Develop and Validate a Prognostic Index With Laboratory Tests to Predict Mortality in Middle-Aged and Older Adults Using Machine Learning Models: A Prospective Cohort Study.
J Gerontol A Biol Sci Med Sci
; 79(5)2024 May 01.
Article
en En
| MEDLINE
| ID: mdl-38349645
ABSTRACT
BACKGROUND:
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 older 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 ratio 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, 6 laboratory tests (low-density lipoprotein, albumin, aspartate aminotransferase, lymphocyte count, high-sensitivity C-reactive protein, and creatinine) emerged as pivotal predictors via stepwise logistic regression (LR) for 5-year mortality. The area under curves of MARBE-PI constructed by LR were 0.799 (95% confidence interval [95% CI] 0.778-0.819) and 0.756 (95% CI 0.694-0.814) for the internal and external validation data sets, 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 data sets, 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 older individuals at elevated mortality risk, thereby aiding clinical decision-making.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Vida Independiente
/
Aprendizaje Automático
Tipo de estudio:
Etiology_studies
/
Observational_studies
/
Prognostic_studies
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Risk_factors_studies
Límite:
Aged
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Humans
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Middle aged
Idioma:
En
Revista:
J Gerontol A Biol Sci Med Sci
Asunto de la revista:
GERIATRIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Taiwán