Korean J Radiol.  2020 Aug;21(8):1007-1017. 10.3348/kjr.2020.0485.

Development and Validation of a Prognostic NomogramBased on Clinical and CT Features for Adverse OutcomePrediction in Patients with COVID-19

  • 1Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
  • 2Department of Radiology, FuYang No.2 People’s Hospital, Fuyang, China
  • 3Department of Radiology, Zhuhai People’s Hospital, Zhuhai Hospital affiliated with Jinan University, Zhuhai, China


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 hospitalswere retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in thetraining cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in thevalidation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, ordeath. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. Anomogram was constructed based on the combination of clinical and CT features, and its prognostic performance wasexternally tested in the validation group. The predictive value of the combined model was compared with models built on theclinical and radiological attributes alone.
Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohortexperienced 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 combinedmodel provided the best performance over the clinical or radiological model (p < 0.050).
Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverseoutcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predictingadverse outcomes of patients with COVID-19.


COVID-19; Coronavirus; Nomogram; Prognosis; CT
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