Development and validation of a knowledge-driven risk calculator for critical illness in COVID-19 patients

Facing the novel coronavirus disease (COVID-19) pandemic, evidence to inform decision-making at all care levels is essential. Based on the results of a study by Petrilli et al., we have developed a calculator using patient data at admission to predict critical illness (intensive care, mechanical ven...

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Bibliographic Details
Published in:The American journal of emergency medicine 2021-01, Vol.39, p.143-145
Main Authors: Cahan, Amos, Gottesman, Tamar, Katz, Michal Tzuchman, Masad, Roee, Azulay, Gal, Dicker, Dror, Zeidman, Aliza, Berkov, Evgeny, Tadmor, Boaz, Lev, Shaul
Format: Article
Language:English
Subjects:
Aged
Clinical Laboratory Techniques - standards
Cognitive ability
Coronaviruses
COVID-19
COVID-19 - diagnosis
COVID-19 - physiopathology
COVID-19 Testing
Critical Care - organization & administration
Critical Illness - therapy
Decision making
Disease transmission
Electronic health records
Emergency medical care
Female
Health care
Hospitalization
Hospitalization - statistics & numerical data
Hospitals
Humans
Illnesses
Israel
Laboratories
Male
Mechanical ventilation
Middle Aged
Oxygen saturation
Pandemics
Patients
Population studies
Retrospective Studies
Risk assessment
Risk Assessment - standards
Risk Factors
Ventilators
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Summary:Facing the novel coronavirus disease (COVID-19) pandemic, evidence to inform decision-making at all care levels is essential. Based on the results of a study by Petrilli et al., we have developed a calculator using patient data at admission to predict critical illness (intensive care, mechanical ventilation, hospice care, or death). We report a retrospective validation of the calculator on 145 consecutive patients admitted with COVID-19 to a single hospital in Israel. Despite considerable differences between the original and validation study populations, of 18 patients with critical illness, 17 were correctly identified (sensitivity: 94.4%, 95% CI, 72.7%–99.9%; specificity: 81.9%, 95% CI, 74.1%–88.2%). Of 127 patients with non-critical illness, 104 were correctly identified. Our results indicate that published knowledge can be reliably applied to assess patient risk, potentially reducing the cognitive burden on physicians, and helping policymakers better prepare for future needs.
ISSN:0735-6757
1532-8171
DOI:10.1016/j.ajem.2020.09.051