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Development and validation of a risk score using complete blood count to predict in-hospital mortality in COVID-19

Hui Liu1,2, Jing Chen3,4, Qin Yang5

  • 1Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, China.

Med (New York, N.Y.)
|February 1, 2021
PubMed

Insights

A new PAWNN score, using complete blood count (CBC) parameters like platelet count and age, accurately predicts COVID-19 patient mortality. This simple tool aids clinicians in prioritizing care for critically ill patients.

Area of Science:

  • Hematology
  • Infectious Diseases
  • Critical Care Medicine

Background:

  • Developing accurate prognostic tools for coronavirus disease 2019 (COVID-19) is crucial for patient management.
  • Complete blood count (CBC) parameters offer a readily available resource for risk stratification.

Purpose of the Study:

  • To develop and validate a sensitive risk score for predicting mortality in COVID-19 patients using CBC parameters.
  • To create a simple yet accurate tool to aid clinical decision-making.

Main Methods:

  • A retrospective cohort study involving 13,138 COVID-19 inpatients from China and Italy.
  • Generalized linear mixed models (GLMM) and Cox regression were used to identify predictors and construct the PAWNN score.
  • The score was validated using 10-fold cross-validation and independent cohorts.

Main Results:

  • The PAWNN score, incorporating platelet counts, age, white blood cell counts, neutrophil counts, and neutrophil:lymphocyte ratio, demonstrated high accuracy (AUROCs 0.92-0.97) in predicting mortality.
  • The score showed consistent performance across different patient subgroups and validation cohorts.
  • Latent Markov models confirmed the score's predictive power for disease progression.

Conclusions:

  • The PAWNN score is a validated, simple, and accurate tool for assessing COVID-19 patient mortality risk throughout hospitalization.
  • This risk assessment tool can assist clinicians in prioritizing treatment for COVID-19 patients, potentially improving outcomes.
Abstract

Keywords:
COVID-19complete blood countlatent markov modelmortalityprediction modelrisk score

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