Development and validation of a prognostic model for COVID-19: a population-based cohort study in Iceland

Abstract

Background: The severity of SARS-CoV-2 infection varies from asymptomatic state to severe respiratory failure and the clinical course is difficult to predict. The aim of the study was to develop a prognostic model to predict the severity of COVID-19 at the time of diagnosis and determine risk factors for severe disease. Methods: All SARS-CoV-2-positive adults in Iceland were prospectively enrolled into a telehealth service at diagnosis. A multivariable proportional-odds logistic regression model was derived from information obtained during the enrollment interview with those diagnosed before May 1, 2020 and validated in those diagnosed between May 1 and December 31, 2020. Outcomes were defined on an ordinal scale; no need for escalation of care during follow-up, need for outpatient visit, hospitalization, and admission to intensive care unit (ICU) or death. Risk factors were summarized as odds ratios (OR) adjusted for confounders identified by a directed acyclic graph. Results: The prognostic model was derived from and validated in 1,625 and 3,131 individuals, respectively. In total, 375 (7.9%) only required outpatient visits, 188 (4.0%) were hospitalized and 50 (1.1%) were either admitted to ICU or died due to complications of COVID-19. The model included age, sex, body mass index (BMI), current smoking, underlying conditions, and symptoms and clinical severity score at enrollment. Discrimination and calibration were excellent for outpatient visit or worse (C-statistic 0.75, calibration intercept 0.04 and slope 0.93) and hospitalization or worse (C-statistic 0.81, calibration intercept 0.16 and slope 1.03). Age was the strongest risk factor for adverse outcomes with OR of 75- compared to 45- year-olds, ranging from 5.29-17.3. Higher BMI consistently increased the risk and chronic obstructive pulmonary disease and chronic kidney disease correlated with worse outcomes. Conclusion: Our prognostic model can accurately predict the outcome of SARS-CoV-2 infection using information that is available at the time of diagnosis.