PRCTC : a machine learning model for prediction of response to corticosteroid therapy in COVID-19 patients

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Yue Gao
  • Xiaoming Xiong
  • Xiaofei Jiao
  • Yang Yu
  • Jianhua Chi
  • Wei Zhang
  • Qinglei Gao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)54-72
Journal / PublicationAging
Volume14
Issue number1
Online published12 Jan 2022
Publication statusPublished - 15 Jan 2022

Link(s)

Abstract

Corticosteroid has been proved to be one of the few effective treatments for COVID-19 patients. However, not all the patients were suitable for corticosteroid therapy. In this study, we aimed to propose a machine learning model to forecast the response to corticosteroid therapy in COVID-19 patients. We retrospectively collected the clinical data about 666 COVID-19 patients receiving corticosteroid therapy between January 27, 2020, and March 30, 2020, from two hospitals in China. The response to corticosteroid therapy was evaluated by hospitalization time, oxygen supply duration, and the outcomes of patients. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five prediction models were applied in the training cohort and assessed in an internal and an external validation dataset, respectively. Finally, two (C reactive protein, lymphocyte percent) of 36 candidate immune/inflammatory features were finally used for model development. All five models displayed promising predictive performance. Notably, the ensemble model, PRCTC (prediction of response to corticosteroid therapy in COVID-19 patients), derived from three prediction models including Gradient Boosted Decision Tree (GBDT), Neural Network (NN), and logistic regression (LR), achieved the best performance with an area under the curve (AUC) of 0.810 (95% confidence interval [CI] 0.760-0.861) in internal validation cohort and 0.845 (95% CI 0.779-0.911) in external validation cohort to predict patients' response to corticosteroid therapy. In conclusion, PRCTC proposed with universality and scalability is hopeful to provide tangible and prompt clinical decision support in management of COVID-19 patients and potentially extends to other medication predictions.

Research Area(s)

  • COVID-19, corticosteroid, machine learning, lymphocyte percent, Creactive protein

Citation Format(s)

PRCTC : a machine learning model for prediction of response to corticosteroid therapy in COVID-19 patients. / Gao, Yue; Xiong, Xiaoming; Jiao, Xiaofei; Yu, Yang; Chi, Jianhua; Zhang, Wei; Chen, Lingxi; Li, Shuaicheng; Gao, Qinglei.

In: Aging, Vol. 14, No. 1, 15.01.2022, p. 54-72.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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