Details

Autor(en) / Beteiligte

• 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

Titel

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

Ist Teil von

• Infection, 2022-04, Vol.50 (2), p.359-370

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.