开发和验证 COVID-19 死亡率预测算法的多中心回顾性队列研究。
Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study.
机构信息
Central Laboratory, Ningbo First Hospital, Zhejiang University, Ningbo, China.
Department of Respiratory and Critical Care, Jingzhou First People's Hospital, Jingzhou, China.
出版信息
BMJ Open. 2020 Dec 24;10(12):e044028. doi: 10.1136/bmjopen-2020-044028.
OBJECTIVE
This study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.
DESIGN
Retrospective cohort study.
SETTING
Five designated tertiary hospitals for COVID-19 in Hubei province, China.
PARTICIPANTS
We routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19.
METHODS
The model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers' D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted.
MAIN OUTCOME MEASURES
The primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.
RESULTS
The full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort.
CONCLUSION
The prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification.
WEB CALCULATOR
We provided a freely accessible web calculator (https://www.whuyijia.com/).
目的
本研究旨在开发和外部验证一种 COVID-19 死亡率风险预测算法。
设计
回顾性队列研究。
地点
中国湖北省五所指定的 COVID-19 三级医院。
参与者
我们常规收集了 2020 年 1 月 8 日至 3 月 19 日期间 1364 例确诊成人 COVID-19 患者的医疗数据。其中，来自武汉两家指定医院的 1088 例患者用于开发预测模型，来自武汉以外的三家医院的 276 例患者用于外部验证。所有患者在 COVID-19 诊断后最多随访 60 天。
方法
通过接受者操作特征曲线（ROC）下面积（AUC）和 Somers' D 检验评估模型的区分度，通过校准图评估校准度，并进行决策曲线分析。
主要观察指标
主要结局为 COVID-19 诊断后 60 天内的全因死亡率。
结果
全模型包含年龄、呼吸衰竭、白细胞计数、淋巴细胞、血小板、D-二聚体和乳酸脱氢酶 7 个预测因素。简单模型包含年龄、呼吸衰竭、冠心病、肾功能衰竭和心力衰竭 5 个指标。经交叉验证，基于推导队列的 AUC 统计数据，全模型为 0.96（95%CI，0.96 至 0.97），简单模型为 0.92（95%CI，0.89 至 0.95）。基于外部验证队列的 AUC 统计数据，全模型为 0.97（95%CI，0.96 至 0.98），简单模型为 0.88（95%CI，0.80 至 0.96）。这两个模型在推导和验证队列中均具有良好的校准准确性。
结论
这些预测模型在识别 COVID-19 患者 60 天内死亡风险较高方面表现出良好的模型性能。它可能有助于急性风险分类。
网络计算器
我们提供了一个免费的在线计算器（https://www.whuyijia.com/）。
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