Identifying patterns of multimorbidity, polypharmacy and frailty in the elderly: a clustering analysis of baseline data from a French, randomised, controlled trial in primary care
Objectives To identify distinct profiles among elderly patients in primary care so that general practitioners (GPs) can develop more targeted care strategies.Design A cross-sectional analysis of baseline data from the French nationwide ‘Elderly Appropriate Treatment in Primary Care’ trial.Setting Pr...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2025-06-01
|
| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/6/e083584.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850110235995275264 |
|---|---|
| author | Étienne Audureau Julien Le Breton Pascal Clerc Joël Cogneau Nadia Oubaya Aziz Guellich François Lacoin |
| author_facet | Étienne Audureau Julien Le Breton Pascal Clerc Joël Cogneau Nadia Oubaya Aziz Guellich François Lacoin |
| author_sort | Étienne Audureau |
| collection | DOAJ |
| description | Objectives To identify distinct profiles among elderly patients in primary care so that general practitioners (GPs) can develop more targeted care strategies.Design A cross-sectional analysis of baseline data from the French nationwide ‘Elderly Appropriate Treatment in Primary Care’ trial.Setting Primary care in France: 277 GPs included patients.Participants The study participants were aged 75 or over, living at home, and taking five or more prescription medications. Of the 2724 patients included, 2651 were analysed.Primary and secondary outcome measures To identify specific patterns of multimorbidity, polypharmacy and frailty, we applied an unsupervised clustering analysis with self-organising maps.Results Seven clusters were identified: cluster 1 (16% of the patients) comprised frail men and women with cardiovascular, respiratory, musculoskeletal and endocrine diseases and marked polypharmacy; cluster 2 (9.3%, mainly men) comprised frail patients with cancer and cardiovascular or urogenital/renal diseases; cluster 3 (15.5%, mainly men) comprised not-very-frail patients with cardiovascular and urogenital/renal diseases; cluster 4 (18.1%) comprised not-very-frail men and women with cardiovascular diseases; cluster 5 (13.5%, mainly women) comprised mainly lonely, very frail patients with hypertension and endocrine, musculoskeletal and neuropsychiatric disorders; cluster 6 (19.1%, mainly women) comprised frail, socially isolated patients with digestive, musculoskeletal and neuropsychiatric diseases; lastly, cluster 7 (8.6%, mainly women) comprised frail, socially isolated patients with hypertension, cancer, or musculoskeletal, psychological and digestive disorders.Conclusion Our phenotypic classification of elderly patients might facilitate efforts to align healthcare services with the care needs that are encountered by GPs in their everyday practice.Trial regestration number (NCT03298386). |
| format | Article |
| id | doaj-art-1cf96f6c18714c78a86200b0dd351a35 |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-1cf96f6c18714c78a86200b0dd351a352025-08-20T02:37:52ZengBMJ Publishing GroupBMJ Open2044-60552025-06-0115610.1136/bmjopen-2023-083584Identifying patterns of multimorbidity, polypharmacy and frailty in the elderly: a clustering analysis of baseline data from a French, randomised, controlled trial in primary careÉtienne Audureau0Julien Le Breton1Pascal Clerc2Joël Cogneau3Nadia Oubaya4Aziz Guellich5François Lacoin6Service de Santé Publique, Hôpital Henri Mondor, F-94010 Creteil, FranceSociété Française de Médecine Générale (SFMG), F-92130 Issy-les-Moulineaux, FranceIMRB, CEpiA Team, Univ Paris Est Creteil, INSERM, F-94010 Creteil, FranceInstitut de Recherche en Médecine Générale (IRMG), F-75005 Paris, FranceIMRB, CEpiA Team, Univ Paris Est Creteil, INSERM, F-94010 Creteil, FranceSociété Française de Médecine Générale (SFMG), F-92130 Issy-les-Moulineaux, FranceInstitut de Recherche en Médecine Générale (IRMG), F-75005 Paris, FranceObjectives To identify distinct profiles among elderly patients in primary care so that general practitioners (GPs) can develop more targeted care strategies.Design A cross-sectional analysis of baseline data from the French nationwide ‘Elderly Appropriate Treatment in Primary Care’ trial.Setting Primary care in France: 277 GPs included patients.Participants The study