Beyond Chronological Age: A Multidimensional Approach to Survival Prediction in Older Adults. Issue 1 (8th September 2022)

• Record Type: Journal Article
• Authors: Salignon, Jérôme; Rizzuto, Debora; Calderón-Larrañaga, Amaia; Zucchelli, Alberto; Fratiglioni, Laura; Riedel, Christian G; Vetrano, Davide L
• Editors: Lipsitz, Lewis A
• Is Part Of: Journals of gerontology. Volume 78:Issue 1(2023)
• Page Start: 158
• Page End: 166
• Publication Date: 2022-09-08
• Subjects: Biological age -- Chronological age -- Multidimensional assessment -- Personalized medicine -- Survival; Geriatrics -- Periodicals; Gerontology -- Periodicals; 618.97
• DOI: 10.1093/gerona/glac186
• Languages: English
• ISSNs: 1079-5006

Abstract

Abstract: Background: There is a growing interest in generating precise predictions of survival to improve the assessment of health and life-improving interventions. We aimed to (a) test if observable characteristics may provide a survival prediction independent of chronological age; (b) identify the most relevant predictors of survival; and (c) build a metric of multidimensional age. Methods: Data from 3 095 individuals aged ≥60 from the Swedish National Study on Aging and Care in Kungsholmen. Eighty-three variables covering 5 domains (diseases, risk factors, sociodemographics, functional status, and blood tests) were tested in penalized Cox regressions to predict 18-year mortality. Results: The best prediction of mortality at different follow-ups (area under the receiver operating characteristic curves [AUROCs] 0.878–0.909) was obtained when 15 variables from all 5 domains were tested simultaneously in a penalized Cox regression. Significant prediction improvements were observed when chronological age was included as a covariate for 15- but not for 5- and 10-year survival. When comparing individual domains, we find that a combination of functional characteristics (ie, gait speed, cognition) gave the most accurate prediction, with estimates similar to chronological age for 5- (AUROC 0.836) and 10-year (AUROC 0.830) survival. Finally, we built a multidimensional measure of age by regressing the predicted mortality risk on chronological age, which displayed a stronger correlation with time to death ( R = −0.760) than chronological age ( R = −0.660) and predicted mortality better than widely used geriatric indices. Conclusions: Combining easily accessible characteristics can help in building highly accurate survival models and multidimensional age metrics with potentially broad geriatric and biomedical applications.