Dataset Information

COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.

ABSTRACT: We present COMPASS, a COmputational Model to Predict the development of Alzheimer's diSease Spectrum, to model Alzheimer's disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer's Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores, and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall, and 91% accuracy at 10% recall. For (3), "genetic only" model has Pearson's correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele.

SUBMITTER: Zhu F

PROVIDER: S-EPMC5050516 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.

Zhu Fan F Panwar Bharat B Dodge Hiroko H HH Li Hongdong H Hampstead Benjamin M BM Albin Roger L RL Paulson Henry L HL Guan Yuanfang Y

Scientific reports 20161005

We present COMPASS, a COmputational Model to Predict the development of Alzheimer's diSease Spectrum, to model Alzheimer's disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer's Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical co ...[more]

PMID: 27703197

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Project description:BackgroundMissing data are a notable problem in Alzheimer's disease clinical trials. One cause of missing data is participant dropout. The Research Attitudes Questionnaire is a 7-item instrument that measures an individual's attitudes toward biomedical research, with higher scores indicating more favorable attitudes. The objective of this study was to describe the performance of the Research Attitudes Questionnaire over time and to examine whether Research Attitudes Questionnaire scores predict study dropout and other participant behaviors that affect trial integrity.MethodsThe Research Attitudes Questionnaire was collected at baseline and weeks 26 and 52 from each member of 119 participant/study partner dyads enrolled in a Phase 2, randomized, double-blind, placebo-controlled mild-to-moderate Alzheimer's disease clinical trial. Within-subject longitudinal analyses examined change in Research Attitudes Questionnaire scores over time in each population. Logistic regression analyses that controlled for trial arm and clustering in trial sites were used to assess whether baseline Research Attitudes Questionnaire scores predicted trial completion, study medication compliance, and enrollment in optional substudies.ResultsParticipants and study partners endorsed statistically similar ratings on the Research Attitudes Questionnaire that were stable over time. Participants with baseline Research Attitudes Questionnaire scores above 28.5 were 4.7 (95% confidence interval = 1.01 to 21.95) times as likely to complete the trial compared to those with lower scores. Applying the same cutoff, baseline study partner Research Attitudes Questionnaire scores were similarly able to predict study completion (odds ratio = 4.2, 95% confidence interval = 1.71 to 10.32). Using a score cutoff of 27.5, higher participant Research Attitudes Questionnaire scores predicted study medication compliance (odds ratio = 5.85, 95% confidence interval = 1.34 to 25.54). No relationship was observed between Research Attitudes Questionnaire score and participation in optional substudies.ConclusionThis brief instrument that measures research attitudes may identify participants at risk for behaviors that cause missing data.

Project description:BackgroundRetaining participants in observational longitudinal studies after bariatric surgery is difficult yet critical because the retention rate affects interpretation and generalizability of results. Strategies for keeping participants involved in such studies are not commonly published. The objective of this study was to review LABS retention strategies and present the 24-month retention data.MethodsThe LABS Consortium monitors an observational cohort study of 2458 adults enrolled before bariatric surgery at 10 centers within the United States (LABS-2). To maximize data completeness, the investigators developed retention strategies, including flexible scheduling, a call protocol, reminder letters, abbreviated visit options, honoraria, travel reimbursement, providing research progress reports, laboratory results, newsletters, study website, and retention surveys. Strategies for locating participants included frequent updates of contact information, sending registered letters, and searching medical and public records.ResultsAt 12 and 24 months, 2426 and 2405 participants remained active, with vital status known for 98.7% and 97.3% and weight obtained for 95.2% and 92.2%, respectively. There were 148 missed visits (6.2%) at 24 months primarily because of inability to contact the participant. Only 15 (0.6%) active participants at 24 months missed all follow-up visits. Although 42 participants could not be located or contacted at 6 months, data were obtained for 23 (54.7%) of them at 12 months, and of the 52 participants who could not be located or contacted at 12 months, data were obtained for 18 (34.6%) at 24 months.ConclusionLongitudinal studies provide the ability to evaluate long-term effects of bariatric surgical procedures. The retention achieved in LABS is superior to that of many published reports but requires extensive effort and resources. This report identifies useful retention strategies. Further research is needed to identify the efficacy and cost-effectiveness of specific retention strategies.

Dataset Information

COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.

Publications

COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.

Similar Datasets

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