Zhang Rui, Long Fang, Wu Jingyi, Tan Ruoming
Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Critical Care Medicine, Zhuzhou Lukou District People's Hospital, Zhuzhou, Hunan, China.
Front Med (Lausanne). 2025 Apr 17;12:1575237. doi: 10.3389/fmed.2025.1575237. eCollection 2025.
Understanding heterogeneous recovery patterns in sepsis is crucial for personalizing treatment strategies and improving outcomes.
To identify distinct recovery trajectories in sepsis and develop a prediction model using early clinical and immunological markers.
Retrospective cohort study using data from 28,745 adult patients admitted to 12 intensive care units (ICUs) with sepsis between January 2014 and December 2024.
Primary outcome was the 28-day trajectory of Sequential Organ Failure Assessment (SOFA) scores. Secondary outcomes included 90-day mortality and hospital length of stay.
Among 24,450 eligible patients (mean [SD] age, 64.5 [15.3] years; 54.2% male), three distinct recovery trajectories were identified: rapid recovery (42.3%), slow recovery (35.8%), and deterioration (21.9%). The machine learning model achieved an AUROC of 0.85 (95% CI, 0.83-0.87) for trajectory prediction. Key predictors included initial SOFA score, lactate levels, and inflammatory markers. Mortality rates were 12.3, 28.7, and 45.6% for rapid, slow, and deterioration groups, respectively.
Early prediction of sepsis recovery trajectories is feasible and may facilitate personalized treatment strategies. The developed model could assist clinical decision-making and resource allocation in critical care settings.
了解脓毒症的异质性恢复模式对于个性化治疗策略和改善治疗结果至关重要。
识别脓毒症中不同的恢复轨迹，并使用早期临床和免疫标志物开发预测模型。
设计、设置和参与者：回顾性队列研究，使用2014年1月至2024年12月期间入住12个重症监护病房（ICU）的28745例成年脓毒症患者的数据。
主要结局是序贯器官衰竭评估（SOFA）评分的28天轨迹。次要结局包括90天死亡率和住院时间。
在24450例符合条件的患者中（平均[标准差]年龄，64.5[15.3]岁；54.2%为男性），识别出三种不同的恢复轨迹：快速恢复（42.3%）、缓慢恢复（35.8%）和恶化（21.9%）。机器学习模型在轨迹预测方面的曲线下面积（AUROC）为0.85（95%CI，0.83-0.87）。关键预测因素包括初始SOFA评分、乳酸水平和炎症标志物。快速、缓慢和恶化组的死亡率分别为12.3%、28.7%和45.6%。
脓毒症恢复轨迹的早期预测是可行的，可能有助于制定个性化治疗策略。所开发的模型可协助重症监护环境中的临床决策和资源分配。
