Treatment Response Trajectories Research Articles

Distinct trajectories of symptomatic response in ulcerative colitis during filgotinib therapy: A post hoc analysis from the SELECTION study.

Filgotinib is an oral, once-daily, Janus kinase 1 preferential inhibitor approved for treatment of ulcerative colitis (UC) following the phase 2b/3 SELECTION trial. Identification of patient populations and factors associated with long-term treatment response trajectories may improve UC management. We aimed to identify and describe distinct patient subgroups of response to filgotinib based on partial Mayo Clinic Score (pMCS) trajectories over time. In these post hoc analyses of SELECTION, group-based trajectory modeling (GBTM) was applied to pMCS to describe groups of distinct, symptom-based patient trajectories using data from patients who responded to filgotinib 200 or 100mg and continued receiving filgotinib up to week 58. Patient demographics, disease characteristics, and week 10 response were compared between the groups. Achievement of a patient-level multi-component endpoint of comprehensive disease control (CDC) was assessed in each group. GBTM identified five distinct patient populations with different response trajectories; 67.5% of patients had beneficial trajectories. The beneficial trajectory groups generally had higher proportions of patients who were recently diagnosed (<1year), were receiving filgotinib 200mg and were biologic-naive versus the relapsing trajectory groups (4%-9% vs. 4%-5%; 43%-65% vs. 36%-46%; 54%-70% vs. 35%-58%, respectively). Furthermore, 55.4% of patients had sustained beneficial trajectories, with low baseline endoscopic subscores (≥43% of patients had a subscore of 2) and strong week 10 FCP responses (≥61% of patients with >50% decrease in FCP from baseline). Sustained beneficial trajectory groups had a higher probability of achieving CDC at week 58 than other groups (31%-32% vs. 0%-7%). Beneficial long-term response trajectories and achievement of CDC with filgotinib were associated with being biologic-naive and having less severe disease at baseline. Early estimation of sustained and CDC may facilitate patient identification and development of personalized management strategies in UC. NCT02914522.

Predictors of treatment response trajectories to cognitive behavioral therapy for chronic fatigue syndrome: A cohort study

BackgroundThe response to cognitive behavioral therapy (CBT) for chronic fatigue syndrome (CFS) varies greatly between patients, but predictors of treatment success remain to be elucidated. We aimed to identify patient subgroups based on fatigue trajectory during CBT, identify pre-treatment predictors of subgroup membership, and disentangle the direction of predictor – outcome relationships over time. Methods297 individuals with CFS were enrolled in a standardized CBT program consisting of 17 sessions, with session timing variable between participants. Self-reported levels of fatigue, depressive, anxiety, and somatic symptoms, perceived stress, and positive affect were collected pre-treatment, and after 3, 10, and 15 sessions. Latent Class Growth Analysis (LCGA) was used to identify subgroups based on fatigue trajectories and baseline predictors of group membership. Cross-lagged structural equation models were used to disentangle predictor-outcome relationships. ResultsLCGA identified four fatigue trajectory subgroups, which were labelled as “no improvement” (23 %), “weak improvement” (45 %), “moderate improvement” (23 %), and “strong improvement” (9 %) classes. Higher pre-treatment levels of depressive, anxiety, and somatic symptoms, stress, and lower levels of positive affect predicted membership of the “no improvement” subgroup. Reductions in anxiety preceded reductions in fatigue, while the depressive symptoms – fatigue relationship was bidirectional. ConclusionsOn a group level, there were statistically significant reductions in fatigue after 15 sessions of CBT, with important individual differences in treatment response. Higher pre-treatment levels of anxious, depressive, and somatic symptoms and perceived stress are predictors of lack of response, with reductions in anxiety and stress preceding improvements in fatigue.

Distinct trajectories of treatment response to mepolizumab toward remission in patients with severe eosinophilic asthma.

