Incubation Period, Clinical and Lung CT Features for Early Prediction of COVID-19 Deterioration

Development and Internal Verification of a Risk Model

Hongbing Peng; Chao Hu; Wusheng Deng; Lingmei Huang; Yushan Zhang; Baowei Luo; Xingxing Wang; Xiaodan Long; Xiaoying Huang


BMC Pulm Med. 2022;22(188) 

In This Article

Abstract and Introduction


Background: Most severe, critical, or mortal COVID-19 cases often had a relatively stable period before their status worsened. We developed a deterioration risk model of COVID-19 (DRM-COVID-19) to predict exacerbation risk and optimize disease management on admission.

Method: We conducted a multicenter retrospective cohort study with 239 confirmed symptomatic COVID-19 patients. A combination of the least absolute shrinkage and selection operator (LASSO), change-in-estimate (CIE) screened out independent risk factors for the multivariate logistic regression model (DRM-COVID-19) from 44 variables, including epidemiological, demographic, clinical, and lung CT features. The compound study endpoint was progression to severe, critical, or mortal status. Additionally, the model's performance was evaluated for discrimination, accuracy, calibration, and clinical utility, through internal validation using bootstrap resampling (1000 times). We used a nomogram and a network platform for model visualization.

Results: In the cohort study, 62 cases reached the compound endpoint, including 42 severe, 18 critical, and two mortal cases. DRM-COVID-19 included six factors: dyspnea [odds ratio (OR) 4.89;confidence interval (95% CI) 1.53–15.80], incubation period (OR 0.83; 95% CI 0.68–0.99), number of comorbidities (OR 1.76; 95% CI 1.03–3.05), D-dimer (OR 7.05; 95% CI, 1.35–45.7), C-reactive protein (OR 1.06; 95% CI 1.02–1.1), and semi-quantitative CT score (OR 1.50; 95% CI 1.27–1.82). The model showed good fitting (Hosmer–Lemeshow goodness, X2(8) = 7.0194, P = 0.53), high discrimination (the area under the receiver operating characteristic curve, AUROC, 0.971; 95% CI, 0.949–0.992), precision (Brier score = 0.051) as well as excellent calibration and clinical benefits. The precision-recall (PR) curve showed excellent classification performance of the model (AUCPR = 0.934). We prepared a nomogram and a freely available online prediction platform (

Conclusion: We developed a predictive model, which includes the including incubation period along with clinical and lung CT features. The model presented satisfactory prediction and discrimination performance for COVID-19 patients who might progress from mild or moderate to severe or critical on admission, improving the clinical prognosis and optimizing the medical resources.


The global pandemic of COVID-19 caused by the severe acute respiratory syndrome coronavirus (SARS-COV-2) has started in December 2019 and it has been around for 2 year now.[1,2] As of May 9, 2021, the World Health Organization (WHO) reported that more than 1.5 billion infected people worldwide, and more than 3.29 million deaths occurred. The fatality rate in the early stage of the disease is more than 7.0%.[3,4] This pandemic poses a significant threat to global health.

The reported clinical outcomes of different severity grades are heterogeneous, and the mild and moderate cases often rely on their immune ability to recover.[5,6] However, most severe or critical COVID-19 patients are asymptomatic at the initial stage of onset, and the median time from onset to sepsis is 10.0 days [interquartile range (IQR) 7.0–14.0].[7] Early screening and active intervention in critical patients could reduce mortality.[8] A deterioration model of early prediction of COVID-19 progression from mild or moderate to severe, critical or mortal might help front-line clinicians to optimize the patient triage and develop appropriate treatment strategies.

Many multivariate clinical prognostic models for predicting the deterioration of COVID-19 have been published.[9–13] The predictors mainly include demographic, clinical, and laboratory factors. However, the included factors are rarely involved in epidemiology and chest imaging features, such as the incubation period. Although the incubation period was the key feature and essential basis in the study of epidemic control and prediction,[14] there were relatively few studies on the deterioration of COVID-19. Early studies found that the incubation period of travelers to Hubei was shorter than that of non-travelers.[15] The incubation period was negatively correlated with the severity of COVID-19.[14] Furthermore, high CT scores characterized severe/critical COVID-19 pneumonia.[16,17] Further research is still needed to determine whether epidemiology and lung CT features can improve the predictive ability of the deterioration model.

In this study, we present a prediction model of COVID-19 (DRM-COVID-19) with epidemiological, clinical, and pulmonary CT characteristics, which could predict the risk of COVID-19 deterioration on admission. The COVID-19 epidemic is still raging globally, and we hope our model can provide convenience for front-line clinicians to make individualized treatment decisions, reduce the deterioration and optimize the use of medical resources.