Journal
INFECTION
Volume 50, Issue 2, Pages 359-370Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s15010-021-01656-z
Keywords
COVID-19; Machine learning; Predictive model; Advanced stage; Complicated stage; LEOSS
Categories
Funding
- Federal Ministry of Education and Research (BMBF), Germany [FKZ: 01EO1502, 01KI2015OA]
- PePPP center of excellence [MV ESF/14-BM-A55-0045/16, ESF MV V-630-S-150-2012/132/133]
- Deutsche Forschungsgemeinschaft [SFB1321/1, 329628492]
- Forderprogramm fur Forschung und Lehre (FoFoLe) [1028]
- Friedrich-Baur-Stiftung [42/17]
- Bundesministerium fur Bildung und Forschung [01EK1511A]
- German Center for Infection Research (DZIF)
- Willy Robert Pitzer Foundation
- Projekt DEAL
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This study developed a machine learning-based predictor model and a clinical score for identifying patients at risk of progressing to advanced COVID-19, with a focus on better prioritizing patients in need for hospitalization. The predictor model included variables such as temperature, age, body mass index, smoking habit, pulmonary involvement, inflammation markers, and acute kidney injury to predict the disease severity of COVID-19 patients.
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.
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