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The aim of this study was to determine the predictability of clinical complications by analyzing the perfusion index historical behavior patterns with polynomial group-based trajectory model.

Objectives Occipitocervical fusion (OCF) is an effective treatment for instability of occipitocervical junction (OCJ). The occipital condyle screw serves as a novel surgical technique for occipitocervical fixation. However, the intraoperative procedures for the occipital condyle screw technique have relied on surgeons' experience, so the pool of surgeons who are able to perform this surgery safely is limited. The present study aims to evaluate the feasibility and safety of the occipital condyle screw technique using human cadavers and to provide image anatomy for clinical application basis. Methods The scientific study comprised 10 fresh-frozen cadaveric specimens from the anatomy department of Qingdao University. Placement of the occipital condyle screws (3.5 mm diameter and 20.0 mm length) was performed in the 10 fresh-frozen cadaveric specimens with intact occipitocervical junctions, respectively. Occipitocervical CT was performed for all specimens and the DICOM data was obtained. Occipitocervical CT three-dimensional (3D) reconstruction was performed for the cadavers. Morphometric analysis was performed on the bilateral occipitocervical junction of 10 cadaveric specimens based on the 3D reconstruction CT images. Detailed morphometric measurements of the 20 occipital condyles screws were conducted including the average length of the screw trajectory, inside and upper tilting angles of screws, distance to the hypoglossal canal, and to the medial wall of occipital condyle. Results Placement of the occipital condyle screws into the 20 occipital condyles of the 10 cadaveric specimens was performed successfully and the trajectory of implantation was satisfactory according to 3D CT reconstruction images, respectively. There was no obvious injury to the spinal cord, nerve root, and vertebral artery. The length of the bilateral screw trajectory was, respectively, 20.96 ± 0.91 mm (left) and 20.59 ± 0.77 mm (right) (t = 1.306, P > 0.05). The upper tilting angle of bilateral screws was, respectively, 11.24° ± 0.74° (left) and 11.11° ± 0.64° (right) (t = 0.681, P > 0.05). The inside tilting angle of bilateral screws was, respectively, 31.00° ± 1.32° (left) and 30.85° ± 1.27° (right) (t = 0.307, P > 0.05). The screw's distance to the bilateral hypoglossal canal was, respectively, 4.84 ± 0.54 mm (left) and 4.70 ± 0.54 mm (right) (t = 0.685, P > 0.05). The screw's distance to the medial wall of the bilateral occipital condyle was, respectively, 5.13 ± 0.77 mm (left) and 5.04 ± 0.71 mm (right) (t = 0.384, P > 0.05). Conclusion The occipital condyle screw technique can serve as a feasible and safe treatment for instability of the occipitocervical junction with meticulous preoperative planning of the screw entry point and direction based on individual differences. Morphometric trajectory analysis is also an effective way to evaluate the surgical procedure.

Summary Background Children exposed to poverty and family adversities including domestic violence, parental mental ill health and parental alcohol misuse may experience poor outcomes across the life course. However, the complex interrelationships between these exposures in childhood are unclear. We therefore assessed the clustering of trajectories of household poverty and family adversities and their impacts on adolescent health outcomes. Methods We used longitudinal data from the UK Millennium Cohort study on 11564 children followed to age 14 years. Family adversities included parent reported domestic violence and abuse, poor mental health and frequent alcohol use. We used a group-based multi-trajectory cluster model to identify trajectories of poverty and family adversity for children. We assessed associations of these trajectories with child physical, mental and behavioural outcomes at age 14 years using multivariable logistic regression, adjusting for confounders. Findings Six trajectories were identified: low poverty and family adversity (43·2%), persistent parental alcohol use (7·7%), persistent domestic violence and abuse (3·4%), persistent poor parental mental health (11·9%), persistent poverty (22·6%) and persistent poverty and poor parental mental health (11·1%). Compared with children exposed to low poverty and adversity, children in the persistent adversity trajectory groups experienced worse outcomes; those exposed to persistent poor parental mental health and poverty were particularly at increased risk of socioemotional behavioural problems (adjusted odds ratio 6·4; 95% CI 5·0 – 8·3), cognitive disability (aOR 2·1; CI 1·5 – 2·8), drug experimentation (aOR 2·8; CI 1·8 – 4·2) and obesity (aOR 1·8; CI 1·3 – 2·5). Interpretation In a contemporary UK cohort, persistent poverty and/or persistent poor parental mental health affects over four in ten children. The combination of both affects one in ten children and is strongly associated with adverse child outcomes, particularly poor child mental health. Funding The National Institute for Health Research (NIHR) Policy Research Programme, NIHR Applied Research Collaboration South London (ARC South London) at King's College Hospital NHS Foundation Trust and the Medical Research Council (MRC).

BACKGROUND The current study aimed to investigate the patterns of postoperative pain trajectories over time and their associated risk factors in patients receiving video-assisted thoracoscopic surgery (VATS) and epidural analgesia (EA) for non-small cell lung cancer (NSCLC). METHODS This retrospective study was conducted at a tertiary medical center and included patients undergoing VATS for stage I NSCLC between 2011 and 2015. Maximal pain intensity was recorded daily during the first postoperative week. Group-based trajectory analysis was performed to categorize variations in pain scores over time. Associations between pain trajectory classification and amount of EA administered and length of hospital stay (LOS) after surgery were also evaluated. RESULTS A total of 635 patients with 4,647 pain scores were included in the analysis, and two postoperative pain trajectory groups were identified: group 1, mild pain trajectory (78%); and group 2, rebound pain trajectory (22%). Risk factors for rebound pain trajectory were a surgical time longer than 3 hours (odds ratio, OR: 1.97; 95% confidence interval, CI: 1.27 - 3.07), female sex (OR: 1.62; 95% CI: 1.04 - 2.53) and higher pain score on postoperative day 0 (OR: 1.21; 95% CI: 1.08 - 1.36, linear effect). Although group 2 had a longer LOS (p < 0.001), they did not receive more EA than group 1 (p = 0.805). CONCLUSION Surgical time, sex and pain intensity after surgery were major determinants of rebound pain trajectory, and more aggressive pain control strategies should be considered in high-risk patients.

