Determinants of patient-reported outcome trajectories and symptomatic recovery in Improving Access to Psychological Therapies (IAPT) services

Predictors of Symptom Reduction and Remission Among People with Anxiety: Secondary Analyses from a Randomized Controlled Trial

Despite the substantial disease burden of anxiety disorders, only limited or conflicting data on prognostic factors is available. Most studies include patients in the secondary healthcare sector thus, the generalizability of findings is limited. The present study examines predictors of symptom reduction and remission in patients with anxiety disorders in a primary care setting. 214 patients with anxiety disorders, recruited as part of the Collabri Flex trial, were included in secondary analyses. Data on potential predictors of anxiety symptoms at 6-month follow-up was collected at baseline, including patient characteristics related to demography, illness, comorbidity, functional level, life quality, and self-efficacy. The outcomes were symptom reduction and remission. Univariate and multivariate linear and logistic regression analyses were conducted to assess the associations between predictor variables and the outcome, and machine-learning methods were also applied. In multiple linear regression analysis, anxiety severity at baseline (β = -6.05, 95% CI = -7.54,-4.56, p < 0.001) and general psychological problems and symptoms of psychopathology (SCL-90-R score) (β = 2.19, 95% CI = 0.24,4.14, p = 0.028) were significantly associated with symptom change at 6 months. Moreover, self-efficacy was associated with the outcome, however no longer significant in the multiple regression model. In multiple logistic regression analysis, anxiety severity at baseline (OR = 0.54, 95% CI = -1.13,-0.12, p = 0.018) was significantly associated with remission at 6 months. There was no predictive performance of the machine-learning models. Our study contributes with information that could be valuable knowledge for managing anxiety disorders in primary care.

Real-World Effectiveness and Predictors of Nurse-Led Individual Cognitive Behavioral Therapy for Mental Disorders: An Updated Pragmatic Retrospective Cohort Study

The importance of nurses integrating effective psychological techniques into their clinical practice is widely recognized. Nevertheless, further evidence from real-world settings is needed to establish nurse-led cognitive behavioural therapy (CBT) as an effective approach in clinical practice. This study aimed to examine the clinical effectiveness and predictors of individual CBT for mental disorders delivered by nurses in various routine clinical settings. This pragmatic retrospective cohort study collected data from participants who received nurse-led individual CBT at four institutions from different prefectures in Japan between April 2015 and March 2023. During the study period, 280 clients were referred to nurses for CBT, 240 of whom received nurse-led individual CBT of at least one session. The common primary diagnoses among participants were major depressive disorder (33.8%), social phobia (12.9%), and obsessive-compulsive disorder (10.0%). Of these, 23 participants were ongoing cases at the end of the observation period, and 217 who had completed the course of therapy or discontinued/dropped out from the therapy were included in the analysis (173 completed and 44 discontinued/dropped out (i.e., dropout rate = 20.3%)). Based on the clinical significance definition (primary outcome), 62.4% of the participants who completed the therapy were judged to demonstrate positive clinical significance (recovered or improved), with only a few participants (6.9%) demonstrating deterioration. Significant improvements were observed before and after nurse-led individual CBT across all secondary outcomes, including depression and anxiety symptoms, health-related quality of life, and functional disability (all ps ≤ 0.001). Univariate logistic regression revealed that clients with higher baseline severity of depression and anxiety symptoms were less likely to achieve positive clinical significance following nurse-led individual CBT. The real-world evidence gained through this study will encourage frontline nurses and motivate institutional/organizational leaders and policymakers to employ nurse-led individual CBT, especially for depression and anxiety-related disorders.

Predictors of primary care psychological therapy outcomes for depression and anxiety in people living with dementia: evidence from national healthcare records in England

BACKGROUND

Psychological therapies can be effective in reducing symptoms of depression and anxiety in people living with dementia (PLWD). However, factors associated with better therapy outcomes in PLWD are currently unknown.

AIMS

To investigate whether dementia-specific and non-dementia-specific factors are associated with therapy outcomes in PLWD.

METHOD

National linked healthcare records were used to identify 1522 PLWD who attended psychological therapy services across England. Associations between various factors and therapy outcomes were explored.

RESULTS

People with frontotemporal dementia were more likely to experience reliable deterioration in depression/anxiety symptoms compared with people with vascular dementia (odds ratio 2.98, 95% CI 1.08-8.22; P = 0.03) or Alzheimer's disease (odds ratio 2.95, 95% CI 1.15-7.55; P = 0.03). Greater depression severity (reliable recovery: odds ratio 0.95, 95% CI 0.92-0.98, P < 0.001; reliable deterioration: odds ratio 1.73, 95% CI 1.04-2.90, P = 0.04), lower work and social functioning (recovery: odds ratio 0.98, 95% CI 0.96-0.99, P = 0.002), psychotropic medication use (recovery: odds ratio 0.67, 95% CI 0.51-0.90, P = 0.01), being of working age (recovery: odds ratio 2.03, 95% CI 1.10-3.73, P = 0.02) and fewer therapy sessions (recovery: odds ratio 1.12, 95% CI 1.09-1.16, P < 0.001) were associated with worse therapy outcomes in PLWD.

CONCLUSIONS

Dementia type was generally not associated with outcomes, whereas clinical factors were consistent with those identified for the general population. Additional support and adaptations may be required to improve therapy outcomes in PLWD, particularly in those who are younger and have more severe depression.

