Several prediction models for frailty in older adults have been published, but their characteristics and methodological quality are unclear. This review aims to summarize and critically appraise the prediction models. Studies describing multivariable prediction models for frailty among older adults were included. PubMed, Embase, Web of Science, and PsycINFO were searched from outset to Feb 21, 2021. Methodological and reporting quality of included models were evaluated by PROBAST and TRIPOD, respectively. All results were descriptively summarized. Twenty articles including 39 models were identified. The included models showed good predictive discrimination with C indices ranging from 0.70 to 0.98. However, all studies except one were assessed as high risk of bias mainly due to inappropriate analysis; meanwhile, poor reporting quality was also frequently observed. Few mature prediction models can be used in practice. Researchers should pay more attention to external validation and improving the quality both in methodology and reporting.
Abellan van KanG.RollandY. M.MorleyJ. E.VellasB. (2008). Frailty: toward a clinical definition. Journal of the American Medical Directors Association, 9(2), 71–72. https://doi.org/10.1016/j.jamda.2007.11.005
2.
AmbagtsheerR. C.ShafiabadyN.DentE.SeibothC.BeilbyJ. (2020). The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set. International Journal of Medical Informatics, 136(2020), 104094. https://doi.org/10.1016/j.ijmedinf.2020.104094
3.
Aznar-TortondaV.Palazón-BruA.la RosaD. M. F.Espínola-MorelV.Pérez-PérezB. F.León-RuizA. B.Gil-GuillénV. F. (2020). Detection of frailty in older patients using a mobile app: cross-sectional observational study in primary care. British Journal of General Practice, 70(690), e29–e35. https://doi.org/10.3399/bjgp19X706577
BastaniH. (2021). Predicting with proxies: Transfer learning in high dimension. Management Science, 67(5), 2964–2984. https://doi.org/10.1287/mnsc.2020.3729
6.
BertiniF.BergamiG.MontesiD.VeroneseG.MarchesiniG.PandolfiP. (2018). Predicting frailty condition in elderly using multidimensional socioclinical databases. Proceedings of the IEEE, 106(4), 723–737. https://doi.org/10.1109/jproc.2018.2791463
7.
BrodyK. K.JohnsonR. E.Douglas RiedL. (1997). Evaluation of a self-report screening instrument to predict frailty outcomes in aging populations. Gerontologist, 37(2), 182–191. https://doi.org/10.1093/geront/37.2.182
CollardR. M.BoterH.SchoeversR. A.Oude VoshaarR. C. (2012). Prevalence of frailty in community-dwelling older persons: a systematic review. Journal of the American Geriatrics Society, 60(8), 1487–1492. https://doi.org/10.1111/j.1532-5415.2012.04054.x
10.
CollinsG. S.ReitsmaJ. B.AltmanD. G.MoonsK. G. (2015). Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. J Clin Epidemiol, 68(2), 134–143. https://doi.org/10.1016/j.jclinepi.2014.11.010.3
11.
CuthbertsonC. C.Kucharska-NewtonA.FaurotK. R.StürmerT.Jonsson FunkM.PaltaP.WindhamB. G.ThaiS.LundJ. L. (2018). Controlling for frailty in pharmacoepidemiologic studies of older adults: validation of an existing medicare claims-based algorithm. Epidemiology, 29(4), 556–561. https://doi.org/10.1097/EDE.0000000000000833
12.
DamenJ. A.HooftL.SchuitE.DebrayT. P.CollinsG. S.TzoulakiI.LassaleC. M.SiontisG. C.ChiocchiaV.RobertsC.SchlüsselM. M.GerryS.BlackJ. A.HeusP.van der SchouwY. T.PeelenL. M.MoonsK. G. (2016). Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 353(2016), i2416. https://doi.org/10.1136/bmj.i2416
13.
