference Cluster E (HR 3.21, 95% CI 2.32–4.45; p < 0.001). Cluster C also showed an elevated mortality risk (HR 2.09, 95% CI 1.53–2.85; p < 0.001), whereas clusters characterized by lower or early-normalizing heart-rate trajectories demonstrated more favorable survival profiles. Kaplan-Meier analyses confirmed significant differences in survival across clusters (log-rank p < 0.001). Machine learning-based phenotyping of 24-hour HR trajectories stratified short-term mortality risk and revealed time-dependent patterns potentially reflecting treatment responsiveness. Early adverse heart-rate trajectories, particularly persistent tachycardia, may serve as prognostic signals of physiological stress or poor treatment response during the initial ICU course, supporting early risk stratification and clinical reassessment.