Evidence map›Paper›PMID 40175586›Full record

ArticleScientific reports2025

Development and indirect validation of a model predicting frailty in the French healthcare claims database.

Hana Lahbib, Laurence Mandereau-Bruno, Sarah Goria, Vérène Wagner, Marion J Torres, Catherine Féart, Catherine Helmer, Karine Pérès, Laure Carcaillon-Bentata

Abstract readValidation Study
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

9 authors.

Hana LahbibSanté publique France, French National Public Health Agency, Saint-Maurice, 94410, France.ORCID http://orcid.org/0000-0002-8010-3928
Laurence Mandereau-BrunoSanté publique France, French National Public Health Agency, Saint-Maurice, 94410, France.ORCID http://orcid.org/0009-0001-6829-8531
Sarah GoriaSanté publique France, French National Public Health Agency, Saint-Maurice, 94410, France.ORCID http://orcid.org/0000-0001-8620-9195
Vérène WagnerSanté publique France, French National Public Health Agency, Saint-Maurice, 94410, France.ORCID http://orcid.org/0000-0001-9804-069X
Marion J TorresSanté publique France, French National Public Health Agency, Saint-Maurice, 94410, France.ORCID http://orcid.org/0000-0002-7609-5531
Catherine FéartBordeaux Population Health Research Center, Bordeaux University, INSERM, UMR U1219, Bordeaux, 33000, France.ORCID http://orcid.org/0000-0002-7959-1610
Catherine HelmerBordeaux Population Health Research Center, Bordeaux University, INSERM, UMR U1219, Bordeaux, 33000, France.ORCID http://orcid.org/0000-0002-5169-7421
Karine PérèsBordeaux Population Health Research Center, Bordeaux University, INSERM, UMR U1219, Bordeaux, 33000, France.ORCID http://orcid.org/0000-0002-0720-0684
Laure Carcaillon-BentataSanté publique France, French National Public Health Agency, Saint-Maurice, 94410, France. laure.carcaillon-bentata@u-bordeaux.fr.ORCID http://orcid.org/0000-0003-1556-8590

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to build a predictive model to identify frailty in the French national health data system (SNDS) so as to create a new tool to monitor and anticipate the disability burden associated with population ageing. We developed the model using the 2012 wave of the French Health, Healthcare, and Insurance Survey (ESPS) linked to the SNDS (n = 2,829). This survey used Fried's frailty phenotype as the gold standard. We compared two statistical approaches - logistic regressions (stepwise and LASSO selection) and random forest - to predict frailty probability based on different SNDS healthcare claims. We indirectly validated the model by comparing (1) the predicted frailty prevalence in the overall French population in the SNDS with the expected prevalence and (2) the predictive ability of the model for 6-year mortality with that of Fried's frailty phenotype. Logistic regression with LASSO selection was retained as the best method to predict frailty. After stratification for age, we obtained three models for individuals aged 55-64, 65-74, and ≥ 75 years (AUC = 0.61, 0.76, and 0.80 respectively). Applying these models to the SNDS, frailty prevalence was comparable to expected prevalence in all sex and age groups: overall prevalence = 12.9% (95%CI: 12.9-12.9) in the SNDS versus 12.0% (95%CI: 10.8-13.2) in the ESPS. Predicted frailty probabilities in the SNDS showed a similar strong association with 6-year mortality (HR

Indexed as

FrailtyAgedAged, 80 and overDatabases, FactualFemaleFrail ElderlyFranceHumansLogistic ModelsMaleMiddle AgedPrevalenceAlgorithmFrailtyFrench national health data systemPredictive model

Identifiers

PMID40175586
PMCPMC11965288

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Registered trials

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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.