Pain Trajectories and Predictors: A 7-Year Longitudinal Study of Women With Vulvodynia

IntroductionA significant proportion of women report a reduction of symptoms over time—even without treatment—yet the natural progression of vulvodynia and which factors may explain decrease vs persistence of pain remain unclear.AimTo identify subgroups of pain trajectories in women with vulvodynia and to predict these different trajectories by treatments undertaken, pain characteristics, and psychosocial factors.MethodsData on pain intensity, treatments undertaken, pain characteristics, and psychosocial factors were collected 3 times over a 7-year period from 173 women who screened positive for vulvodynia. Latent class growth analysis was conducted to identify homogeneous subgroups with distinct pain trajectories. A multivariate binomial logistic regression was used to examine whether treatments, pain characteristics, and psychosocial factors predicted these trajectories.Main Outcome MeasureThe main outcome was pain intensity (0–10), measured at 3 time points with the numerical rating scale.Results2 pain trajectories were identified: 1 where pain persisted (28.9%), and 1 where pain decreased over time (71.1%). Whether a treatment had been undertaken was not predictive of the course of pain over time. Women who were older at first pain onset, had pain at another location than the entrance of the vagina, and reported more anxiety were more likely to have a persistent pain trajectory relative to the decreased pain trajectory.Clinical ImplicationsFindings suggest that the evolution of pain differs among women with vulvodynia depending on pain characteristics and anxiety.Strengths & LimitationsStrengths of the study include the 7-year longitudinal design to examine the natural history of provoked vestibulodynia and the inclusion of biopsychosocial factors as predictors of pain trajectories. However, women with major medical and psychiatric illnesses or deep dyspareunia were not included, and, thus, these factors could not be examined as predictors.ConclusionAssessing baseline characteristics associated with different pain trajectories during medical visits could have positive implications for the management of vulvodynia.Pâquet M, Vaillancourt-Morel M-P, Jodouin J-F, et al. Pain Trajectories and Predictors: A 7-Year Longitudinal Study of Women With Vulvodynia. J Sex Med 2019;16:1606–1614.     

Description synthétique des trajectoires des patients insuffisants rénaux chroniques terminaux à partir du registre REIN

Context and objectivesSince 2001, the aim of the REIN registry has been to identify patients suffering from end-stage renal disease and benefiting from replacement therapy in France. The analysis of trajectories aims to evaluate the flow of patients between the different types of treatment in order to better understand and predict patient pathways. The objective of this study was to analyse the incoming and outgoing flows at 1 year of patients prevalent in the REIN registry on 12/31/2017.MethodsFlow analysis was carried out on patients prevalent on 12/31/2017 in the REIN registry by studying the before and after treatment modalities on 12/31/2016 and 12/31/2018. This analysis was initially carried out on all patients, then in sub-groups for each of the 5 treatment modalities.ResultsThe analyses covered 85,472 patients prevalent on 12/31/2017. The overall analysis showed that more than 20% of patients had been diagnosed with end-stage renal disease the year before. Regarding inflow, there was a relative stability for patients treated with self-care hemodialysis, in-center haemodialysis, peritoneal dialysis, and graft, in contrast to patients treated with hemodialysis in a medical unit. Regarding outgoing flows, proportion of deaths at one year was 9%. Peritoneal dialysis was the modality with the highest outflow proportion at one year.ConclusionAnalysis of patients’ trajectories shows variable evolution profiles according to treatment modality and thus could be a valuable tool in the evaluation and improvement of management and care in the field of end-stage renal disease.     

Latent class analysis was accurate but sensitive in data simulations

ObjectivesLatent class methods are increasingly being used in analysis of developmental trajectories. A recent simulation study by Twisk and Hoekstra (2012) suggested caution in use of these methods because they failed to accurately identify developmental patterns that had been artificially imposed on a real data set. This article tests whether existing developmental patterns within the data set used might have obscured the imposed patterns.Study Design and SettingData were simulated to match the latent class pattern in the previous article, but with varying levels of randomly generated variance, rather than variance carried over from a real data set. Latent class analysis (LCA) was then used to see if the latent class structure could be accurately identified.ResultsLCA performed very well at identifying the simulated latent class structure, even when the level of variance was similar to that reported in the previous study, although misclassification began to be more problematic with considerably higher levels of variance.ConclusionThe failure of LCA to replicate the imposed patterns in the previous study may have been because it was sensitive enough to detect residual patterns of population heterogeneity within the altered data. LCA performs well at classifying developmental trajectories.     

Psychosocial predictors and outcomes of loneliness trajectories from childhood to early adolescence

Using latent class growth analysis, we were interested in investigating how experiences of loneliness emerge in distinct developmental patterns over the course of middle childhood and adolescence (NICHD Study of Early Child Care, N = 832). Second, we examined the role of demographic, mental health, and behavioral variables in association with these discrete patterns of loneliness. Loneliness was measured at 3 time points: age 9, age 11, and age 15. Results indicated five discrete trajectories of loneliness from middle childhood to adolescence. Most children exhibited a stable and low level of loneliness over time. The remaining children were split among moderate increasing, high increasing, decreasing, and chronic loneliness groups. Ethnicity, income, age 7 social skills, age 7 depression, and age 7 aggression were associated with trajectory membership. In addition, the loneliness trajectories predicted self-reports of social skills deficits, depression, aggression, and suicidal ideation at age 15.     

