Objectives: To visualise the trajectories of pulmonary arterial pressure (PAP) in systemic sclerosis (SSc) and identify the clinical phenotypes for each trajectory, by applying latent trajectory modelling for PAP repeatedly estimated by echocardiography.
Methods: This was a multicentre, retrospective cohort study conducted at four referral hospitals in Kyoto, Japan. Patients with SSc who were treated at study sites between 2008 and 2021 and who had at least three echocardiographic measurements of systolic PAP (sPAP) were included. A group-based trajectory model was applied to the change in sPAP over time, and patients were classified into distinct subgroups that followed similar trajectories. Pulmonary hypertension (PH)-free survival was compared for each trajectory. Multinomial logistic regression analysis was performed for baseline clinical characteristics associated with trajectory assignment.
Results: A total of 236 patients with 1097 sPAP measurements were included. We identified five trajectories: rapid progression (n=9, 3.8%), early elevation (n=30, 12.7%), middle elevation (n=54, 22.9%), late elevation (n=24, 10.2%) and low stable (n=119, 50.4%). The trajectories, in the listed order, showed progressively earlier elevation of sPAP and shorter PH-free survival. In the multinomial logistic regression analysis with the low stable as a reference, cardiac involvement was associated with rapid progression, diffuse cutaneous SSc was associated with early elevation and anti-centromere antibody was associated with middle elevation; older age of onset was associated with all three of these trajectories.
Conclusion: The pattern of changes in PAP over time in SSc can be classified into five trajectories with distinctly different clinical characteristics and outcomes.
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http://dx.doi.org/10.1136/rmdopen-2022-002673 | DOI Listing |
Pain
January 2025
Temple University, Philadelphia, PA, United States.
A variety of minimal clinically important difference (MCID) estimates are available to distinguish subgroups with differing outcomes. When a true gold standard is absent, latent class growth curve analysis (LCGC) has been proposed as a suitable alternative for important change. Our purpose was to evaluate the performance of individual and baseline quartile-stratified MCIDs.
View Article and Find Full Text PDFPsychol Addict Behav
January 2025
Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill.
Objective: Although research supports an association between increased alternative reinforcement and decreased substance use, the impact of substance use on changes in reinforcement during posttreatment recovery remains untested. This study tested the effect of abstinence duration and substance use frequency on the trajectories of four reinforcement mechanisms, behavioral activation, reward probability, reward barriers, and valued living, from pre- to 12-month posttreatment.
Method: Adults in intensive outpatient substance use disorder treatment ( = 206) completed self-report measures of the four reinforcement constructs and substance use over six timepoints from pre- to 12-month posttreatment.
Child Psychiatry Hum Dev
January 2025
Department of Psychology, Yale University, New Haven, CT, 06520, USA.
Among a large sample of youth (9-10 years old at baseline) from the Adolescent Brain Cognitive Development (ABCD) Study® (n = 11,661) we modeled trajectories of psychopathology over three years and associated risk and protective factors. Growth mixture modeling characterized latent classes with distinct psychopathology trajectories. Results indicated four different internalizing trajectories: a high-decreasing class, a moderate-decreasing class, a moderate-increasing class, and a low-stable class.
View Article and Find Full Text PDFBackground: The early diagnosis and monitoring of Alzheimer's disease (AD) presents a significant challenge due to its heterogeneous nature, which includes variability in cognitive symptoms, diagnostic test results, and progression rates. This study aims to enhance the understanding of AD progression by integrating neuroimaging metrics with demographic data using a novel machine learning technique.
Method: We used supervised Variational Autoencoders (VAEs), a generative AI method, to analyze high-dimensional neuroimaging data for AD progression, incorporating age and gender as covariates.
Background: A lesson of the recent progress in Alzheimer's Disease therapy is that biomarker-driven trials will be crucial to demonstrating efficacy in the clinic. Many studies have demonstrated the potential predictive power of fluid and imaging biomarkers in guiding patient selection and continued progress of precision medicine approaches will demand development of multi-dimensional biomarker arrays. However, correlations between candidate biomarkers change non-linearly with time, requiring methodologies to align biomarkers across a common disease timescale (time from amyloid positivity; TFAP).
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