|Title||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.|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Ajnakina, O, Agbedjro, D, McCammon, RJ, Faul, J, Murray, RM, Stahl, D, Steptoe, A|
|Journal||BMC Medical Research Methodology|
|Keywords||Absolute risk, Mortality, Population-based longitudinal study, Prognostic factors, Statistical learning, Survival|
BACKGROUND: In 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.
METHODS: For 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.
RESULTS: The 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.
CONCLUSIONS: A 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.
|PubMed Central ID||PMC7789636|
|Grant List||RO1AG7644 / AG / NIA NIH HHS / United States |
PDF-2018-11-ST2-020 / / National Institute for Health Research (NIHR) /