Dataset Information

Measuring Alzheimer disease progression with transition probabilities: estimates from NACC-UDS.

ABSTRACT: OBJECTIVES:Estimate the probabilities, for Alzheimer's disease (AD) patients, of transitioning between stages of disease severity (mild, moderate, severe, dead) and care settings (community, institutional). METHODS:Data were compiled by the National Alzheimer Coordinating Center. The main analyses were limited to 3,852 patients who were 50 years old, diagnosed with possible/probable AD and had at least two center visits. A multinomial logistic model accounting for patient and center level correlation was used to calculate transition probabilities between stages of the Clinical Dementia Rating (CDR). Separately we calculated the probabilities of being institutionalized based on CDR stage. Both analyses controlled for baseline age, time between visits, sex, marital status, whether white, whether Hispanic and number of years of education. RESULTS:The annual probabilities of dying for patients in mild, moderate and severe health states were 5.5%, 21.5% and 48.0%, respectively, while the annual probabilities for institutionalization were 1.2%, 3.4% and 6.6%, respectively. The majority of mild and moderate patients remain in the same health state after one year, 77.4% and 50.1% respectively. Progressing patients are most likely to transition one stage, but 1.3% of mild patients become severe in one year. Some patients revert to lower severity stages, 7% from moderate to mild. CONCLUSIONS:Transition probabilities to higher CDR stages and to institutionalization are lower than those published previously, but the probability of death is higher. These results are useful for understanding AD progression and can be used in simulation models to evaluate costs and compare new treatments or policies.

SUBMITTER: Spackman DE

PROVIDER: S-EPMC3690579 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Measuring Alzheimer disease progression with transition probabilities: estimates from NACC-UDS.

Spackman D Eldon DE Kadiyala Srikanth S Neumann Peter J PJ Veenstra David L DL Sullivan Sean D SD

Current Alzheimer research 20121101 9

<h4>Objectives</h4>Estimate the probabilities, for Alzheimer's disease (AD) patients, of transitioning between stages of disease severity (mild, moderate, severe, dead) and care settings (community, institutional).<h4>Methods</h4>Data were compiled by the National Alzheimer Coordinating Center. The main analyses were limited to 3,852 patients who were 50 years old, diagnosed with possible/probable AD and had at least two center visits. A multinomial logistic model accounting for patient and cent ...[more]

PMID: 22175655

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Project description:Background: The characterizing symptom of Alzheimer disease (AD) is cognitive deterioration. While much recent work has focused on defining AD as a biological construct, most patients are still diagnosed, staged, and treated based on their cognitive symptoms. But the cognitive capability of a patient at any time throughout this deterioration reflects not only the disease state, but also the effect of the cognitive decline on the patient's pre-disease cognitive capability. Patients with high pre-disease cognitive capabilities tend to score better on cognitive tests that are sensitive early in disease relative to patients with low pre-disease cognitive capabilities at a similar disease stage. Thus, a single assessment with a cognitive test is often not adequate for determining the stage of an AD patient. Repeated evaluation of patients' cognition over time may improve the ability to stage AD patients, and such longitudinal assessments in combinations with biomarker assessments can help elucidate the time dynamics of biomarkers. In turn, this can potentially lead to identification of markers that are predictive of disease stage and future cognitive decline, possibly before any cognitive deficit is measurable. Methods and Findings: This article presents a class of statistical disease progression models and applies them to longitudinal cognitive scores. These non-linear mixed-effects disease progression models explicitly model disease stage, baseline cognition, and the patients' individual changes in cognitive ability as latent variables. Maximum-likelihood estimation in these models induces a data-driven criterion for separating disease progression and baseline cognition. Applied to data from the Alzheimer's Disease Neuroimaging Initiative, the model estimated a timeline of cognitive decline that spans ~15 years from the earliest subjective cognitive deficits to severe AD dementia. Subsequent analyses demonstrated how direct modeling of latent factors that modify the observed data patterns provides a scaffold for understanding disease progression, biomarkers, and treatment effects along the continuous time progression of disease. Conclusions: The presented framework enables direct interpretations of factors that modify cognitive decline. The results give new insights to the value of biomarkers for staging patients and suggest alternative explanations for previous findings related to accelerated cognitive decline among highly educated patients and patients on symptomatic treatments.

Project description:ImportancePredictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.ObjectiveTo characterize the algorithmic fairness of longitudinal prediction models for AD progression.Design, setting, and participantsThis prognostic study investigated the algorithmic fairness of logistic regression, support vector machines, and recurrent neural networks for predicting progression to mild cognitive impairment (MCI) and AD using data from participants in the Alzheimer Disease Neuroimaging Initiative evaluated at 57 sites in the US and Canada. Participants aged 54 to 91 years who contributed data on at least 2 visits between September 2005 and May 2017 were included. Data were analyzed in October 2022.ExposuresFairness was quantified across sex, ethnicity, and race groups. Neuropsychological test scores, anatomical features from T1 magnetic resonance imaging, measures extracted from positron emission tomography, and cerebrospinal fluid biomarkers were included as predictors.Main outcomes and measuresOutcome measures quantified fairness of prediction models (logistic regression [LR], support vector machine [SVM], and recurrent neural network [RNN] models), including equal opportunity, equalized odds, and demographic parity. Specifically, if the model exhibited equal sensitivity for all groups, it aligned with the principle of equal opportunity, indicating fairness in predictive performance.ResultsA total of 1730 participants in the cohort (mean [SD] age, 73.81 [6.92] years; 776 females [44.9%]; 69 Hispanic [4.0%] and 1661 non-Hispanic [96.0%]; 29 Asian [1.7%], 77 Black [4.5%], 1599 White [92.4%], and 25 other race [1.4%]) were included. Sensitivity for predicting progression to MCI and AD was lower for Hispanic participants compared with non-Hispanic participants; the difference (SD) in true positive rate ranged from 20.9% (5.5%) for the RNN model to 27.8% (9.8%) for the SVM model in MCI and 24.1% (5.4%) for the RNN model to 48.2% (17.3%) for the LR model in AD. Sensitivity was similarly lower for Black and Asian participants compared with non-Hispanic White participants; for example, the difference (SD) in AD true positive rate was 14.5% (51.6%) in the LR model, 12.3% (35.1%) in the SVM model, and 28.4% (16.8%) in the RNN model for Black vs White participants, and the difference (SD) in MCI true positive rate was 25.6% (13.1%) in the LR model, 24.3% (13.1%) in the SVM model, and 6.8% (18.7%) in the RNN model for Asian vs White participants. Models generally satisfied metrics of fairness with respect to sex, with no significant differences by group, except for cognitively normal (CN)-MCI and MCI-AD transitions (eg, an absolute increase [SD] in the true positive rate of CN-MCI transitions of 10.3% [27.8%] for the LR model).Conclusions and relevanceIn this study, models were accurate in aggregate but failed to satisfy fairness metrics. These findings suggest that fairness should be considered in the development and use of machine learning models for AD progression.

Dataset Information

Measuring Alzheimer disease progression with transition probabilities: estimates from NACC-UDS.

Publications

Measuring Alzheimer disease progression with transition probabilities: estimates from NACC-UDS.

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