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      The effectiveness of high-intensity CBT and counselling alone and following low-intensity CBT: a reanalysis of the 2nd UK National Audit of Psychological Therapies data

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      BMC Psychiatry
      BioMed Central

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          Abstract

          Background

          A previously published article in this journal reported the service effects from 103 services within the UK Improving Access to Psychological Therapies (IAPT) initiative and the comparative effectiveness of CBT and Counselling provision. All patients received High-intensity CBT or High-intensity Counselling, but some also received Low-intensity CBT before being stepped-up to High intensity treatments. The report did not distinguish between patients who received low-intensity CBT before being stepped-up. This article clarifies the basis for collapsing low- and high-intensity interventions by analysing the four treatment conditions separately.

          Method

          Data from 33,243 patients included in the second round of the National Audit of Psychological Therapies (NAPT) were re-analysed as four separate conditions: High-intensity CBT only ( n = 5975); High-intensity Counselling only ( n = 3003); Low-intensity CBT plus High-intensity CBT ( n = 17,620); and Low-intensity CBT plus High-intensity Counselling ( n = 6645). Analyses considered levels of pre-post therapy effect sizes (ESs), reliable improvement (RI) and reliable and clinically significant improvement (RCSI). Multilevel modelling was used to model predictors of outcome, namely patient pre-post change on PHQ-9 scores at last therapy session.

          Results

          Significant differences obtained on various outcome indices but were so small they carried no clinical significance. Including the four treatment groups in a multilevel model comprising patient intake severity, patient ethnicity and number of sessions attended showed no significant differences between the four treatment groups. Comparisons between the two high-intensity interventions only ( N = 8978) indicated Counselling showed more improvement than CBT by 0.3 of a point on PHQ-9 for the mean number of sessions attended. However, this result was moderated by the number of sessions and for 12 or more sessions, the advantage went to CBT.

          Conclusions

          This re-analysis showed no evidence of clinically meaningful differences between the four treatment conditions using standard indices of patient outcomes. However, a differential advantage to high-intensity Counselling for fewer than average sessions attended and high-intensity CBT for more than average sessions attended has important service implications. The finding of equivalent outcomes between high-intensity CBT and Counselling for more severe patients also has important policy implications. Empirically-informed procedures (e.g., predictive modelling) for assigning patients to interventions need to be considered to improve patient outcomes.

          Electronic supplementary material

          The online version of this article (10.1186/s12888-018-1899-0) contains supplementary material, which is available to authorized users.

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          Most cited references3

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          Predicting Optimal Outcomes in Cognitive Therapy or Interpersonal Psychotherapy for Depressed Individuals Using the Personalized Advantage Index Approach

          Introduction Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works best for the depressed individual. In this paper, we aim to replicate a recently developed treatment selection method, using data from an RCT comparing the effects of cognitive therapy (CT) and interpersonal psychotherapy (IPT). Methods 134 depressed patients completed the pre- and post-treatment BDI-II assessment. First, we identified baseline predictors and moderators. Second, individual treatment recommendations were generated by combining the identified predictors and moderators in an algorithm that produces the Personalized Advantage Index (PAI), a measure of the predicted advantage in one therapy compared to the other, using standard regression analyses and the leave-one-out cross-validation approach. Results We found five predictors (gender, employment status, anxiety, personality disorder and quality of life) and six moderators (somatic complaints, cognitive problems, paranoid symptoms, interpersonal self-sacrificing, attributional style and number of life events) of treatment outcome. The mean average PAI value was 8.9 BDI points, and 63% of the sample was predicted to have a clinically meaningful advantage in one of the therapies. Those who were randomized to their predicted optimal treatment (either CT or IPT) had an observed mean end-BDI of 11.8, while those who received their predicted non-optimal treatment had an end-BDI of 17.8 (effect size for the difference = 0.51). Discussion Depressed patients who were randomized to their predicted optimal treatment fared much better than those randomized to their predicted non-optimal treatment. The PAI provides a great opportunity for formal decision-making to improve individual patient outcomes in depression. Although the utility of the PAI approach will need to be evaluated in prospective research, this study promotes the development of a treatment selection approach that can be used in regular mental health care, advancing the goals of personalized medicine.
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            The comparative effectiveness and efficiency of cognitive behaviour therapy and generic counselling in the treatment of depression: evidence from the 2nd UK National Audit of psychological therapies

            Background Cognitive Behaviour Therapy (CBT) is the front-line psychological intervention for step 3 within UK psychological therapy services. Counselling is recommended only when other interventions have failed and its effectiveness has been questioned. Method A secondary data analysis was conducted of data collected from 33,243 patients across 103 Improving Access to Psychological Therapies (IAPT) services as part of the second round of the National Audit of Psychological Therapies (NAPT). Initial analysis considered levels of pre-post therapy effect sizes (ESs) and reliable improvement (RI) and reliable and clinically significant improvement (RCSI). Multilevel modelling was used to model predictors of outcome, namely patient pre-post change on PHQ-9 scores at last therapy session. Results Counselling received more referrals from patients experiencing moderate to severe depression than CBT. For patients scoring above the clinical cut-off on the PHQ-9 at intake, the pre-post ES (95% CI) for CBT was 1.59 (1.58, 1.62) with 46.6% making RCSI criteria and for counselling the pre-post ES was 1.55 (1.52, 1.59) with 44.3% of patients meeting RCSI criteria. Multilevel modelling revealed a significant site effect of 1.8%, while therapy type was not a predictor of outcome. A significant interaction was found between the number of sessions attended and therapy type, with patients attending fewer sessions on average for counselling [M = 7.5 (5.54) sessions and a median (IQR) of 6 (3–10)] than CBT [M = 8.9 (6.34) sessions and a median (IQR) of 7 (4–12)]. Only where patients had 18 or 20 sessions was CBT significantly more effective than counselling, with recovery rates (95% CIs) of 62.2% (57.1, 66.9) and 62.4% (56.5, 68.0) respectively, compared with 44.4% (32.7, 56.6) and 42.6% (30.0, 55.9) for counselling. Counselling was significantly more effective at two sessions with a recovery rate of 34.9% (31.9, 37.9) compared with 22.2% (20.5, 24.0) for CBT. Conclusions Outcomes for counselling and CBT in the treatment of depression were comparable. Research efforts should focus on factors other than therapy type that may influence outcomes, namely the inherent variability between services, and adopt multilevel modelling as the given analytic approach in order to capture the naturally nested nature of the implementation and delivery of psychological therapies. It is of concern that half of all patients, regardless of type of intervention, did not show reliable improvement. Electronic supplementary material The online version of this article (doi:10.1186/s12888-017-1370-7) contains supplementary material, which is available to authorized users.
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              Case complexity as a guide for psychological treatment selection.

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                Author and article information

                Contributors
                m.barkham@sheffield.ac.uk
                Journal
                BMC Psychiatry
                BMC Psychiatry
                BMC Psychiatry
                BioMed Central (London )
                1471-244X
                3 October 2018
                3 October 2018
                2018
                : 18
                : 321
                Affiliations
                ISNI 0000 0004 1936 9262, GRID grid.11835.3e, Department of Psychology, , University of Sheffield, ; Sheffield, UK
                Author information
                http://orcid.org/0000-0003-1687-6376
                Article
                1899
                10.1186/s12888-018-1899-0
                6171289
                30285674
                874d1be0-f907-43a5-93e6-db890a87e417
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 14 June 2018
                : 19 September 2018
                Categories
                Correspondence
                Custom metadata
                © The Author(s) 2018

                Clinical Psychology & Psychiatry
                Clinical Psychology & Psychiatry

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