TY  - JOUR
A1  - Jakob, Carolin Ellen Marianne
A1  - Mahajan, Ujjwal Mukund
A1  - Oswald, Marcus
A1  - Stecher, Melanie
A1  - Schons, Maximilian
A1  - Mayerle, Julia
A1  - Rieg, Siegbert
A1  - Pletz, Mathias
A1  - Merle, Uta
A1  - Wille, Kai
A1  - Borgmann, Stefan
A1  - Spinner, Christoph Daniel
A1  - Dolff, Sebastian Conrad Johannes
A1  - Scherer, Clemens Martin
A1  - Pilgram, Lisa
A1  - Rüthrich, Maria Madeleine
A1  - Hanses, Frank
A1  - Hower, Martin
A1  - Strauß, Richard
A1  - Maßberg, Steffen
A1  - Er, Ahmet Görkem
A1  - Jung, Norma
A1  - Vehreschild, Jörg Janne
A1  - Stubbe, Hans Christian
A1  - Tometten, Lukas
A1  - König, Rainer
T1  - Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning
T2  - Infection
N2  - 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.
KW  - COVID-19
KW  - Machine learning
KW  - Predictive model
KW  - Advanced stage
KW  - Complicated stage
KW  - LEOSS
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/63782
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-637823
SN  - 1439-0973
N1  - Further Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s15010-021-01656-z
N1  - Open Access funding enabled and organized by Projekt DEAL. MO and RK were supported by the Federal Ministry of Education and Research (BMBF), Germany, FKZ: 01EO1502 (CSCC) and 01KI2015OA (SARSiRNA). UMM, JM and HS were supported by the PePPP center of excellence MV ESF/14-BM-A55-0045/16; ESF MV V-630-S-150-2012/132/133); Deutsche Forschungsgemeinschaft, SFB1321/1 (Project P14, 329628492), Förderprogramm für Forschung und Lehre (FöFoLe, Reg. Nr. 1028), Friedrich-Baur-Stiftung (Reg. Nr. 42/17), and Bundesministerium für Bildung und Forschung 01EK1511A. The LEOSS study was supported by the German Center for Infection Research (DZIF) and the Willy Robert Pitzer Foundation.
VL  - 50.2021
IS  - 2
SP  - 359
EP  - 370
PB  - Urban & Vogel ; Springer
CY  - München ; Heidelberg
ER  -