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. 2022 Aug 5;14(1):110.
doi: 10.1186/s13195-022-01053-0.

Development of multivariable prediction models for institutionalization and mortality in the full spectrum of Alzheimer's disease

Arenda Mank  1   2   3 , Ingrid S van Maurik  4   5   6 , Judith J M Rijnhart  6 , Els D Bakker  4   5 , Vincent Bouteloup  7 , Lisa Le Scouarnec  7 , Charlotte E Teunissen  8 , Frederik Barkhof  9   10 , Philip Scheltens  4   5 , Johannes Berkhof  6 , Wiesje M van der Flier  4   5
Affiliations

Development of multivariable prediction models for institutionalization and mortality in the full spectrum of Alzheimer's disease

Arenda Mank et al. Alzheimers Res Ther. .

Abstract

Background: Patients and caregivers express a desire for accurate prognostic information about time to institutionalization and mortality. Previous studies predicting institutionalization and mortality focused on the dementia stage. However, Alzheimer's disease (AD) is characterized by a long pre-dementia stage. Therefore, we developed prediction models to predict institutionalization and mortality along the AD continuum of cognitively normal to dementia.

Methods: This study included SCD/MCI patients (subjective cognitive decline (SCD) or mild cognitive impairment (MCI)) and patients with AD dementia from the Amsterdam Dementia Cohort. We developed internally and externally validated prediction models with biomarkers and without biomarkers, stratified by dementia status. Determinants were selected using backward selection (p<0.10). All models included age and sex. Discriminative performance of the models was assessed with Harrell's C statistics.

Results: We included n=1418 SCD/MCI patients (n=123 died, n=74 were institutionalized) and n=1179 patients with AD dementia (n=413 died, n=453 were institutionalized). For both SCD/MCI and dementia stages, the models for institutionalization and mortality included after backward selection clinical characteristics, imaging, and cerebrospinal fluid (CSF) biomarkers. In SCD/MCI, the Harrell's C-statistics of the models were 0.81 (model without biomarkers: 0.76) for institutionalization and 0.79 (model without biomarker: 0.76) for mortality. In AD-dementia, the Harrell's C-statistics of the models were 0.68 (model without biomarkers: 0.67) for institutionalization and 0.65 (model without biomarker: 0.65) for mortality. Models based on data from amyloid-positive patients only had similar discrimination.

Conclusions: We constructed prediction models to predict institutionalization and mortality with good accuracy for SCD/MCI patients and moderate accuracy for patients with AD dementia. The developed prediction models can be used to provide patients and their caregivers with prognostic information on time to institutionalization and mortality along the cognitive continuum of AD.

Keywords: Alzheimer’s disease; Institutionalization; Mild cognitive impairment; Mortality; Prognosis; Subjective cognitive decline.

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Conflict of interest statement

Arenda Mank, Ingrid S. van Maurik, Els D. Bakker, Vincent Bouteloup, Lisa Le Scouarnec, and Johannes Berkhof report no financial disclosures or conflicts of interest.

Judith J.M. Rijnhart received a grant from the Amsterdam Public Health Research Institute, which was paid to the Amsterdam UMC.

Charlotte E. Teunissen was supported by funding from the European Commission (Marie Curie International Training Network, grant agreement No 860197 (MIRIADE), and JPND), Health Holland, the Dutch Research Council (ZonMW), Alzheimer Drug Discovery Foundation, The Selfridges Group Foundation, Alzheimer Netherlands, Alzheimer Association. C.E. Teunissen has a collaboration contract with ADx Neurosciences, Quanterix, and Eli Lilly, performed contract research, or received grants from AC-Immune, Axon Neurosciences, Biogen, Brainstorm Therapeutics, Celgene, EIP Pharma, Eisai, PeopleBio, Roche, Toyama, and Vivoryon. C.E. Teunissen serves on editorial boards of Medidact Neurologie/Springer, Alzheimer Research and Therapy, and Neurology: Neuroimmunology & Neuroinflammation and is editor of a Neuromethods book Springer.

