Scientific Reports (Apr 2021)

U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19

  • Janne J. Näppi,
  • Tomoki Uemura,
  • Chinatsu Watari,
  • Toru Hironaka,
  • Tohru Kamiya,
  • Hiroyuki Yoshida

DOI
https://doi.org/10.1038/s41598-021-88591-z
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 11

Abstract

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Abstract The rapid increase of patients with coronavirus disease 2019 (COVID-19) has introduced major challenges to healthcare services worldwide. Therefore, fast and accurate clinical assessment of COVID-19 progression and mortality is vital for the management of COVID-19 patients. We developed an automated image-based survival prediction model, called U-survival, which combines deep learning of chest CT images with the established survival analysis methodology of an elastic-net Cox survival model. In an evaluation of 383 COVID-19 positive patients from two hospitals, the prognostic bootstrap prediction performance of U-survival was significantly higher (P < 0.0001) than those of existing laboratory and image-based reference predictors both for COVID-19 progression (maximum concordance index: 91.6% [95% confidence interval 91.5, 91.7]) and for mortality (88.7% [88.6, 88.9]), and the separation between the Kaplan–Meier survival curves of patients stratified into low- and high-risk groups was largest for U-survival (P < 3 × 10–14). The results indicate that U-survival can be used to provide automated and objective prognostic predictions for the management of COVID-19 patients.