Hou Grace Yao, Lal Amos, Schulte Phillip J, Dong Yue, Kilickaya Oguz, Gajic Ognjen, Zhong Xiang
Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida.
Division of Pulmonary and Critical Care Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, Minnesota.
Shock. 2025 Apr 1;63(4):573-578. doi: 10.1097/SHK.0000000000002536. Epub 2025 Jan 23.
Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to intensive care units (ICUs) of Mayo Clinic Hospitals over 8-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status. Of 19,177 patients, 42% were female with a median age of 65 (interquartile range [IQR], 55-76) years, The Acute Physiology, Age, and Chronic Health Evaluation III score of 70 (IQR, 56-87), hospital length of stay (LOS) of 7 (IQR, 4-12) days, and ICU LOS of 2 (IQR, 1-4) days. Four distinct trajectories were identified: fast recovery (27% with a mortality rate of 3.5% and median hospital LOS of 3 (IQR, 2-15) days), slow recovery (62% with a mortality rate of 3.6% and hospital LOS of 8 (IQR, 6-13) days), fast decline (4% with a mortality rate of 99.7% and hospital LOS of 1 (IQR, 0-1) day), and delayed decline (7% with a mortality rate of 97.9% and hospital LOS of 5 (IQR, 3-8) days). Distinct trajectories remained robust and were distinguished by Charlson Comorbidity Index, The Acute Physiology, Age, and Chronic Health Evaluation III scores, as well as day 1 and day 3 SOFA ( P < 0.001 ANOVA). These findings provide a foundation for developing prediction models and digital twin decision support tools, improving both shared decision making and resource planning.
了解脓毒症患者的临床轨迹对于预后评估、资源规划以及为危重症数字孪生模型提供信息至关重要。本研究旨在基于对心肺支持的动态评估，利用经过验证的电子健康记录数据，识别常见的临床轨迹，该数据涵盖了梅奥诊所医院重症监护病房（ICU）在8年期间收治的19177例脓毒症患者的回顾性队列。使用基于ICU中心肺支持和医院出院状态的无监督机器学习两阶段聚类方法，对患者从ICU入院至14天的轨迹进行建模。在19177例患者中，42%为女性，中位年龄为65岁（四分位间距[IQR]，55 - 76岁），急性生理学与慢性健康状况评分系统III（APACHE III）评分为70分（IQR，56 - 87分），住院时间（LOS）为7天（IQR，4 - 12天），ICU住院时间为2天（IQR，1 - 4天）。识别出四种不同的轨迹：快速康复（27%，死亡率为3.5%，中位住院时间为3天（IQR，2 - 15天））、缓慢康复（62%，死亡率为3.6%，住院时间为8天（IQR，6 - 13天））、快速恶化（4%，死亡率为99.7%，住院时间为1天（IQR，0 - 1天））和延迟恶化（7%，死亡率为97.9%，住院时间为5天（IQR，3 - 8天））。不同的轨迹仍然稳健，并通过查尔森合并症指数、急性生理学与慢性健康状况评分系统III评分以及第1天和第3天的序贯器官衰竭评估（SOFA）进行区分（方差分析，P < 0.001）。这些发现为开发预测模型和数字孪生决策支持工具奠定了基础，有助于改善共同决策和资源规划。