Patients with severe eosinophilic asthma, characterised by a high disease burden, benefit from mepolizumab, which improves symptoms and reduces exacerbations, potentially leading to clinical remission in a subgroup. This study aimed to identify treatment response trajectories to mepolizumab for severe eosinophilic asthma and to assess the achievement of clinical remission.Data from the Australian Mepolizumab Registry were used to assess treatment responses at 3, 6, and 12 months. The treatment response trajectories were identified using a group-based trajectory model. The proportions achieving clinical remission at 12 months, which was defined as well-controlled symptoms, no exacerbations, and no oral corticosteroid (OCS) use for asthma management, were compared between trajectories, and baseline predictors of the trajectories were identified using logistic regression analysis.We identified three trajectory groups: group 1, responsive asthma with less OCS use (n=170); group 2, responsive late-onset asthma (n=58); and group 3, obstructed and less responsive asthma (n=70). Groups 1 and 2 demonstrated higher proportions achieving clinical remission at 36.5% and 25.9%, respectively, compared to group 3 with 5.7% (p <0.001). Baseline predictors for assigned groups included lower OCS dose in group 1; greater FEV1% predicted, higher Asthma Quality of Life Questionnaire score, higher OCS dose, and nasal polyps in group 2; with group 3 as the reference.Treatment response to mepolizumab in severe eosinophilic asthma follows 3 trajectories with varying proportions achieving clinical remission and differing baseline characteristics. Treatment response variability may influence the achievement of clinical remission with mepolizumab therapy.

Higher-order functional brain networks and anterior cingulate glutamate + glutamine (Glx) in antipsychotic-naïve first episode psychosis patients

Human connectome studies have provided abundant data consistent with the hypothesis that functional dysconnectivity is predominant in psychosis spectrum disorders. Converging lines of evidence also suggest an interaction between dorsal anterior cingulate cortex (dACC) cortical glutamate with higher-order functional brain networks (FC) such as the default mode (DMN), dorsal attention (DAN), and executive control networks (ECN) in healthy controls (HC) and this mechanism may be impaired in psychosis. Data from 70 antipsychotic-medication naïve first-episode psychosis (FEP) and 52 HC were analyzed. 3T Proton magnetic resonance spectroscopy (1H-MRS) data were acquired from a voxel in the dACC and assessed correlations (positive FC) and anticorrelations (negative FC) of the DMN, DAN, and ECN. We then performed regressions to assess associations between glutamate + glutamine (Glx) with positive and negative FC of these same networks and compared them between groups. We found alterations in positive and negative FC in all networks (HC > FEP). A relationship between dACC Glx and positive and negative FC was found in both groups, but when comparing these relationships between groups, we found contrasting associations between these variables in FEP patients compared to HC. We demonstrated that both positive and negative FC in three higher-order resting state networks are already altered in antipsychotic-naïve FEP, underscoring the importance of also considering anticorrelations for optimal characterization of large-scale functional brain networks as these represent biological processes as well. Our data also adds to the growing body of evidence supporting the role of dACC cortical Glx as a mechanism underlying alterations in functional brain network connectivity. Overall, the implications for these findings are imperative as this particular mechanism may differ in untreated or chronic psychotic patients; therefore, understanding this mechanism prior to treatment could better inform clinicians.Clinical trial registration: Trajectories of Treatment Response as Window into the Heterogeneity of Psychosis: A Longitudinal Multimodal Imaging Study, NCT03442101. Glutamate, Brain Connectivity and Duration of Untreated Psychosis (DUP), NCT02034253.

School-Based Intervention for Adolescents With ADHD: Predictors of Effects on Academic, Behavioral, and Social Functioning

Adolescents with attention-deficit/hyperactivity disorder (ADHD) experience significant academic, behavioral, and social skill difficulties including underachievement, risk for school dropout, poor peer relations, and emotion dysregulation. Although stimulant medication reduces ADHD symptoms, psychosocial and educational interventions are necessary to address functional impairments. We examined the nature and predictors of academic, behavioral, and social skills trajectories in response to multicomponent organizational and interpersonal skills training in 92 high school students with ADHD. Latent trajectory class analyses revealed positive treatment response ranging from 61.5% (report card grades) to 100% (inattention symptoms, organizational skills, social skills). Organizational skill and academic grade treatment response trajectories were predicted by assigned sex, pretreatment anxiety, and treatment dosage, while improvement in behavioral and social functioning was associated with better emotion regulation and family relations prior to treatment along with stronger working alliance with treatment coach at midtreatment. Multicomponent organizational and interpersonal skills training appears effective for most high school students with ADHD and the degree treatment-induced change is associated with multiple malleable factors can be leveraged to enhance intervention response.

