Explore the vast range of titles available.

Introduction This study applies a novel algorithm to longitudinal amyloid positron emission tomography (PET) imaging to identify age‐heterogeneous amyloid trajectory groups, estimate the age and duration (chronicity) of amyloid positivity, and investigate chronicity in relation to cognitive decline and tau burden. Methods Cognitively unimpaired participants (n = 257) underwent one to four amyloid PET scans (Pittsburgh Compound B, PiB). Group‐based trajectory modeling was applied to participants with longitudinal scans (n = 171) to identify and model amyloid trajectory groups, which were combined with Bayes theorem to estimate age and chronicity of amyloid positivity. Relationships between chronicity, cognition, clinical progression, and tau PET (MK‐6240) were investigated using regression models. Results Chronicity explained more heterogeneity in amyloid burden than age and binary amyloid status. Chronicity was associated with faster cognitive decline, increased risk of abnormal cognition, and higher entorhinal tau. Discussion Amyloid chronicity provides unique information about cognitive decline and neurofibrillary tangle development and may be useful to investigate preclinical Alzheimer's disease.

Objective The objective of the study was to characterize the longitudinal, dynamic intracranial pressure (ICP) trajectory in acute brain injury (ABI) patients admitted to intensive care unit (ICU) and explore whether it added sights over traditional thresholds in predicting outcomes. Methods ABI patients with ICP monitoring were identified from two public databases named Medical Information Mart for the Intensive Care (MIMIC)‐IV and eICU Collaborative Research Database (eICU‐CRD). Group‐based trajectory modeling (GBTM) was employed to identify 4‐h ICP trajectories in days 0–5 post‐ICU admission. Then, logistic regression was used to compare clinical outcomes across distinct groups. To further validate previously reported thresholds, we created the receiver operating characteristic (ROC) curve in our dataset. Results A total of 810 eligible patients were ultimately enrolled in the study. GBTM analyses generated 6 distinct ICP trajectories, differing in the initial ICP, evolution pattern, and number/proportion of spikes >20/22 mmHg. Compared with patients in “the highest, declined then rose” trajectory, those belonging to the “lowest, stable,” “low, stable,” and “medium, stable” ICP trajectories were at lower risks of 30‐day mortality (odds ratio [OR] 0.04; 95% confidence interval [CI] 0.01, 0.21), (OR 0.04; 95% CI 0.01, 0.19), (OR 0.08; 95% CI 0.01, 0.42), respectively. ROC analysis demonstrated an unfavorable result, for example, 30‐day mortality in total cohort: an area under the curve (AUC): 0.528, sensitivity: 0.11, and specificity: 0.94. Conclusions This study identified three ICP trajectories associated with elevated risk, three with reduced risks for mortality during ICU hospitalization. Notably, a fixed ICP threshold should not be applied to all kinds of patients. GBTM, a granular method for describing ICP evolution and their association with clinical outcomes, may add to the current knowledge in intracranial hypertension treatment. To summarize, a novel ICP trajectory that could enable us to move the treatment of ABI from a fixed threshold approach to a more individualized treatment was proposed. ICP values and variability differed across these six identified trajectory groups with favorable vs. unfavorable outcomes. The epidemiological shift toward a larger proportion of physiologically fragile elderly patients calls for more attention.

Background Adolescent depression prevention research has focused on mean intervention outcomes, but has not considered heterogeneity in symptom course. Here, we empirically identify subgroups with distinct trajectories of depressive symptom change among adolescents enrolled in two indicated depression prevention trials and examine how cognitive‐behavioral (CB) interventions and baseline predictors relate to trajectory membership. Methods Six hundred thirty‐one participants were assigned to one of three conditions: CB group intervention, CB bibliotherapy, and brochure control. We used group‐based trajectory modeling to identify trajectories of depressive symptoms from pretest to 2‐year follow‐up. We examined associations between class membership and conditions using chi‐square tests and baseline predictors using multinomial regressions. Results We identified four trajectories in the full sample. Qualitatively similar trajectories were found in each condition separately. Two trajectories of positive symptom course (low‐declining, high‐declining) had declining symptoms and were distinguished by baseline symptom severity. Two trajectories of negative course (high‐persistent, resurging), respectively, showed no decline in symptoms or decline followed by symptom reappearance. Participants in the brochure control condition were significantly more likely to populate the high‐persistent trajectory relative to either CB condition and were significantly less likely to populate the low‐declining trajectory relative to CB group. Several baseline factors predicted trajectory classes, but gender was the most informative prognostic factor, with males having increased odds of membership in a high‐persistent trajectory relative to other trajectories. Conclusions Findings suggest that CB preventive interventions do not alter the nature of trajectories, but reduce the risk that adolescents follow a trajectory of chronically elevated symptoms.

This article provides an overview of a group-based statistical methodology for analyzing developmental trajectories – the evolution of an outcome over age or time. Across all application domains, this group-based statistical method lends itself to the presentation of findings in the form of easily understood graphical and tabular data summaries. In so doing, the method provides statistical researchers with a tool for figuratively painting a statistical portrait of the predictors and consequences of distinct trajectories of development. Data summaries of this form have the great advantage of being accessible to nontechnical audiences and quickly comprehensible to audiences that are technically sophisticated. Examples of the application of the method are provided. A detailed account of the statistical underpinnings of the method and a full range of applications are provided by the author in a previous study.