Introduction Type 2 diabetes mellitus (T2DM) frequently associates with increasing multi-morbidity/treatment complexity. Some headway has been made to identify genetic and non-genetic risk factors for T2DM. However longitudinal clinical histories of individuals both before and after diagnosis of T2DM are likely to provide additional insight into both diabetes aetiology/further complex trajectory of multi-morbidity. Methods This study utilised diabetes patients/controls enrolled in the DARE (Diabetes Alliance for Research in England) study where pre- and post-T2DM diagnosis longitudinal data was available for trajectory analysis. Longitudinal data of 281 individuals (T2DM n=237 vs matched non-T2DM controls n=44) were extracted, checked for errors and logical inconsistencies and then subjected to Trajectory Analysis over a period of up to 70 years based on calculations of the proportions of most prominent clinical conditions for each year. Results For individuals who eventually had a diagnosis of T2DM made, a number of clinical phenotypes were seen to increase consistently in the years leading up to diagnosis of T2DM. Of these documented phenotypes, the most striking were diagnosed hypertension (more than in the control group) and asthma. This trajectory over time was much less dramatic in the matched control group. Immediately prior to T2DM diagnosis a greater indication of ischaemic heart disease proportions was observed. Post-T2DM diagnosis, the proportions of T2DM patients exhibiting hypertension and infection continued to climb rapidly before plateauing. Ischaemic heart disease continued to increase in this group as well as retinopathy, impaired renal function and heart failure. Conclusion These observations provide an intriguing and novel insight into the onset and natural progression of T2DM. They suggest an early phase of potentially-related disease activity well before any clinical diagnosis of diabetes is made. Further studies on a larger cohort of DARE patients are underway to explore the utility of establishing predictive risk scores.

Blood-drop trajectory analysis can provide investigators with retrospective information regarding the spatial positioning of an injured person. To assist with bloodstain pattern analysis, various commercially available software have been developed and deployed. A systematic review was conducted to understand the extent of experimental validation and applications of blood-drop trajectory analysis software to case work. Ninety-two sources between 1987 and 2020 were identified including peer-reviewed studies and commercial websites. Thirty-four of these were validation studies, of which, only two involved impact patterns generated from greater than 1 m from the main target surface. Fifteen software were identified during this review with six documented to have been applied in casework. The reviewed software do not appear to fully satisfy relevant forensic validation criteria, based on publicly available literature. In some cases, software underwent limited experimental validation prior to real-world application with subsequent references to this in later literature. This review provides forensic investigators and bloodstain pattern analysts with a comprehensive overview of all available software options, knowledge of the extent of research into validating these techniques and highlights documented applications of these software in criminal cases.

Objective In this study, we aimed to inform clinical practice by identifying distinct subgroups of U.S. veteran psychiatry inpatients on their alcohol and drug use severity, posttraumatic stress disorder (PTSD) symptoms, and suicidal ideation over time. Method Participants were 406 patients with co-occurring substance use and mental health disorders. A parallel latent growth trajectory model was used to characterize participants' symptom severity across 15 months posttreatment intake. Results Four distinct classes were identified: 47% "normative improvement," 32% "high PTSD," 11% "high drug use," and 9% "high alcohol use." Eighty percent of the sample had reduced their drinking and drug intake by half from baseline to 3 months, and those levels remained stable from 3 to 15 months. The High PTSD, High Drug Use, and High Alcohol Use classes all reported levels of PTSD symptomatology at baseline consistent with a clinical diagnosis, and symptom levels remained high and stable across all 15 months. The Normative Improvement class showed declining drug and alcohol intake and was the only class exhibiting reductions in PTSD symptomatology over time. High substance use classes showed initial declines in suicidal ideation, then an increase from 9 to 15 months. Conclusions The reduction in frequency of drinking and drug use for 80% of the sample was substantial and supports the potential efficacy of current treatment approaches. However, the high and stable levels of PTSD for more than 50% of the sample, as well as the reemergence of suicidal ideation in a sizable subgroup, underscore the difficulty in finding and linking patients to effective interventions to decrease symptomatology over time.

BACKGROUND: The current state of the science regarding the care and prognosis of patients with disorders of consciousness is limited. Scientific advances are needed to improve the accuracy, relevance, and approach to prognostication, thereby providing the foundation to develop meaningful and effective interventions. METHODS: To address this need, an interdisciplinary expert panel was created as part of the Coma Science Working Group of the Neurocritical Care Society Curing Coma Campaign. RESULTS: The panel performed a gap analysis which identified seven research needs for prognostic modeling and trajectory analysis (“recovery science”) in patients with disorders of consciousness: (1) to define the variables that predict outcomes; (2) to define meaningful intermediate outcomes at specific time points for different endotypes; (3) to describe recovery trajectories in the absence of limitations to care; (4) to harness big data and develop analytic methods to prognosticate more accurately; (5) to identify key elements and processes for communicating prognostic uncertainty over time; (6) to identify health care delivery models that facilitate recovery and recovery science; and (7) to advocate for changes in the health care delivery system needed to advance recovery science and implement already-known best practices. CONCLUSION: This report summarizes the current research available to inform the proposed research needs, articulates key elements within each area, and discusses the goals and advances in recovery science and care anticipated by successfully addressing these needs.

The aim of this study was to demonstrate that a stochastic vector-based mapping approach could guide ablation of atrial fibrillation (AF) drivers as evidenced by ablation response and long-term follow-up outcomes.The optimal method for mapping and ablation of AF drivers is yet to be defined.Patients undergoing persistent AF ablation were recruited. Patients underwent pulmonary vein isolation (PVI) with further ablation guided by the stochastic trajectory analysis of ranked signals (STAR) mapping method. The proportion of the time an electrode's activation was seen to precede its neighboring electrodes activation was used to determine early sites of activation (ESA). A positive ablation response at ESA was defined as AF termination or cycle length slowing of ≥30 ms. Clinical outcome was defined as recurrence of AF/atrial tachycardia (AT) during a follow-up of 12 months.Thirty-five patients were included (AF duration of 14.4 ± 5.3 months). After PVI, an average of 2.6 ± 0.8 ESA were ablated per patient with study-defined ablation response achieved in all patients. Of the 86 STAR maps created post-PVI, the same ESA was identified on 73.8 ± 26.1% of maps. ESA that resulted in AF termination were more likely to be identified on both pre- and post-PVI maps than those associated with cycle length slowing (23 of 24 vs. 16 of 49; p 0.001). During a follow-up of 18.5 ± 3.7 months, 28 (80%) patients were free from AF/AT.The ablation response at ESA suggests they may be drivers of AF. Ablation guided by STAR mapping produced a favorable clinical outcome and warrants testing through a randomized controlled trial. (Identification, Electro-mechanical Characterisation and Ablation of Driver Regions in Persistent Atrial Fibrillation [STAR MAPPING]; NCT02950844).