DebrayT. P.DamenJ. A.SnellK. I.EnsorJ.HooftL.ReitsmaJ. B.RileyR. D.MoonsK. G. (2017). A guide to systematic review and meta-analysis of prediction model performance. BMJ, 356(2017), i6460. https://doi.org/10.1136/bmj.i6460
14.
de BreijS.van HoutH. P. J.de BruinS. R.SchusterN. A.DeegD. J. H.HuismanM.HoogendijkE. O. (2021). Predictors of frailty and vitality in older adults aged 75 years and over: results from the longitudinal aging study amsterdam. Gerontology, 67(1), 69–77. https://doi.org/10.1159/000512049
15.
de JongY.RamspekC. L.van der EndtV. H. W.RookmaakerM. B.BlankestijnP. J.VernooijR. W. M.VerhaarM. C.BosW. J. W.DekkerF. W.OcakG.van DiepenM. (2020). A systematic review and external validation of stroke prediction models demonstrates poor performance in dialysis patients. Journal of Clinical Epidemiology, 123(2020), 69–79. https://doi.org/10.1016/j.jclinepi.2020.03.015
16.
de LabraC.Guimaraes-PinheiroC.MasedaA.LorenzoT.Millán-CalentiJ. C. (2015). Effects of physical exercise interventions in frail older adults: a systematic review of randomized controlled trials. BMC Geriatrics, 15(2015), 154. https://doi.org/10.1186/s12877-015-0155-4
17.
DentE.MartinF. C.BergmanH.WooJ.Romero-OrtunoR.WalstonJ. D. (2019). Management of frailty: opportunities, challenges, and future directions. Lancet, 394(10206), 1376–1386. https://doi.org/10.1016/S0140-6736(19)31785-4
18.
de SousaJ. A. V.LenardtM. H.GrdenC. R. B.KusomotaL.DellarozaM. S. G.BetioIliS. E. (2018). Physical frailty prediction model for the oldest olds. Revista Latino-Americana De Enfermagem, 26(2018), e3023. https://doi.org/10.1590/1518-8345.2346.3023
19.
FalconerN.BarrasM.CottrellN. (2018). Systematic review of predictive risk models for adverse drug events in hospitalized patients. British Journal of Clinical Pharmacology, 84(5), 846–864. https://doi.org/10.1111/bcp.13514
20.
FaurotK. R.Jonsson FunkM.PateV.BrookhartM. A.PatrickA.HansonL. C.CastilloW. C.StürmerT. (2015). Using claims data to predict dependency in activities of daily living as a proxy for frailty. Pharmacoepidemiology and Drug Safety, 24(1), 59–66. https://doi.org/10.1002/pds.3719
21.
FeartC. (2019). Nutrition and frailty: Current knowledge. Progress in Neuro-psychopharmacology & Biological Psychiatry, 95(2019), 109703. https://doi.org/10.1016/j.pnpbp.2019.109703
22.
FriedL. P.TangenC. M.WalstonJ.NewmanA. B.HirschC.GottdienerJ.SeemanT.TracyR.KopW. J.BurkeG.McBurnieM. A. (2001). Frailty in older adults: evidence for a phenotype. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 56(3), M146–M156. https://doi.org/10.1093/gerona/56.3.m146
23.
Garcia-MorenoF. M.Bermudez-EdoM.GarridoJ. L.Rodríguez-GarcíaE.Pérez-MármolJ. M.Rodríguez-FórtizM. J. (2020). A microservices e-health system for ecological frailty assessment using wearables dagger. Sensors Electronic Resource, 20(12), 3427. https://doi.org/10.3390/s20123427
24.
GoonawardeneN.TanH.-P.TanL. B. (2018). Unobtrusive detection of frailty in older adults. In ZhouJ.SalvendyG. (eds.) Human aspects of it for the aged population: applications in health, assistance, and entertainment, Pt Ii, (10927, pp. 290–302). https://doi.org/10.1007/978-3-319-92037-5_22
25.
HasslerA. P.MenasalvasE.García-GarcíaF. J.Rodríguez-MañasL.HolzingerA. (2019). Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome. BMC Medical Informatics and Decision Making, 19(1), 33. https://doi.org/10.1186/s12911-019-0747-6
26.
HirschC.AndersonM. L.NewmanA.KopW.JacksonS.GottdienerJ.TracyR.FriedL. P. (2006). The association of race with frailty: the cardiovascular health study. Annals of Epidemiology, 16(7), 545–553. https://doi.org/10.1016/j.annepidem.2005.10.003
27.
HoogendijkE. O.AfilaloJ.EnsrudK. E.KowalP.OnderG.FriedL. P. (2019). Frailty: implications for clinical practice and public health. Lancet, 394(10206), 1365–1375. https://doi.org/10.1016/S0140-6736(19)31786-6
28.
