Lancet Respir Med：通过机器学习使用临床数据区分ARDS亚表型的验证与应用

Summary

Background

Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS.

在7个单独的队列中，使用潜在类分析(LCA)确定了具有不同临床和生物学特征以及不同治疗反应的两种急性呼吸窘迫综合征(ARDS)亚表型（高炎性和低炎性）。为了便于亚表型的床旁识别，在4项随机对照试验中描述了使用现成临床变量的临床分类器模型。我们的目的是评估这些模型在ARDS观察队列中的性能。

Methods

In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable.

在这项观察性、多队列、回顾性研究中，我们验证了在两个ARDS患者观察队列中分配ARDS亚表型的两种机器学习临床分类器模型：肾和肺损伤早期评估(EARLI；n = 335)和急性肺损伤诊断标志物验证(VALID；n = 452)，以LCA来源的亚表型为金标准。主要模型仅包括生命体征和实验室变量，次要模型包括主要模型中的所有预测因素，并增加了通气变量和人口统计学。通过计算受试者工作特征曲线下面积(AUC)和校准图，并使用0.5的概率临界值分配亚表型来评估模型性能，以确定分配的灵敏度、特异性和准确度。我们还使用从电子健康记录中自动提取的数据（EHR；EHR衍生的EARLI队列）评估了EARLI中主要模型的性能。在了解重度急性呼吸衰竭全球影响的大型观察性研究(LUNG SAFE；n = 2813)中，一项多国、观察性ARDS队列研究，我们应用自定义分类器模型（变量少于主要模型）来确定亚表型的预后价值，并测试它们与呼气末正压(PEEP)策略的相互作用，以90天死亡率作为因变量。

Findings

The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90–0·95) in EARLI and 0·88 (0·84–0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81–0·94] vs 0·92 [0·88–0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs233 [32%] of 734 patients in the low PEEP group).

主要临床分类器模型的受试者工作特征曲线下面积(AUC)在EARLI中为0.92(95%CI 0.90–0.95)，在VALID中为0.88(0.84-0.91)。当仅使用EHR推导的预测因子与手动绘制的预测因子相比时，主要模型的性能相似(AUC = 0.88[95%CI 0.81-0.94]vs 0.92[0.88-0.97])。在LUNG SAFE中，高炎症亚表型患者的90天死亡率高于低炎症表型患者（725例中414例[57%]vs 2088例中694例[33%]；p < 0.0001）。PEEP策略和ARDS亚表型之间存在显著的治疗交互作用(p = 0.041)，高PEEP组高炎症亚表型患者的90天死亡率较低（高炎症亚表型：高PEEP组313例患者中的169例[54%]vs低PEEP组205例患者中的127例[62%]；低炎症亚表型：高PEEP组675例患者中的231例[34%]与低PEEP组734例患者中的233例[32%]）。

Interpretation

Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated.

单独使用临床变量的分类器模型可以准确分配观察队列中的ARDS亚表型。这些模型的应用可以提供有价值的预后信息，并可以为个性化治疗的管理策略提供信息，包括应用PEEP，一旦前瞻性验证。

Funding

US National Institutes of Health and European Society of Intensive Care Medicine.