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

被引:0
|
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.
引用
收藏
页数:16
相关论文
共 50 条
  • [41] Predicting in-Hospital Mortality of Patients with COVID-19 Using Machine Learning Techniques
    Tezza, Fabiana
    Lorenzoni, Giulia
    Azzolina, Danila
    Barbar, Sofia
    Leone, Lucia Anna Carmela
    Gregori, Dario
    JOURNAL OF PERSONALIZED MEDICINE, 2021, 11 (05):
  • [42] Systemic Inflammatory Predictors of In-Hospital Mortality in COVID-19 Patients: A Retrospective Study
    Kudlinski, Bartosz
    Zgola, Dominika
    Stolinska, Marta
    Murkos, Magdalena
    Kania, Jagoda
    Nowak, Pawel
    Noga, Anna
    Wojciech, Magdalena
    Zaborniak, Gabriel
    Zembron-Lacny, Agnieszka
    DIAGNOSTICS, 2022, 12 (04)
  • [43] Azvudine reduces the in-hospital mortality of COVID-19 patients:A retrospective cohort study
    Kaican Zong
    Hui Zhou
    Wen Li
    E Jiang
    Yi Liu
    Shiying Li
    Acta Pharmaceutica Sinica B, 2023, (11) : 4655 - 4660
  • [44] Electrocardiographic Findings and In-Hospital Mortality of COVID-19 Patients; a Retrospective Cohort Study
    Aghajani, Mohammad Haji
    Toloui, Amirmohammad
    Aghamohammadi, Moazzameh
    Pourhoseingholi, Asma
    Taherpour, Niloufar
    Sistanizad, Mohammad
    Neishaboori, Arian Madani
    Asadpoordezaki, Ziba
    Miri, Reza
    ARCHIVES OF ACADEMIC EMERGENCY MEDICINE, 2021, 9 (01) : 1 - 11
  • [45] Significance of hemogram-derived ratios for predicting in-hospital mortality in COVID-19: A multicenter study
    Asaduzzaman, M. D.
    Romel Bhuia, Mohammad
    Nazmul Alam, Z. H. M.
    Zabed Jillul Bari, Mohammad
    Ferdousi, Tasnim
    HEALTH SCIENCE REPORTS, 2022, 5 (04)
  • [46] Azvudine reduces the in-hospital mortality of COVID-19 patients: A retrospective cohort study
    Zong, Kaican
    Zhou, Hui
    Li, Wen
    Jiang, E.
    Liu, Yi
    Li, Shiying
    ACTA PHARMACEUTICA SINICA B, 2023, 13 (11) : 4655 - 4660
  • [47] Comparison of in-hospital mortality risk prediction models from COVID-19
    El-Solh, Ali A.
    Lawson, Yolanda
    Carter, Michael
    El-Solh, Daniel A.
    Mergenhagen, Kari A.
    PLOS ONE, 2020, 15 (12):
  • [48] Rapid prediction of in-hospital mortality among adults with COVID-19 disease
    Kim, Kyoung Min
    Evans, Daniel S.
    Jacobson, Jessica
    Jiang, Xiaqing
    Browner, Warren
    Cummings, Steven R.
    PLOS ONE, 2022, 17 (07):
  • [49] Artificial intelligence for predicting mortality in hospitalized COVID-19 patients
    Korsakov, Igor N.
    Karonova, Tatiana L.
    Mikhaylova, Arina A.
    Loboda, Alexander A.
    Chernikova, Alyona T.
    Mikheeva, Anna G.
    Sharypova, Marina V.
    Konradi, Alexandra O.
    Shlyakhto, Evgeny V.
    DIGITAL HEALTH, 2024, 10
  • [50] Healthcare Disparities Correlated with In-Hospital Mortality in COVID-19 Patients
    Harvey, Rachel
    Hernnez, Maryan
    Schanz, Luke
    Karabon, Patrick
    Wunderlich-Barillas, Tracy
    Halalau, Alexandra
    INTERNATIONAL JOURNAL OF GENERAL MEDICINE, 2021, 14 : 5593 - 5596