Background Identifying factors that can predict severe disease in patients needing hospitalization for COVID-19 is crucial for early recognition of patients at greatest risk. Objective (1) Identify factors predicting intensive care unit (ICU) transfer and (2) develop a simple calculator for clinicians managing patients hospitalized with COVID-19. Methods A total of 2,685 patients with laboratory-confirmed COVID-19 admitted to a large metropolitan health system in Georgia, USA between March and July 2020 were included in the study. Seventy-five percent of patients were included in the training dataset (admitted March 1 to July 10). Through multivariable logistic regression, we developed a prediction model (probability score) for ICU transfer. Then, we validated the model by estimating its performance accuracy (area under the curve [AUC]) using data from the remaining 25% of patients (admitted July 11 to July 31). Results We included 2,014 and 671 patients in the training and validation datasets, respectively. Diabetes mellitus, coronary artery disease, chronic kidney disease, serum C-reactive protein, and serum lactate dehydrogenase were identified as significant risk factors for ICU transfer, and a prediction model was developed. The AUC was 0.752 for the training dataset and 0.769 for the validation dataset. We developed a free, web-based calculator to facilitate use of the prediction model ( https://icucovid19.shinyapps.io/ICUCOVID19/ ). Conclusion Our validated, simple, and accessible prediction model and web-based calculator for ICU transfer may be useful in assisting healthcare providers in identifying hospitalized patients with COVID-19 who are at high risk for clinical deterioration. Triage of such patients for early aggressive treatment can impact clinical outcomes for this potentially deadly disease.
【초록키워드】 COVID-19, Treatment, intensive care, Hospitalization, Triage, C-reactive protein, risk, Chronic kidney disease, risk factor, ICU, Clinical outcome, Coronary artery disease, Probability, serum, Accuracy, Patient, dataset, health system, USA, disease, predict, severe disease, Clinical deterioration, AUC, high risk, Factor, Healthcare provider, clinician, multivariable logistic regression, transfer, datasets, laboratory-confirmed, identifying, training dataset, objective, Result, develop, facilitate, hospitalized patient, calculator, patients hospitalized, serum lactate, with COVID-19, 【제목키워드】 COVID-19, Hospitalization, Patient, development, Critical, Predictive,