ArticleBMC medical research methodology2021
Development and validation of prediction model to estimate 10-year risk of all-cause mortality using modern statistical learning methods: a large population-based cohort study and external validation.
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The Utility of Measures of Physical Behavior, Function, and Fitness as Predictors of Mortality.Mayo Clinic proceedings. Innovations, quality & outcomes · 2026Article
- A simulation study of the impact of population-wide lifestyle modifications on life expectancy in the Chinese population.Nature communications · 2025Article
- New horizons in prediction modelling using machine learning in older people's healthcare research.Age and ageing · 2024Article
- The development and validation of a nomogram-based risk prediction model for mortality among older adults.SSM - population health · 2024Article
- Do LUTS Predict Mortality? An Analysis Using Random Forest Algorithms.Clinical interventions in aging · 2024Article
- Genetic propensity, socioeconomic status, and trajectories of depression over a course of 14 years in older adults.Translational psychiatry · 2023Article
- Development and Validation of Predictive Model for a Diagnosis of First Episode Psychosis Using the Multinational EU-GEI Case-control Study and Modern Statistical Learning Methods.Schizophrenia bulletin open · 2023Article
- Development and validation of questionnaire-based machine learning models for predicting all-cause mortality in a representative population of China.Frontiers in public health · 2023Article
- Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19-a data-driven retrospective cohort study.Journal of the Royal Society of Medicine · 2023Article
- A Novel Score for mHealth Apps to Predict and Prevent Mortality: Further Validation and Adaptation to the US Population Using the US National Health and Nutrition Examination Survey Data Set.Journal of medical Internet research · 2022Review
- Interplay between polygenic propensity for ageing-related traits and the consumption of fruits and vegetables on future dementia diagnosis.BMC psychiatry · 2022Article
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7 authors.
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Abstract
backgroundIn increasingly ageing populations, there is an emergent need to develop a robust prediction model for estimating an individual absolute risk for all-cause mortality, so that relevant assessments and interventions can be targeted appropriately. The objective of the study was to derive, evaluate and validate (internally and externally) a risk prediction model allowing rapid estimations of an absolute risk of all-cause mortality in the following 10 years.
methodsFor the model development, data came from English Longitudinal Study of Ageing study, which comprised 9154 population-representative individuals aged 50-75 years, 1240 (13.5%) of whom died during the 10-year follow-up. Internal validation was carried out using Harrell's optimism-correction procedure; external validation was carried out using Health and Retirement Study (HRS), which is a nationally representative longitudinal survey of adults aged ≥50 years residing in the United States. Cox proportional hazards model with regularisation by the least absolute shrinkage and selection operator, where optimisation parameters were chosen based on repeated cross-validation, was employed for variable selection and model fitting. Measures of calibration, discrimination, sensitivity and specificity were determined in the development and validation cohorts.
resultsThe model selected 13 prognostic factors of all-cause mortality encompassing information on demographic characteristics, health comorbidity, lifestyle and cognitive functioning. The internally validated model had good discriminatory ability (c-index=0.74), specificity (72.5%) and sensitivity (73.0%). Following external validation, the model's prediction accuracy remained within a clinically acceptable range (c-index=0.69, calibration slope β=0.80, specificity=71.5% and sensitivity=70.6%). The main limitation of our model is twofold: 1) it may not be applicable to nursing home and other institutional populations, and 2) it was developed and validated in the cohorts with predominately white ethnicity.
conclusionsA new prediction model that quantifies absolute risk of all-cause mortality in the following 10-years in the general population has been developed and externally validated. It has good prediction accuracy and is based on variables that are available in a variety of care and research settings. This model can facilitate identification of high risk for all-cause mortality older adults for further assessment or interventions.
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