In:
The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, Oxford University Press (OUP)
Abstract:
Prognostic indices can enhance personalized predictions of health burdens. However, a simple, practical and reproducible tool is lacking for clinical use. This study aimed to develop a machine learning-based prognostic index for predicting all-cause mortality in community-dwelling elderly individuals. Methods We utilized the Healthy Aging Longitudinal Study in Taiwan (HALST) cohort, encompassing data from 5,663 participants. Over the 5-year follow-up, 447 deaths were confirmed. A machine learning-based routine blood examination prognostic index (MARBE-PI) was developed using common laboratory tests based on machine learning techniques. Participants were grouped into multiple risk categories by stratum-specific likelihood ratios analysis based on their MARBE-PI scores. The MARBE-PI was subsequently externally validated with an independent population-based cohort from Japan. Results Beyond age, sex, education level and BMI, six laboratory tests (LDL, albumin, AST, lymphocyte count, hsCRP, and creatinine) emerged as pivotal predictors via stepwise logistic regression for 5-year mortality. The AUCs of MARBE-PI constructed by logistic regression were 0.799 (95% CI: 0.778–0.819) and 0.756 (95% CI: 0.694–0.814) for the internal and external validation datasets, and were 0.801 (95% CI: 0.790–0.811) and 0.809 (95% CI: 0.774–0.845) for the extended 10-year mortality in both datasets, respectively. Risk categories stratified by MARBE-PI showed a consistent dose–response association with mortality. The MARBE-PI also performed comparably with indices constructed with clinical health deficits and/or laboratory results. Conclusions The MARBE-PI is considered the most applicable measure for risk stratification in busy clinical settings. It holds potential to pinpoint elderly individuals at elevated mortality risk, thereby aiding clinical decision-making.
Type of Medium:
Online Resource
ISSN:
1079-5006
,
1758-535X
DOI:
10.1093/gerona/glae041
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2024
detail.hit.zdb_id:
2043927-1
SSG:
12