Introduction Stochastic trajectory analysis of ranked signals (STAR) is a novel method for mapping arrhythmia. The aim was to describe its development and validation as a mapping tool. Methods and Results The method ranks electrodes in terms of the proportion of the time they lead relative to neighboring electrodes and ascribes a predominant direction of activation between electrodes. This was conceived with the aim of mapping atrial fibrillation (AF) drivers. Validation of this approach was performed in stages. First, in vitro simultaneous multi‐electrode array and optical mapping were performed on spontaneously fibrillating HL1 cell cultures, to determine if such a method would be able to determine early sites of activation (ESA). A clinical study acquiring unipolar electrograms using a 64‐pole basket for the purposes of STAR mapping in patients undergoing atrial tachycardia (AT) ablation. STAR maps were analyzed by physicians to see if arrhythmia mechanisms could be correctly determined. Mapping was then repeated during atrial pacing. STAR mapping of in vitro activation sequences accurately correlated to the optical maps of planar and rotational activation. Thirty‐two ATs were mapped in 25 patients. The ESA accurately identified focal/micro‐reentrant ATs and the mechanism of macro‐reentrant ATs was effectively demonstrated. STAR method accurately identified four pacing sites in all patients. Conclusions This novel STAR method correlated well with the gold standard of optical mapping in vitro and was able to accurately identify AT mechanisms. Further analysis is needed to determine whether the method might be of use mapping AF.

We present an enhanced version of the input device evaluation application (IDEA) system as an objective method for evaluating upper limb motor skills performance. By introducing three new metrics for mouse cursor trajectory analysis, along with the application of the two-dimensional (2D) experiment in the case of multiple sclerosis (MS), we examine the sensitivity of the IDEA system for differentiating patients with early-stage MS and healthy participants. The IDEA system calculates multiple kinematic metrics for point-and-click tasks: movement time, index of difficulty, effective target width, effective index of difficulty, throughput, missed clicks, target re-entry, task axis crossing, movement direction change, orthogonal direction change, movement variability, movement error, movement offset, mean velocity, velocity peaks, and maximum/mean velocity ratio. The results reveal that the IDEA system sensitivity has been improved in comparison with previous studies, which is high enough to detect the presence of early-stage MS with a 70.9% success rate in the 2D experiment.

The combined role of objectively assessed moderate-vigorous intensity physical activity (MVPA) and sedentary behaviour (SB) is unclear in obesity prevention. This study aimed to identify latent groups for MVPA and SB trajectories from childhood to adolescence and examine their relationship with obesity risk at adolescence. From the Gateshead Millennium Study, accelerometer-based trajectories of time spent in MVPA and SB at ages 7, 9, 12, and 15 were derived as assigned as the predictor variable. Fat mass index (FMI), using bioelectrical impedance at age 15, was the outcome variable. From 672 children recruited, we identified three distinct multiple trajectory groups for time spent in MVPA and SB. The group with majority membership (54% of the cohort) had high MVPA and low SB at childhood, but MVPA declined and SB increased by age 15. One third of the cohort (31%) belonged to the trajectory with low MVPA and high time spent sedentary throughout. The third trajectory group (15% of the cohort) that had relatively high MVPA and relatively low SB throughout had lower FMI (−1.7, 95% CI (−3.4 to −1.0) kg/m2, p = 0.034) at age 15 compared to the inactive throughout group. High MVPA and low SB trajectories when combined are protective against obesity.

Double Asteroid Redirection Test will be the first mission to demonstrate and characterize the concept of a kinetic impactor for planetary defense, by impacting the smaller member of a binary aster...

WOS:000614806200005; Ecological research focuses on the spatio-temporal patterns of ecosystems and communities. The recently proposed framework of Community Trajectory Analysis considers community dynamics as trajectories in a chosen space of community resemblance and utilizes geometrical properties of trajectories to compare and analyse temporal changes. Here, we extend the initial framework, which focused on consecutive trajectory segments, by considering additional metrics with respect to initial or baseline states. Addressing questions about community dynamics and more generally temporal and spatial ecological variability requires synthetic and efficient modes of representation. Hence, we propose a set of innovative maps, charts and trajectory roses to represent trajectory properties and complement the panel of traditional modes of representation used in community ecology. We use four case studies to highlight the complementarity and the ability of the new metrics and innovative figures to illustrate ecological trajectories and to facilitate their interpretation. Finally, we encourage ecologists skilled in multivariate analysis to integrate CTA into their toolbox in order to quantitatively evaluate spatio-temporal changes.

Background Although previous studies have shown connections between pain and worse cancer outcomes, few clinical studies have evaluated their direct association, and the current study aimed to investigate the potential association between acute pain trajectories and postoperative outcomes after liver cancer surgery. Methods This retrospective study was conducted in a single medical center and included patients who received liver cancer surgery between January 2010 and December 2016. Maximal pain intensity was recorded daily using a numerical rating scale during the first postoperative week. Group-based trajectory analysis was performed to classify the variations in pain scores over time. Cox and linear regression analyses were used to assess the effect of pain trajectories on recurrence-free survival, overall survival, and length of hospital stay (LOS) after surgery and to explore predictors of these outcomes. Results A total of 804 patients with 5396 pain score observations were analyzed within the present study. Group-based trajectory analysis categorized the changes in postoperative pain into three groups: group 1 had constantly mild pain (76.6%), group 2 had moderate/severe pain dropping to mild (10.1%), and group 3 had mild pain rebounding to moderate (13.3%). Multivariable analysis demonstrated that on average, group 3 had a 7% increase in LOS compared with the group 1 (p = 0.02) and no significant difference in the LOS was noted between pain trajectory groups 2 and 1 (p = 0.93). Pain trajectories were not associated with recurrence-free survival or overall survival after liver cancer surgery. Conclusion Acute pain trajectories were associated with LOS but not cancer recurrence and survival after liver cancer surgery. Group-based trajectory analysis provided a promising approach for investigating the complex relationships between variations in postoperative pain over time and clinical outcomes.