Frederik Barkhof is a steering committee or iDMC member for Biogen, Merck, Roche, EISAI, and Prothena. FB is a consultant for Roche, Biogen, Merck, IXICO, Jansen, and Combinostics. Research agreements with Merck, Biogen, GE Healthcare, Roche. Co-founder and shareholder of Queen Square Analytics LTD. FB is supported by the NIHR biomedical research centre at UCLH.

Philip Scheltens has received consultancy fees (paid to the university) from Alzheon, Brainstorm Cell, and Green Valley. Within his university affiliation, he is global PI of the phase 1b study of AC Immune, phase 2b study with FUJI-film/Toyama, phase 2 study of UCB, and phase 1 study with ImmunoBrain Checkpoint. He is chair of the EU steering committee of the phase 2b program of Vivoryon, the phase 2b study of Novartis Cardiology, and co-chair of the phase 3 study with NOVO-Nordisk. He is also an employee of EQT Life Sciences (formerly LSP).

Wiesje M. van der Flier: Research programs of W.M. van der Flier have been funded by ZonMW, NWO, EU-FP7, EU-JPND, Alzheimer Nederland, Hersenstichting CardioVascular Onderzoek Nederland, Health~Holland, Topsector Life Sciences & Health, stichting Dioraphte, Gieskes-Strijbis fonds, stichting Equilibrio, Edwin Bouw fonds, Pasman stichting, stichting Alzheimer & Neuropsychiatrie Foundation, Philips, Biogen MA Inc, Novartis-NL, Life-MI, AVID, Roche BV, Fujifilm, Combinostics. WF holds the Pasman chair. WF is a recipient of ABOARD, which is a public-private partnership receiving funding from ZonMW (#73305095007) and Health~Holland, Topsector Life Sciences & Health (PPP-allowance; #LSHM20106). WF has performed contract research for Biogen MA Inc, and Boehringer Ingelheim. WF has been an invited speaker at Boehringer Ingelheim, Biogen MA Inc, Danone, Eisai, WebMD Neurology (Medscape), and Springer Healthcare. WF is a consultant to Oxford Health Policy Forum CIC, Roche, and Biogen MA Inc. WF participated in advisory boards of Biogen MA Inc and Roche. All funding is paid to her institution. WF was associate editor of Alzheimer, Research & Therapy in 2020/2021. WF is an associate editor at Brain.

Figures

Fig. 1
Fig. 1
Fitting of the models, stratified by dementia status, to predict institutionalization and mortality. The concordance between the predicted and observed outcomes was assessed by comparing the probabilities of institutionalization and mortality for each time point as estimated by the Cox model to those obtained by the Kaplan-Meier method, respectively. For this purpose, the prognostic index was calculated based on the models (model 2; constructed with all candidate predictors) for each test set in the fivefold cross-validation based on the linear predictor from the corresponding training set and we categorized the prognostic index into four risk groups
See this image and copyright information in PMC

References

    1. Scheltens P, Blennow K, Breteler MM, de Strooper B, Frisoni GB, Salloway S, et al. Alzheimer's disease. Lancet (London, England) 2016;388(10043):505–517. - PubMed
    1. Mank A, van Maurik IS, Bakker ED, van de Glind EMM, Jönsson L, Kramberger MG, et al. Prognosis of Alzheimer's disease; what do patients and care partners want to know? Transl Res Clin Intervent. 2021;7(1):e12189. - PMC - PubMed
    1. Cepoiu-Martin M, Tam-Tham H, Patten S, Maxwell CJ, Hogan DB. Predictors of long-term care placement in persons with dementia: a systematic review and meta-analysis. Int J Geriatr Psychiatry. 2016;31(11):1151–1171. - PubMed
    1. Hatoum HT, Thomas SK, Lin SJ, Lane R, Bullock R. Predicting time to nursing home placement based on activities of daily living scores--a modelling analysis using data on Alzheimer's disease patients receiving rivastigmine or donepezil. J Med Econ. 2009;12(2):98–103. - PubMed
    1. Joling KJ, Janssen O, Francke AL, Verheij RA, Lissenberg-Witte BI, Visser PJ, et al. Time from diagnosis to institutionalization and death in people with dementia. Alzheimer's Dement. 2020;16(4):662–671. - PMC - PubMed

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