LONGITUDINAL TRAJECTORIES OF CLINICAL DECLINE IN AMYLOID POSITIVE AND NEGATIVE POPULATIONS

2016 ◽  
pp. 1-9
Author(s):  
P.T. Trzepacz ◽  
H. Hochstetler ◽  
S. Wang ◽  
P. Yu ◽  
M. Case ◽  
...  
Keyword(s):  
Latent Class ◽  
Regression Tree ◽  
Growth Mixture Modeling ◽  
Classification And Regression Tree ◽  
Disease Assessment ◽  
Cart Analysis ◽  
Growth Mixture ◽  
Baseline Variable ◽  
Healthy Control ◽  
And Function

BACKGROUND: Brain beta-amyloid status portends different trajectories of clinical decline. OBJECTIVE: Determine trajectories and predictive baseline variable(s). DESIGN: Longitudinal, up to 24 months. SETTING: ADNI sites. PARTICIPANTS: Healthy control (n=325), early and late mild cognitive impairment (n=279; n=372), and Alzheimer’s dementia (n=216) subjects from ADNI-1/GO/2. MEASUREMENTS: Baseline amyloid status was based on first available CSF Aβ1-42 or, [11C]PiB or [18F]florbetapir (FBP) PET. Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog13) and Functional Activities Questionnaire (FAQ) were co-analyzed using Growth Mixture Modeling (GMM) to define latent class trajectories for each amyloid group. Classification and Regression Tree (CART) analysis determined which variables best predicted trajectory class membership using a number of variables available to clinicians. RESULTS: GMMs found two trajectory classes (C1, C2) each for amyloid-positive (P; n=722) and negative (N; n=470) groups. Most (90%) in the negative group were C2N with mildly impaired baseline ADAS-Cog13, normal FAQ and nonprogression; 10% were C1N with moderately impaired baseline FAQ and ADAS-Cog13 and trajectory of moderately worsening scores on the FAQ. C1P (26%) had more impaired baseline FAQ and ADAS-Cog13 than C2P (74%) and a steeper declining trajectory. CART yielded 4 decision nodes (FAQ <10.5, FAQ <6.5, MMSE ≥26.5, age <75.5) in positive and 1 node (FAQ <6.5) in negative groups, with 91.4% and 92.8% accuracy for class assignments, respectively. CONSLUSIONS: The trajectory pattern of greater decline in amyloid positive subjects was predicted by greater baseline impairment of cognition and function. While most amyloid-negative subjects had nonprogression irrespective of their diagnosis, a subgroup declined similarly to the gradually declining amyloid-positive group. CART predicted likely trajectory class, with known amyloid status, using variables accessible in a clinical setting, but needs replication.

scholarly journals Validation and verification of predictive salivary biomarkers for oral health

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nagihan Bostanci ◽  
Konstantinos Mitsakakis ◽  
Beral Afacan ◽  
Kai Bao ◽  
Benita Johannsen ◽  
...  
Keyword(s):  
Oral Health ◽  
Predictive Accuracy ◽  
Statistical Tests ◽  
Area Under The Curve ◽  
Regression Tree ◽  
High Sensitivity ◽  
Oral Health Status ◽  
Classification And Regression Tree ◽  
Cart Analysis ◽  
Salivary Biomarkers

AbstractOral health is important not only due to the diseases emerging in the oral cavity but also due to the direct relation to systemic health. Thus, early and accurate characterization of the oral health status is of utmost importance. There are several salivary biomarkers as candidates for gingivitis and periodontitis, which are major oral health threats, affecting the gums. These need to be verified and validated for their potential use as differentiators of health, gingivitis and periodontitis status, before they are translated to chair-side for diagnostics and personalized monitoring. We aimed to measure 10 candidates using high sensitivity ELISAs in a well-controlled cohort of 127 individuals from three groups: periodontitis (60), gingivitis (31) and healthy (36). The statistical approaches included univariate statistical tests, receiver operating characteristic curves (ROC) with the corresponding Area Under the Curve (AUC) and Classification and Regression Tree (CART) analysis. The main outcomes were that the combination of multiple biomarker assays, rather than the use of single ones, can offer a predictive accuracy of > 90% for gingivitis versus health groups; and 100% for periodontitis versus health and periodontitis versus gingivitis groups. Furthermore, ratios of biomarkers MMP-8, MMP-9 and TIMP-1 were also proven to be powerful differentiating values compared to the single biomarkers.

scholarly journals Latent Class Growth Analysis and Growth Mixture Modeling using R: A tutorial for two R-packages and a comparison with Mplus.

