Corona Virus Disease 2019 (COVID-19): Intensive Care Admission Prediction Model

Abstract Background: Identifying clinical-features or a scoring-system to predict a benefit from hospital admission for patients with COVID-19 can be of great value for the decision-makers in the health-sector. We aim to identify differences in patients' demographic, clinical, laboratory and radiological findings of COVID-19 positive cases to develop and validate a diagnostic-model predicting who will develop severe-form and who will need critical-care in the future.Methodology: Patients were classified according to their clinical state into mild, moderate, severe, and critical. All their baseline clinical data, laboratory, and radiological results were used to construct a prediction-model that can predict if the COVID-19 patients will develop a severe condition that will necessitate their ICU-admission. An ensemble feature selection tool was used to identify the relative importance of each variable. The performance of the selected features compared to all features using logistic regression and area under curve test.Results: Patients with ICU admission showed a distinct clinical, demographic as well as laboratory features when compared to pattients that did not need ICU admission. This includes elder age group, male gender and presence of comorbidities like diabetis and history of hypertension.Out of the different demographic, clinical and laboratory charasteristics of these patients, Age at diagnosis, Lymphocyte count, C-reactive protein (CRP), lactate dehydrogenase (LDH), Albumin, Urea, and Procalcitonin levels were found to be able to predict which patients may need ICU admission. Conclusion: Higher CRP, LDH, Age at diagnosis, Urea, Procalcitonin, and lower Albumin, Lymphocyte count are significant determinant in ICU admission for COVID-19 patients.