Prediction of COVID-19 in-hospital mortality in older patients using artificial intelligence: a multicenter study

被引:0
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作者
Fedecostante, Massimiliano [1 ]
Sabbatinelli, Jacopo [2 ,3 ]
Dell'Aquila, Giuseppina [1 ]
Salvi, Fabio [1 ]
Bonfigli, Anna Rita [4 ]
Volpato, Stefano [5 ]
Trevisan, Caterina [5 ]
Fumagalli, Stefano [6 ]
Monzani, Fabio [7 ]
Antonelli Incalzi, Raffaele [8 ]
Olivieri, Fabiola [2 ,4 ]
Cherubini, Antonio [1 ,2 ]
机构
[1] IRCCS INRCA, Accettaz Geriatr & Ctr Ric invecchiamento, Geriatria, Ancona, Italy
[2] Univ Politecn Marche, Dept Clin & Mol Sci, Ancona, Italy
[3] IRCCS INRCA, Clin Lab & Precis Med, Ancona, Italy
[4] IRCCS INRCA, Sci Direct, Ancona, Italy
[5] Univ Ferrara, Dept Med Sci, Ferrara, Italy
[6] Univ Florence, Geriatr Intens Care Unit, Dept Expt & Clin Med, Florence, Italy
[7] Nursing Home Misericordia, Intermediate Care Unit, Pisa, Italy
[8] Campus Biomed Univ & Teaching Hosp, Unit Geriatr, Dept Med, Rome, Italy
来源
FRONTIERS IN AGING | 2024年 / 5卷
关键词
COVID-19; mobility; neutrophil-to-limphocyte ratio; in-hospital mortality; artificial intelligence; LYMPHOCYTE RATIO; NEUTROPHIL;
D O I
10.3389/fragi.2024.1473632
中图分类号
R592 [老年病学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 100203 ;
摘要
Background Once the pandemic ended, SARS-CoV-2 became endemic, with flare-up phases. COVID-19 disease can still have a significant clinical impact, especially in older patients with multimorbidity and frailty.Objective This study aims at evaluating the main characteristics associated to in-hospital mortality among data routinely collected upon admission to identify older patients at higher risk of death.Methods The present study used data from Gerocovid-acute wards, an observational multicenter retrospective-prospective study conducted in geriatric and internal medicine wards in subjects >= 60 years old during the COVID-19 pandemic. Seventy-one routinely collected variables, including demographic data, living arrangements, smoking habits, pre-COVID-19 mobility, chronic diseases, and clinical and laboratory parameters were integrated into a web-based machine learning platform (Just Add Data Bio) to identify factors with the highest prognostic relevance. The use of artificial intelligence allowed us to avoid variable selection bias, to test a large number of models and to perform an internal validation.Results The dataset was split into training and test sets, based on a 70:30 ratio and matching on age, sex, and proportion of events; 3,520 models were set out to train. The three predictive algorithms (optimized for performance, interpretability, or aggressive feature selection) converged on the same model, including 12 variables: pre-COVID-19 mobility, World Health Organization disease severity, age, heart rate, arterial blood gases bicarbonate and oxygen saturation, serum potassium, systolic blood pressure, blood glucose, aspartate aminotransferase, PaO2/FiO2 ratio and derived neutrophil-to-lymphocyte ratio.Conclusion Beyond variables reflecting the severity of COVID-19 disease failure, pre-morbid mobility level was the strongest factor associated with in-hospital mortality reflecting the importance of functional status as a synthetic measure of health in older adults, while the association between derived neutrophil-to-lymphocyte ratio and mortality, confirms the fundamental role played by neutrophils in SARS-CoV-2 disease.
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页数:16
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