2020 ◽  
Author(s):  
Klaas J Wardenaar
Keyword(s):  
Latent Class ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Growth Trajectories ◽  
Growth Mixture ◽  
Software Packages ◽  
Model Configuration ◽  
R Packages ◽  
Latent Class Growth ◽  
Growth Analyses

Latent Class Growth Analyses (LCGA) and Growth Mixture Modeling (GMM) analyses are used to explain between-subject heterogeneity in growth on an outcome, by identifying latent classes with different growth trajectories. Dedicated software packages are available to estimate these models, with Mplus (Muthén &amp; Muthén, 2019) being widely used . Although this and other available commercial software packages are of good quality, very flexible and rich in options, they can be costly and fit poorly into the analytical workflow of researchers that increasingly depend on the open-source R-platform. Interestingly, although plenty of R-packages to conduct mixture analyses are available, there is little documentation on how to conduct LCGA/GMM in R. Therefore, the current paper aims to provide applied researchers with a tutorial and coding examples for conducting LCGA and GMM in R. Furthermore, it will be evaluated how results obtained with R and the modeling approaches (e.g., default settings, model configuration) of the used R-packages compare to each other and to Mplus.

scholarly journals Cognitive and Behavioral Domains That Reliably Differentiate Normal Aging and Dementia in Down Syndrome

2021 ◽  
Vol 11 (9) ◽  
pp. 1128
Author(s):  
Jordan P. Harp ◽  
Lisa M. Koehl ◽  
Kathryn L. Van Pelt ◽  
Christy L. Hom ◽  
Eric Doran ◽  
...  
Keyword(s):  
Primary Care ◽  
Down Syndrome ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Vineland Adaptive Behavior Scales ◽  
Cart Analysis ◽  
Dementia Screening ◽  
Significant Difference ◽  
Unit Increase

Primary care integration of Down syndrome (DS)-specific dementia screening is strongly advised. The current study employed principal components analysis (PCA) and classification and regression tree (CART) analyses to identify an abbreviated battery for dementia classification. Scale- and subscale-level scores from 141 participants (no dementia n = 68; probable Alzheimer’s disease n = 73), for the Severe Impairment Battery (SIB), Dementia Scale for People with Learning Disabilities (DLD), and Vineland Adaptive Behavior Scales—Second Edition (Vineland-II) were analyzed. Two principle components (PC1, PC2) were identified with the odds of a probable dementia diagnosis increasing 2.54 times per PC1 unit increase and by 3.73 times per PC2 unit increase. CART analysis identified that the DLD sum of cognitive scores (SCS < 35 raw) and Vineland-II community subdomain (<36 raw) scores best classified dementia. No significant difference in the PCA versus CART area under the curve (AUC) was noted (D(65.196) = −0.57683; p = 0.57; PCA AUC = 0.87; CART AUC = 0.91). The PCA sensitivity was 80% and specificity was 70%; CART was 100% and specificity was 81%. These results support an abbreviated dementia screening battery to identify at-risk individuals with DS in primary care settings to guide specialized diagnostic referral.

scholarly journals Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kaizhou Huang ◽  
Feiyang Ji ◽  
Zhongyang Xie ◽  
Daxian Wu ◽  
Xiaowei Xu ◽  
...  
Keyword(s):  
High Risk ◽  
Hepatitis B ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Artificial Liver ◽  
Liver Support ◽  
Artificial Liver Support ◽  
Cart Analysis ◽  
Classification And Regression ◽  
Cart Model

Abstract Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The aims of the present study were to investigate the subgroups of patients with HBV-ACLF who may benefit from ALSS therapy, and the relevant patient-specific factors. 489 ALSS-treated HBV-ACLF patients were enrolled, and served as derivation and validation cohorts for classification and regression tree (CART) analysis. CART analysis identified three factors prognostic of survival: hepatic encephalopathy (HE), prothrombin time (PT), and total bilirubin (TBil) level; and two distinct risk groups: low (28-day mortality 10.2–39.5%) and high risk (63.8–91.1%). The CART model showed that patients lacking HE and with a PT ≤ 27.8 s and a TBil level ≤455 μmol/L experienced less 28-day mortality after ALSS therapy. For HBV-ACLF patients with HE and a PT > 27.8 s, mortality remained high after such therapy. Patients lacking HE with a PT ≤ 27.8 s and TBil level ≤ 455 μmol/L may benefit markedly from ALSS therapy. For HBV-ACLF patients at high risk, unnecessary ALSS therapy should be avoided. The CART model is a novel user-friendly tool for screening HBV-ACLF patient eligibility for ALSS therapy, and will aid clinicians via ACLF risk stratification and therapeutic guidance.

scholarly journals A Novel Comprehensive Clinical Stratification Model to Refine Prognosis of Glioblastoma Patients Undergoing Surgical Resection

