Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People.
clustering
electronic health record
frailty
multimorbidity
primary care
trajectory
Journal
JMIR public health and surveillance
ISSN: 2369-2960
Titre abrégé: JMIR Public Health Surveill
Pays: Canada
ID NLM: 101669345
Informations de publication
Date de publication:
27 06 2023
27 06 2023
Historique:
received:
19
01
2023
accepted:
25
05
2023
revised:
02
05
2023
medline:
29
6
2023
pubmed:
27
6
2023
entrez:
27
6
2023
Statut:
epublish
Résumé
Multimorbidity and frailty are characteristics of aging that need individualized evaluation, and there is a 2-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people. This study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older. Longitudinal data were drawn from electronic health records through the SIDIAP (Sistema d'Informació pel Desenvolupament de la Investigació a l'Atenció Primària) primary care database for the population aged 65 years or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and Swedish National Study of Aging and Care in Kungsholmen [SNAC-K], respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants. In addition, one set included age, and the other included frailty. Cox models were used to test their associations with death, nursing home admission, and home care need. Trajectories were defined as the evolution of the patterns over the follow-up period. The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like chronic ulcers &peripheral vascular. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty. The change in patterns when considering frailty also led to a change in trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45.1% (656,778/1,456,052) remained in the same pattern. Our results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes, such as nursing home admission or home care need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories.
Sections du résumé
BACKGROUND
Multimorbidity and frailty are characteristics of aging that need individualized evaluation, and there is a 2-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people.
OBJECTIVE
This study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older.
METHODS
Longitudinal data were drawn from electronic health records through the SIDIAP (Sistema d'Informació pel Desenvolupament de la Investigació a l'Atenció Primària) primary care database for the population aged 65 years or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and Swedish National Study of Aging and Care in Kungsholmen [SNAC-K], respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants. In addition, one set included age, and the other included frailty. Cox models were used to test their associations with death, nursing home admission, and home care need. Trajectories were defined as the evolution of the patterns over the follow-up period.
RESULTS
The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like chronic ulcers &peripheral vascular. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty. The change in patterns when considering frailty also led to a change in trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45.1% (656,778/1,456,052) remained in the same pattern.
CONCLUSIONS
Our results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes, such as nursing home admission or home care need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories.
Identifiants
pubmed: 37368462
pii: v9i1e45848
doi: 10.2196/45848
pmc: PMC10365626
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e45848Informations de copyright
©Lucía A Carrasco-Ribelles, Margarita Cabrera-Bean, Marc Danés-Castells, Edurne Zabaleta-del-Olmo, Albert Roso-Llorach, Concepción Violán. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 27.06.2023.
Références
Med Sci Monit. 2019 Sep 11;25:6820-6835
pubmed: 31507272
Int J Epidemiol. 2022 Dec 13;51(6):e324-e336
pubmed: 35415748
J Gerontol A Biol Sci Med Sci. 2013 Jan;68(1):62-7
pubmed: 22511289
Nat Aging. 2021 Aug;1(8):651-665
pubmed: 37117769
PLoS Med. 2015 Oct 06;12(10):e1001885
pubmed: 26440803
Ageing Res Rev. 2011 Sep;10(4):430-9
pubmed: 21402176
BMC Geriatr. 2018 Jan 16;18(1):16
pubmed: 29338690
J Clin Med. 2021 May 13;10(10):
pubmed: 34068296
JAMA. 2023 Apr 25;329(16):1376-1385
pubmed: 37097356
J Gerontol A Biol Sci Med Sci. 2017 Oct 01;72(10):1417-1423
pubmed: 28003375
Lancet. 2013 Mar 2;381(9868):752-62
pubmed: 23395245
Age Ageing. 2021 Nov 10;50(6):2183-2191
pubmed: 34228784
J Nutr Health Aging. 2017;21(2):207-214
pubmed: 28112778
Age Ageing. 2019 Sep 1;48(5):665-671
pubmed: 31297511
Lancet. 2012 Jul 7;380(9836):37-43
pubmed: 22579043
BMC Geriatr. 2022 May 7;22(1):404
pubmed: 35525922
J Am Geriatr Soc. 2006 Jun;54(6):975-9
pubmed: 16776795
Lancet Public Health. 2021 Aug;6(8):e587-e597
pubmed: 34166630
CMAJ. 2018 Aug 27;190(34):E1004-E1012
pubmed: 30150242
EClinicalMedicine. 2022 Aug 11;52:101610
pubmed: 36034409
BMJ Open. 2019 Aug 30;9(8):e029594
pubmed: 31471439
Maturitas. 2017 Jan;95:31-35
pubmed: 27889050
Nat Rev Dis Primers. 2022 Jul 14;8(1):48
pubmed: 35835758
ScientificWorldJournal. 2001 Aug 08;1:323-36
pubmed: 12806071
Aging Clin Exp Res. 2021 Feb;33(2):457-462
pubmed: 33580869
Int J Epidemiol. 2018 Oct 1;47(5):1687-1704
pubmed: 30016472
Gac Sanit. 2021 Mar-Apr;35(2):113-122
pubmed: 32014314
J Clin Epidemiol. 1999 Mar;52(3):171-9
pubmed: 10210233
Eur J Epidemiol. 2019 Nov;34(11):1025-1053
pubmed: 31624969
J Gerontol A Biol Sci Med Sci. 2019 Apr 23;74(5):659-666
pubmed: 29726918
Clin Transl Sci. 2020 Jan;13(1):4-7
pubmed: 31456349
PLOS Digit Health. 2022 Jan 18;1(1):e0000003
pubmed: 36812509
Age Ageing. 2016 May;45(3):353-60
pubmed: 26944937
J Gerontol A Biol Sci Med Sci. 2021 Oct 13;76(11):e347-e353
pubmed: 34244759
BMC Geriatr. 2017 Sep 5;17(1):203
pubmed: 28874140
Adv Ther. 2018 Nov;35(11):1763-1774
pubmed: 30357570
Age Ageing. 2022 Dec 5;51(12):
pubmed: 36469092
Prev Med. 2015 Dec;81:92-8
pubmed: 26311587
Inform Prim Care. 2011;19(3):135-45
pubmed: 22688222
PLoS Med. 2018 Mar 13;15(3):e1002516
pubmed: 29534066
J Gerontol A Biol Sci Med Sci. 2001 Mar;56(3):M146-56
pubmed: 11253156
Sci Rep. 2020 Oct 9;10(1):16879
pubmed: 33037233
Sci Rep. 2022 Oct 1;12(1):16485
pubmed: 36182953
JAMA Netw Open. 2021 Nov 1;4(11):e2134427
pubmed: 34817584
BMJ Open. 2021 Nov 22;11(11):e048485
pubmed: 34810182
Methods Inf Med. 2017;56(5):391-400
pubmed: 29582934
Lancet Public Health. 2018 Jul;3(7):e323-e332
pubmed: 29908859
BMJ. 2018 Apr 25;361:k1315
pubmed: 29695481
J Clin Epidemiol. 2014 Mar;67(3):254-66
pubmed: 24472295
PLoS One. 2014 Jul 21;9(7):e102149
pubmed: 25048354
J Am Coll Cardiol. 2018 May 15;71(19):2149-2161
pubmed: 29747836
Lancet. 2018 Nov 10;392(10159):2052-2090
pubmed: 30340847