Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning.
Carolin Jakob,Ujjwal M. Mahajan,Marcus Oswald,Melanie Stecher,M. Schons,Julia Mayerle,Siegbert Rieg,Mathias W. Pletz,Uta Merle,Kai Wille,Stefan Borgmann,Christoph D. Spinner,Sebastian Dolff,Clemens Scherer,Lisa Pilgram,Maria Rüthrich,Frank Hanses,Martin Hower,Richard Strauß,Steffen Massberg,Ahmet Görkem Er,Norma Jung,Jörg Janne Vehreschild,Jörg Janne Vehreschild,Hans Stubbe,Lukas Tometten,Rainer König +26 more
TLDR
A machine learning-based predictor model and a clinical score are presented for identifying patients at risk of developing advanced COVID-19 and better prioritizing patients in need for hospitalization.Abstract:
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. 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). 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. We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.read more
Citations
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Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study
Fei Zhou,Ting Yu,Ronghui Du,Guohui Fan,Ying Liu,Zhibo Liu,Jie Xiang,Yeming Wang,Bin Song,Xiaoying Gu,Xiaoying Gu,Lulu Guan,Yuan Wei,Li Hui,Xudong Wu,Jiuyang Xu,Shengjin Tu,Yi Zhang,Hua Chen,Bin Cao +19 more
TL;DR: Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.
Journal ArticleDOI
Specific Risk Factors for Fatal Outcome in Critically Ill COVID-19 Patients: Results from a European Multicenter Study.
David Meintrup,Stefan Borgmann,Karlheinz Seidl,Melanie Stecher,Carolin Jakob,Lisa Pilgram,Christoph D. Spinner,Siegbert Rieg,Nora Isberner,Martin Hower,Maria J G T Vehreschild,Siri Göpel,Frank Hanses,Martina Nowak-Machen +13 more
TL;DR: In this article, a multifactorial logistic regression model was used to identify specific risk factors for fatal outcome in critically ill COVID-19 patients and their odds ratios were derived.
Journal ArticleDOI
Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data.
Thomas Campbell,Melissa P Wilson,Heinrich Roder,Samantha MaWhinney,Robert W. Georgantas,Laura Maguire,Joanna Roder,Kristine M. Erlandson +7 more
TL;DR: In this paper, the authors developed models to stratify patients by risk of severe outcomes during COVID-19 hospitalization using readily available information at hospital admission, including basic patient characteristics, vital signs at admission, and basic lab results collected at time of presentation.
Journal ArticleDOI
Development of Machine-Learning Model to Predict COVID-19 Mortality: Application of Ensemble Model and Regarding Feature Impacts
TL;DR: This study demonstrated both the applicability of DL and ML models for classifying COVID-19 mortality using hospital-structured data and that the ensemble model had the best predictive ability.
Journal ArticleDOI
Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques
Yacheng Fu,Weijun Zhong,Taoze Liu,Jianmin Li,Kui Xiao,Xinhua Ma,L. Xie,Junyi Jiang,Hong-Hao Zhou,Rong-Rong Liu,Wei Zhang +10 more
TL;DR: A risk prediction model based on laboratory findings of patients with COVID-19 was developed and might contribute to the treatment of critical illness disease as early as possible and allow for optimized use of medical resources.
References
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