How has our primary-care NHS-IAPT provision for PTSD adapted to the pandemic? A service evaluation of recovery pre-COVID-19 and peri-COVID-19

BACKGROUND

Mental health issues have been an ongoing major cause of global disability exacerbated by the COVID-19 pandemic. The unique challenges have been the high contagiousness of COVID-19 and atypical PTSD presentations e.g., ICU-PTSD. This has led to increased demand on mental health services which have had to vary their provision for example working remotely vs. the traditional face-to-face. The pandemic has also exposed the preexisting health inequalities related to sociodemographic variables. In the UK, NHS-IAPT is the main primary-care provider which has suffered these repercussions. Research from COVID-19 and previous viral outbreaks has estimated an increase in the prevalence of PTSD. Although services have been urged to monitor their provision, research on PTSD remains scanty. The current NHS-IAPT service was concerned about these ramifications of the pandemic and also wished to address the gap in the research. The aim was to conduct an evaluation of the impact of the COVID-19 on PTSD recovery. The first question evaluated the impact, and the second question evaluated the associated variables.

METHODS

The study employed a quantitative data analysis method. Data were extracted and analysed from the electronic database, IAPTus. The study evaluated PTSD recovery rates during pre-pandemic and peri-pandemic periods. The comparisons determined the impact of the pandemic as well as what recovery variables were significant. The data were analysed statistically using both descriptive statistics and inferential statistics (t-test and Chi-square). The data were analyzed in reference to the national NHS-IAPT standards via NHS-Digital.

RESULTS

The findings suggest that the pandemic had no significant impact on overall PTSD recovery rates, which also aligned with the national standards. These recovery rates fell below the target national standard of 50% regardless of the pandemic. Several client, service and treatment variables were shown to be associated with PTSD recovery rates.

CONCLUSIONS

This evaluation highlights a pre-existing problem around the persistently low PTSD recovery rates. It also identifies variables that warrant further research in order to improve PTSD service-provision and mitigate any long-term pandemic impacts. This study also provides information for other services wishing to enhance their PTSD recovery rates.

Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach

Objectives

Anxiety and mood disorders greatly affect the quality of life for individuals worldwide. A substantial proportion of patients do not sufficiently improve during evidence-based treatments in mental healthcare. It remains challenging to predict which patients will or will not benefit. Moreover, the limited research available on predictors of treatment outcomes comes from efficacy RCTs with strict selection criteria which may limit generalizability to a real-world context. The current study evaluates the performance of different machine learning (ML) models in predicting non-improvement in an observational sample of patients treated in routine specialized mental healthcare.

Methods

In the current longitudinal exploratory prediction study diagnosis-related, sociodemographic, clinical and routinely collected patient-reported quantitative outcome measures were acquired during treatment as usual of 755 patients with a primary anxiety, depressive, obsessive compulsive or trauma-related disorder in a specialized outpatient mental healthcare center. ML algorithms were trained to predict non-response (< 0.5 standard deviation improvement) in symptomatic distress 6 months after baseline. Different models were trained, including models with and without early change scores in psychopathology and well-being and models with a trimmed set of predictor variables. Performance of trained models was evaluated in a hold-out sample (30%) as a proxy for unseen data.

Results

ML models without early change scores performed poorly in predicting six-month non-response in the hold-out sample with Area Under the Curves (AUCs) < 0.63. Including early change scores slightly improved the models' performance (AUC range: 0.68-0.73). Computationally-intensive ML models did not significantly outperform logistic regression (AUC: 0.69). Reduced prediction models performed similar to the full prediction models in both the models without (AUC: 0.58-0.62 vs. 0.58-0.63) and models with early change scores (AUC: 0.69-0.73 vs. 0.68-0.71). Across different ML algorithms, early change scores in psychopathology and well-being consistently emerged as important predictors for non-improvement.

Conclusion

Accurately predicting treatment outcomes in a mental healthcare context remains challenging. While advanced ML algorithms offer flexibility, they showed limited additional value compared to traditional logistic regression in this study. The current study confirmed the importance of taking early change scores in both psychopathology and well-being into account for predicting longer-term outcomes in symptomatic distress.

Accessibility and efficiency of mental health services, United Kingdom of Great Britain and Northern Ireland

Problem

Mental ill health in the United Kingdom of Great Britain and Northern Ireland has been a major driver of labour market exclusion through sickness absence, reduced productivity and job loss.

Approach

A government-supported programme for improving access to psychological therapies was launched in 2008 and expanded across England in 2010. The aim was to provide evidence-based treatments for people with common mental disorders through three principal strategies: (i) routine session-by-session outcome monitoring; (ii) integration with the wider care system; and (iii) delivery of psychological therapies as part of a stepped-care approach.

Local setting

Access to effective psychological therapies was previously low in the United Kingdom. In 2010, only about 35% of people with moderately severe mental disorders were in specialist or non-specialist treatment.

Relevant changes

The accessibility of quality mental health services has increased, as has the efficiency of the country’s mental health system. The numbers of people entering treatment have increased steadily from 0.43 million in 2012–2013 to 1.09 million in 2018–2019. The recovery rate of patients in treatment increased from 42.8% to 52.1% during 2012–2018. The number of people moved off sick pay and benefits rose from 3683 to 18 039 over the same period.

Lessons learnt

A clinical guideline on psychological therapies is a prerequisite for increasing the accessibility and efficiency of mental health services. An integrated approach allows mental health services to have better reach. Routine collection of patient-level outcome data plays an important role in the value and function of the mental health care system.