Dataset Information

Prognostic subgroups for remission and response in the Coordinated Anxiety Learning and Management (CALM) trial.

ABSTRACT: Most patients with anxiety disorders receive treatment in primary care settings. Limited moderator data are available to inform clinicians of likely prognostic outcomes for individual patients. We identify baseline characteristics associated with outcome in adults seeking treatment for anxiety disorders.We conducted an exploratory moderator analysis from the Coordinated Anxiety Learning and Management (CALM) trial. In the CALM trial, 1,004 adults who met DSM-IV criteria for generalized anxiety disorder (GAD), panic disorder, social anxiety disorder, and/or posttraumatic stress disorder (PTSD) were randomized to usual care (UC) or a collaborative care intervention (ITV) of cognitive-behavioral therapy and/or pharmacotherapy between June 2006 and April 2008. Logistic regression was used to examine baseline characteristics associated with remission and response overall and by treatment condition. Receiver operating curve (ROC) analyses identified subgroups associated with similar likelihood of response and remission of global anxiety symptoms. Remission was defined as score < 6 on the 12-item Brief Symptom Inventory (BSI-12) anxiety and somatization subscales. Response was defined as at least 50% reduction on BSI-12, or meeting remission criteria.Randomization to ITV over UC was often the strongest predictor of outcome. Several baseline patient characteristics were associated with poor treatment outcome including comorbid depression, increased severity of underlying anxiety disorder(s) (P < .001), low socioeconomic status (perceived [P < .001] and actual [P < .05]), and limited social support (P < .001). Patient characteristics associated with particular benefit from ITV were being female (P < .05), increased depression (P < .01)/GAD severity (P < .05), and low socioeconomic status (P < .05). ROC analysis demonstrated prognostic subgroups with large differences in response likelihood.Further research should focus on the effectiveness of implementing the ITV intervention of CALM in community treatment centers where patients typically are of low socioeconomic status and may particularly benefit from ITV.ClinicalTrials.gov identifier: NCT00347269.

SUBMITTER: Kelly JM

PROVIDER: S-EPMC4930076 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Prognostic subgroups for remission and response in the Coordinated Anxiety Learning and Management (CALM) trial.

Kelly J MacLaren JM Jakubovski Ewgeni E Bloch Michael H MH

The Journal of clinical psychiatry 20150301 3

<h4>Objective</h4>Most patients with anxiety disorders receive treatment in primary care settings. Limited moderator data are available to inform clinicians of likely prognostic outcomes for individual patients. We identify baseline characteristics associated with outcome in adults seeking treatment for anxiety disorders.<h4>Method</h4>We conducted an exploratory moderator analysis from the Coordinated Anxiety Learning and Management (CALM) trial. In the CALM trial, 1,004 adults who met DSM-IV c ...[more]

PMID: 25562579

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Project description:ObjectiveFew data exist to help clinicians predict likelihood of treatment response in individual patients with major depressive disorder (MDD). Our aim was to identify subgroups of MDD patients with differential treatment outcomes based on presenting clinical characteristics. We also sought to quantify the likelihood of treatment success based on the degree of improvement and side effects after 2 and 4 weeks of selective serotonin reuptake inhibitor (SSRI) pharmacotherapy.MethodWe analyzed data from the first treatment phase of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial, in which subjects with a DSM-IV diagnosis of MDD were treated for 8-14 weeks with open-label citalopram. A receiver operating characteristic (ROC) analysis was conducted to determine homogenous subgroups with different rates of response and remission in depressive symptoms. Included predictor variables were initial clinical characteristics, initial improvement, and side effects after 2 and 4 weeks of SSRI treatment. The primary outcome measures were treatment response (defined as a greater than 50% reduction in 17-item Hamilton Depression Rating Scale [HDRS-17] score from baseline) and remission (defined as an HDRS-17 score ≤ 17).ResultsBaseline clinical characteristics were able to identify subgroups from a low likelihood of response of 18% (income < $10,000, comorbid generalized anxiety disorder, < 16 years of education; P < .01) to a high likelihood of response of 68% (income ≥ $40,000, no comorbid posttraumatic stress disorder; P < .01). Among baseline clinical characteristics, employment status (N = 2,477; χ²₁ = 78.1; P < .001) and income level (N = 2,512; χ²₁ = 77.7; P < .001) were the most informative in predicting treatment outcome. For the models at weeks 2 and 4, treatment success was best predicted by early symptom improvement.ConclusionsSocioeconomic data such as low income, education, and unemployment were most discriminative in predicting a poor response to citalopram, even with disparities in access to care accounted for. This finding implies that socioeconomic factors may be more useful predictors of medication response than traditional psychiatric diagnoses or past treatment history.Trial registrationClinicalTrials.gov identifier: NCT00021528.

Project description:Investigators recently tested the effectiveness of a collaborative-care intervention for anxiety disorders: Coordinated Anxiety Learning and Management(CALM) []) in 17 primary care clinics around the United States. Investigators also conducted a qualitative process evaluation. Key research questions were as follows: (1) What were the facilitators/barriers to implementing CALM? (2) What were the facilitators/barriers to sustaining CALM after the study was completed?Key informant interviews were conducted with 47 clinic staff members (18 primary care providers, 13 nurses, 8 clinic administrators, and 8 clinic staff) and 14 study-trained anxiety clinical specialists (ACSs) who coordinated the collaborative care and provided cognitive behavioral therapy. The interviews were semistructured and conducted by phone. Data were content analyzed with line-by-line analyses leading to the development and refinement of themes.Similar themes emerged across stakeholders. Important facilitators to implementation included the perception of "low burden" to implement, provider satisfaction with the intervention, and frequent provider interaction with ACSs. Barriers to implementation included variable provider interest in mental health, high rates of part-time providers in clinics, and high social stressors of lower socioeconomic-status patients interfering with adherence. Key sustainability facilitators were if a clinic had already incorporated collaborative care for another disorder and presence of onsite mental health staff. The main barrier to sustainability was funding for the ACS.The CALM intervention was relatively easy to incorporate during the effectiveness trial, and satisfaction was generally high. Numerous implementation and sustainability barriers could limit the reach and impact of widespread adoption. Findings should be interpreted with the knowledge that the ACSs in this study were provided and trained by the study. Future research should explore uptake of CALM and similar interventions without the aid of an effectiveness trial.

