Abstract
The Post COVID-19 condition (PCC) is a complex disease affecting health and everyday functioning. This is well reflected by a patient's inability to work (ITW). In this study, we aimed to investigate factors associated with ITW (1) and to design a machine learning-based model for predicting ITW (2) twelve months after baseline. We selected patients from the post COVID care study (PCC-study) with data on their ability to work. To identify factors associated with ITW, we compared PCC patients with and without ITW. For constructing a predictive model, we selected nine clinical parameters: hospitalization during the acute SARS-CoV-2 infection, WHO severity of acute infection, presence of somatic comorbidities, presence of psychiatric comorbidities, age, height, weight, Karnofsky index, and symptoms. The model was trained to predict ITW twelve months after baseline using TensorFlow Decision Forests. Its performance was investigated using cross-validation and an independent testing dataset. In total, 259 PCC patients were included in this analysis. We observed that ITW was associated with dyslipidemia, worse patient reported outcomes (FSS, WHOQOL-BREF, PHQ-9), a higher rate of preexisting psychiatric conditions, and a more extensive medical work-up. The predictive model exhibited a mean AUC of 0.83 (95% CI: 0.78; 0.88) in the 10-fold cross-validation. In the testing dataset, the AUC was 0.76 (95% CI: 0.58; 0.93). In conclusion, we identified several factors associated with ITW. The predictive model performed very well. It could guide management decisions and help setting mid- to long-term treatment goals by aiding the identification of patients at risk of extended ITW.