A O03 Host phenotypic adversity independently predicts progression to resection in neoadjuvantly-treated pancreatic cancer

Abstract Background Neoadjuvant treatment (NAT) offers a modest survival benefit to the otherwise dismal outcomes of pancreatic ductal adenocarcinoma (PDAC), however methods for assessing treatment response and disease trajectory during NAT are limited. Cachexia and sarcopenia are hallmark features of PDAC and defining host phenotypic adversity is crucial in understanding the tumour-host relationship and its influence on clinical outcomes. We sought to determine the relationship between body composition, systemic inflammation, and outcomes in NAT PDAC to lay foundations for improving assessment of treatment response. Methods This is a retrospective, observational study with an intention-to-treat design, analysing resected (n = 109) and non-resected (n = 117) NAT PDAC (total n = 226) from the tertiary West of Scotland Pancreatic Unit, Glasgow Royal Infirmary. Patients were included if they had resectable or borderline resectable disease and received NAT. Patients were excluded if they had locally advanced or metastatic disease. Body composition was determined by single-slice L3 cross-sectional measurements. Results Patients with an adverse phenotype defined by change in body composition were less likely to progress to resection (40.9% vs 68.8%, p &amp;lt; 0.001). Logistic regression confirms the independence of this effect (OR 3.79, 95% CI 1.99-7.22, p &amp;lt; 0.001). There was a trend towards systemic inflammation in the non-resected group (CRP rise 51.4% vs 29.9%, p = 0.008). Patients treated with Folfirinox who required dose reduction received higher doses of irinotecan, 5-fluorouracil (bolus) and 5-fluorouracil (infusion) per unit muscle mass (7.3 vs 6.9mg/cm2⋅m-2, p = 0.010; 16.2 vs 14.7mg/cm2⋅m-2, p = 0.002; 96.5 vs 91.1mg/cm2⋅m-2, p = 0.026). Conclusions Host phenotypic adversity during NAT presents a potentially valuable addition to a limited arsenal of tools to aid decision making in resectable and borderline resectable PDAC. Early assessment of body composition trend during chemotherapy may present an opportunity to switch the host phenotype from adverse to favourable in some patients, or highlight the futility of continuing aggressive treatment in others. Chemotherapeutic prescribing by body surface area overlooks crucial differences in lean body mass and may be exposing patients to avoidable and burdensome toxicities.

Single trajectory treatment response for predominant negative symptoms: Post-hoc analysis of a clinical trial with cariprazine and risperidone.

Examining the heterogeneity of negative symptoms of schizophrenia contributes to the identification of available treatment targets. Generally, prior evidence classified three to four symptom treatment response trajectory groups over the course of positive symptoms, yet, no evidence exists regarding the heterogeneity of medium-term response to predominant negative symptoms. The current post-hoc analysis aims to identify the heterogeneity in negative symptom treatment response trajectories among patients with predominant negative symptoms who received either cariprazine or risperidone for 26weeks. Treatment response was analyzed based on the: the Positive and Negative Syndrome Scale Factor Score for Negative Symptoms (PANSS-FSNS), and the Clinical Global Impression Severity (CGIS) and Improvement (CGII) scales. To identify subgroups of patients with a similar course of treatment response, group-based trajectory modelling was utilized. Results demonstrated that in comparison with competing models, a single trajectory best described the treatment response of patients with predominant negative symptoms. The results indicate that patients with predominant negative symptoms with over ten years of schizophrenia respond rapidly to adequate treatment and follow a course of steady improvement.

