Beyond Chronological Age: A Multidimensional Approach to Survival Prediction in Older Adults

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.

Keywords: Biological age; Chronological age; Multidimensional assessment; Personalized medicine; Survival.

Figure 1.

Accuracy (AUROC) of different health domains in predicting mortality. A 10-fold cross-validation strategy was used to estimate through penalized Cox regressions the predictive abilities of models built from various domains, after adjusting and not adjusting by chronological age. Domains were sorted by their mean AUROC with age across the 3 follow-up time points. Bars indicate 95% confidence intervals of the estimations. Analyses were carried after imputing missing data. AUROC = area under the receiver operating characteristic curve.

Figure 2.

Significant predictors of mortality in different health domains Coefficients were derived by penalized Cox regression models with mortality as the outcome. (A) Number of total or significant features by domain when adjusting or not by chronological age. (B, C) Coefficients of the multidimensional model including (B) or not including (C) chronological age among the potential predictors. Positive coefficients indicate a positive association with mortality risk. Negative coefficients indicate a negative association with mortality risk. Bars indicate 95% confidence intervals of the estimations. Analyses were carried out after imputing missing data. ADL = activities of daily living; BMI = body mass index; COPD = chronic obstructive pulmonary disease; MMSE = Mini-Mental State Examination.

Figure 3.

Multidimensional age prediction and its correlation with time to death. Relative risks from the multidomain models were averaged across imputed data sets, and then log2-transformed. Multidimensional age was obtained by regressing averaged risks on chronological age, using weights by age groups (see Materials and Methods section). For each panel, the red line shows the weighted regression (by age groups) of x on y, and R shows the Pearson correlation coefficients between the 2 variables. Highlighted points show 2 individuals with 36 years of age difference but with similar relative risk (B), multidimensional age (B, C), and time until death (C). Analyses were carried out after imputing missing data.

Figure 4.

Comparison of the predictive accuracy for mortality of multidimensional age, chronological age, and other clinical and functional geriatric indices. The predictive accuracy was compared by mean of AUROC values. Bars indicate 95% confidence intervals of the estimations. Analyses were carried out after imputing missing data. ADL = activities of daily living; AUROC = area under the receiver operating characteristic curve; HAT = Health Assessment Tool.