@article {11279, 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.}, journal = {BMC Medical Research Methodology}, volume = {21}, year = {2021}, pages = {8}, abstract = {

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{\textquoteright}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{\textquoteright}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.

}, keywords = {Absolute risk, Mortality, Population-based longitudinal study, Prognostic factors, Statistical learning, Survival}, issn = {1471-2288}, doi = {10.1186/s12874-020-01204-7}, author = {Ajnakina, Olesya and Agbedjro, Deborah and Ryan J McCammon and Jessica Faul and Murray, Robin M and Stahl, Daniel and Andrew Steptoe} }