Development and internal validation of a prediction model for post-COVID-19 condition 2 years after infection-results of the CORFU study.

Abstract

Background: A subset of COVID-19 patients develops post-COVID-19 condition (PCC). This condition results in disability in numerous areas of patients' lives and a reduced health-related quality of life, with societal impact including work absences and increased healthcare utilization. There is a scarcity of models predicting PCC, especially those considering the severity of the initial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and incorporating long-term follow-up data. Therefore, we developed and internally validated a prediction model for PCC 2 years after SARS-CoV-2 infection in a cohort of COVID-19 patients.

Methods: Data from the CORona Follow-Up (CORFU) study were used. This research initiative integrated data from multiple Dutch COVID-19 cohort studies. We utilized 2-year follow-up data collected via the questionnaires between October 1st of 2021 and December 31st of 2022. Participants were former COVID-19 patients, approximately 2-year post-SARS-CoV-2 infection. Candidate predictors were selected based on literature and availability across cohorts. The outcome of interest was the prevalence of PCC at 2 years after the initial infection. Logistic regression with backward stepwise elimination identified significant predictors such as sex, BMI and initial disease severity. The model was internally validated using bootstrapping. Model performance was quantified as model fit, discrimination and calibration.

Results: In total 904 former COVID-19 patients were included in the analysis. The cohort included 146 (16.2%) non-hospitalized patients, 511 (56.5%) ward admitted patients, and 247 (27.3%) intensive care unit (ICU) admitted patients. Of all participants, 551 (61.0%) participants suffered from PCC. We included 20 candidate predictors in the multivariable analysis. The final model, after backward elimination, identified sex, body mass index (BMI), ward admission, ICU admission, and comorbidities such as arrhythmia, asthma, angina pectoris, previous stroke, hernia, osteoarthritis, and rheumatoid arthritis as predictors of post-COVID-19 condition. Nagelkerke's R-squared value for the model was 0.19. The optimism-adjusted AUC was 71.2%, and calibration was good across predicted probabilities.

Conclusions: This internally validated prediction model demonstrated moderate discriminative ability to predict PCC 2 years after COVID-19 based on sex, BMI, initial disease severity, and a collection of comorbidities.