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

IF 3.3 Q2 GERIATRICS & GERONTOLOGY
Frontiers in aging Pub Date : 2024-10-17 eCollection Date: 2024-01-01 DOI:10.3389/fragi.2024.1473632
Massimiliano Fedecostante, Jacopo Sabbatinelli, Giuseppina Dell'Aquila, Fabio Salvi, Anna Rita Bonfigli, Stefano Volpato, Caterina Trevisan, Stefano Fumagalli, Fabio Monzani, Raffaele Antonelli Incalzi, Fabiola Olivieri, Antonio Cherubini
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引用次数: 0

Abstract

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.

利用人工智能预测老年患者 COVID-19 院内死亡率:一项多中心研究。
背景:大流行结束后,SARS-CoV-2开始流行,并有爆发阶段。COVID-19 疾病仍会对临床产生重大影响,尤其是对多病和体弱的老年患者:本研究旨在评估入院时常规收集的数据中与院内死亡率相关的主要特征,以识别死亡风险较高的老年患者:本研究使用了Gerocovid-acute病房的数据,这是一项多中心回顾性-前瞻性观察研究,在COVID-19大流行期间在老年病科和内科病房对年龄≥60岁的受试者进行了观察。我们将人口统计学数据、生活安排、吸烟习惯、COVID-19 流行前的行动能力、慢性疾病以及临床和实验室参数等 71 个常规收集的变量整合到一个基于网络的机器学习平台(Just Add Data Bio)中,以确定与预后相关性最高的因素。人工智能的使用使我们能够避免变量选择偏差,测试大量模型并进行内部验证:数据集按照 70:30 的比例分成训练集和测试集,并根据年龄、性别和事件比例进行匹配;共设置了 3520 个模型进行训练。三种预测算法(针对性能、可解释性或积极特征选择进行了优化)趋同于同一个模型,其中包括12个变量:COVID-19前的活动能力、世界卫生组织疾病严重程度、年龄、心率、动脉血气碳酸氢盐和血氧饱和度、血清钾、收缩压、血糖、天冬氨酸氨基转移酶、PaO2/FiO2比值和衍生的中性粒细胞与淋巴细胞比值:除了反映 COVID-19 疾病衰竭严重程度的变量外,病前活动能力水平是与院内死亡率关联最大的因素,这反映了功能状态作为老年人健康综合衡量标准的重要性,而中性粒细胞与淋巴细胞比值与死亡率之间的关联证实了中性粒细胞在 SARS-CoV-2 疾病中的重要作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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审稿时长
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