scholarly journals Cognitive and functional progression along the continuum from mild cognitive impairment (MCI) to Alzheimer’s disease (AD): A latent class analysis using the U.S. National Alzheimer’s Coordinating Center (NACC) data set

2020 ◽  
Vol 16 (S10) ◽  
Author(s):  
Julie Mouchet ◽  
Keith A. Betts ◽  
Raluca Ionescu‐Ittu ◽  
Lesley M. Butler ◽  
Paul Delmar ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Data Set ◽  
The U.S ◽  
The Continuum

scholarly journals Identifying and predicting heterogeneity in cognitive decline among individuals with prodromal Alzheimer's disease using a latent class analysis

2020 ◽  
Vol 16 (S6) ◽  
Author(s):  
Roos J. Jutten ◽  
Sietske A.M. Sikkes ◽  
Kathryn V. Papp ◽  
Bart N.M. Van Berckel ◽  
Charlotte E. Teunissen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Prodromal Alzheimer’S Disease

scholarly journals Prospective predictors of decline v. stability in mild cognitive impairment with Lewy bodies or Alzheimer's disease

2020 ◽  
pp. 1-9
Author(s):  
Calum A. Hamilton ◽  
Fiona E. Matthews ◽  
Paul C. Donaghy ◽  
John-Paul Taylor ◽  
John T. O'Brien ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Latent Class ◽  
Lewy Bodies ◽  
Cognitive Status ◽  
Rapid Decline ◽  
Visual Hallucinations ◽  
Full Cohort

Abstract Background Mild cognitive impairment (MCI) may gradually worsen to dementia, but often remains stable for extended periods of time. Little is known about the predictors of decline to help explain this variation. We aimed to explore whether this heterogeneous course of MCI may be predicted by the presence of Lewy body (LB) symptoms in a prospectively-recruited longitudinal cohort of MCI with Lewy bodies (MCI-LB) and Alzheimer's disease (MCI-AD). Methods A prospective cohort (n = 76) aged ⩾60 years underwent detailed assessment after recent MCI diagnosis, and were followed up annually with repeated neuropsychological testing and clinical review of cognitive status and LB symptoms. Latent class mixture modelling identified data-driven sub-groups with distinct trajectories of global cognitive function. Results Three distinct trajectories were identified in the full cohort: slow/stable progression (46%), intermediate progressive decline (41%) and a small group with a much faster decline (13%). The presence of LB symptomology, and visual hallucinations in particular, predicted decline v. a stable cognitive trajectory. With time zeroed on study end (death, dementia or withdrawal) where available (n = 39), the same subgroups were identified. Adjustment for baseline functioning obscured the presence of any latent classes, suggesting that baseline function is an important parameter in prospective decline. Conclusions These results highlight some potential signals for impending decline in MCI; poorer baseline function and the presence of probable LB symptoms – particularly visual hallucinations. Identifying people with a rapid decline is important but our findings are preliminary given the modest cohort size.

Latent class analysis identification of syndromes in Alzheimer's disease

2006 ◽  
Vol 3 (1) ◽  
Author(s):  
Cathal Walsh
Keyword(s):  
Social Sciences ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Latent Class Analysis ◽  
Posterior Distribution ◽  
Latent Variable ◽  
Latent Class ◽  
Bayesian Framework ◽  
Class Analysis ◽  
The Social

Latent variable models have been used extensively in the social sciences. In this work a latent class analysis is used to identify syndromes within Alzheimer's disease. The fitting of the model is done in a Bayesian framework, and this is examined in detail here. In particular, the label switching problem is identified, and solutions presented. Graphical summaries of the posterior distribution are included.

O2-07-04: COGNITIVE SUBTYPES IN DEMENTIA DUE TO ALZHEIMER'S DISEASE IDENTIFIED BY LATENT CLASS ANALYSIS

2014 ◽  
Vol 10 ◽  
pp. P178-P179
Author(s):  
Nienke Scheltens ◽  
Francisca Galindo Garre ◽  
Yolande A.L. Pijnenburg ◽  
Annelies E. van der Vlies ◽  
Lieke L. Smits ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis

scholarly journals Physical frailty and cognitive impairment in older nursing home residents: a latent class analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yiyang Yuan ◽  
Kate L. Lapane ◽  
Jennifer Tjia ◽  
Jonggyu Baek ◽  
Shao-Hsien Liu ◽  
...  
Keyword(s):  
Nursing Home ◽  
Cognitive Impairment ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Nursing Home Residents ◽  
Class Analysis ◽  
Physical Frailty ◽  
Data Set ◽  
Severe Cognitive Impairment ◽  
Moderate Cognitive Impairment

