Evidence map›Paper›PMID 33407175›Full record

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.

Olesya Ajnakina, Deborah Agbedjro, Ryan McCammon, Jessica Faul, Robin M Murray, Daniel Stahl, Andrew Steptoe

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. The Utility of Measures of Physical Behavior, Function, and Fitness as Predictors of Mortality.Mayo Clinic proceedings. Innovations, quality & outcomes · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Olesya AjnakinaDepartment of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, UK. o.ajnakina@ucl.ac.uk.ORCID 0000-0003-3987-1236
Deborah AgbedjroDepartment of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Ryan McCammonSurvey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, USA.
Jessica FaulSurvey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, USA.
Robin M MurrayDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Daniel StahlDepartment of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Andrew SteptoeDepartment of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, UK.

Funding

HRS Yrs29-34: Y33 SSA CoFundingU01AG009740 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jessica Faul, KENNETH M LANGA · 1990 to 2026
$555.8M
Project-001R01AG017644 · NIA · UNIVERSITY OF LONDON INST OF NEUROLOGY · PI Andrew Steptoe · 2000 to 2026
$56.2M
Department of Health PDF-2018-11-ST2-020NIA NIH HHS R01 AG017644NIA NIH HHS U01 AG009740
6 · The paper itself

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.

Indexed as

AgingAgedCohort StudiesHumansLongitudinal StudiesProportional Hazards ModelsRisk AssessmentSensitivity and SpecificityAbsolute riskMortalityPopulation-based longitudinal studyPrognostic factorsStatistical learningSurvival

Identifiers

PMID33407175
PMCPMC7789636

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.