Background. Saudi Arabia is reported to have the highest number of children and adolescents with T1DM. However, data concerning glycemic control during adolescence are lacking. Objectives. To determine glycemic control at transition stage from pediatric to adult clinics, determine HBA1c patterns during follow-up, and identify any clinical or demographic variables that may predict a distinctive glycemic pattern. Design. Observational retrospective study. Setting. Dammam Medical Complex, secondary care hospital. Patients and Method. Adolescents aged ≥12 years, with HbA1c recorded at least once a year over 4 years of follow-up, were eligible for inclusion. A trajectory analysis from 2008 to 2019 was conducted, using latent class growth modelling (LCGM), and two-sample t-tests and Fisher’s exact tests were conducted to determine whether there was a statistically significant difference in demographic and clinical variables. Sample Size. 44 patients. Results. 61.36% were referred from pediatric clinics, and 84% were on multiple insulin daily injections. For the trajectory prediction, two groups were identified. Group 1 comprised 71.7%, had high HbA1c values at age 13 (HbA1c, 11.28%), and had a significant and stable decrease in HbA1c values with age (−0.32, p < 0.00 ). Group 2 comprised 28.2%, showed poor HbA1c values at age 13 (HbA1c, 13.28%), and showed increase in HbA1c values slightly by age 15, which then steadily decreased with age (−0.27). Results indicated that the initial HBA1c value was a significant predictor for group trajectory p = 0.01 , while the remaining variables did not have any significance. Conclusion. Our study identified two groups with poorly controlled diabetes; however, the first group performed relatively better than the second group. Both groups almost doubled their targets, with a trend towards HbA1c reduction by the age of 19 in both groups. Limitations. Retrospective study with convenient, small sample size.

Most of the existing single object trackers track the target in a unitary local search window, making them particularly vulnerable to challenging factors such as heavy occlusions and out-of-view movements. Despite the attempts to further incorporate global search, prevailing mechanisms that cooperate local and global search are relatively static, thus are still sub-optimal for improving tracking performance. By further studying the local and global search results, we raise a question: can we allow more dynamics for cooperating both results? In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy. In particular, we construct dynamic appearance model that contains multiple target templates, each of which provides its own attention for locating the target in the new frame. Guided by different attention, we maintain diversified tracking results for the target to build multi-trajectory tracking history, allowing more candidates to represent the true target trajectory. After spanning the whole sequence, we introduce a multi-trajectory selection network to find the best trajectory that delivers improved tracking performance. Extensive experimental results show that our proposed tracking strategy achieves compelling performance on various large-scale tracking benchmarks. The project page of this paper can be found at https://sites.google.com/view/mt-track/., Comment: Accepted by IEEE T-CSVT 2021

IntroductionEmerging evidence suggests working with men to prevent intimate partner violence (IPV) perpetration can be effective. However, it is unknown whether all men benefit equally, or whether different groups of men respond differentially to interventions.MethodsWe conducted trajectory modelling using longitudinal data from men enrolled in intervention arms of three IPV trials in South Africa and Rwanda to identify trajectories of IPV perpetration. We then use multinomial regression to describe baseline characteristics associated with group allocation.ResultsIn South Africa, the Stepping Stones and Creating Futures (SS-CF) trial had 289 men and the CHANGE trial had 803 men, and in Rwanda, Indashyikirwa had 821 men. We identified three trajectories of IPV perpetration: a low-flat (60%–67% of men), high with large reduction (19%–24%) and high with slight increase (10%–21%). Baseline factors associated men in high-start IPV trajectories, compared with low-flat trajectory, varied by study, but included higher poverty, poorer mental health, greater substance use, younger age and more childhood traumas. Attitudes supportive of IPV were consistently associated with high-start trajectories. In separate models comparing high-reducing to high-increasing trajectories, baseline factors associated with reduced IPV perpetration were depressive symptoms (relative risk ratio, RRR=3.06, p=0.01 SS-CF); living separately from their partner (RRR=2.14, p=0.01 CHANGE); recent employment (RRR=1.85, p=0.04 CHANGE) and lower acceptability of IPV (RRR=0.60, p=0.08 Indashyikirwa). Older aged men had a trend towards reducing IPV perpetration in CHANGE (p=0.06) and younger men in Indashyikirwa (p=0.07).ConclusionsThree distinct groups of men differed in their response to IPV prevention interventions. Baseline characteristics of past traumas and current poverty, mental health and gender beliefs predicted trajectory group allocation. The analysis may inform targeting of interventions towards those who have propensity to change or guide how contextual factors may alter intervention effects.Trial registration numbersNCT03022370; NCT02823288; NCT03477877.

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge&ndash, desire&ndash, intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users&rsquo, beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.

Background We investigated differing trajectories of childhood obesity prevalence amongst English local authorities (LAs). Methods Data on prevalence of childhood obesity (BMI ≥ 95th centile) for Reception year and Year 6 for 150 LAs in England from 2006/07 to 2015/16 were obtained from the National Child Measurement Programme (NCMP). Latent class mixture modelling (LCCM) was used to identify classes of change in obesity prevalence. Results In Reception, most LAs showed little change across the period (Class 1; stable, moderate obesity prevalence;84%), with a smaller group with a high prevalence that fell thereafter (Class 2; high but falling obesity prevalence; 16%). In Year 6 we identified three classes: moderate obesity prevalence (Class 3; 43%); high and rising obesity prevalence (Class 2; 36%); and stable low obesity prevalence (Class 1; 21%). Greater LA deprivation and higher LA proportion of non-white ethnicity increased risk of being in Class 2 (Reception) or Class 2 or 3 (Year 6) compared with Class 1. Conclusions The prevalence of childhood obesity in LAs in England follow a small number of differing trajectories that are influenced by LA deprivation and ethnic composition. LAs following a stable low obesity trajectory for Year 6 are targets for further investigation.