“Trajectories of treatment response in a cognitive-behavioral therapy intervention for depression and adherence in persons with HIV in South Africa”

BackgroundPeople with HIV (PHW) are at greater risk of depression than the general population. Insight into the time-to-treatment-response and predictors of response to psychotherapy may improve implementation in primary care. MethodsWe assessed depression treatment response among 80 participants in a trial of cognitive-behavioral therapy for adherence and depression (CBT-AD) for PWH with MDD and suboptimal antiretroviral therapy (ART) adherence. Participants self-reported depressive symptoms (CESD) at each therapy session. Clinicians assessed participants' depression (HAMD), along with potential predictors of response, every four months for one year. Latent class analyses examined classes of responders for the active and the post-treatment phases. Regression analyses identified predictors of class membership for each phase. ResultsDuring the active treatment phase (CESD) we identified an early response (at session 2 and with continued trajectory of improvement) and a non-response group. There were also two classes during post-treatment (HAM-D): early responders (4-month) and late responders (12-month). Distress aversion was associated with lower likelihood of early response to CBT-AD (aOR = 0.74, 95%CI[0.56–0.90], p = .009), and social support was associated with increased likelihood of early response (aOR = 2.24, 95%CI[1.07–5.46], p = .045). LimitationsSelf-reported depression during the treatment phase may have resulted from social desirability bias. ConclusionsMost participants responded to CBT-AD early during treatment (89 %) and had sustained improvements in depression by 4 months (80 %). Distress aversion was a risk factor for late response, and social support was protective. Future research is needed to assess the optimal dose of CBT-AD in resource limited settings.

The levels of immunosuppressive checkpoint protein PD-L1 and tumor-infiltrating lymphocytes were integrated to reveal the glioma tumor microenvironment.

In spite of significant strides in the realm of cancer biology and therapeutic interventions, the clinical prognosis for patients afflicted with glioblastoma (GBM) remains distressingly dismal. The tumor immune microenvironment (TIME), a crucial player in the progression, treatment response, and prognostic trajectory of glioma, warrants thorough exploration. Within this intricate microcosm, the immunosuppressive checkpoint protein PD-L1 and tumor-infiltrating lymphocytes (TILs) emerge as pivotal constituents, underscoring their potential role in deciphering glioma biology and informing treatment strategies. However, prognostic models based on the association between PD-L1 expression and TIL infiltration in the tumor immune microenvironment have not been established. The aim of this study was to explore TIME genes associated with PD-L1 expression and TIL invasion and to construct a risk score for predicting the overall survival (OS) of GBM patients based on these genes. The samples were separately classified according to the PD-L1 expression level and TIL score and TIME-related genes were identified using differential expression and weighted gene co-expression network analysis. The DEGs were subjected to least absolute contraction and selection operator (LASSO) -Cox regression to construct TIME associated risk score (TIMErisk). A TIMErisk was developed based on STEAP3 and CXCL13 genes. The STLEAP3 was demonstrated to be involved in glioma progression. The results showed that the patients in the high TIMErisk group had poor OS compared with subjects in the low TIMErisk group. The biological phenotypes associated with TIMErisk were analyzed in terms of functional enrichment, tumor immune profile, and tumor mutation profile. The results on tumor immune dysfunction and exclusion dysfunction (TIDE) score and immune surface score (IPS) showed that GBM patients with different TIME risks had different responses to immunotherapy. Tumor purity analysis indicated that PD-L1 and TIL scores were positively correlated with TIMErisk score and negatively correlated with tumor purity. These results show that the TIMErisk-based prognostic model had high predictive value for the prognosis and immune characteristics of GBM patients. Immunohistochemical staining images of patients in the high and low TIMErisk groups were analyzed, showing that the degree of immune cell infiltration was higher in the high TIMErisk group relative to the low TIMErisk group. The present study provides a basis for understanding glioma tumor microenvironment and a foundation for conducting comprehensive immunogenomic analysis.

