skip to main content

Explore Research Products in the NSF-PAR

Title: Predicting 5‐year dementia conversion in veterans with mild cognitive impairment
Abstract INTRODUCTION

Identifying mild cognitive impairment (MCI) patients at risk for dementia could facilitate early interventions. Using electronic health records (EHRs), we developed a model to predict MCI to all‐cause dementia (ACD) conversion at 5 years.

 METHODS

Cox proportional hazards model was used to identify predictors of ACD conversion from EHR data in veterans with MCI. Model performance (area under the receiver operating characteristic curve [AUC] and Brier score) was evaluated on a held‐out data subset.

 RESULTS

Of 59,782 MCI patients, 15,420 (25.8%) converted to ACD. The model had good discriminative performance (AUC 0.73 [95% confidence interval (CI) 0.72–0.74]), and calibration (Brier score 0.18 [95% CI 0.17–0.18]). Age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors, while body mass index, alcohol abuse, and sleep apnea were protective factors.

 DISCUSSION

EHR‐based prediction model had good performance in identifying 5‐year MCI to ACD conversion and has potential to assist triaging of at‐risk patients.

 Highlights

Of 59,782 veterans with mild cognitive impairment (MCI), 15,420 (25.8%) converted to all‐cause dementia within 5 years.

Electronic health record prediction models demonstrated good performance (area under the receiver operating characteristic curve 0.73; Brier 0.18).

Age and vascular‐related morbidities were predictors of dementia conversion.

Synthetic data was comparable to real data in modeling MCI to dementia conversion.

 Key Points

An electronic health record–based model using demographic and co‐morbidity data had good performance in identifying veterans who convert from mild cognitive impairment (MCI) to all‐cause dementia (ACD) within 5 years.

Increased age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors for 5‐year conversion from MCI to ACD.

High body mass index, alcohol abuse, and sleep apnea were protective factors for 5‐year conversion from MCI to ACD.

Models using synthetic data, analogs of real patient data that retain the distribution, density, and covariance between variables of real patient data but are not attributable to any specific patient, performed just as well as models using real patient data. This could have significant implications in facilitating widely distributed computing of health‐care data with minimized patient privacy concern that could accelerate scientific discoveries.

  more » « less
NSF-PAR ID:
10497313
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
Volume:
16
Issue:
1
ISSN:
2352-8729
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ; ; et al ( , Alzheimer's & Dementia)
    Abstract INTRODUCTION

    Sleep duration has been associated with dementia and stroke. Few studies have evaluated sleep pattern–related outcomes of brain disease in diverse Hispanics/Latinos.

     METHODS

    The SOL‐INCA (Study of Latinos‐Investigation of Neurocognitive Aging) magnetic resonance imaging (MRI) study recruited diverse Hispanics/Latinos (35–85 years) who underwent neuroimaging. The main exposure was self‐reported sleep duration. Our main outcomes were total and regional brain volumes.

     RESULTS

    The final analytic sample includedn = 2334 participants. Increased sleep was associated with smaller brain volume (βtotal_brain = −0.05,p < 0.01) and consistently so in the 50+ subpopulation even after adjusting for mild cognitive impairment status. Sleeping >9 hours was associated with smaller gray (βcombined_gray = −0.17,p < 0.05) and occipital matter volumes (βoccipital_gray = −0.18,p < 0.05).

     DISCUSSION

    We found that longer sleep duration was associated with lower total brain and gray matter volume among diverse Hispanics/Latinos across sex and background. These results reinforce the importance of sleep on brain aging in this understudied population.

     Highlights

    Longer sleep was linked to smaller total brain and gray matter volumes.

    Longer sleep duration was linked to larger white matter hyperintensities (WMHs) and smaller hippocampal volume in an obstructive sleep apnea (OSA) risk group.

    These associations were consistent across sex and Hispanic/Latino heritage groups.

     
    more » « less
  2. ; ; ; ; ; ; ; ( , Sleep)
    Abstract Study Objectives

    Dementia is a growing cause of disability and loss of independence in the elderly, yet remains largely underdiagnosed. Early detection and classification of dementia can help close this diagnostic gap and improve management of disease progression. Altered oscillations in brain activity during sleep are an early feature of neurodegenerative diseases and be used to identify those on the verge of cognitive decline.

