利用加速失效时间模型对新型冠状病毒肺炎患者的康复时间进行建模。
Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model.
机构信息
Faculty of Allied Health Sciences, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, India.
Collegiate Education, Chennai Region, Chennai, India.
出版信息
J Int Med Res. 2021 Aug;49(8):3000605211040263. doi: 10.1177/03000605211040263.
OBJECTIVE
To identify factors associated with recovery time from coronavirus disease 2019 (COVID-19).
METHODS
In this retrospective study, data for patients with COVID-19 were obtained between 21 June and 30 August 2020. An accelerated failure time (AFT) model was used to identify covariates associated with recovery time (days from hospital admission to discharge). AFT models with different distributions (exponential, log-normal, Weibull, generalized gamma, and log-logistic) were generated. Akaike's information criterion (AIC) was used to identify the most suitable model.
RESULTS
A total of 730 patients with COVID-19 were included (92.5% recovered and 7.5% censored). Based on its low AIC value, the log-logistic AFT model was the best fit for the data. The covariates affecting length of hospital stay were oxygen saturation, lactate dehydrogenase, neutrophil-lymphocyte ratio, D-dimer, ferritin, creatinine, total leucocyte count, age > 80 years, and coronary artery disease.
CONCLUSIONS
The log-logistic AFT model accurately described the recovery time of patients with COVID-19.
目的
确定与 2019 年冠状病毒病（COVID-19）康复时间相关的因素。
方法
本回顾性研究于 2020 年 6 月 21 日至 8 月 30 日期间收集了 COVID-19 患者的数据。采用加速失效时间（AFT）模型确定与康复时间（从入院到出院的天数）相关的协变量。生成了具有不同分布（指数、对数正态、威布尔、广义伽马和对数逻辑）的 AFT 模型。采用赤池信息量准则（AIC）来确定最合适的模型。
结果
共纳入 730 例 COVID-19 患者（92.5%痊愈，7.5%删失）。基于其较低的 AIC 值，对数逻辑 AFT 模型最适合该数据。影响住院时间的协变量包括氧饱和度、乳酸脱氢酶、中性粒细胞-淋巴细胞比值、D-二聚体、铁蛋白、肌酐、白细胞总数、年龄＞80 岁和冠状动脉疾病。
结论
对数逻辑 AFT 模型准确描述了 COVID-19 患者的康复时间。
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