![]() ![]() ![]() Development of critical illness is driven by systemic inflammation, leading to acute respiratory distress syndrome (ARDS), respiratory failure, septic shock, multi-organ failure, and/or disseminated coagulopathy 4, 5, 8. Progression to severe disease occurs within 1–2 weeks from symptom onset and is characterized by clinical signs of pneumonia with dyspnea, increased respiratory rate, and decreased blood oxygen saturation requiring supplemental oxygen 3, 4, 5, 6, 7. Although the majority of SARS-CoV-2 positive cases experience mild to moderate disease approximately 15% were estimated to develop severe disease 3. COVID-19 is caused by the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and infected individuals present with a variety of symptoms ranging from mild to life-threatening 2. Upon further validation, this model may allow direct reporting of personalized survival probabilities in routine care.īy April 2022 the Coronavirus disease 2019 (COVID-19) had claimed over 6 million lives since its outbreak in late 2019 1. Our explainable survival model developed on EHR data also revealed temporal dynamics of the 22 selected risk factors. ![]() ![]() Age, sex, number of medications, previous hospitalizations and lymphocyte counts were identified as top mortality risk factors. Performance on the test set was measured with a weighted concordance index of 0.95 and an area under the curve for precision-recall of 0.71. A discrete-time framework for survival modelling enabled us to predict personalized survival curves and explain individual risk factors. By leveraging data on 33,938 confirmed SARS-CoV-2 cases in eastern Denmark, we considered 2723 variables extracted from electronic health records (EHR) including demographics, diagnoses, medications, laboratory test results and vital parameters. Here we trained a machine learning model to predict mortality within 12 weeks of a first positive SARS-CoV-2 test. Interpretable risk assessment of SARS-CoV-2 positive patients can aid clinicians to implement precision medicine. ![]()
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