Background: The obesity epidemic is a global concern. Standard behavioral treatment including increased physical activity, reduced energy intake, and behavioral change counseling is an effective lifestyle intervention for weight loss. Purpose: To identify distinct step count patterns among weight loss intervention participants, examine weight loss differences by trajectory group, and examine baseline factors associated with trajectory group membership. Methods: Both groups received group-based standard behavioral treatment while the experimental group received up to 30 additional, one-on-one self-efficacy enhancement sessions. Data were analyzed using group-based trajectory modeling, analysis of variance, chi-square tests, and multinomial logistic regression. Results: Participants (N = 120) were mostly female (81.8%) and white (73.6%) with a mean (SD) body mass index of 33.2 (3.8) kg/m2. Four step count trajectory groups were identified: active (>10,000 steps/day; 11.7%), somewhat active (7500–10,000 steps/day; 28.3%), low active (5000–7500 steps/day; 27.5%), and sedentary (P = .001). At baseline, lower body mass index and higher perceived health predicted membership in the better performing trajectory groups. Conclusions: Within a larger group of adults in a weight loss intervention, 4 distinct trajectory groups were identified and group membership was associated with differential weight loss.

Rationale: A model for stratifying progression of respiratory muscle weakness in amyotrophic lateral sclerosis (ALS) would identify disease mechanisms and phenotypes suitable for future investigations. This study sought to categorize progression of FVC after presentation to an outpatient ALS clinic. Objectives: To identify clinical phenotypes of ALS respiratory progression based on FVC trajectories over time. Methods: We derived a group-based trajectory model from a single-center cohort of 837 patients with ALS who presented between 2006 and 2015. We applied our model to the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database with 7,461 patients with ALS. Baseline characteristics at first visit were used as predictors of trajectory group membership. The primary outcome was trajectory of FVC over time in months. Measurements and Main Results: We found three trajectories of FVC over time, termed “stable low,” “rapid progressor,” and “slow progressor.” Compared with the slow progressors, the rapid progressors had shorter diagnosis delay, more bulbar-onset disease, and a lower ALS Functional Rating Scale–Revised (ALSFRS-R) total score at baseline. The stable low group had a shorter diagnosis delay, lower body mass index, more bulbar-onset disease, lower ALSFRS-R total score, and were more likely to have an ALSFRS-R orthopnea score lower than 4 compared with the slow progressors. We found that projected group membership predicted respiratory insufficiency in the PRO-ACT cohort (concordance statistic = 0.78, 95% CI, 0.76–0.79). Conclusions: We derived a group-based trajectory model for FVC progression in ALS, which validated against the outcome of respiratory insufficiency in an external cohort. Future studies may focus on patients predicted to be rapid progressors.

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Classical Pre-Post Intervention Studies are often analyzed using traditional statistics. Nevertheless, the nutritional interventions have small effects on the metabolism and traditional statistics are not enough to detect these subtle nutrient effects. Generally, this kind of studies assumes that the participants are adhered to the assigned dietary intervention and directly analyzes its effects over the target parameters. Thus, the evaluation of adherence is generally omitted. Although, sometimes, participants do not effectively adhere to the assigned dietary guidelines. For this reason, the Trajectory Map is proposed as a visual tool where dietary patterns of individuals can be followed during the intervention and can also be related with nutritional prescriptions. The Trajectory Analysis is also proposed allowing both analysis: 1) adherence to the intervention and 2) intervention effects. The analysis is made by projecting the differences of the target parameters over the resulting trajectories between states of different time-stamps which might be considered either individually or by groups. The proposal has been applied over a real nutritional study showing that some individuals adhere better than others and some individuals of the control group modify their habits during the intervention. In addition, the intervention effects are different depending on the type of individuals, even some subgroups have opposite response to the same intervention.

Study objective More than 17 million people have gained health insurance coverage through the Patient Protection and Affordable Care Act’s Medicaid expansion. Few studies have examined heterogeneity within the Medicaid expansion population. We do so based on time-varying patterns of emergency department (ED) and ambulatory care use, and characterize diagnoses associated with ED and ambulatory care visits to evaluate whether certain diagnoses predominate in individual trajectories. Method We used group-based multitrajectory modeling to jointly estimate trajectories of ambulatory care and ED utilization in the first 12 months of enrollment among Pennsylvania Medicaid expansion enrollees from 2015 to 2017. Results Among 601,877 expansion enrollees, we identified 6 distinct groups based on joint trajectories of ED and ambulatory care use. Mean ED use varied across groups from 3.4 to 48.7 visits per 100 enrollees in the first month and between 2.8 and 44.0 visits per 100 enrollees in month 12. Mean ambulatory visit rates varied from 0.0 to 179 visits per 100 enrollees in the first month and from 0.0 to 274 visits in month 12. Rates of ED visits did not change over time, but rates of ambulatory care visits increased by at least 50% among 4 groups during the study period. Groups varied on chronic condition diagnoses, including mental health and substance use disorders, as well as diagnoses associated with ambulatory care visits. Conclusion We found substantial variation in rates of ED and ambulatory care use across empirically defined subgroups of Medicaid expansion enrollees. We also identified heterogeneity among the diagnoses associated with these visits. This data-driven approach may be used to target resources to encourage efficient use of ED services and support engagement with ambulatory care clinicians.

The key objective that is envisaged for this paper is predicting the future changes of Sand Forest that will take place as a result of continued informal wood harvesting. A change prediction model was undertaken through the use of the IDRISI Land Change Modeller, to predict the changes that would occur by 2024 within Sand Forest, over a 10 year period from 2014 base data available. Using Markov chain, the anticipated loss in extent of Sand Forest that may occur from 2014 to 2024 is 10.51 km2, over the entire Maputaland. The change prediction model illustrates that the expected loss centres on the larger communities that have established road networks. However, as these resources become depleted, this may change after 2024. The ability of trajectory analysis to predict potential changes based on observed and quantified trends provides a new dynamic to conservation and management strategies. In understanding where and how much Sand Forest will be lost in the forthcoming 10 years, more appropriate and accurate recommendations on conservation and management can be made. Furthermore, priority areas can be more readily identified for both conservation and for management intervention.

