PDF(Pubmed)
Abstract：
UNASSIGNED: Septic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of these patients may reveal insights about the broader heterogeneity in the clinical course of sepsis. We aimed to derive novel phenotypes of sepsis-induced ARF using observational clinical data and investigate their generalizability across multi-ICU specialties, considering multi-organ dynamics.
UNASSIGNED: We performed a multi-center retrospective study of ICU patients with sepsis who required mechanical ventilation for ≥24 hours. Data from two different high-volume academic hospital systems were used as a derivation set with N=3,225 medical ICU (MICU) patients and a validation set with N=848 MICU patients. For the multi-ICU validation, we utilized retrospective data from two surgical ICUs at the same hospitals (N=1,577). Clinical data from 24 hours preceding intubation was used to derive distinct phenotypes using an explainable machine learning-based clustering model interpreted by clinical experts.
UNASSIGNED: Four distinct ARF phenotypes were identified: A (severe multi-organ dysfunction (MOD) with a high likelihood of kidney injury and heart failure), B (severe hypoxemic respiratory failure [median P/F=123]), C (mild hypoxia [median P/F=240]), and D (severe MOD with a high likelihood of hepatic injury, coagulopathy, and lactic acidosis). Patients in each phenotype showed differences in clinical course and mortality rates despite similarities in demographics and admission co-morbidities. The phenotypes were reproduced in external validation utilizing an external MICU from second hospital and SICUs from both centers. Kaplan-Meier analysis showed significant difference in 28-day mortality across the phenotypes (p<0.01) and consistent across both centers. The phenotypes demonstrated differences in treatment effects associated with high positive end-expiratory pressure (PEEP) strategy.
UNASSIGNED: The phenotypes demonstrated unique patterns of organ injury and differences in clinical outcomes, which may help inform future research and clinical trial design for tailored management strategies.
UNASSIGNED: We performed a multi-center retrospective study of ICU patients with sepsis who required mechanical ventilation for ≥24 hours. Data from two different high-volume academic hospital systems were used as a derivation set with N=3,225 medical ICU (MICU) patients and a validation set with N=848 MICU patients. For the multi-ICU validation, we utilized retrospective data from two surgical ICUs at the same hospitals (N=1,577). Clinical data from 24 hours preceding intubation was used to derive distinct phenotypes using an explainable machine learning-based clustering model interpreted by clinical experts.
UNASSIGNED: Four distinct ARF phenotypes were identified: A (severe multi-organ dysfunction (MOD) with a high likelihood of kidney injury and heart failure), B (severe hypoxemic respiratory failure [median P/F=123]), C (mild hypoxia [median P/F=240]), and D (severe MOD with a high likelihood of hepatic injury, coagulopathy, and lactic acidosis). Patients in each phenotype showed differences in clinical course and mortality rates despite similarities in demographics and admission co-morbidities. The phenotypes were reproduced in external validation utilizing an external MICU from second hospital and SICUs from both centers. Kaplan-Meier analysis showed significant difference in 28-day mortality across the phenotypes (p<0.01) and consistent across both centers. The phenotypes demonstrated differences in treatment effects associated with high positive end-expiratory pressure (PEEP) strategy.
UNASSIGNED: The phenotypes demonstrated unique patterns of organ injury and differences in clinical outcomes, which may help inform future research and clinical trial design for tailored management strategies.
摘要：
背景：发生需要机械通气的急性呼吸衰竭（ARF）的脓毒症患者代表了具有广泛可变临床特征的危重患者的异质性亚组。识别这些患者的不同表型可能揭示了脓毒症临床过程中更广泛的异质性。我们旨在使用观察性临床数据得出脓毒症诱导的ARF的新表型，并研究其在多个ICU专业的普适性。考虑多器官动力学。方法：我们对需要机械通气≥24小时的ICU脓毒症患者进行了多中心回顾性研究。来自两个不同的大批量学术医院系统的数据被用作N=3,225名医疗ICU（MICU）患者的推导集和N=848名MICU患者的验证集。对于多ICU验证，我们利用了来自同一医院的两个外科ICU的回顾性数据(N=1,577).插管前24小时的临床数据用于使用由临床专家解释的可解释的基于机器学习的聚类模型来得出不同的表型。结果：确定了四种不同的ARF表型：A（严重的多器官功能障碍（MOD），肾脏损伤和心力衰竭的可能性很高），B（严重低氧性呼吸衰竭[中位数P/F=123]），C（轻度缺氧[中位数P/F=240]），和D（严重的MOD，肝脏损伤的可能性很高，凝血病,和乳酸性酸中毒)。尽管人口统计学和入院合并症相似，但每种表型的患者在临床病程和死亡率方面均存在差异。利用来自第二医院的外部MICU和来自两个中心的SICU在外部验证中再现表型。Kaplan-Meier分析显示在表型之间28天死亡率的显著差异(p<0.01),并且在两个中心之间是一致的。表型显示与高呼气末正压（PEEP）策略相关的治疗效果差异。结论：表型表现出独特的器官损伤模式和临床结果的差异，这可能有助于为未来的研究和临床试验设计提供信息，以制定量身定制的管理策略。
