Validation of a fall rate prediction model for community-dwelling older adults: a combined analysis of three cohorts with 1850 participants.
Count regression
Falls
Fragility fractures
Model validation
Older adults
Journal
BMC geriatrics
ISSN: 1471-2318
Titre abrégé: BMC Geriatr
Pays: England
ID NLM: 100968548
Informations de publication
Date de publication:
27 Mar 2024
27 Mar 2024
Historique:
received:
07
11
2023
accepted:
14
02
2024
medline:
28
3
2024
pubmed:
28
3
2024
entrez:
28
3
2024
Statut:
epublish
Résumé
Fragility fractures in older adults are often caused by fall events. The estimation of an expected fall rate might improve the identification of individuals at risk of fragility fractures and improve fracture prediction. A combined analysis of three previously developed fall rate models using individual participant data (n = 1850) was conducted using the methodology of a two-stage meta-analysis to derive an overall model. These previously developed models included the fall history as a predictor recorded as the number of experienced falls within 12 months, treated as a factor variable with the levels 0, 1, 2, 3, 4 and ≥ 5 falls. In the first stage, negative binomial regression models for every cohort were fit. In the second stage, the coefficients were compared and used to derive overall coefficients with a random effect meta-analysis. Additionally, external validation was performed by applying the three data sets to the models derived in the first stage. The coefficient estimates for the prior number of falls were consistent among the three studies. Higgin's I This analysis suggests that the fall history treated as a factor variable is a robust predictor of estimating future falls among different cohorts.
Sections du résumé
BACKGROUND
BACKGROUND
Fragility fractures in older adults are often caused by fall events. The estimation of an expected fall rate might improve the identification of individuals at risk of fragility fractures and improve fracture prediction.
METHODS
METHODS
A combined analysis of three previously developed fall rate models using individual participant data (n = 1850) was conducted using the methodology of a two-stage meta-analysis to derive an overall model. These previously developed models included the fall history as a predictor recorded as the number of experienced falls within 12 months, treated as a factor variable with the levels 0, 1, 2, 3, 4 and ≥ 5 falls. In the first stage, negative binomial regression models for every cohort were fit. In the second stage, the coefficients were compared and used to derive overall coefficients with a random effect meta-analysis. Additionally, external validation was performed by applying the three data sets to the models derived in the first stage.
RESULTS
RESULTS
The coefficient estimates for the prior number of falls were consistent among the three studies. Higgin's I
CONCLUSION
CONCLUSIONS
This analysis suggests that the fall history treated as a factor variable is a robust predictor of estimating future falls among different cohorts.
Identifiants
pubmed: 38539089
doi: 10.1186/s12877-024-04811-x
pii: 10.1186/s12877-024-04811-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
287Subventions
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
ID : 183584
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
ID : 183584
Informations de copyright
© 2024. The Author(s).
Références
James SL, Lucchesi LR, Bisignano C, Castle CD, Dingels ZV, Fox JT, et al. The global burden of falls: global, regional and national estimates of morbidity and mortality from the global burden of Disease Study 2017. Inj Prev. 2020;26(Suppl 2):i3–11.
doi: 10.1136/injuryprev-2019-043286
pubmed: 31941758
Court-Brown CM, Clement ND, Duckworth AD, Biant LC, McQueen MM. The changing epidemiology of fall-related fractures in adults. Injury. 2017;48(4):819–24.
doi: 10.1016/j.injury.2017.02.021
pubmed: 28283181
Harvey NC, Odén A, Orwoll E, Lapidus J, Kwok T, Karlsson MK, et al. Falls Predict fractures independently of FRAX Probability: a Meta-analysis of the osteoporotic fractures in men (MrOS) study. J Bone Min Res. 2018;33(3):510–6.
doi: 10.1002/jbmr.3331
Home| FRAXplus® [Internet]. [cited 2023 Dec 22]. Available from: https://www.fraxplus.org/ .
Komisar V, Robinovitch SN. The role of fall biomechanics in the cause and Prevention of Bone fractures in older adults. Curr Osteoporos Rep. 2021;19(4):381–90.
doi: 10.1007/s11914-021-00685-9
pubmed: 34105101
Ensrud KE, Epidemiology of Fracture Risk With Advancing Age. Journals Gerontol Ser A: Biol Sci Med Sci. 2013;68(10):1236–42.
doi: 10.1093/gerona/glt092
Lusardi MM, Fritz S, Middleton A, Allison L, Wingood M, Phillips E, et al. Determining risk of Falls in Community Dwelling older adults: a systematic review and Meta-analysis using Posttest Probability. J Geriatr Phys Ther. 2017;40(1):1–36.
doi: 10.1519/JPT.0000000000000099
pubmed: 27537070
Fabre J, Ellis R, Kosma M, Wood R. Falls Risk factors and a compendium of Falls Risk Screening instruments. J Geriatr Phys Ther. 2010;33(4):184–97.
doi: 10.1519/JPT.0b013e3181ff2a24
pubmed: 21717922
Strini V, Schiavolin R, Prendin A. Fall risk Assessment Scales: a systematic literature review. Nurs Rep. 2021;11(2):430–43.
doi: 10.3390/nursrep11020041
pubmed: 34968219
pmcid: 8608097
Ullah S, Finch CF, Day L. Statistical modelling for falls count data. Accid Anal Prev. 2010;42(2):384–92.