© 2019 Hebert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Objective Identify patient subgroups defined by trajectories of pain and disability following surgery for degenerative lumbar spinal stenosis, and investigate the construct validity of the subgroups by evaluating for meaningful differences in clinical outcomes. Methods We recruited patients with degenerative lumbar spinal stenosis from 13 surgical spine centers who were deemed to be surgical candidates. Study outcomes (leg and back pain numeric rating scales, modified Oswestry disability index) were measured before surgery, and after 3, 12, and 24 months. Group-based trajectory models were developed to identify trajectory subgroups for leg pain, back pain, and pain-related disability. We examined for differences in the proportion of patients achieving minimum clinically important change in pain and disability (30%) and clinical success (50% reduction in disability or Oswestry score ≤22) 12 months from surgery. Results Data from 548 patients (mean[SD] age = 66.7[9.1] years; 46% female) were included. The models estimated 3 unique trajectories for leg pain (excellent outcome = 14.4%, good outcome = 49.5%, poor outcome = 36.1%), back pain (excellent outcome = 13.1%, good outcome = 45.0%, poor outcome = 41.9%), and disability (excellent outcome = 30.8%, fair outcome = 40.1%, poor outcome = 29.1%). The construct validity of the trajectory subgroups was confirmed by between-trajectory group differences in the proportion of patients meeting thresholds for minimum clinically important change and clinical success after 12 postoperative months (p < .001). Conclusion Subgroups of patients with degenerative lumbar spinal stenosis can be identified by their trajectories of pain and disability following surgery. Although most patients experienced important reductions in pain and disability, 29% to 42% of patients were classified as members of an outcome trajectory subgroup that experienced little to no benefit from surgery. These findings may inform appropriate expectation setting for patients and clinicians and highlight the need for better methods of treatment selection for patients with degenerative lumbar spinal stenosis.

Background Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to facilitate workflows and analysis of molecular dynamics simulation data to fully harness the power of PCA is lacking. The Java Essential Dynamics inspector (JEDi) software is a major upgrade from the previous JED software. Results Employing multi-threading, JEDi features a user-friendly interface to control rapid workflows for interrogating conformational motions of biopolymers at various spatial resolutions and within subregions, including multiple chain proteins. JEDi has options for Cartesian-based coordinates (cPCA) and internal distance pair coordinates (dpPCA) to construct covariance (Q), correlation (R), and partial correlation (P) matrices. Shrinkage and outlier thresholding are implemented for the accurate estimation of covariance. The effect of rare events is quantified using outlier and inlier filters. Applying sparsity thresholds in statistical models identifies latent correlated motions. Within a hierarchical approach, small-scale atomic motion is first calculated with a separate local cPCA calculation per residue to obtain eigenresidues. Then PCA on the eigenresidues yields rapid and accurate description of large-scale motions. Local cPCA on all residue pairs creates a map of all residue-residue dynamical couplings. Additionally, kernel PCA is implemented. JEDi output gives high quality PNG images by default, with options for text files that include aligned coordinates, several metrics that quantify mobility, PCA modes with their eigenvalues, and displacement vector projections onto the top principal modes. JEDi provides PyMol scripts together with PDB files to visualize individual cPCA modes and the essential dynamics occurring within user-selected time scales. Subspace comparisons performed on the most relevant eigenvectors using several statistical metrics quantify similarity/overlap of high dimensional vector spaces. Free energy landscapes are available for both cPCA and dpPCA. Conclusion JEDi is a convenient toolkit that applies best practices in multivariate statistics for comparative studies on the essential dynamics of similar biopolymers. JEDi helps identify functional mechanisms through many integrated tools and visual aids for inspecting and quantifying similarity/differences in mobility and dynamic correlations.

Background: Estrogen such as estradiol (E2) is known to promote ER+ breast cancer. However, several clinical trials reported the unexpected therapeutic benefit of E2 for aromatase inhibitor (AI)-resistant cases of ER+ postmenopausal breast cancer. The objective of this study is to uncover the mechanisms of E2-induced tumor regression, leading to an unconventional treatment of AI resistance. Methods: An E2-suppressive patient-derived xenograft model (named GS3) was established from an AI resistant ER+/PR-/HER2- brain metastatic breast cancer. Placebo or E2 pellets were implanted in mice carrying GS3 for evaluating the effects of E2. Immunohistochemistry (IHC) and RNA sequencing of GS3 were conducted to decipher molecular changes after E2 treatments. Since the cancer tissue has a heterogeneous structure, the single-cell analysis was further performed to examine gene expression profiles in individual cells. In addition, in vitro cell proliferation analysis was carried out using organoids from GS3. Results: E2 inhibited the growth of GS3 both in vivo and in vitro. ERα and ERβ genes in GS3 are wild-type and not amplified. ERα is involved because E2-mediated inhibition of GS3-organoids can be reversed by the co-treatment of ERα antagonist, not by ERβ antagonist. IHC showed that ER, Ki-67 and CEA expressions decreased and PR expression appeared after E2 treatment. Gene set enrichment analysis (GSEA) using RNAseq results showed that the E2 response gene sets were significantly up-regulated after E2 treatment. However, the cell cycle gene sets and the TNFA/NFKB gene set were down-regulated. GS3 gained an E2 independence after three cycles of intermittent E2 treatment (E2 pellet on/off every 4 weeks; Int-E2). Interestingly, the cell cycle and TNFA/NFKB gene sets were up-regulated after Int-E2 treatment. Single-cell RNAseq analysis revealed that cells from one-week E2-treated and Placebo-treated GS3 were placed in different clusters based on principle component analysis of Highly Variable Genes. Although E2 response genes were up-regulated, the percent of ESR1+ cells decreased after E2 treatment (41.3% vs. 31.5%). The number of cells arrested at the G1 phase increased (+12.5%) after E2 treatment. GSEA using genes expressed in only ESR1+ cells showed that the TNFA/NFKB gene set was significantly down-regulated after E2 treatment. Meanwhile, GSEA using genes expressed in only ESR1– cells showed that cell cycle gene sets were significantly down-regulated. Single-cell trajectory analysis disclosed three major branches; 1) common E2 and placebo, 2) E2, and 3) placebo. In the E2 only branch, the cell cycle arrested at the G1 phase, the E2 response gene sets were up-regulated, and the NFKB gene set was down-regulated in ESR1+ cells. Significantly, E2 response gene sets were also up-regulated and cell cycle genes were down-regulated even in ESR1– cells. In the placebo branch, E2 response gene sets were not up-regulated and cell cycle genes were not down regulated. A group of MKI67+ cells (at G2M phase), including some ESR1+ cells, were present in both E2-treated and placebo-treated tumors. Conclusions: E2-induced suppression is an unexpected outcome of AI resistance. In these cases, elimination of estrogen by AI results in maintaining tumor growth. Analysis of GS3 PDX has revealed that estrogen can induce cell cycle arrest and the expression of estrogen-regulated genes. Our results also suggest the cross-talk between ESR1+ and ESR1- cells as well as potential roles of the TNFA/NFKB pathway. Our findings point out the need of markers for such patients who can benefit from E2 treatment after AI resistance, and measurements of ER and PR expression are not sufficient. An intermittent treatment strategy does not sustain the effect of estrogen-mediated suppression. Citation Format: Hitomi Mori, Kohei Saeki, Gregory Chang, Xiwei Wu, Pei-Yin Hsu, Noriko Kanaya, George Somlo, Shiuan Chen. Estrogen-induced cell cycle arrest as an unexpected outcome of aromatase inhibitor-resistance: Insights from single-cell trajectory analysis of a patient-derived xenograft model [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD7-02.

