期刊
BMC MEDICAL RESEARCH METHODOLOGY
卷 21, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s12874-020-01204-7
关键词
Mortality; Survival; Prognostic factors; Statistical learning; Absolute risk; Population-based longitudinal study
资金
- National Institute on Aging [RO1AG7644, NIA U01AG009740]
- Economic and Social Research Council (ESRC)
- National Institute for Health Research (NIHR) [PDF-2018-11-ST2-020]
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London
- US Social Security Administration
- National Institutes of Health Research (NIHR) [PDF-2018-11-ST2-020] Funding Source: National Institutes of Health Research (NIHR)
A new prediction model has been developed to quantify the absolute risk of all-cause mortality in the next 10 years. The model includes 13 prognostic factors and has good prediction accuracy, sensitivity, and specificity.
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 beta=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.
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