Hua Yiting, Zhou Yutong, Qin Ziyue, Mu Yuan, Wang Ting, Ruan Haoyu
Department of Laboratory Medicine, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.
Branch of National Clinical Research Center for Laboratory Medicine, Nanjing, Jiangsu, People's Republic of China.
Infect Drug Resist. 2024 Mar 23;17:1171-1184. doi: 10.2147/IDR.S447326. eCollection 2024.
The surge in the number of patients diagnosed with COVID-19 since China's open-door policy has placed a huge burden on the public healthcare system, especially the intensive care system. This study's objective was to discover possible clinical outcome predictors in COVID-19 patients treated in intensive care units (ICUs) and to provide useful information for future preventative efforts and therapies.
This retrospective study included 173 COVID-19 critically ill patients and reviewed the 28-day survival outcome in the First Affiliated Hospital of Nanjing Medical University. Competing risk analysis was performed to predict the cumulative incidence function (CIF) of mortality in hospital. The independent prognostic factors were identified by applying the Fine-Gray proportional subdistribution hazard model. Receiver operating characteristic (ROC) curves were used to evaluate model efficacy, and calibration curves were used to validate the model. Finally, we compared the competing risk model with the traditional proportional hazards model (Cox regression model) using CIF.
Of these 173 patients, 66 (38.2%) survived, 55 (31.8%) died, and 52 (30.0%) discharged. In univariate analysis, 12 variables were significantly correlated with mortality. In multivariate analysis, Age, Neutrophil ratio, Direct Bilirubin (DBIL) and Renal disease were independent predictors of 28-day outcome. The ROC curve of the multivariate prediction model showed an AUC (area under the curve) of 0.790. The results of the calibration curve and the concordance index (C-index) show that the model has good discriminatory power. The competing risk model we applied was more accurate than the Cox model.
We presented a more accurate multivariate prediction model for 28-day in-hospital mortality for ICU COVID-19 patients using a competing risk model.
自中国开放政策以来，新冠病毒病（COVID-19）确诊患者数量激增，给公共医疗系统，尤其是重症监护系统带来了巨大负担。本研究的目的是发现重症监护病房（ICU）中接受治疗的COVID-19患者可能的临床结局预测因素，并为未来的预防措施和治疗提供有用信息。
这项回顾性研究纳入了173例COVID-19危重症患者，并回顾了南京医科大学第一附属医院的28天生存结局。进行竞争风险分析以预测住院死亡率的累积发病率函数（CIF）。应用Fine-Gray比例子分布风险模型确定独立的预后因素。采用受试者工作特征（ROC）曲线评估模型效能，并用校准曲线验证模型。最后，我们使用CIF将竞争风险模型与传统的比例风险模型（Cox回归模型）进行比较。
在这173例患者中，66例（38.2%）存活，55例（31.8%）死亡，52例（30.0%）出院。单因素分析中，12个变量与死亡率显著相关。多因素分析中，年龄、中性粒细胞比例、直接胆红素（DBIL）和肾脏疾病是28天结局的独立预测因素。多因素预测模型的ROC曲线显示曲线下面积（AUC）为0.790。校准曲线和一致性指数（C指数）结果表明该模型具有良好的区分能力。我们应用的竞争风险模型比Cox模型更准确。
我们使用竞争风险模型为ICU中COVID-19患者的28天住院死亡率提出了一个更准确的多因素预测模型。
