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Application of a prediction model with laboratory indexes in the risk stratification of patients with COVID-19

Jiru Ye1, Xiaoqing Zhang2, Feng Zhu2

  • 1Department of Respiratory and Critical Care Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, P.R. China.

Experimental and Therapeutic Medicine
|January 25, 2021
PubMed

Insights

A new prediction model combining laboratory indexes effectively stratifies COVID-19 patients by severity. This model, using red blood cell count, high-sensitivity C-reactive protein (hsCRP), and procalcitonin (PCT), shows promise for clinical risk assessment.

Area of Science:

  • Clinical Medicine
  • Infectious Diseases
  • Biomarkers

Background:

  • Accurate risk stratification is crucial for managing Coronavirus Disease 2019 (COVID-19) patients.
  • Identifying reliable laboratory predictors for COVID-19 severity is essential for timely clinical intervention.

Purpose of the Study:

  • To establish and validate a prediction model using combined laboratory indexes for COVID-19 patient risk stratification.
  • To compare the diagnostic efficacy and clinical utility of individual laboratory markers and the developed prediction model.

Main Methods:

  • Retrospective analysis of 170 COVID-19 patients, categorized into asymptomatic-moderate and severe or above groups.
  • Multivariate logistic regression was employed to construct a prediction model based on significant laboratory indexes.
  • Receiver operating characteristic (ROC) curve and Decision Curve Analysis (DCA) were used to evaluate diagnostic performance and clinical validity.

Main Results:

  • Significant differences in blood cell count, high-sensitivity C-reactive protein (hsCRP), and procalcitonin (PCT) were observed between severity groups (P<0.05).
  • Individual hsCRP demonstrated high diagnostic efficacy (AUC=0.870).
  • The combined prediction model (red blood cell count, hsCRP, PCT) showed a higher AUC (0.912) and superior net benefit in DCA compared to individual markers.

Conclusions:

  • A prediction model integrating red blood cell count, hsCRP, and PCT effectively predicts clinical classification in COVID-19 patients.
  • This model offers improved clinical utility for risk stratification compared to individual laboratory indexes.
  • The developed model serves as a valuable tool for assessing COVID-19 patient severity and guiding treatment decisions.

Keywords:
C-reactive proteinCOVID-19decision support techniques

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