JanssenK. J.MoonsK. G.KalkmanC. J.GrobbeeD. E.VergouweY. (2008). Updating methods improved the performance of a clinical prediction model in new patients. Journal of Clinical Epidemiology, 61(1), 76–86. https://doi.org/10.1016/j.jclinepi.2007.04.018
KimB.McKayS. M.LeeJ. (2020). Consumer-grade wearable device for predicting frailty in canadian home care service clients: prospective observational proof-of-concept study. Journal of Medical Internet Research Electronic Resource, 22(9), e19732. https://doi.org/10.2196/19732
31.
KojimaG. (2015). Frailty as a predictor of future falls among community-dwelling older people: a systematic review and meta-analysis. Journal of the American Medical Directors Association, 16(12), 1027–1033. https://doi.org/10.1016/j.jamda.2015.06.018
32.
KojimaG. (2016). Frailty as a predictor of fractures among community-dwelling older people: A systematic review and meta-analysis. Bone, 90(2016), 116–122. https://doi.org/10.1016/j.bone.2016.06.009
33.
KojimaG.IliffeS.WaltersK. (2018). Frailty index as a predictor of mortality: a systematic review and meta-analysis. Age and Ageing, 47(2), 193–200. https://doi.org/10.1093/ageing/afx162
34.
KreuzbergerN.DamenJ. A.TrivellaM.EstcourtL. J.AldinA.UmlauffL.Vazquez-MontesM. D.WolffR.MoonsK. G.MonsefI.ForoutanF.KreuzerK. A.SkoetzN. (2020). Prognostic models for newly-diagnosed chronic lymphocytic leukaemia in adults: a systematic review and meta-analysis. The Cochrane Database of Systematic Reviews Electronic Resource, 7(7), Cd012022. https://doi.org/10.1002/14651858.CD012022.pub2
35.
MandrekarJ. N. (2010). Receiver operating characteristic curve in diagnostic test assessment. Journal of Thoracic Oncology: Official Publication of the International Association for the Study of Lung Cancer, 5(9), 1315–1316. https://doi.org/10.1097/JTO.0b013e3181ec173d
36.
MoonsK. G.de GrootJ. A.BouwmeesterW.VergouweY.MallettS.AltmanD. G.ReitsmaJ. B.CollinsG. S. (2014). Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. Plos Medicine, 11(10), e1001744. https://doi.org/10.1371/journal.pmed.1001744
37.
MoonsK. G.KengneA. P.GrobbeeD. E.RoystonP.VergouweY.AltmanD. G.WoodwardM. (2012). Risk prediction models: II. External validation, model updating, and impact assessment. Heart, 98(9), 691–698. https://doi.org/10.1136/heartjnl-2011-301247
38.
MoonsK. G.RoystonP.VergouweY.GrobbeeD. E.AltmanD. G. (2009). Prognosis and prognostic research: what, why, and how? BMJ, 338(2009), b375. https://doi.org/10.1136/bmj.b375.
39.
NgT. P.FengL.NyuntM. S.LarbiA.YapK. B. (2014). Frailty in older persons: multisystem risk factors and the frailty risk index (FRI). Journal of the American Medical Directors Association, 15(9), 635–642. https://doi.org/10.1016/j.jamda.2014.03.008
40.
PageM. J.McKenzieJ. E.BossuytP. M.BoutronI.HoffmannT. C.MulrowC. D.ShamseerL.TetzlaffJ. M.AklE. A.BrennanS. E.ChouR.GlanvilleJ.GrimshawJ. M.HróbjartssonA.LaluM. M.LiT.LoderE. W.Mayo-WilsonE.McDonaldS.MoherD. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372(2021), n71. https://doi.org/10.1136/bmj.n71
41.
RazjouyanJ.NaikA. D.HorstmanM. J.KunikM. E.AmirmazaheriM.ZhouH.SharafkhanehA.NajafiB. (2018). Wearable sensors and the assessment of frailty among vulnerable older adults: an observational cohort study. Sensors (Basel), 18(5), 1336. https://doi.org/10.3390/s18051336
42.
RileyR. D.EnsorJ.SnellK. I. E.HarrellF. E.Jr.MartinG. P.ReitsmaJ. B.MoonsK. G. M.CollinsG.van SmedenM. (2020). Calculating the sample size required for developing a clinical prediction model. BMJ, 368(2020), m441. https://doi.org/10.1136/bmj.m441
43.
RockwoodK.MitnitskiA. (2007). Frailty in relation to the accumulation of deficits. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 62(7), 722–727. https://doi.org/10.1093/gerona/62.7.722
44.
