Meng Lingzhong, Li Jiangqiong, Liu Xiang, Sun Yanhua, Li Zuotian, Cai Jinjin, Parab Ameya D, Lu George, Budhkar Aishwarya, Kanakasabai Saravanan, Adams David C, Liu Ziyue, Zhang Xuhong, Su Jing
Department of Anesthesia, Indiana University School of Medicine, Indianapolis, IN, USA.
Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA.
NPJ Digit Med. 2025 Jul 24;8(1):474. doi: 10.1038/s41746-025-01863-0.
Effective hemodynamic management in the intensive care unit requires individualized targets that adapt to dynamic clinical conditions. We developed Dynamic Cohort Ensemble Learning (DynaCEL), a real-time framework that recommends personalized heart rate and systolic blood pressure targets by modeling each time point post-intensive care unit admission as a distinct temporal cohort. Trained on eICU data and validated on MIMIC-IV and Indiana University Health datasets, DynaCEL demonstrated robust predictive performance (AUCs 0.83-0.91). In the MIMIC-IV cohort, proximity to DynaCEL-predicted targets was associated with lower 24-hour mortality compared to fixed targets, after adjustment using propensity score matching. Dose-response and comparative analyses revealed that greater deviations from personalized targets were associated with higher mortality. Case studies illustrated temporal and inter-individual variation in optimal targets. DynaCEL offers interpretable and scalable support for exploring precision hemodynamic management, although its clinical utility remains to be established in prospective trials.
重症监护病房中有效的血流动力学管理需要适应动态临床状况的个体化目标。我们开发了动态队列集成学习（DynaCEL），这是一个实时框架，通过将重症监护病房入院后的每个时间点建模为一个独特的时间队列，来推荐个性化的心率和收缩压目标。DynaCEL在eICU数据上进行训练，并在MIMIC-IV和印第安纳大学健康数据集上进行验证，显示出强大的预测性能（AUC为0.83 - 0.91）。在MIMIC-IV队列中，使用倾向得分匹配进行调整后，与固定目标相比，接近DynaCEL预测目标与较低的24小时死亡率相关。剂量反应和比较分析表明，与个性化目标的偏差越大，死亡率越高。案例研究说明了最佳目标的时间和个体间差异。DynaCEL为探索精准血流动力学管理提供了可解释且可扩展的支持，尽管其临床效用仍有待在前瞻性试验中确定。
