Dataset Information

Prediction of Conversion to Alzheimer's Disease with Longitudinal Measures and Time-To-Event Data.

ABSTRACT: Identifying predictors of conversion to Alzheimer's disease (AD) is critically important for AD prevention and targeted treatment.To compare various clinical and biomarker trajectories for tracking progression and predicting conversion from amnestic mild cognitive impairment to probable AD.Participants were from the ADNI-1 study. We assessed the ability of 33 longitudinal biomarkers to predict time to AD conversion, accounting for demographic and genetic factors. We used joint modelling of longitudinal and survival data to examine the association between changes of measures and disease progression. We also employed time-dependent receiver operating characteristic method to assess the discriminating capability of the measures.23 of 33 longitudinal clinical and imaging measures are significant predictors of AD conversion beyond demographic and genetic factors. The strong phenotypic and biological predictors are in the cognitive domain (ADAS-Cog; RAVLT), functional domain (FAQ), and neuroimaging domain (middle temporal gyrus and hippocampal volume). The strongest predictor is ADAS-Cog 13 with an increase of one SD in ADAS-Cog 13 increased the risk of AD conversion by 2.92 times.Prediction of AD conversion can be improved by incorporating longitudinal change information, in addition to baseline characteristics. Cognitive measures are consistently significant and generally stronger predictors than imaging measures.

SUBMITTER: Li K

PROVIDER: S-EPMC5477671 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Prediction of Conversion to Alzheimer's Disease with Longitudinal Measures and Time-To-Event Data.

Li Kan K Chan Wenyaw W Doody Rachelle S RS Quinn Joseph J Luo Sheng S

Journal of Alzheimer's disease : JAD 20170101 2

<h4>Background</h4>Identifying predictors of conversion to Alzheimer's disease (AD) is critically important for AD prevention and targeted treatment.<h4>Objective</h4>To compare various clinical and biomarker trajectories for tracking progression and predicting conversion from amnestic mild cognitive impairment to probable AD.<h4>Methods</h4>Participants were from the ADNI-1 study. We assessed the ability of 33 longitudinal biomarkers to predict time to AD conversion, accounting for demographic ...[more]

PMID: 28436391

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Project description:IntroductionMany research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous-time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time.MethodologiesWe simulate the disease progression based on patient profiles using non-linear survival methods-non-linear Cox proportional hazard model (Cox-PH) and neural multi-task logistic regression (N-MTLR). In addition, we evaluate the concordance index (C-index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple-visit details between 2005 and 2017.ResultsThe experiment results show that our N-MTLR based survival models outperform the CoxPH models, the best of which gives Concordance-Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval.ConclusionsThe proposed deep learning-based survival method and model can be used by medical practitioners to predict the patients' AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.

Dataset Information

Prediction of Conversion to Alzheimer's Disease with Longitudinal Measures and Time-To-Event Data.

Publications

Prediction of Conversion to Alzheimer's Disease with Longitudinal Measures and Time-To-Event Data.

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