Early effects predict trajectories of response to esketamine in treatment-resistant depression

BackgroundThe efficacy of esketamine in treatment-resistant depression (TRD) has been confirmed. However, its administration is expensive and restrictive, with limited knowledge on how long the treatment should be continued. Predicting the treatment outcome would benefit patients and alleviate the global treatment cost. We aimed to define distinct trajectories of treatment response and assess their predictability. MethodsIn this longitudinal study, two independent samples of patients with unipolar or bipolar TRD were treated with esketamine in real-world settings. Depression severity was assessed using the Montgomery-Åsberg Depression Rating Scale (MADRS) before each esketamine administration. Latent class analyses were used to define trajectories of response. ResultsIn the original sample (N = 50), we identified two classes whose trajectories depicted response and non-response, respectively. The model was validated in the confirmatory sample (N = 55). Class membership was influenced by a few baseline characteristics such as concomitant benzodiazepine medication, number of depressive episodes or polarity. On the other hand, after only two esketamine administrations, the MADRS score predicted the 90-day trajectory of response with an accuracy of 80 %. LimitationsThis observational study is not placebo-controlled. Therefore, its results and their generalizability need to be confirmed in experimental settings. ConclusionsAfter the first administrations of esketamine, the MADRS score has a good capacity to predict the most plausible trajectory of response. While thresholds and their predictive values need to be confirmed, this finding suggests that clinicians could base on MADRS scores their decision to discontinue treatment because of poor remaining chances of treatment response.

Supervised learning of high-confidence phenotypic subpopulations from single-cell data

Accurately identifying phenotype-relevant cell subsets from heterogeneous cell populations is crucial for delineating the underlying mechanisms driving biological or clinical phenotypes. Here by deploying a Learning with Rejection strategy, we developed a novel supervised learning framework called PENCIL to identify subpopulations associated with categorical or continuous phenotypes from single-cell data. By embedding a feature selection function into this flexible framework, for the first time, we were able to simultaneously select informative features and identify cell subpopulations, enabling accurate identification of phenotypic subpopulations otherwise missed by methods incapable of concurrent gene selection. Furthermore, the regression mode of PENCIL presents a novel ability for supervised phenotypic trajectory learning of subpopulations from single-cell data. We conducted comprehensive simulations to evaluate PENCIL’s versatility in simultaneous gene selection, subpopulation identification and phenotypic trajectory prediction. PENCIL is fast and scalable to analyse one million cells within 1 h. Using the classification mode, PENCIL detected T-cell subpopulations associated with melanoma immunotherapy outcomes. Moreover, when applied to single-cell RNA sequencing of a patient with mantle cell lymphoma with drug treatment across multiple timepoints, the regression mode of PENCIL revealed a transcriptional treatment response trajectory. Collectively, our work introduces a scalable and flexible infrastructure to accurately identify phenotype-associated subpopulations from single-cell data. To detect phenotype-related cell subpopulations from single-cell data, appropriate feature sets need to be chosen or learned simultaneously. Ren et al. present here a tool based on Learning with Rejection, a method that during training learns features from cells that can be predicted with high confidence, while cells that the model is not yet certain about are rejected.

Modeling the antidepressant treatment response to transcranial magnetic stimulation using an exponential decay function

Recovery from depression often demonstrates a nonlinear pattern of treatment response, where the largest reduction in symptoms is observed early followed by smaller improvements. This study investigated whether this exponential pattern could model the antidepressant response to repetitive transcranial magnetic stimulation (TMS). Symptom ratings from 97 patients treated with TMS for depression were collected at baseline and after every five sessions. A nonlinear mixed-effects model was constructed using an exponential decay function. This model was also applied to group-level data from several published clinical trials of TMS for treatment-resistant depression. These nonlinear models were compared to corresponding linear models. In our clinical sample, response to TMS was well modeled with the exponential decay function, yielding significant estimates for all parameters and demonstrating superior fit compared to a linear model. Similarly, when applied to multiple studies comparing TMS modalities as well as to previously identified treatment response trajectories, the exponential decay models yielded consistently better fits compared to linear models. These results demonstrate that the antidepressant response to TMS follows a nonlinear pattern of improvement that is well modeled with an exponential decay function. This modeling offers a simple and useful framework to inform clinical decisions and future studies.

Machine Learning of Multi-Modal Tumor Imaging Reveals Trajectories of Response to Precision Treatment.

