Artificial neural network based prediction of the lung tissue involvement as an independent in‐hospital mortality and mechanical ventilation risk factor in COVID‐19

Introduction During COVID‐19 pandemic, artificial neural network (ANN) systems have been providing aid for clinical decisions. However, to achieve optimal results, these models should link multiple clinical data points to simple models. This study aimed to model the in‐hospital mortality and mechani...

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Published in:	Journal of medical virology Vol. 95; no. 5; pp. e28787 - n/a
Main Authors:	Parczewski, Miłosz, Kufel, Jakub, Aksak‐Wąs, Bogusz, Piwnik, Joanna, Chober, Daniel, Puzio, Tomasz, Lesiewska, Laura, Białkowski, Sebastian, Rafalska‐Kosior, Milena, Wydra, Jacek, Awgul, Krystian, Grobelna, Milena, Majchrzak, Adam, Dunikowski, Kosma, Jurczyk, Krzysztof, Podyma, Marek, Serwin, Karol, Musiałek, Jakub
Format:	Journal Article
Language:	English
Published:	United States Wiley Subscription Services, Inc 01-05-2023
Subjects:	Aged, 80 and over artificial intelligence artificial neural network Artificial neural networks Cerebrovascular diseases Computed tomography COVID-19 Data analysis Data points Decisions Diabetes mellitus Epidermal growth factor receptors Glomerular filtration rate Health hazards Hospital Mortality Humans Inflammation Lung - diagnostic imaging Lung diseases Lungs Mechanical ventilation Mortality multivariate models Neural networks Neural Networks, Computer Pandemics Patients Pneumonia Procalcitonin Respiration, Artificial respiratory failure Retrospective Studies Risk Factors SARS-CoV-2 Statistical models Tissues Troponin Ventilation Virology COVID-19 mortality respiratory failure artificial neural network multivariate models artificial intelligence
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Summary:	Introduction During COVID‐19 pandemic, artificial neural network (ANN) systems have been providing aid for clinical decisions. However, to achieve optimal results, these models should link multiple clinical data points to simple models. This study aimed to model the in‐hospital mortality and mechanical ventilation risk using a two step approach combining clinical variables and ANN‐analyzed lung inflammation data. Methods A data set of 4317 COVID‐19 hospitalized patients, including 266 patients requiring mechanical ventilation, was analyzed. Demographic and clinical data (including the length of hospital stay and mortality) and chest computed tomography (CT) data were collected. Lung involvement was analyzed using a trained ANN. The combined data were then analyzed using unadjusted and multivariate Cox proportional hazards models. Results Overall in‐hospital mortality associated with ANN‐assigned percentage of the lung involvement (hazard ratio [HR]: 5.72, 95% confidence interval [CI]: 4.4–7.43, p < 0.001 for the patients with >50% of lung tissue affected by COVID‐19 pneumonia), age category (HR: 5.34, 95% CI: 3.32–8.59 for cases >80 years, p < 0.001), procalcitonin (HR: 2.1, 95% CI: 1.59–2.76, p < 0.001, C‐reactive protein level (CRP) (HR: 2.11, 95% CI: 1.25–3.56, p = 0.004), glomerular filtration rate (eGFR) (HR: 1.82, 95% CI: 1.37–2.42, p < 0.001) and troponin (HR: 2.14, 95% CI: 1.69–2.72, p < 0.001). Furthermore, the risk of mechanical ventilation is also associated with ANN‐based percentage of lung inflammation (HR: 13.2, 95% CI: 8.65–20.4, p < 0.001 for patients with >50% involvement), age, procalcitonin (HR: 1.91, 95% CI: 1.14–3.2, p = 0.14, eGFR (HR: 1.82, 95% CI: 1.2–2.74, p = 0.004) and clinical variables, including diabetes (HR: 2.5, 95% CI: 1.91–3.27, p < 0.001), cardiovascular and cerebrovascular disease (HR: 3.16, 95% CI: 2.38–4.2, p < 0.001) and chronic pulmonary disease (HR: 2.31, 95% CI: 1.44–3.7, p < 0.001). Conclusions ANN‐based lung tissue involvement is the strongest predictor of unfavorable outcomes in COVID‐19 and represents a valuable support tool for clinical decisions.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0146-6615 1096-9071
DOI:	10.1002/jmv.28787