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 386 ◽  
Author(s):  
Tamara Ius ◽  
Fabrizio Pignotti ◽  
Giuseppe Maria Della Pepa ◽  
Giuseppe La Rocca ◽  
Teresa Somma ◽  
...  
Keyword(s):  
Surgical Resection ◽  
Classification Tree ◽  
Prognostic Score ◽  
Regression Tree ◽  
Image Data ◽  
Extent Of Resection ◽  
Classification And Regression Tree ◽  
Postoperative Treatment ◽  
Time Data ◽  
Cart Analysis

Despite recent discoveries in genetics and molecular fields, glioblastoma (GBM) prognosis still remains unfavorable with less than 10% of patients alive 5 years after diagnosis. Numerous studies have focused on the research of biological biomarkers to stratify GBM patients. We addressed this issue in our study by using clinical/molecular and image data, which is generally available to Neurosurgical Departments in order to create a prognostic score that can be useful to stratify GBM patients undergoing surgical resection. By using the random forest approach [CART analysis (classification and regression tree)] on Survival time data of 465 cases, we developed a new prediction score resulting in 10 groups based on extent of resection (EOR), age, tumor volumetric features, intraoperative protocols and tumor molecular classes. The resulting tree was trimmed according to similarities in the relative hazard ratios amongst groups, giving rise to a 5-group classification tree. These 5 groups were different in terms of overall survival (OS) (p < 0.000). The score performance in predicting death was defined by a Harrell’s c-index of 0.79 (95% confidence interval [0.76–0.81]). The proposed score could be useful in a clinical setting to refine the prognosis of GBM patients after surgery and prior to postoperative treatment.

scholarly journals WEALTH PROFILES AND VARIATIONS BY GENDER AND RACE: A GROWTH-MIXTURE MULTIGROUP COMPARISON

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S733-S733
Author(s):  
Yu-Chih Chen ◽  
Nancy Morrow-Howell
Keyword(s):  
Life Course ◽  
Latent Class ◽  
Dispersion Model ◽  
Latent Class Model ◽  
Growth Mixture Modeling ◽  
Later Life ◽  
Gender And Race ◽  
Race And Gender ◽  
Growth Mixture ◽  
And Gender

Abstract Wealth is fundamentally affected by various life course characteristics. However, little is known about the role of life course factors in shaping wealth trajectories in later life. This study explored how the longitudinal profiles of wealth varied by gender and race (white and non-white populations). Data came from the 2004-2014 Health and Retirement Study with 16,189 older adults aged 51 and older. With corrections for clustered effect within household, this study used growth mixture modeling (GMM) to identify the longitudinal patterns of wealth, and how these profiles varied by these two important life course attributes. The model began with a separate GMM model for race and gender to investigate the optimal latent class model. These results were combined using multi-group approach to incrementally examine the gender and race invariance using configural (same form), structural (same trajectory mean), dispersion (same trajectory variance), and distributional (same latent class size) test. Results identified four distinct wealth profiles—Stable high, Low and increasing, Stable low, and High but decline—for each race and gender category. The multigroup GMM analyses revealed that the wealth profiles varied by gender and race, but the degrees of variation differed a great deal, with results supporting a dispersion model for gender but a configural model for race. Results indicate that race has a stronger effect in shaping wealth development compared to gender. The findings suggest that understanding wealth disparities in later life could be facilitated by examining how wealth varies by gender and race.

scholarly journals Classification and regression tree (CART) analyses of genomic signatures reveal sets of tetramers that discriminate temperature optima of archaea and bacteria

Archaea ◽  
2008 ◽  
Vol 2 (3) ◽  
pp. 159-167 ◽  
Author(s):  
Betsey Dexter Dyer ◽  
Michael J. Kahn ◽  
Mark D. LeBlanc
Keyword(s):  
Environmental Conditions ◽  
Convergent Evolution ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Evolutionary Divergence ◽  
Cart Analysis ◽  
Genomic Signatures ◽  
Genome Wide ◽  
Classification And Regression ◽  
Temperature Optima

Classification and regression tree (CART) analysis was applied to genome-wide tetranucleotide frequencies (genomic signatures) of 195 archaea and bacteria. Although genomic signatures have typically been used to classify evolutionary divergence, in this study, convergent evolution was the focus. Temperature optima for most of the organisms examined could be distinguished by CART analyses of tetranucleotide frequencies. This suggests that pervasive (nonlinear) qualities of genomes may reflect certain environmental conditions (such as temperature) in which those genomes evolved. The predominant use of GAGA and AGGA as the discriminating tetramers in CART models suggests that purine-loading and codon biases of thermophiles may explain some of the results.

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