Identification and validation of clinical phenotypes with prognostic implications in patients admitted to hospital with COVID-19: a multicentre cohort study.
Identification and validation of clinical phenotypes with prognostic implications in patients admitted to hospital with COVID-19: a multicentre cohort study.
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DOI：
10.1016/s1473-3099(21)00019-0
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
REIPI-SEIMC COVID-19 group and COVID@HULP groups
中科院分区：
文献类型：
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作者：
Gutiérrez-Gutiérrez B;Del Toro MD;Borobia AM;Carcas A;Jarrín I;Yllescas M;Ryan P;Pachón J;Carratalà J;Berenguer J;Arribas JR;Rodríguez-Baño J;REIPI-SEIMC COVID-19 group and COVID@HULP groups
入院患者的COVID-19临床表现各不相同。我们的目的是确定COVID-19患者的临床表型是否可以从临床数据中推导出来，评估这些表型的可重复性及其与预后的相关性，并推导和验证表型分配的简化概率模型。表型鉴定并非主要用作死亡率的预测工具。在这项研究中，我们使用了来自两个队列的数据：COVID-19@Spain队列是一个回顾性队列，包括2020年2月2日至3月17日期间在西班牙127家医院收治的4035名连续COVID-19成人患者，COVID-19@HULP队列包括2月25日至4月19日期间在马德里一家教学医院收治的2226名连续成人患者，2020. COVID-19@Spain队列分为一个推导队列，包括2667名随机选择的患者，以及一个内部验证队列，包括其余1368名患者。COVID-19@HULP队列被用作外部验证队列。在推导队列中使用多项逻辑回归推导出表型分配的概率模型，并在内部验证队列中进行验证。该模型也适用于外部验证队列。30-在衍生的表型和概率模型指定的表型中评估日死亡率和其他预后变量。在衍生队列（n = 2667）中衍生出三种不同的表型-表型A（516例[19%]患者），表型B（1955年[73%]）和表型C（196 [7%]）-并在内部验证队列（n = 1368）中重现-表型A（233例[17%]患者）、表型B（1019例[74%]）和表型C（116例[8%]）。表型A患者较年轻，男性较少，病毒症状轻微，炎症参数正常。具有表型B的患者包括更多的肥胖、淋巴细胞减少和中度升高的炎症参数的患者。具有表型C的患者包括比表型B具有更多合并症和甚至更高的炎症参数的老年患者。我们开发了一个简化的概率模型（在内部验证队列中验证）用于表型分配，包括16个变量。在衍生队列中，表型A患者的30天死亡率为2.5%（95% CI 1.4 - 4.3），表型B患者为30.5%（28.5 - 32.6），表型C患者为60.7%（53.7 - 67.2）（对数秩检验p <0.0001）。在内部验证队列和外部验证队列中预测的表型显示出与指定表型相似的死亡率（内部验证队列：表型A为5.3%[95% CI 3.4 - 8.1]，表型B为31.3%[28.5 - 34.2]，表型C为59.5%[48.8 - 69.3];外部验证队列：表型A为3.7%[2.0 - 6.4]，表型B为23.7%[21.8 - 25.7]，表型C为51.4%[41.9 - 60.7]。因COVID-19入院的患者可分为三种与死亡率相关的表型。我们开发并验证了一种简化的工具，用于将患者概率分配到表型中。这些结果可能有助于更好地分类患者的临床管理，但表型的病理生理机制必须进行调查。卡洛斯三世健康研究所、西班牙科学与创新部和SEIMC/GeSIDA基金会。
The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. In this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Three distinct phenotypes were derived in the derivation cohort (n=2667)—phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])—and reproduced in the internal validation cohort (n=1368)—phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2·5% (95% CI 1·4–4·3) for patients with phenotype A, 30·5% (28·5–32·6) for patients with phenotype B, and 60·7% (53·7–67·2) for patients with phenotype C (log-rank test p<0·0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5·3% [95% CI 3·4–8·1] for phenotype A, 31·3% [28·5–34·2] for phenotype B, and 59·5% [48·8–69·3] for phenotype C; external validation cohort: 3·7% [2·0–6·4] for phenotype A, 23·7% [21·8–25·7] for phenotype B, and 51·4% [41·9–60·7] for phenotype C). Patients admitted to hospital with COVID-19 can be classified into three phenotypes that correlate with mortality. We developed and validated a simplified tool for the probabilistic assignment of patients into phenotypes. These results might help to better classify patients for clinical management, but the pathophysiological mechanisms of the phenotypes must be investigated. Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation, and Fundación SEIMC/GeSIDA.