Identification of psychopathological course trajectories in schizophrenia

Course trajectory analyses have been performed primarily for treatment response in acute episodes of schizophrenic disorders. As yet, corresponding data for the long-term course are lacking. Within a multicenter prospective observational study, 268 patients with schizophrenia were assessed at discharge from hospital and followed up after 6, 12, 18, and 24 months. A latent class growth analysis was performed on the scores from the Positive and Negative Syndrome Scale (PANSS). A two-class conditional latent class model showed the best data fit (Entropy: 0.924). The model divided the sample into a group with amelioration in all PANSS subscales (60%) and a group with stable positive/negative and deteriorating general psychopathology symptoms (40%). Global functioning (GAF score), gender, age, living situation and involuntary admission predicted course trajectory class membership. The model was predictive of significant differences between the two groups in health care service costs and quality of life. The results underline the heterogeneous course of the illness, which ranged from amelioration to deterioration over a 2-year period. Statistical models such as trajectory analysis could help to identify more homogenous subtypes in schizophrenia.     

Trajectories of delinquent behaviour in adolescence and their covariates: relations with initial and time-averaged factors

This longitudinal study examined individual, family, and peer covariates of distinctive trajectories of juvenile delinquency, using data from a community sample of 318 German adolescents (mean age at the first wave was 11.45 years). Latent growth mixture modelling analysis revealed four trajectory groups: high-level offenders, medium-level offenders, low-level offenders, and rare offenders. The trajectory groups were discriminated better by time-averaged covariates than by initial status covariates. High peer tolerance of deviance and low parental empathy were consistently linked to varying offending trajectories, whereas gender, low academic achievement, and low parental monitoring appeared to be trajectory-specific covariates.     

Do 12-step meeting attendance trajectories over 9 years predict abstinence?

This study grouped treatment-seeking individuals (n = 1825) by common patterns of 12-step attendance using 5 waves of data (75% interviewed Year 9) to isolate unique characteristics and use-related outcomes distinguishing each class profile. The "high" class reported the highest attendance and abstention. The "descending" class reported high baseline alcohol severity, long treatment episodes, and high initial attendance and abstinence, but by Year 5, their attendance and abstinence dropped. The "early-drop" class, which started with high attendance and abstinence but with low problem severity, reported no attendance after Year 1. The "rising" class, with fairly high alcohol and psychiatric severity throughout, reported initially low attendance, followed by increasing attendance paralleling their abstention. Last, the "low" and "no" classes, which reported low problem severity and very low/no attendance, had the lowest abstention. Female gender and high alcohol severity predicted attendance all years. Consistent with a sustained benefit for 12-step exposure, abstinence patterns aligned much like attendance profiles.     

Contrast circulation in adult fontan patients using MR Time Resolved Angiography: Application for CT pulmonary angiography

BackgroundWhen patients with Fontan circulation require a computed tomographic pulmonary angiogram (CTPA), there are significant challenges in achieving adequate contrast opacification due to the altered anatomical connections. This study used Time Resolved Angiography with Interleaved Stochastic Trajectories (TWIST) Magnetic Resonance Angiography (MRA) to examine contrast circulation in a cohort of patients with Fontan circulation who were having routine MRI follow up to inform the contrast timing of any subsequent CT.MethodsThis is a single centre, cross-sectional, observational, retrospective study. The time to peak (TTP) signal intensity from the MRA was recorded using regions of interest on the aorta, pulmonary arteries, cavae and Fontan conduit. Patients were grouped by ejection fraction, global longitudinal strain, indexed stroke volume and cardiac index to examine if these cardiac performance parameters affected the mean TTP. Statistical analysis was performed to find the mean TTP for each of the vessels, which was consequently compared between the different cardiac performance parameters.Results35 patients were included in the study. Mean TTP contrast enhancement was 31s in the thoracic aorta, 46s in the right pulmonary artery, 41s in the left pulmonary artery and 55s in the Fontan conduit. Cardiac performance shows no statistically significant relationship to the peak contrast enhancement whether measured by ejection fraction, global longitudinal strain, stroke volume index or cardiac index.ConclusionThe mean optimal timing for a single-phase examination of the Fontan circulation, following an upper limb injection, was 55 s following start of contrast injection irrespective of cardiac performance. In TWIST MRA, the IV bolus is 4–5 s duration. A longer bolus is required for CTA, around 20s, suggesting an additional delay will be required. We propose that an optimal single phase CTPA to be protocolled at 70 s following the start of contrast injection, assuming adequate iodinated contrast dose.