Project description:BackgroundMiddle-aged adults (40-65 years) report higher stress levels than most other age groups. There is a need to determine the feasibility of using a meditation app to reduce stress and improve stress-related outcomes in middle-aged adults with a focus on men, as previous meditation app-based studies have reported a low proportion of or even no male participants.ObjectiveThis study aims to (1) determine the feasibility (ie, acceptability and demand with a focus on men) of a consumer-based meditation app (Calm), to reduce stress among middle-aged adults reporting elevated stress levels, and (2) explore the preliminary effects of Calm on perceived stress, psychological outcomes (anxiety, depressive symptoms, mindfulness, and general coping), health behaviors (physical activity and eating habits), and COVID-19 perceptions.MethodsThis feasibility randomized controlled trial evaluated an app-based meditation intervention in middle-aged adults (N=83) with elevated stress levels (ie, Perceived Stress Scale score ≥15) and limited or no previous experience with meditation. Participants were randomized to the intervention group (Calm app) or a control (educational podcasts; POD) group. Participants completed self-report assessments at baseline and postintervention (week 4). Feasibility was measured as acceptability and demand using Bowen framework. Feasibility and COVID-19 perceptions data were examined using descriptive statistics, and preliminary effects were evaluated using repeated measures analysis of variance.ResultsParticipants were satisfied with Calm (27/28, 96%) and found it appropriate or useful (26/28, 93%). Most reported they would likely continue using the Calm app (18/28, 64%). More Calm users reported satisfaction, appropriateness or usefulness, and intent to continue app use than POD users. Calm users (n=33) completed a mean of 20 (SD 31.1) minutes of meditation on the days they meditated and 103 (SD 109.1) minutes of meditation per week. The average adherence rate to the prescribed meditation was 71% among Calm app users, compared to 62% among POD users. Recruitment rate of men was 35% (29/83). Of those randomized to Calm, 55% (15/29) were men, and retention among them was higher (14/15, 93%) than that among women (12/20, 60%). No significant within or between group differences were observed.ConclusionsA 4-week, app-based mindfulness meditation intervention (Calm) may be feasible for middle-aged adults and a useful stress-management tool. Calm users expressed satisfaction with the app and felt it was appropriate and useful. Significant improvements in perceived stress and psychological outcomes or stress-related health behaviors were not observed. Even though men spent less time in meditation than women did and completed fewer weekly sessions, they were more likely to adhere to the prescription. Further research is needed for improving stress and stress-related outcomes among middle-aged adults with emphasis on the effects of mindfulness meditation apps for men.Trial registrationClinicalTrials.gov NCT04272138; https://clinicaltrials.gov/ct2/show/NCT04272138.

Project description:BackgroundLiver cancer (Hepatocellular carcinoma; HCC) prevalence is increasing and with poor clinical outcome expected it means greater understanding of HCC aetiology is urgently required. This study explored a deep learning solution to detect biologically important features that distinguish prognostic subgroups. A novel architecture of an Artificial Neural Network (ANN) trained with a customised objective function (LRSC) was developed. The ANN should discover new data representations, to detect patient subgroups that are biologically homogenous (clustering loss) and similar in survival (survival loss) while removing noise from the data (reconstruction loss). The model was applied to TCGA-HCC multi-omics data and benchmarked against baseline models that only use a reconstruction objective function (BCE, MSE) for learning. With the baseline models, the new features are then filtered based on survival information and used for clustering patients. Different variants of the customised objective function, incorporating only reconstruction and clustering losses (LRC); and reconstruction and survival losses (LRS) were also evaluated. Robust features consistently detected were compared between models and validated in TCGA and LIRI-JP HCC cohorts.ResultsThe combined loss (LRSC) discovered highly significant prognostic subgroups (P-value = 1.55E-77) with more accurate sample assignment (Silhouette scores: 0.59-0.7) compared to baseline models (0.18-0.3). All LRSC bottleneck features (N = 100) were significant for survival, compared to only 11-21 for baseline models. Prognostic subgroups were not explained by disease grade or risk factors. Instead LRSC identified robust features including 377 mRNAs, many of which were novel (61.27%) compared to those identified by the other losses. Some 75 mRNAs were prognostic in TCGA, while 29 were prognostic in LIRI-JP also. LRSC also identified 15 robust miRNAs including two novel (hsa-let-7g; hsa-mir-550a-1) and 328 methylation features with 71% being prognostic. Gene-enrichment and Functional Annotation Analysis identified seven pathways differentiating prognostic clusters.ConclusionsCombining cluster and survival metrics with the reconstruction objective function facilitated superior prognostic subgroup identification. The hybrid model identified more homogeneous clusters that consequently were more biologically meaningful. The novel and prognostic robust features extracted provide additional information to improve our understanding of a complex disease to help reveal its aetiology. Moreover, the gene features identified may have clinical applications as therapeutic targets.

Dataset Information

Prognostic subgroups for remission and response in the Coordinated Anxiety Learning and Management (CALM) trial.

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Prognostic subgroups for remission and response in the Coordinated Anxiety Learning and Management (CALM) trial.

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