A nomogram for predicting mortality in patients with COVID-19 and solid tumors: a multicenter retrospective cohort study

J Immunother Cancer. 2020 Sep;8(2):e001314. doi: 10.1136/jitc-2020-001314.

Abstract

Background: Individualized prediction of mortality risk can inform the treatment strategy for patients with COVID-19 and solid tumors and potentially improve patient outcomes. We aimed to develop a nomogram for predicting in-hospital mortality of patients with COVID-19 with solid tumors.

Methods: We enrolled patients with COVID-19 with solid tumors admitted to 32 hospitals in China between December 17, 2020, and March 18, 2020. A multivariate logistic regression model was constructed via stepwise regression analysis, and a nomogram was subsequently developed based on the fitted multivariate logistic regression model. Discrimination and calibration of the nomogram were evaluated by estimating the area under the receiver operator characteristic curve (AUC) for the model and by bootstrap resampling, a Hosmer-Lemeshow test, and visual inspection of the calibration curve.

Results: There were 216 patients with COVID-19 with solid tumors included in the present study, of whom 37 (17%) died and the other 179 all recovered from COVID-19 and were discharged. The median age of the enrolled patients was 63.0 years and 113 (52.3%) were men. Multivariate logistic regression revealed that increasing age (OR=1.08, 95% CI 1.00 to 1.16), receipt of antitumor treatment within 3 months before COVID-19 (OR=28.65, 95% CI 3.54 to 231.97), peripheral white blood cell (WBC) count ≥6.93 ×109/L (OR=14.52, 95% CI 2.45 to 86.14), derived neutrophil-to-lymphocyte ratio (dNLR; neutrophil count/(WBC count minus neutrophil count)) ≥4.19 (OR=18.99, 95% CI 3.58 to 100.65), and dyspnea on admission (OR=20.38, 95% CI 3.55 to 117.02) were associated with elevated mortality risk. The performance of the established nomogram was satisfactory, with an AUC of 0.953 (95% CI 0.908 to 0.997) for the model, non-significant findings on the Hosmer-Lemeshow test, and rough agreement between predicted and observed probabilities as suggested in calibration curves. The sensitivity and specificity of the model were 86.4% and 92.5%.

Conclusion: Increasing age, receipt of antitumor treatment within 3 months before COVID-19 diagnosis, elevated WBC count and dNLR, and having dyspnea on admission were independent risk factors for mortality among patients with COVID-19 and solid tumors. The nomogram based on these factors accurately predicted mortality risk for individual patients.

Keywords: biomarkers; biostatistics; immunization; inflammation; tumor.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Age Factors
  • Aged
  • Area Under Curve
  • Betacoronavirus
  • China / epidemiology
  • Cohort Studies
  • Coronavirus Infections / blood
  • Coronavirus Infections / complications
  • Coronavirus Infections / mortality*
  • Coronavirus Infections / physiopathology
  • Dyspnea / physiopathology
  • Fatigue / physiopathology
  • Female
  • Heart Rate
  • Hospital Mortality*
  • Humans
  • Leukocyte Count
  • Logistic Models
  • Lung Neoplasms / complications
  • Lung Neoplasms / therapy
  • Lymphocyte Count
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Neoplasm Staging
  • Neoplasms / complications
  • Neoplasms / pathology
  • Neoplasms / therapy*
  • Neutrophils
  • Nomograms*
  • Pandemics
  • Pneumonia, Viral / blood
  • Pneumonia, Viral / complications
  • Pneumonia, Viral / mortality*
  • Pneumonia, Viral / physiopathology
  • Pulmonary Disease, Chronic Obstructive / complications
  • ROC Curve
  • Retrospective Studies
  • Risk Assessment

Supplementary concepts

  • COVID-19
  • severe acute respiratory syndrome coronavirus 2