The standard assessment of response to cancer treatments is based on gross tumor characteristics, such as tumor size or glycolysis, which provide very indirect information about the effect of precision treatments on the pharmacological targets of tumors. Several advanced imaging modalities allow for the visualization of targeted tumor hallmarks. Descriptors extracted from these images can help establishing new classifications of precision treatment response. We propose a machine learning (ML) framework to analyze metabolic-anatomical-vascular imaging features from positron emission tomography, ultrafast Doppler, and computed tomography in a mouse model of paraganglioma undergoing anti-angiogenic treatment with sunitinib. Imaging features from the follow-up of sunitinib-treated (n = 8, imaged once-per-week/6-weeks) and sham-treated (n = 8, imaged once-per-week/3-weeks) mice groups were dimensionally reduced and analyzed with hierarchical clustering Analysis (HCA). The classes extracted from HCA were used with 10 ML classifiers to find a generalized tumor stage prediction model, which was validated with an independent dataset of sunitinib-treated mice. HCA provided three stages of treatment response that were validated using the best-performing ML classifier. The Gaussian naive Bayes classifier showed the best performance, with a training accuracy of 98.7 and an average area under curve of 100. Our results show that metabolic-anatomical-vascular markers allow defining treatment response trajectories that reflect the efficacy of an anti-angiogenic drug on the tumor target hallmark.

Child-therapist interaction features impact Autism treatment response trajectories

BackgroundIdentifying mechanisms of change in Autism treatment may help explain response variability and maximize efficacy. For this, the child-therapist interaction could have a key role as stressed by developmental models of intervention, but still remains under-investigated. AimsThe longitudinal study of treatment response trajectories considering both baseline and child-therapist interaction features by means of predictive modeling. Methods and ProceduresN = 25 preschool children were monitored for one year during Naturalistic Developmental Behavioral Intervention. N = 100 video-recorded sessions were annotated with an observational coding system at four time points, to extract quantitative interaction features. Outcomes and ResultsBaseline and interaction variables were combined to predict response trajectories at one year, and achieved the best predictive performance. The baseline developmental gap, therapist’s efficacy in child engagement, respecting children’s timing after fast behavioral synchronization, and modulating the interplay to prevent child withdrawal emerged as key factors. Further, changes in interaction patterns in the early phase of the intervention were predictive of the overall response to treatment. Conclusions and ImplicationsClinical implications are discussed, stressing the importance of promoting emotional self-regulation during intervention and the possible relevance of the first period of intervention for later response.

Specific Contamination Symptoms are Associated with Experiencing a Limited Response of Cognitive-Behavioral Therapy in Pediatric Patients with OCD.

A recent study identified three distinct treatment-response trajectories in pediatric OCD where higher levels of contamination symptoms predicted a limited response to cognitive-behavioral therapy (CBT). This study extends these findings by examining which specific symptoms characterize limited CBT response from baseline to 3-year follow-up, with an emphasis on contamination symptoms. The study sample comprised 269 pediatric patients with OCD, all receiving stepped-care treatment with manualized CBT. Differences in single item-reporting between the three trajectory groups were examined using linear mixed-effect modeling. Limited responders displayed a higher symptom load across all OCD symptom categories at 3-year follow-up, dominated by contamination symptoms. Five of these (obsessions about dirt and germs, about bodily fluids, about the feeling of contamination and compulsions regarding handwashing and showering) showed persistence from baseline to 3-year follow-up. The results indicate that presence of specific contamination symptoms may influence long-term symptom severity trajectories in young patients with OCD.

Cognitive function and treatment response trajectories in first-episode schizophrenia: evidence from a prospective cohort study

ObjectivesThis prospective cohort study tested for associations between baseline cognitive performance in individuals early within their first episode and antipsychotic treatment of psychosis. We hypothesised that poorer cognitive functioning at...

