Open Access

Carolin Ellen Marianne Jakob, Ujjwal Mukund Mahajan, Marcus Oswald, Melanie Stecher, Maximilian Schons, Julia Mayerle, Siegbert Rieg, Mathias Pletz, Uta Merle, Kai Wille, Stefan Borgmann, Christoph Daniel Spinner, Sebastian Conrad Johannes Dolff, Clemens Martin Scherer, Lisa Pilgram, Maria Madeleine Rüthrich, Frank Hanses, Martin Hower, Richard Strauß, Steffen Maßberg, Ahmet Görkem Er, Norma Jung, Jörg Janne Vehreschild, Hans Christian Stubbe, Lukas Tometten, Rainer König

• Purpose: While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization. Methods: We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16). Results: The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface. Conclusion: We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.