Salinas-EscuderoG.Carrillo-VegaM. F.García-PeñaC.Martínez-ValverdeS.Jácome-MaldonadoL. D.CesariM.Pérez-ZepedaM. U. (2021). Last year of life, frailty, and out-of-pocket expenses in older adults: a secondary analysis of the mexican health and aging study. Journal of Applied Gerontology, 41(2), 462–470. https://doi.org/10.1177/07334648211024790
45.
SanchisJ.NúñezE.RuizV.BonanadC.FernándezJ.CauliO.García-BlasS.MainarL.ValeroE.Rodríguez-BorjaE.ChorroF. J.HermenegildoC.NúñezJ. (2015). Usefulness of clinical data and biomarkers for the identification of frailty after acute coronary syndromes. Canadian Journal of Cardiology, 31(12), 1462–1468. https://doi.org/10.1016/j.cjca.2015.07.737
46.
SegalJ. B.ChangH.-Y.DuY.WalstonJ. D.CarlsonM. C.VaradhanR. (2017). Development of a claims-based frailty indicator anchored to a well-established frailty phenotype. Medical Care, 55(7), 716–722. https://doi.org/10.1097/MLR.0000000000000729
47.
StevensR. J.PoppeK. K. (2020). Validation of clinical prediction models: what does the “calibration slope” really measure? Journal of Clinical Epidemiology, 118, 93-99. https://doi.org/10.1016/j.jclinepi.2019.09.016
48.
SteyerbergE. (2009). Clinical prediction models: A practical approach to development. Springer US.
49.
SteyerbergE. W. (2010). Clinical prediction models: a practical approach to development, validation, and updating by Ewout W. Steyerberg. Journal of the Royal Statistical Society, 66(2), 661–662. https://doi.org/10.1111/j.1541-0420.2010.01431.x
TaiC. J.ChenJ. H.TsengT. G.LinY. T.HsiaoY. H.LeeM. C.YangY. H. (2020). Prediction of frailty and dementia using oral health impact profile from a population-based survey. International Journal of Environmental Research and Public Health, 17(6), 1997. https://doi.org/10.3390/ijerph17061997
52.
TalbotD.MassambaV. K. (2019). A descriptive review of variable selection methods in four epidemiologic journals: there is still room for improvement. European Journal of Epidemiology, 34(8), 725–730. https://doi.org/10.1007/s10654-019-00529-y
53.
TegouT.KalamarasI.TsipourasM.GiannakeasN.VotisK.TzovarasD. (2019). A low-cost indoor activity monitoring system for detecting frailty in older adults. Sensors (Basel), 19(3), 452. https://doi.org/10.3390/s19030452
54.
ToosizadehN.WendelC.HsuC. H.ZamriniE.MohlerJ. (2017). Frailty assessment in older adults using upper-extremity function: index development. BMC Geriatrics, 17(1), 117. https://doi.org/10.1186/s12877-017-0509-1
55.
Van HouwelingenJ. C.Le CessieS. (1990). Predictive value of statistical models. Statistics in Medicine, 9(11), 1303–1325. https://doi.org/10.1002/sim.4780091109
56.
VernereyD.AnotaA.VandelP.Paget-BaillyS.DionM.BaillyV.BoninM.PozetA.FoubertA.BenetkiewiczM.ManckoundiaP.BonnetainF. (2016). Development and validation of the FRAGIRE tool for assessment an older person’s risk for frailty. BMC Geriatrics, 16(1), 187. https://doi.org/10.1186/s12877-016-0360-9
WolffR. F.MoonsK. G. M.RileyR. D.WhitingP. F.WestwoodM.CollinsG. S.ReitsmaJ. B.KleijnenJ.MallettS. (2019). PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Annals of Internal Medicine, 170(1), 51–58. https://doi.org/10.7326/M18-1376
59.
WynantsL.Van CalsterB.CollinsG. S.RileyR. D.HeinzeG.SchuitE.BontenM. M. J.DahlyD. L.DamenJ. A. A.DebrayT. P. A.de JongV. M. T.De VosM.DhimanP.HallerM. C.HarhayM. O.HenckaertsL.HeusP.KammerM.KreuzbergerN.van SmedenM. (2020). Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ, 369(2020), m1328. https://doi.org/10.1136/bmj.m1328
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
