Published online May 6, 2021. doi: 10.12998/wjcc.v9.i13.2994
Peer-review started: November 28, 2020
First decision: December 24, 2020
Revised: December 30, 2020
Accepted: February 24, 2021
Article in press: February 24, 2021
Published online: May 6, 2021
Processing time: 140 Days and 3.4 Hours
The outbreak of coronavirus disease 2019 (COVID-19) led to high mortality and the intensive care unit (ICU) is needed to reduce the number of deaths. This causes a shortage of medical resources, especially in ICU settings.
The goal of this study was to develop and externally validate prediction rules to risk stratify patients by severity at an early stage.
The study aimed to risk stratify patients by severity of the illness at an early stage to better microallocate limited medical resources.
This multicenter retrospective cohort study included 487 adult patients with confirmed COVID-19 between January 19, 2020, and March 14, 2020, in Shenzhen Third People’s Hospital and the Wuhan Asia General Hospital. Independent variables included sociodemographic factors, clinical symptoms, comorbidity, travel and contact history, and laboratory tests. The outcome variables were whether patients were defined as severe or critical. Logistic regression was applied to identify the independent factors that were associated with critical COVID-19 patients. Stepwise multivariate regression was used to select adjusted independent predictors (P < 0.05 was considered significant). The 10-fold cross-validations were included to internally validate the performance of the newly proposed predictive model. The data were randomly split into 10 approximately equal sizes, in which 9 were used to develop the model and one was used for internal validation. Furthermore, the newly developed predictive model was externally validated by the data collected from the Wuhan Asia General Hospital. In addition to area under the receiver operating curve, the predictive performance of the proposed model was measured by its sensitivity, specificity, and precision. The goodness-of-fit test was applied by comparing the observed and predicted events of COVID-19 using the risk group deciles by the Hosmer-Lemeshow χ2 test.
The model with the aforementioned six predictors could be used in clinical practice to identify high-risk dialysis patients. A simple form of a probability prediction model was put forward to identify patients with severe COVID-19. The proposed predictive model, including the aforementioned 6 factors, assists in predicting the prognosis with high accuracy.
The proposed risk-prediction model for COVID-19 in this study showed significant association with the presence of obesity, delayed admission, fever, existing comorbidities, hypoxemia, and higher neutrophil/lymphocyte ratio. The model accurately predicted the severity of patients with COVID-19. This could practically help clinicians perform timely interventions and control measures to prevent overcrowding or delayed diagnosis of severe COVID-19 in hospital settings.
The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU. It assisted the ICU clinicians in making timely decisions for the target population.