COVID-19和流感肺炎的医院预后和临床亚表型预测因子不同

P. Lyons, S. Bhavani, A. Michelson, T. Kannampallil, P. Sinha
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摘要

导语:由SARS-CoV-2(2019冠状病毒病,COVID-19)引起的肺炎经常与其他病毒性肺炎(包括流感)进行比较。虽然一些数据表明生物反应存在显著差异,但SARS-COV-2和流感肺炎在临床病程和特征上的差异尚不清楚。我们评估了COVID-19和流感肺炎预后和早期临床亚表型的临床预测因素的差异。方法:我们对所有因>住院的患者进行回顾性队列研究;因COVID-19(2020年3月至7月)或流感(2012年1月至2018年12月)在巴恩斯犹太医院住院24小时,需要吸氧支持。住院死亡率或临终关怀出院是主要结局。首先,使用每个病毒队列的自举复制来训练监督机器学习分类器模型(XGBoost),以预测主要结果。在住院24小时内最极端生命体征和实验室值中预选28个候选预测变量,排除高度相关的变量。我们比较了每个模型的内部判别与其在替代队列中的表现,并评估了两种病毒性肺炎模型之间变量重要性的差异。接下来,我们通过两种方式评估临床亚表型的差异:1)先前验证的算法,根据住院72小时内的温度轨迹将患者分为四个不同的亚表型;2)潜在类分析(LCA),根据上述预测变量确定每个病毒队列中未测量的亚组。在这两项分析中,我们比较了病毒队列中亚表型成员的频率和每个亚表型的主要结局。结果:我们评估了321例新冠肺炎住院病例和535例流感住院病例。主要结局分别为23%和9.5%的患者。流感预测模型对COVID-19患者预后的区分差于内部评估(A组),表明病毒性肺炎患者的预后变量不同。在两种模型中,前5个贡献变量中只有一个是共享的(图B)。病毒性肺炎的温度轨迹亚表型患病率也存在显著差异。所有COVID-19温度轨迹亚表型比流感亚型更频繁地经历主要结局(图C)。LCA在每个队列中确定了两个不同的类别,每个病毒性肺炎的少数类别比多数类别经历更差的结局。在每个模型的前5个分类定义变量中,只有2个是共享的(图D)。结论:COVID-19和流感肺炎在预测结果和临床亚表型方面存在显著差异。这些发现强调了病毒性肺炎中可观察到的病原体特异性差异反应,并建议对这些疾病研究不同的管理方法。(表)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictors of Hospital Outcomes and Clinical Subphenotypes Differ Between COVID-19 and Influenza Pneumonia
Introduction: Pneumonia due to SARS-CoV-2 (Coronavirus Disease 2019, COVID-19) has frequently been compared to other viral pneumonias, including influenza. While some data suggest significant differences in biological responses, dissimilarities in the clinical course and characteristics between SARS-COV-2 and influenza pneumonia remain unknown. We evaluated differences in clinical predictors of outcomes and early clinical subphenotypes in COVID-19 and influenza pneumonia. Methods: We performed a retrospective cohort study of all patients hospitalized for > 24 hours, requiring oxygen support, at Barnes-Jewish Hospital with COVID-19 (March-July 2020) or influenza (Jan 2012-Dec 2018). In-hospital mortality or hospice discharge was the primary outcome. First, supervised machine learning classifier models (XGBoost) were trained using bootstrap replications of each viral cohort to predict the primary outcome. 28 candidate predictor variables among the most extreme vital signs and laboratory values within 24 hours of hospitalization were preselected, excluding highly correlated variables. We compared each model's internal discrimination to its performance in the alternate cohort and evaluated differences in variable importance between the two viral pneumonia models. Next, we evaluated differences in clinical subphenotypes in two ways: 1) a previously-validated algorithm to group patients into four distinct subphenotypes based on temperature trajectories within 72 hours of hospitalization;2) latent class analysis (LCA) to identify unmeasured subgroups within each viral cohort based on the predictor variables described above. In both analyses, we compared frequency of subphenotype membership and each subphenotype's primary outcome between viral cohorts. Results: We evaluated 321 unique hospitalizations with COVID-19 and 535 with influenza. The primary outcome was experienced in 23% and 9.5% of patients, respectively. Influenza predictor model discriminated outcomes worse in COVID-19 than on internal evaluation (Panel A), suggesting prognostic variables differ between the viral pneumonias. Only one of the top five contributory variables was shared between the two models (Panel B). Prevalences of temperature trajectory subphenotype also differed significantly between viral pneumonias. All COVID-19 temperature trajectory subphenotypes experienced the primary outcome more frequently than their influenza counterparts (Panel C). LCA identified two distinct classes in each cohort, with each viral pneumonia's minority class experiencing worse outcomes than the majority class. Of each model's top 5 classdefining variables, only 2 were shared (Panel D). Conclusions: COVID-19 and influenza pneumonia differ markedly in predictors of outcome and in clinical subphenotypes. These findings emphasize observable pathogen-specific differential responses in viral pneumonias and suggest that distinct management approaches should be investigated for these diseases. (Table Presented).
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