Treatment response trajectories in residential PTSD programs for veterans: A national cohort investigation

Although improving residential PTSD care is a priority for the Department of Veterans Affairs, previous evaluations have been limited by a lack of systematic data collection across more than two timepoints. This study used recently available data to assess symptom trajectories in a large, national sample of veterans who engaged in residential PTSD treatment. Group-based trajectory analysis PROC TRAJ was used to identify PTSD residential treatment response in a national cohort of veterans (n = 10,832) and the subset of veterans (n = 6515) receiving evidence-based psychotherapy (EBP). PTSD symptoms were assessed at intake, discharge, and 4-month follow-up. Predictors of trajectory membership were estimated using multinomial models. For the full cohort, a three-group trajectory model provided the best fit with the following identified groups: “Severe/Stable” (51.8%), “Moderate/Rebound” (40.1%), and “Mild/Rebound” (8.1%). For the EBP sub-cohort, a three-group trajectory model was selected with the following groups: “Severe/Stable” (58.5%), “Moderate/Rebound” (34.1%), and “Mild/Rebound” (7.4%). Across all trajectories, psychological distress, pain severity, substance use, Iraq/Afghanistan combat era, non-White race, and treatment dropout were associated with poorer treatment response. In the EBP sub-cohort, homelessness and unemployment at the time of admission were also associated with poorer treatment outcomes to varying degrees. This study demonstrates that residential treatment for PTSD is associated with heterogeneous treatment trajectories which highlight the need to continue to explore and improve residential PTSD treatment outcomes. Our results underscore the importance of obtaining follow-up data and identifying ways to maintain therapeutic gains following discharge.

Heterogeneity in Response to MCT and Psychoeducation: A Feasibility Study Using Latent Class Mixed Models in First-Episode Psychosis.

Metacognitive training (MCT) is an effective treatment for psychosis. Longitudinal trajectories of treatment response are unknown but could point to strategies to maximize treatment efficacy during the first episodes. This work aims to explore the possible benefit of using latent class mixed models (LCMMs) to understand how treatment response differs between metacognitive training and psychoeducation. We conducted LCMMs in 28 patients that received MCT and 34 patients that received psychoeducation. We found that MCT is effective in improving cognitive insight in all patients but that these effects wane at follow-up. In contrast, psychoeducation does not improve cognitive insight, and may increase self-certainty in a group of patients. These results suggest that LCMMs are valuable tools that can aid in treatment prescription and in predicting response to specific treatments.

Using machine learning to investigate earning capacity in patients undergoing psychosomatic rehabilitation-A retrospective health data analysis.

Psychiatric disorders increasingly contribute to disability and early retirement. This study was conducted to investigate whether machine learning can contribute to a better understanding and assessment of such a reduced earning capacity. It analyzed whether impaired earning capacity is reflected in missing treatment effects, and which interventions drive treatment effects during psychosomatic rehabilitation. Analyses were based on routine clinical data encompassing demographics, diagnoses, psychological questionnaires before, and after treatment, interventions, and an interdisciplinary assessment of earning capacity for N = 1,054 patients undergoing psychosomatic rehabilitation in 2019. Classification of patients by changes in self-reported mental health and interventions predictive of changes were analyzed by gradient boosted model. Clustering results revealed three major groups, one of which was comprised almost exclusively of patients with full earning capacity, one of patients with reduced or lost earning capacity and a third group with mixed assessments. Classification results (Kappa = 0.22) indicated that patients experienced modestly divergent changes over the course of rehabilitation. Relative variable influence in the best model was highest for changes in psychological wellbeing (HEALTH-49). Regression analysis identified intervention A620 (physical exercise therapy with psychological goal setting) as most influential variable predicting changes in psychological wellbeing with a model fit of R 2 = 0.05 (SD = 0.007). Results suggest that disability due to psychiatric disorders does associate with distinct demographic and clinical characteristics but may be less clear-cut in a subgroup of patients. Trajectories of treatment response show moderately divergent paths between patient groups. Moreover, results support both physical exercise therapy as efficient intervention in reducing disability-associated impairments and the complementarity of a multimodal treatment plan.

3.92 Specific Contamination Symptoms Are Associated With Experiencing a Limited Response From CBT in Pediatric OCD Patients

The first-line treatment for pediatric OCD is CBT. A recent study identified 3 distinct treatment-response trajectories during and after treatment in a large group of children with OCD and found that higher levels of contamination symptoms predicted being a limited responder to CBT. This study is an extension of this, examining what characterizes this group of limited responders to CBT regarding contamination symptoms from baseline to 3-year follow-up compared to acute and slow responders to CBT.