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, considering multi-organ dynamics. We aimed to derive novel phenotypes of sepsis-induced ARF using observational clinical data and investigate the generalizability of the derived phenotypes. We performed a multi-center retrospective study of ICU patients with sepsis who required mechanical ventilation for ≥ 24 h. Data from two different high-volume academic hospital centers were used, where all phenotypes were derived in MICU of Hospital-I (N = 3225). The derived phenotypes were validated in MICU of Hospital-II (N = 848), SICU of Hospital-I (N = 1112), and SICU of Hospital-II (N = 465). Clinical data from 24 h preceding intubation was used to derive distinct phenotypes using an explainable machine learning-based clustering model interpreted by clinical experts. 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 the MICU of Hospital-II and SICUs from Hospital-I and -II. Kaplan–Meier analysis showed significant difference in 28-day mortality across the phenotypes (p < 0.01) and consistent across MICU and SICU of both Hospital-I and -II. The phenotypes demonstrated differences in treatment effects associated with high positive end-expiratory pressure (PEEP) strategy. 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.

中文翻译：

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危重症患者全身性脓毒症诱导的急性呼吸衰竭表型的推导和验证：一项回顾性研究


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临床特征差异很大的危重症患者的异质性亚组。考虑到多器官动力学，识别这些患者的不同表型可能会揭示有关脓毒症临床病程中更广泛的异质性的见解。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，并研究衍生表型的普遍性。我们对需要机械通气 ≥ 24 h 的脓毒症 ICU 患者进行了多中心回顾性研究。使用了来自两个不同的高容量学术医院中心的数据，其中所有表型均源自 Hospital-I 的 MICU （N = 3225）。衍生的表型在 II 医院的 MICU （n = 848） 、 I 医院的 SICU （n = 1112） 和 II 医院的 SICU （n = 465） 中得到验证。使用由临床专家解释的基于机器学习的可解释的聚类模型，使用插管前 24 小时的临床数据来得出不同的表型。确定了四种不同的 ARF 表型：A （严重多器官功能障碍 （MOD），肾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 P/F = 123]），C （轻度缺氧 [中位 P/F = 240]），和 D （严重 MOD，肝损伤、凝血病和乳酸酸中毒的可能性很高）。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都表现出差异。利用医院 II 的 MICU 和医院 I 和 II 的 SICU 在外部验证中复制表型。 Kaplan-Meier 分析显示，不同表型的 28 天死亡率存在显著差异 （p < 0.01），并且在医院 I 和 II 的 MICU 和 SICU 中保持一致。表型显示与高呼气末正压 （PEEP） 策略相关的治疗效果存在差异。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。