Motion anomaly detection through video analysis is important for delivering autonomous situation awareness in public places. Surveillance scene segmentation and representation is the preliminary step to implementation anomaly detection. Surveillance scene can be represented using Region Association Graph (RAG), where nodes represent regions and edges denote connectivity among the regions. Existing RAG-based analysis algorithms assume simple anomalies such as moving objects visit statistically unimportant or abandoned regions. However, complex anomalies such as an object encircles within a particular region (Type-I) or within a set of regions (Type-II). In this paper, we extract statistical features from a given set of object trajectories and train multi-class support vector machines (SVM) to deal with each type of anomaly. In the testing phase, a given test trajectory is categorized as normal or anomalous with respect to the trained models. Performance evaluation of the proposed algorithm has been carried out on public as well as our own datasets. We have recorded sensitivity as high as 86% and fall-out rate as low as 9% in experimental evaluation of the proposed technique. We have carried out comparative analysis with state-of-the-art techniques to benchmark the method. It has been observed that the proposed model is consistent and highly accurate across challenging datasets.

We present a vision-based motion analysis method for single and multiple mobile robots which allows quantifying the robot’s behaviour. The method defines how often and for how much each of the robots turn and move straight. The motion analysis relies on the robot trajectories acquired online or offline by an external camera and the algorithm is based on iteratively performed a linear regression to detect straight and curved paths for each robot. The method is experimentally validated with the indoor mobile robotic system. Potential applications include remote robot inspection, rescue robotics and multi-robotic system coordination.

Introduction/Objectives Identification of groups of patients following similar trajectories of time-varying patient characteristics are often of considerable clinical value. This study provides an example of how the identification of trajectory groups of patients can be useful. Methods Using clinical and administrative data of a prospective cohort study aiming to improve the secondary prevention of osteoporosis-related fractures with a Fracture Liaison Service (FLS), trajectory groups for visit compliance over time (2-year follow-up) were predicted using group-based trajectory modeling. Predictors of trajectory groups were identified using multinomial logistic regressions. Results Among 532 participants (86% women, mean age 63 years), three trajectories were identified and interpreted as high followers, intermediate followers, and low followers. The predicted probability for group-membership was: 48.4% high followers, 28.1% intermediate followers, 23.5% low followers. A lower femoral bone mineral density and polypharmacy were predictors of being in the high followers compared to the low followers group; predictors for being in the intermediate followers group were polypharmacy and referral to a bone specialist at baseline. Conclusions Results provided information on visit compliance patterns and predictors for the patients undergoing the intervention. This information has important implications when implementing such health services and determining their effectiveness.

Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead.

OBJECTIVE: Investigate temporal patterns of sleep maintenance problems in women who became surgically menopausal (hysterectomy with bilateral oophorectomy) before their final menstrual period and examine whether pre-surgery trajectories of sleep maintenance problems are related to problems staying asleep post-surgery. METHODS: Longitudinal analysis of sleep self-reports collected every 1–2 years from 1996–2013 from 176 surgically menopausal women in the Study of Women’s Health Across the Nation (SWAN), a 7-site community-based, multi-ethnic/multi-racial, cohort study. Median follow-up was 15.3 years (4.2 years pre-surgery, 10.2 years post-surgery). Group-based trajectory modeling was used to identify patterns of problems staying asleep, and the pre-surgery trajectories were used to predict similar post-surgery sleep problems. RESULTS: 4 trajectory patterns of sleep maintenance problems were identified: low (33.5% of women), moderate (33.0%), increasing during pre-surgery (19.9%), and high (13.6%). One-fifth of women reported a pre-surgery increase in these problems. Post-surgically, problems staying asleep remained associated with similar levels of pre-surgical problems, even after adjusting for post-surgical early morning awakening, frequent vasomotor symptoms, and bodily pain score (β(low) = −1.716, β(moderate) = −1.144, β(increasing) = −0.957, β(high) = −1.021; all Ps < 0.01). CONCLUSIONS: Sleep maintenance problems were relatively stable across time post-surgery. These data are remarkably consistent with our trajectory results across the natural menopause, suggesting that pre-surgical assessment of sleep concerns could help guide women’s expectations post-surgically. While reassuring that sleep complaints do not worsen post-surgically for most surgically menopausal women, referral to a sleep specialist should be considered if sleep symptoms persist or worsen after surgery.

Sustainable management of plant disease outbreaks in agriculture is one of the main challenges of the next years to restore economic and environmental viability of farming practices. Improving early-detection capabilities and disease surveillance is increasingly seen as an obligate step to design appropriate and effective prophylactic measures. In this context, plant diseases caused by wind-dispersed pathogens represent an interesting case of study, since they are particularly complex and hard to observe directly, especially if compared to other dissemination means, and demand for a multidisciplinary approach to be dealt with. Wind dispersal could imply a geographic differentiation in pathogens spreading potential, due to the emerging of local meteorological features. In this work we analyze the spatio-temporal patterns of wind connectivity in Europe and the Mediterranean basin in order to identify possible pathways of Puccinia graminis spores, the causal agent of stem rust of wheat. By running backwards Lagrangian simulations merging a biological layer coupled with a pathogen viability model, we investigate possible long-distance connections between regions in the study area across different seasons. We characterized these regions in terms of network centrality indicators to identify possible spreaders of stem rust of wheat, founding that Central and Western European regions appears to provide highest connectivity for the spread of P. graminis.

