Predictors of Intensive Care Unit Admissions in Patients Presenting with Coronavirus Disease 2019

Area of Science:

• Critical Care Medicine
• Infectious Diseases
• Epidemiology

Background:

• High mortality rates in COVID-19 patients admitted to intensive care units (ICUs) necessitate predictive criteria for ICU admission.
• Identifying patients with coronavirus disease 2019 (COVID-19) at elevated risk for ICU admission is critical for resource allocation and timely intervention.

Purpose of the Study:

• To develop and validate a predictive model for ICU admission in hospitalized COVID-19 patients.
• To identify key clinical factors associated with ICU admission in COVID-19 patients.

Main Methods:

• Retrospective review of electronic medical records for 1,094 COVID-19 positive patients hospitalized between March and May 2020.
• Development of a multivariable logistic regression model using a derivation subset to predict ICU admission.
• Validation of the predictive model using a separate validation subset and assessing performance with c-statistic.

Main Results:

• 18.6% of identified COVID-19 patients required ICU admission.
• Significant predictors for ICU admission included age, body mass index (BMI), quick Sequential Organ Failure Assessment (qSOFA) score, arterial oxygen saturation to fraction of inspired oxygen ratio, platelet count, and white blood cell count.
• The predictive model demonstrated excellent comparability between derivation (c-statistic: 0.798) and validation (c-statistic: 0.764) subsets, with a sensitivity of 0.721 and specificity of 0.763 at a 22% predicted probability threshold.

Conclusions:

• A pilot predictive model incorporating age, BMI, qSOFA score, and oxygenation status effectively identifies COVID-19 patients at higher risk for ICU admission.
• These factors can guide clinical decision-making for ICU admission, potentially improving patient management and outcomes.