participants were aged 75 or over, living at home, and taking five or more prescription medications. Of the 2724 patients included, 2651 were analysed.Primary and secondary outcome measures To identify specific patterns of multimorbidity, polypharmacy and frailty, we applied an unsupervised clustering analysis with self-organising maps.Results Seven clusters were identified: cluster 1 (16% of the patients) comprised frail men and women with cardiovascular, respiratory, musculoskeletal and endocrine diseases and marked polypharmacy; cluster 2 (9.3%, mainly men) comprised frail patients with cancer and cardiovascular or urogenital/renal diseases; cluster 3 (15.5%, mainly men) comprised not-very-frail patients with cardiovascular and urogenital/renal diseases; cluster 4 (18.1%) comprised not-very-frail men and women with cardiovascular diseases; cluster 5 (13.5%, mainly women) comprised mainly lonely, very frail patients with hypertension and endocrine, musculoskeletal and neuropsychiatric disorders; cluster 6 (19.1%, mainly women) comprised frail, socially isolated patients with digestive, musculoskeletal and neuropsychiatric diseases; lastly, cluster 7 (8.6%, mainly women) comprised frail, socially isolated patients with hypertension, cancer, or musculoskeletal, psychological and digestive disorders.Conclusion Our phenotypic classification of elderly patients might facilitate efforts to align healthcare services with the care needs that are encountered by GPs in their everyday practice.Trial regestration number (NCT03298386).https://bmjopen.bmj.com/content/15/6/e083584.full |
| spellingShingle | Étienne Audureau Julien Le Breton Pascal Clerc Joël Cogneau Nadia Oubaya Aziz Guellich François Lacoin Identifying patterns of multimorbidity, polypharmacy and frailty in the elderly: a clustering analysis of baseline data from a French, randomised, controlled trial in primary care BMJ Open |
| title | Identifying patterns of multimorbidity, polypharmacy and frailty in the elderly: a clustering analysis of baseline data from a French, randomised, controlled trial in primary care |
| title_full | Identifying patterns of multimorbidity, polypharmacy and frailty in the elderly: a clustering analysis of baseline data from a French, randomised, controlled trial in primary care |
| title_fullStr | Identifying patterns of multimorbidity, polypharmacy and frailty in the elderly: a clustering analysis of baseline data from a French, randomised, controlled trial in primary care |
| title_full_unstemmed | Identifying patterns of multimorbidity, polypharmacy and frailty in the elderly: a clustering analysis of baseline data from a French, randomised, controlled trial in primary care |
| title_short | Identifying patterns of multimorbidity, polypharmacy and frailty in the elderly: a clustering analysis of baseline data from a French, randomised, controlled trial in primary care |
| title_sort | identifying patterns of multimorbidity polypharmacy and frailty in the elderly a clustering analysis of baseline data from a french randomised controlled trial in primary care |
| url | https://bmjopen.bmj.com/content/15/6/e083584.full |
| work_keys_str_mv | AT etienneaudureau identifyingpatternsofmultimorbiditypolypharmacyandfrailtyintheelderlyaclusteringanalysisofbaselinedatafromafrenchrandomisedcontrolledtrialinprimarycare AT julienlebreton identifyingpatternsofmultimorbiditypolypharmacyandfrailtyintheelderlyaclusteringanalysisofbaselinedatafromafrenchrandomisedcontrolledtrialinprimarycare AT pascalclerc identifyingpatternsofmultimorbiditypolypharmacyandfrailtyintheelderlyaclusteringanalysisofbaselinedatafromafrenchrandomisedcontrolledtrialinprimarycare AT joelcogneau identifyingpatternsofmultimorbiditypolypharmacyandfrailtyintheelderlyaclusteringanalysisofbaselinedatafromafrenchrandomisedcontrolledtrialinprimarycare AT nadiaoubaya identifyingpatternsofmultimorbiditypolypharmacyandfrailtyintheelderlyaclusteringanalysisofbaselinedatafromafrenchrandomisedcontrolledtrialinprimarycare AT azizguellich identifyingpatternsofmultimorbiditypolypharmacyandfrailtyintheelderlyaclusteringanalysisofbaselinedatafromafrenchrandomisedcontrolledtrialinprimarycare AT francoislacoin identifyingpatternsofmultimorbiditypolypharmacyandfrailtyintheelderlyaclusteringanalysisofbaselinedatafromafrenchrandomisedcontrolledtrialinprimarycare |