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Predicting critical illness on initial diagnosis of COVID-19 based on easily obtained clinical variables: development

Miguel Martínez-Lacalzada1, Adrián Viteri-Noël1, Luis Manzano2

  • 1Internal Medicine Department, Hospital Universitario Ramón y Cajal, IRYCIS, Madrid, Spain.

Clinical Microbiology and Infection : the Official Publication of the European Society of Clinical Microbiology and Infectious Diseases
|July 18, 2021
PubMed

Insights

A new PRIORITY model accurately identifies patients with coronavirus disease 2019 (COVID-19) at high risk for critical outcomes like death or mechanical ventilation using simple clinical data.

Area of Science:

  • Infectious Diseases
  • Epidemiology
  • Clinical Prediction Modeling

Background:

  • Coronavirus disease 2019 (COVID-19) poses a significant global health threat.
  • Identifying patients at risk of severe outcomes is crucial for resource allocation and timely intervention.
  • Existing prediction models may not fully capture early clinical indicators of critical illness.

Purpose of the Study:

  • To develop and validate a prediction model for critical outcomes in COVID-19 patients.
  • To utilize readily available clinical history and examination findings for risk stratification.
  • To create a tool for early identification of patients requiring intensive care.

Main Methods:

  • Utilized the SEMI-COVID-19 Registry, a large Spanish cohort of hospitalized COVID-19 patients.
  • Employed logistic regression and least absolute shrinkage and selection operator (LASSO) for model development.
  • Validated the model in independent hospital cohorts to assess generalizability.

Main Results:

  • The PRIORITY model incorporates age, dependency, comorbidities (cardiovascular, chronic kidney disease), and clinical signs (dyspnoea, tachypnoea, confusion, low systolic blood pressure, low SpO2).
  • Achieved high predictive performance in both development (C-statistic 0.823) and validation (C-statistic 0.794) cohorts.
  • A web-based calculator is available for practical application of the PRIORITY model.

Conclusions:

  • The PRIORITY model demonstrates good discrimination and generalizability for predicting critical COVID-19 outcomes.
  • Easily obtainable clinical data forms the basis of this effective risk stratification tool.
  • The model aids clinicians in identifying high-risk COVID-19 patients early.
Abstract

Keywords:
COVID-19Critical illnessEasily obtained clinical variablesInitial assessmentPrognostic models

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