Massimiliano Fedecostante ¹ Jacopo Sabbatinelli ^2,3 Giuseppina Dell'aquila ^1* Fabio Salvi ¹ Anna Rita Bonfigli ⁴ Stefano Volpato ^5* Caterina Trevisan ⁶ Stefano Fumagalli ⁷ Fabio Monzani ^8* Raffaele Antonelli Incalzi ^9* Fabiola Olivieri ^2,4* Antonio Cherubini ^1,2*

• ¹ Geriatria, Accettazione Geriatrica e Centro di ricerca per l'invecchiamento, IRCCS INRCA, Ancona, Italy
• ² Dipartimento di Scienze Cliniche e Molecolari, Facoltà di Medicina e Chirurgia, Università Politecnica delle Marche, Ancona, Marche, Italy
• ³ Clinic of Laboratory and Precision Medicine, IRCCS INRCA, Ancona, Italy
• ⁴ Scientific Direction, IRCCS INRCA, Ancona, Italy
• ⁵ Department of Medical Sciences, University of Ferrara, Ferrara, Emilia-Romagna, Italy
• ⁶ University of Ferrara, Ferrara, Emilia-Romagna, Italy
• ⁷ Department of Experimental and Clinical Medicine, University of Florence, Florence, Tuscany, Italy
• ⁸ Intermediate Care Unit, Nursing Home Misericordia, Pisa, Italy
• ⁹ Department of Medicine, Campus Bio-Medico University Hospital, Roma, Sicily, Italy

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; 3520 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 neutrophilto-lymphocyte ratio. Conclusions: 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.