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

Abstract

Background Increased mortality rates among coronavirus disease 2019 (COVID-19) positive patients admitted to intensive care units (ICUs) highlight a compelling need to establish predictive criteria for ICU admissions. The aim of our study was to identify criteria for recognizing patients with COVID-19 at elevated risk for ICU admission. Methods We identified patients who tested positive for COVID-19 and were hospitalized between March and May 2020. Patients' data were manually abstracted through review of electronic medical records. An ICU admission prediction model was derived from a random sample of half the patients using multivariable logistic regression. The model was validated with the remaining half of the patients using c-statistic. Results We identified 1,094 patients; 204 (18.6%) were admitted to the ICU. Correlates of ICU admission were 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 c-statistic in the derivation subset (0.798, 95% confidence interval [CI]: 0.748, 0.848) and the validation subset (0.764, 95% CI: 0.706, 0.822) showed excellent comparability. At 22% predicted probability for ICU admission, the derivation subset estimated sensitivity was 0.721, (95% CI: 0.637, 0.804) and specificity was 0.763, (95% CI: 0.722, 0.804). Our pilot predictive model identified the combination of age, BMI, qSOFA score, and oxygenation status as significant predictors for ICU admission. Conclusion ICU admission among patients with COVID-19 can be predicted by age, BMI, level of hypoxia, and severity of illness.

Keywords: coronavirus disease 2019; intensive care units; mortality; prediction model; risk factors.

Fig. 1

Predicted probability of ICU admission by age ( A ) and body mass index ( B ) at admission. ICU, intensive care unit.

Fig. 2

Receiver operating characteristic (ROC) curves. Derivation curve: c-statistic = 0.798, 95% confidence interval, 0.748 to 0.848. Validation curve: c-statistic = 0.764, 95% confidence interval, 0.706 to 0.822. Diagonal: Reference line.