doi: 10.1016/j.aap.2009.08.018
pubmed: 20159058
Gade GV, Jørgensen MG, Ryg J, Masud T, Jakobsen LH, Andersen S. Development of a multivariable prognostic PREdiction model for 1-year risk of FALLing in a cohort of community-dwelling older adults aged 75 years and above (PREFALL). BMC Geriatr. 2021;21(1):402.
doi: 10.1186/s12877-021-02346-z
pubmed: 34193084
pmcid: 8243769
Damián J, Pastor-Barriuso R, Valderrama-Gama E, de Pedro-Cuesta J. Factors associated with falls among older adults living in institutions. BMC Geriatr. 2013;13(1):6.
doi: 10.1186/1471-2318-13-6
pubmed: 23320746
pmcid: 3566955
Biver E, Durosier-Izart C, Chevalley T, van Rietbergen B, Rizzoli R, Ferrari S. Evaluation of Radius microstructure and areal bone Mineral Density improves fracture prediction in Postmenopausal Women. J Bone Min Res. 2018;33(2):328–37.
doi: 10.1002/jbmr.3299
Mittaz Hager AG, Mathieu N, Lenoble-Hoskovec C, Swanenburg J, de Bie R, Hilfiker R. Effects of three home-based exercise programmes regarding falls, quality of life and exercise-adherence in older adults at risk of falling: protocol for a randomized controlled trial. BMC Geriatr. 2019;19(1):13.
doi: 10.1186/s12877-018-1021-y
pubmed: 30642252
pmcid: 6332592
Vilpunaho T, Kröger H, Honkanen R, Koivumaa-Honkanen H, Sirola J, Kuvaja-Köllner V, et al. Randomised controlled trial (RCT) study design for a large-scale municipal fall prevention exercise programme in community-living older women: study protocol for the Kuopio fall Prevention Study (KFPS). BMJ Open. 2019;9(6):e028716.
doi: 10.1136/bmjopen-2018-028716
pubmed: 31230026
pmcid: 6596943
Rikkonen T, Sund R, Koivumaa-Honkanen H, Sirola J, Honkanen R, Kröger H. Effectiveness of exercise on fall prevention in community-dwelling older adults: a 2-year randomized controlled study of 914 women. Age Ageing. 2023;52(4):afad059.
doi: 10.1093/ageing/afad059
pubmed: 37097767
pmcid: 10128158
Wapp C, Mittaz Hager AG, Hilfiker R, Zysset P. History of falls and fear of falling are predictive of future falls: Outcome of a fall rate model applied to the Swiss CHEF Trial cohort. Frontiers in Aging [Internet]. 2022 [cited 2022 Dec 14];3. Available from: https://www.frontiersin.org/articles/ https://doi.org/10.3389/fragi.2022.1056779 .
Wapp C, Biver E, Ferrari S, Zysset P, Zwahlen M. Development of a personalized fall rate prediction model in community-dwelling older adults: a negative binomial regression modelling approach. BMC Geriatr. 2023;23(1):200.
doi: 10.1186/s12877-023-03922-1
pubmed: 36997882
pmcid: 10064572
Deandrea S, Lucenteforte E, Bravi F, Foschi R, La Vecchia C, Negri E. Risk factors for falls in community-dwelling older people: a systematic review and Meta-analysis. Epidemiology. 2010;21(5):658–68.
doi: 10.1097/EDE.0b013e3181e89905
pubmed: 20585256
Robertson MC, Campbell AJ. Otago exercise programme to prevent falls in older adults. Wellington, N.Z.: ACC Thinksafe; 2003.
Burke DL, Ensor J, Riley RD. Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ. Stat Med. 2017;36(5):855–75.
doi: 10.1002/sim.7141
pubmed: 27747915
Crowson CS, Atkinson EJ, Therneau TM. Assessing calibration of prognostic risk scores. Stat Methods Med Res. 2016;25(4):1692–706.
doi: 10.1177/0962280213497434
pubmed: 23907781
Czado C, Gneiting T, Held L. Predictive Model Assessment for Count Data. Biometrics. 2009;65(4):1254–61.
doi: 10.1111/j.1541-0420.2009.01191.x
pubmed: 19432783
Kleiber C, Zeileis A. Visualizing Count Data regressions using rootograms. Am Stat. 2016;70(3):296–303.
doi: 10.1080/00031305.2016.1173590
Viechtbauer W. Conducting Meta-analyses in R with the metafor Package. J Stat Softw. 2010;36:1–48.
doi: 10.18637/jss.v036.i03
Garcia PA, Dias JMD, Silva SLA, Dias RC. Prospective monitoring and self-report of previous falls among older women at high risk of falls and fractures: a study of comparison and agreement. Braz J Phys Ther. 2015;19(3):218–26.
doi: 10.1590/bjpt-rbf.2014.0095
pubmed: 26083603
pmcid: 4518575
Vilpunaho T, Sund R, Koivumaa-Honkanen H, Honkanen R, Kröger H, Rikkonen T. Urban RCT participants were healthier than non-participants or rural women. J Clin Epidemiol. 2021;140:44–55.
doi: 10.1016/j.jclinepi.2021.08.032
pubmed: 34487834