Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning
Authors: Jakob Carolin E. M. , Mahajan Ujjwal Mukund , Oswald Marcus , Stecher Melanie , Schons Maximilian , Mayerle Julia , Rieg Siegbert , Pletz Mathias , Merle Uta , Wille Kai , Borgmann Stefan , Spinner Christoph D. , Dolff Sebastian , Scherer Clemens , Pilgram Lisa , Rüthrich Maria , Hanses Frank , Hower Martin , Strauß Richard , Massberg Steffen , Er Ahmet Görkem , Jung Norma , Vehreschild Jörg Janne , Stubbe Hans , Tometten Lukas , König Rainer ,   The LEOSS Study group, Tometten Lukas , Rieg Siegbert , Merle Uta , Wille Kai , Borgmann Stefan , Spinner Christoph , Dolff Sebastian , Rüthrich Maria Madeleine , Hanses Frank , Hower Martin , Strauß Richard , Akova Murat , Jung Norma , von Bergwelt-Baildon Michael , Vehreschild Maria , Grüner Beate , Haselberger Martina , Isberner Nora , Piepel Christiane , Hellwig Kerstin , Rauschning Dominic , Eberwein Lukas , Jensen Björn , Raichle Claudia , Müller-Jörger Gabriele , Stieglitz Sven , Kratz Thomas , Degenhardt Christian , Friedrichs Anette , Bals Robert , Rüger Susanne , With Katja , Rothfuss Katja , Goepel Siri , Nattermann Jacob , Jordan Sabine , Rüddel Jessica , Trauth Janina , Beutel Gernot , Aydin Ozlem Altuntas , Milovanovic Milena , Doll Michael , Vehreschild Jörg Janne , Pilgram Lisa , Stecher Melanie , Jakob Carolin E. M. , Schons Maximilian , Claßen Annika , Fuhrmann Sandra , de Miranda Susana Nunes , Franke Bernd , Schulze Nick , Prasser Fabian , Lablans Martin 
Published: July 2021

Abstract
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.