Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Development and Validation of a Prognostic Nomogram Based on Clinical and CT Features for Adverse Outcome Prediction in Patients with COVID-19

  • Yingyan Zheng (Department of Radiology, Huashan Hospital, Fudan University) ;
  • Anling Xiao (Department of Radiology, FuYang No.2 People's Hospital) ;
  • Xiangrong Yu (Department of Radiology, Zhuhai People's Hospital, Zhuhai Hospital affiliated with Jinan University) ;
  • Yajing Zhao (Department of Radiology, Huashan Hospital, Fudan University) ;
  • Yiping Lu (Department of Radiology, Huashan Hospital, Fudan University) ;
  • Xuanxuan Li (Department of Radiology, Huashan Hospital, Fudan University) ;
  • Nan Mei (Department of Radiology, Huashan Hospital, Fudan University) ;
  • Dejun She (Department of Radiology, Huashan Hospital, Fudan University) ;
  • Dongdong Wang (Department of Radiology, Huashan Hospital, Fudan University) ;
  • Daoying Geng (Department of Radiology, Huashan Hospital, Fudan University) ;
  • Bo Yin (Department of Radiology, Huashan Hospital, Fudan University)
  • Received : 2020.04.20
  • Accepted : 2020.05.20
  • Published : 2020.08.01
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Abstract

Objective: The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19). Materials and Methods: The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone. Results: Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67-6.71; p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04-0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03-4.48; p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76-0.88), and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82-0.96). The combined model provided the best performance over the clinical or radiological model (p < 0.050). Conclusion: Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.

Keywords

Acknowledgement

The authors thank Zebin Xiao, at University of Pennsylvania, Philadelphia, US, for manuscript editing. We are also grateful to Weiwei Zheng, at Environmental Health, School of Public Health, Fudan University, Shanghai, China, and Dajun Tian, at Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, Saint Louis, Missouri, USA, for their assistance in statistical analyses.

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