Abstract Background Little is known about the heterogeneous clinical profile of physical frailty and its association with cognitive impairment in older U.S. nursing home (NH) residents. Methods Minimum Data Set 3.0 at admission was used to identify older adults newly-admitted to nursing homes with life expectancy ≥6 months and length of stay ≥100 days (n = 871,801). Latent class analysis was used to identify physical frailty subgroups, using FRAIL-NH items as indicators. The association between the identified physical frailty subgroups and cognitive impairment (measured by Brief Interview for Mental Status/Cognitive Performance Scale: none/mild; moderate; severe), adjusting for demographic and clinical characteristics, was estimated by multinomial logistic regression and presented in adjusted odds ratios (aOR) and 95% confidence intervals (CIs). Results In older nursing home residents at admission, three physical frailty subgroups were identified: “mild physical frailty” (prevalence: 7.6%), “moderate physical frailty” (44.5%) and “severe physical frailty” (47.9%). Those in “moderate physical frailty” or “severe physical frailty” had high probabilities of needing assistance in transferring between locations and inability to walk in a room. Residents in “severe physical frailty” also had greater probability of bowel incontinence. Compared to those with none/mild cognitive impairment, older residents with moderate or severe impairment had slightly higher odds of belonging to “moderate physical frailty” [aOR (95%CI)moderate cognitive impairment: 1.01 (0.99–1.03); aOR (95%CI)severe cognitive impairment: 1.03 (1.01–1.05)] and much higher odds to the “severe physical frailty” subgroup [aOR (95%CI)moderate cognitive impairment: 2.41 (2.35–2.47); aOR (95%CI)severe cognitive impairment: 5.74 (5.58–5.90)]. Conclusions Findings indicate the heterogeneous presentations of physical frailty in older nursing home residents and additional evidence on the interrelationship between physical frailty and cognitive impairment.

scholarly journals O3-11-02: LATENT CLASS ANALYSIS IDENTIFIES FUNCTIONAL DECLINE WITH AMSTERDAM IADL IN PRE-CLINICAL ALZHEIMER'S DISEASE

2019 ◽  
Vol 15 ◽  
pp. P911-P912
Author(s):  
Sarah-Christine Villeneuve ◽  
Marion Houot ◽  
Merike Verrijp ◽  
Marie-Odile Habert ◽  
Bruno Dubois ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Functional Decline ◽  
Class Analysis

scholarly journals Identifying and evaluating clinical subtypes of Alzheimer’s disease in care electronic health records using unsupervised machine learning

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nonie Alexander ◽  
Daniel C. Alexander ◽  
Frederik Barkhof ◽  
Spiros Denaxas
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Electronic Health Records ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Affinity Propagation ◽  
Class Analysis ◽  
Clustering Methods ◽  
Health Records ◽  
Electronic Health

Abstract Background Alzheimer’s disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to date have mainly used imaging or cognition data and have been limited in terms of data breadth and sample size. Here we examine the clinical heterogeneity of Alzheimer's disease patients using electronic health records (EHR) to identify and characterise disease subgroups using multiple clustering methods, identifying clusters which are clinically actionable. Methods We identified AD patients in primary care EHR from the Clinical Practice Research Datalink (CPRD) using a previously validated rule-based phenotyping algorithm. We extracted and included a range of comorbidities, symptoms and demographic features as patient features. We evaluated four different clustering methods (k-means, kernel k-means, affinity propagation and latent class analysis) to cluster Alzheimer’s disease patients. We compared clusters on clinically relevant outcomes and evaluated each method using measures of cluster structure, stability, efficiency of outcome prediction and replicability in external data sets. Results We identified 7,913 AD patients, with a mean age of 82 and 66.2% female. We included 21 features in our analysis. We observed 5, 2, 5 and 6 clusters in k-means, kernel k-means, affinity propagation and latent class analysis respectively. K-means was found to produce the most consistent results based on four evaluative measures. We discovered a consistent cluster found in three of the four methods composed of predominantly female, younger disease onset (43% between ages 42–73) diagnosed with depression and anxiety, with a quicker rate of progression compared to the average across other clusters. Conclusion Each clustering approach produced substantially different clusters and K-Means performed the best out of the four methods based on the four evaluative criteria. However, the consistent appearance of one particular cluster across three of the four methods potentially suggests the presence of a distinct disease subtype that merits further exploration. Our study underlines the variability of the results obtained from different clustering approaches and the importance of systematically evaluating different approaches for identifying disease subtypes in complex EHR.

The identification of cognitive subtypes in Alzheimer's disease dementia using latent class analysis

2015 ◽  
Vol 87 (3) ◽  
pp. 235-243 ◽  
Author(s):  
Nienke M E Scheltens ◽  
Francisca Galindo-Garre ◽  
Yolande A L Pijnenburg ◽  
Annelies E van der Vlies ◽  
Lieke L Smits ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Alzheimer’S Disease Dementia
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