Background Group-based trajectory modelling (GBTM) is increasingly used to identify subgroups of individuals with similar patterns. In this paper, we use simulated and real-life data to illustrate that GBTM is susceptible to generating spurious findings in some circumstances. Methods Six plausible scenarios, two of which mimicked published analyses, were simulated. Models with 1 to 10 trajectory subgroups were estimated and the model that minimized the Bayes criterion was selected. For each scenario, we assessed whether the method identified the correct number of trajectories, the correct shapes of the trajectories, and the mean number of participants of each trajectory subgroup. The performance of the average posterior probabilities, relative entropy and mismatch criteria to assess classification adequacy were compared. Results Among the six scenarios, the correct number of trajectories was identified in two, the correct shapes in four and the mean number of participants of each trajectory subgroup in only one. Relative entropy and mismatch outperformed the average posterior probability in detecting spurious trajectories. Conclusion Researchers should be aware that GBTM can generate spurious findings, especially when the average posterior probability is used as the sole criterion to evaluate model fit. Several model adequacy criteria should be used to assess classification adequacy.

This study aims to investigate the association between trajectories of the cognition and body mass index (BMI) among Chinese middle and old-aged adults. A total of 5693 adults (age 45 +) whose cognitive score is higher than average at the baseline were included from China Health and Retirement Longitudinal Study (CHARLS:2011–2015). Cognitive function was measured by Mini-mental state examination (MMSE) in Chinese version. The Group-based trajectory modeling (GBTM) was adopted to identify the potential heterogeneity of longitudinal changes over the past 5 years and to investigate the relationship between baseline BMI and trajectories of cognitive function. Three trajectories were identified in results: the slow decline (37.92%), the rapid decline (6.71%) and the stable function (55.37%). After controlling for other variables, underweight (BMI < 18.5 kg/m 2 ) was associated with the rapid and slow decline trajectories. Obesity (BMI > 28 kg/m 2 ) was associated with the slow decline trajectory. High-risk people of cognitive decline can be screened by measuring BMI.

Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers' efficiency when it comes to choosing the most appropriate technique that best suits their research questions.

This study aims to investigate the association between lactate trajectories and the risk of acute kidney injury (AKI) and hospital mortality in patients with hyperlactatemia. We conducted a multicenter retrospective study using data from three independent cohorts. By the lactate levels during the first 48 h of ICU admission, patients were classified into distinct lactate trajectories using group-based trajectory modeling (GBTM) method. The primary outcomes were AKI incidence and hospital mortality. Logistic regression analysis assessed the association between lactate trajectories and clinical outcomes, with adjusting potential confounders. Patients were divided into three trajectories: mild hyperlactatemia with rapid recovery (Traj-1), severe hyperlactatemia with gradual recovery (Traj-2), and severe hyperlactatemia with persistence (Traj-3). Traj-3 was an independent risk factor of both hospital mortality (all  < 0.001) and AKI development (all  < 0.001). Notably, Traj-2 was also associated with increased risk of mortality and AKI development (all  < 0.05) using Traj-1 as reference, except for the result in the Tianjin Medical University General Hospital (TMUGH) cohort for mortality in adjusted model (  = 0.123). Our finding was still robust in subgroup and sensitivity analysis. In the combination cohort, both Traj-2 and Traj-3 were considered as independent risk factor for hospital mortality and AKI development (all  < 0.001). When compared with the Traj-3, Traj-2 was only significantly associated with the decreased risk of hospital mortality (OR 0.17, 95% CI 0.14-0.20,  < 0.001), but no with the likelihood of AKI development (OR 0.90, 95% CI 0.77-1.05,  = 0.172). Lactate trajectories provide valuable information for predicting AKI and mortality in critically ill patients.

Premenstrual dysphoric disorder (PMDD) is a new Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 diagnosis characterized by the cyclical emergence of emotional and physical symptoms in the luteal phase of the menstrual cycle, with symptom remission in the follicular phase. Converging evidence highlights the possibility of distinct subtypes of PMDD with unique pathophysiologies, but temporal subgroups have yet to be explored in a systematic way. In the current work, we use group-based trajectory modeling to identify unique trajectory subgroups of core emotional and total PMDD symptoms across the perimenstrual frame (days -14 to +9, where day 0 is menstrual onset) in a sample of 74 individuals prospectively diagnosed with DSM-5 PMDD. For the total daily symptom score, the best-fitting model was comprised of three groups: a group demonstrating moderate symptoms only in the premenstrual week (65%), a group demonstrating severe symptoms across the full 2 weeks of the luteal phase (17.5%), and a group demonstrating severe symptoms in the premenstrual week that were slow to resolve in the follicular phase (17.5%). These trajectory groups are discussed in the context of the latest work on the pathophysiology of PMDD. Experimental work is needed to test for the presence of possible pathophysiologic differences in trajectory groups, and whether unique treatment approaches are needed.