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利用人工智能预测老年患者新冠病毒疾病的院内死亡率:一项多中心研究

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

作者信息

Fedecostante Massimiliano, Sabbatinelli Jacopo, Dell'Aquila Giuseppina, Salvi Fabio, Bonfigli Anna Rita, Volpato Stefano, Trevisan Caterina, Fumagalli Stefano, Monzani Fabio, Antonelli Incalzi Raffaele, Olivieri Fabiola, Cherubini Antonio

机构信息

Geriatria, Accettazione Geriatrica e Centro di ricerca per l'invecchiamento, IRCCS INRCA, Ancona, Italy.

Department of Clinical and Molecular Sciences, Università Politecnica Delle Marche, Ancona, Italy.

出版信息

Front Aging. 2024 Oct 17;5:1473632. doi: 10.3389/fragi.2024.1473632. eCollection 2024.

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.

摘要

背景

疫情结束后,严重急性呼吸综合征冠状病毒2(SARS-CoV-2)成为地方性流行病毒,存在病情突发阶段。新型冠状病毒肺炎(COVID-19)疾病仍会产生重大临床影响,尤其是在患有多种疾病和身体虚弱的老年患者中。

目的

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。

方法

本研究使用了老年COVID-19急性病房的数据,这是一项在COVID-19大流行期间,针对老年医学和内科病房中60岁及以上受试者开展的多中心观察性回顾性前瞻性研究。将71个常规收集的变量,包括人口统计学数据、居住安排、吸烟习惯、COVID-19前的活动能力、慢性病以及临床和实验室参数,整合到一个基于网络的机器学习平台(Just Add Data Bio)中,以识别具有最高预后相关性的因素。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。

结果

数据集按照70:30的比例划分为训练集和测试集,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)收敛于同一模型,该模型包括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氧饱和度、血清钾、收缩压、血糖、天冬氨酸转氨酶、动脉血氧分压/吸入氧分数值(PaO2/FiO2)比值以及衍生的中性粒细胞与淋巴细胞比值。

结论

除了反映COVID-19疾病严重程度的变量外,病前活动能力水平是与院内死亡率相关的最强因素,这反映了功能状态作为老年人健康综合指标的重要性,而衍生的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疾病中所起的重要作用。

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