A new algorithm for predicting time to disease endpoints in Alzheimer's disease patients.
Background: The ability to predict the length of time to death and institutionalization has strong implications for Alzheimer's disease patients and caregivers, health policy, economics, and the design of intervention studies.Objective: To develop and validate a prediction algorithm that uses data f...
| Publicado en: | Journal of Alzheimer's Disease Vol. 38; no. 1; pp. 661 - 669 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Sage Publications Inc.
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103996619&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103996619 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13872877 FLR jtl: Journal of Alzheimer's Disease issn: 13872877 maglogo: N pubinfo: dt: 2014 vid: 38 iid: 1 pid: 20732 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 103996619 103996619 NLM24064468 2012434915 10.3233/JAD-131142 NLM24064468 PMC3864687 103996619 ppf: 661 ppct: 8 formats: tig: atl: A new algorithm for predicting time to disease endpoints in Alzheimer's disease patients. aug: au: Razlighi, Qolamreza R Stallard, Eric Brandt, Jason Blacker, Deborah Albert, Marilyn Scarmeas, Nikolaos Kinosian, Bruce Yashin, Anatoliy I Stern, Yaakov affil: Cognitive Neuroscience Division, Department of Neurology, Columbia University College of Physicians and Surgeons, New York, NY, USA. sug: subj: Algorithms Alzheimer's Disease Mortality Alzheimer's Disease Physiopathology Aged Aged, 80 and Over Prospective Studies Female Human Kaplan-Meier Estimator Male Middle Age Predictive Value of Tests Reproducibility of Results Sex Factors Time Factors Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female Male ab: Background: The ability to predict the length of time to death and institutionalization has strong implications for Alzheimer's disease patients and caregivers, health policy, economics, and the design of intervention studies.Objective: To develop and validate a prediction algorithm that uses data from a single visit to estimate time to important disease endpoints for individual Alzheimer's disease patients.Method: Two separate study cohorts (Predictors 1, N = 252; Predictors 2, N = 254), all initially with mild Alzheimer's disease, were followed for 10 years at three research centers with semiannual assessments that included cognition, functional capacity, and medical, psychiatric, and neurologic information. The prediction algorithm was based on a longitudinal Grade of Membership model developed using the complete series of semiannually-collected Predictors 1 data. The algorithm was validated on the Predictors 2 data using data only from the initial assessment to predict separate survival curves for three outcomes.Results: For each of the three outcome measures, the predicted survival curves fell well within the 95% confidence intervals of the observed survival curves. Patients were also divided into quintiles for each endpoint to assess the calibration of the algorithm for extreme patient profiles. In all cases, the actual and predicted survival curves were statistically equivalent. Predictive accuracy was maintained even when key baseline variables were excluded, demonstrating the high resilience of the algorithm to missing data.Conclusion: The new prediction algorithm accurately predicts time to death, institutionalization, and need for full-time care in individual Alzheimer's disease patients; it can be readily adapted to predict other important disease endpoints. The algorithm will serve an unmet clinical, research, and public health need. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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