BACKGROUND: E5103 was a study designed to evaluate the efficacy and safety of bevacizumab. It was a negative trial for the end points of invasive disease–free survival and overall survival. The current work examines the tolerability of bevacizumab and other medication exposures with respect to clinical outcomes and patient-reported outcomes (PROs). METHODS: Adverse events (AEs) collected from the Common Terminology Criteria for Adverse Events were summarized to form an AE profile at each treatment cycle. All-grade and high-grade events were separately analyzed. The change in the AE profile over the treatment cycle was delineated as distinct AE trajectory clusters. AE-related and any-reason early treatment discontinuations were treated as clinical outcome measures. PROs were measured with the Functional Assessment of Cancer Therapy–Breast + Lymphedema. The relationships between the AE trajectory and early treatment discontinuation as well as PROs were analyzed. RESULTS: More than half of all AEs (57.5%) were low-grade. A cluster of patients with broad and mixed AE (all-grade) trajectory grades was significantly associated with any-reason early treatment discontinuation (odds ratio [OR], 2.87; P = .01) as well as AE-related discontinuation (OR, 4.14; P = .001). This cluster had the highest count of all-grade AEs per cycle in comparison with other clusters. Another cluster of patients with primary neuropathic AEs in their trajectories had poorer physical well-being in comparison with a trajectory of no or few AEs (P < .01). A high-grade AE trajectory did not predict discontinuations. CONCLUSIONS: A sustained and cumulative burden of across-the-board toxicities, which were not necessarily all recognized as high-grade AEs, contributed to early treatment discontinuation. Patients with neuropathic all-grade AEs may require additional attention for preventing deterioration in their physical well-being.

Background Both poverty and family adversities including domestic violence, parental mental illness and parental drug and alcohol use are associated with poor outcomes across the life course. However, the complex relationships between these exposures in childhood are unclear. We therefore assessed the clustering of trajectories of household poverty and family adversities and their impacts on child health outcomes in adolescence. Methods We used longitudinal data from the nationally representative UK Millennium Cohort study on 11564 children born between 2000 and 2002, followed through six survey waves. Family adversities were defined here as parent reported domestic violence and abuse, parental alcohol use and parental mental illness. We used a group-based multi-trajectory cluster model to define trajectories of poverty and family adversity for children aged 9 months to 14 years. We assessed associations of these trajectories and child outcomes at age 14 years (child socioemotional behavioural problems, cognitive disability, obesity, alcohol and drug use) using multivariable logistic regression adjusting for confounders. Results Six trajectories were identified: persistent alcohol misuse (7.7%), low poverty and family adversity (43.2%), persistent domestic violence and abuse (3.4%), persistent parental mental illness (11.9%), persistent poverty (22.6%) and poverty and parental mental illness (11.1%). Compared to the low poverty and family adversity trajectory, children in the other trajectory groups experienced worse outcomes, particularly for combined exposure to poverty and parental mental illness. Compared with children with low adversity, those in the parental mental illness and poverty group were particularly at increased risk of socioemotional behavioural problems (adjusted OR 6.4, 95% CI 5.0 - 8.3), cognitive disability (aOR 3.1, CI 2.4 - 4.2), drug use (aOR 1.7, CI 1.4 - 2.0) and obesity (aOR 1.8, CI 1.3 - 2.5). Conclusion Over half of children in the UK grow up experiencing poverty and adversities associated with poor health in adolescence. Persistent poverty and/or persistent parental mental illness affect over four in ten UK children. The combination of both affects one in ten and is very strongly associated with adverse child outcomes, particularly poor child mental health.

International audience; Objective: To identify temporal trajectories of anxiolytic benzodiazepine (A-BZD) use over 10 years among new A-BZD users aged 50 and older and describe treatment patterns and demographic and clinical characteristics associated with each trajectory. Method: A representative cohort of the French national health insurance fund users was tracked from 2006 through 2015. We used latent class mixed models to identify the trajectories. Results: We observed four trajectories among new users (no A-BZD dispensing in 2005) plus one non-use trajectory. The proportion of occasional use among users was 60%; early increasing use, 10%; late increasing use, 17%; and increasing/decreasing use, 13%. Prevalence of occasional use decreased with age in women, but not men. Duration of treatment episodes and doses differed between trajectories. Multiple regression analyses with occasional use as the reference showed that the other three trajectories shared characteristics (age, coprescriptions of other psychotropic drugs, and more general practitioner consultations) but differed by the presence at inclusion or occurrence during follow-up of psychiatric, neurodegenerative, and somatic conditions. Conclusion: We found four different long-term temporal trajectories in new A-BZD users (occasional, early increasing, late increasing, and increasing/decreasing use). Difficulties quitting or reducing consumption may be very different for each trajectory, requiring tailored care approaches.

Introduction: The objective of the study was to evaluate the acute pain intensity evolution in ED discharged patients using Group-based trajectory modeling (GBTM). This method identified patient groups with similar profiles of change over time without assuming the existence of a particular pattern or number of groups. Methods: This was a prospective cohort study of ED patients aged ≥18 years with an acute pain condition (≤ 2 weeks) and discharged with an opioid prescription. Patients completed a 14-day diary assessing daily pain intensity level (0-10 numeric rating scale) and pain medication use. Results: Among the 372 included patients, six distinct post-ED pain intensity trajectories were identified: two started with severe levels of pain, one remained with severe pain intensity (12.6% of the sample) and the other ended with moderate pain intensity level (26.3%). Two other trajectories had severe initial pain, one decreased to mild pain (21.7%) and the other to no-pain (13.8%). Another trajectory had moderate initial pain which decreased to a mild level (15.9%) and the last one started with mild pain intensity and had no pain at the end of the 14-day (9.7%). The pain trajectory patterns were significantly associated with age, type of painful conditions, pain intensity at ED discharge, and with opioid consumption. Conclusion: Acute pain resolution following an ED visit seems to progress through six different trajectory patterns that are more informative than simple linear models and could be useful to adapt acute pain management in future research.