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Objectives: Evidence on the work-related societal impact of long-term health-related consequences following SARS-CoV-2 is emerging. We characterize the modified work ability index (mWAI) of employees 6 to 12 months after an acute infection compared to pre-infection., Methods: Analyses were based on a population-based, multi-center cross-sectional study including employees aged 18-65 years with positive SARS-CoV-2 polymerase chain reaction (tested between October 2020-April 2021 in defined geographic regions in Germany). Prevalences and results of adjusted logistic regression analyses were given., Results: In 9752 employees (mean age 45.6 years, 58% females, response 24%), n = 1217 (13.1%) participants were regarded as having low mWAI compared to pre-infection. Outpatient medical treatment, inpatient treatment, and admission to intensive care during infection were associated with mWAI <15 th percentile (P15, each odds ratio [OR] >3.0). Post-COVID symptom clusters most strongly linked to mWAI

The aim of this observational retrospective study is to improve early risk stratification of hospitalized Covid-19 patients by predicting in-hospital mortality, transfer to intensive care unit (ICU) and mechanical ventilation from electronic health record data of the first 24 h after admission. Our machine learning model predicts in-hospital mortality (AUC = 0.918), transfer to ICU (AUC = 0.821) and the need for mechanical ventilation (AUC = 0.654) from a few laboratory data of the first 24 h after admission. Models based on dichotomous features indicating whether a laboratory value exceeds or falls below a threshold perform nearly as good as models based on numerical features. We devise completely data-driven and interpretable machine-learning models for the prediction of in-hospital mortality, transfer to ICU and mechanical ventilation for hospitalized Covid-19 patients within 24 h after admission. Numerical values of. CRP and blood sugar and dichotomous indicators for increased partial thromboplastin time (PTT) and glutamic oxaloacetic transaminase (GOT) are amongst the best predictors., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)