     Methods

    Our observational cross-sectional study used a clinical dataset of 10 784 polysomnography from 8044 participants. Sleep macro- and micro-structural features were extracted from the electroencephalogram (EEG). Microstructural features were engineered from spectral band powers, EEG coherence, spindle, and slow oscillations. Participants were classified as dementia (DEM), mild cognitive impairment (MCI), or cognitively normal (CN) based on clinical diagnosis, Montreal Cognitive Assessment, Mini-Mental State Exam scores, clinical dementia rating, and prescribed medications. We trained logistic regression, support vector machine, and random forest models to classify patients into DEM, MCI, and CN groups.

     Results

    For discriminating DEM versus CN, the best model achieved an area under receiver operating characteristic curve (AUROC) of 0.78 and area under precision-recall curve (AUPRC) of 0.22. For discriminating MCI versus CN, the best model achieved an AUROC of 0.73 and AUPRC of 0.18. For discriminating DEM or MCI versus CN, the best model achieved an AUROC of 0.76 and AUPRC of 0.32.

     Conclusions

    Our dementia classification algorithms show promise for incorporating dementia screening techniques using routine sleep EEG. The findings strengthen the concept of sleep as a window into neurodegenerative diseases.

     
    more » « less
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Scientific Reports)
    Abstract

    In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression ofCR1(complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.

     
    more » « less
  4. ; ; ; ; ; ; ; ; ; ( , The Gerontologist)
    Abstract Background and Objectives

    Sleep disorders often predict or co-occur with cognitive decline. Yet, little is known about how the relationship unfolds among older adults at risk for cognitive decline. To examine the associations of sleep disorders with cognitive decline in older adults with unimpaired cognition or impaired cognition (mild cognitive impairment and dementia).

     Research Design and Methods

    A total of 5,822 participants (Mage = 70) of the National Alzheimer’s Coordinating Center database with unimpaired or impaired cognition were followed for 3 subsequent waves. Four types of clinician-diagnosed sleep disorders were reported: sleep apnea, hyposomnia/insomnia, REM sleep behavior disorder, or “other.” Cognition over time was measured by the Montreal Cognitive Assessment (MoCA) or an estimate of general cognitive ability (GCA) derived from scores based on 12 neuropsychological tests. Growth curve models were estimated adjusting for covariates.

     Results

    In participants with impaired cognition, baseline sleep apnea was related to better baseline MoCA performance (b = 0.65, 95% confidence interval [95% CI] = [0.07, 1.23]) and less decline in GCA over time (b = 0.06, 95% CI = [0.001, 0.12]). Baseline insomnia was related to better baseline MoCA (b = 1.54, 95% CI = [0.88, 2.21]) and less decline in MoCA over time (b = 0.56, 95% CI = [0.20, 0.92]). Furthermore, having more sleep disorders (across the 4 types) at baseline predicted better baseline MoCA and GCA, and less decline in MoCA and GCA over time. These results were only found in those with impaired cognition and generally consistent when using self-reported symptoms of sleep apnea or insomnia.

     Discussion and Implications

    Participants with sleep disorder diagnoses may have better access to healthcare, which may help maintain cognition through improved sleep.

     
    more » « less
  5. ; ; ; ; ; ; ; ; ; ( , CNS Neuroscience & Therapeutics)
    Abstract Background

    Alzheimer's disease (AD), the most prevalent form of dementia, affects 6.5 million Americans and over 50 million people globally. Clinical, genetic, and phenotypic studies of dementia provide some insights of the observed progressive neurodegenerative processes, however, the mechanisms underlying AD onset remain enigmatic.

     Aims

    This paper examines late‐onset dementia‐related cognitive impairment utilizing neuroimaging‐genetics biomarker associations.

     Materials and Methods

    The participants, ages 65–85, included 266 healthy controls (HC), 572 volunteers with mild cognitive impairment (MCI), and 188 Alzheimer's disease (AD) patients. Genotype dosage data for AD‐associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics archive using sample‐major additive coding. Such 29 SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta‐GWAS study by Jansen and colleagues.

     Results

    We identified the significant correlations between the 29 genomic markers (GMs) and the 200 neuroimaging markers (NIMs). The odds ratios and relative risks for AD and MCI (relative to HC) were predicted using multinomial linear models.

     Discussion

    In the HC and MCI cohorts, mainly cortical thickness measures were associated with GMs, whereas the AD cohort exhibited different GM‐NIM relations. Network patterns within the HC and AD groups were distinct in cortical thickness, volume, and proportion of White to Gray Matter (pct), but not in the MCI cohort. Multinomial linear models of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and HC, and between MCI and HC.

     Conclusion

    This study suggests that advanced analytics provide mechanisms for exploring the interrelations between morphometric indicators and GMs. The findings may facilitate further clinical investigations of phenotypic associations that support deep systematic understanding of AD pathogenesis.

     
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email