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Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

ABSTRACT: BACKGROUND:There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes. OBJECTIVE:To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT). METHODS:Participants were 61 adolescents (12-17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach. RESULTS:Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy. CONCLUSIONS:The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted.

SUBMITTER: Lenhard F

PROVIDER: S-EPMC6877165 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

Lenhard Fabian F Sauer Sebastian S Andersson Erik E Månsson Kristoffer Nt KN Mataix-Cols David D Rück Christian C Serlachius Eva E

International journal of methods in psychiatric research 20170728 1

<h4>Background</h4>There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes.<h4>Objective</h4>To test four different machine learning methods in the predi ...[more]

PMID: 28752937

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Project description:OBJECTIVES:To evaluate the cost-effectiveness of a therapist-guided internet-delivered cognitive behaviour therapy (ICBT) intervention for adolescents with obsessive-compulsive disorder (OCD) compared with untreated patients on a waitlist. DESIGN:Single-blinded randomised controlled trial. SETTING:A research clinic within the regular child and adolescent mental health service in Stockholm, Sweden. PARTICIPANTS:Sixty-seven adolescents (12-17?years) with a Diagnostic and Statistical Manual of Mental Disorders Fifth Edition diagnosis of OCD. INTERVENTIONS:Either a 12-week, therapist-guided ICBT intervention or a wait list condition of equal duration. PRIMARY OUTCOME MEASURES:Cost data were collected at baseline and after treatment, including healthcare use, supportive resources, prescription drugs, prescription-free drugs, school absence and productivity loss, as well as the cost of ICBT. Health outcomes were defined as treatment responder rate and quality-adjusted life years gain. Bootstrapped mixed model analyses were conducted comparing incremental costs and health outcomes between the groups from the societal and healthcare perspectives. RESULTS:Compared with waitlist control, ICBT generated substantial societal cost savings averaging US$-144.98 (95%?CI -159.79 to -130.16) per patient. The cost reductions were mainly driven by reduced healthcare use in the ICBT group. From the societal perspective, the probability of ICBT being cost saving compared with waitlist control was approximately 60%. From the healthcare perspective, the cost per additional responder to ICBT compared with waitlist control was approximately US$78. CONCLUSIONS:The results suggest that therapist-guided ICBT is a cost-effective treatment and results in societal cost savings, compared with patients who do not receive evidence-based treatment. Since, at present, most patients with OCD do not have access to evidence-based treatments, the results have important implications for the increasingly strained national and healthcare budgets. Future studies should compare the cost-effectiveness of ICBT with regular face-to-face CBT. TRIAL REGISTRATION NUMBER:NCT02191631.

Project description:ImportanceTherapist-guided, internet-delivered cognitive behavioral therapy is an effective treatment option for children and adolescents with obsessive-compulsive disorder, but to our knowledge, its cost-effectiveness compared with traditional in-person treatment has not been established.ObjectiveTo evaluate the cost-effectiveness of guided internet-delivered cognitive behavioral therapy implemented within a stepped-care model compared with in-person cognitive behavioral therapy for young people with obsessive-compulsive disorder.Design, setting, and participantsThis economic evaluation of a randomized noninferiority trial conducted at 2 specialist obsessive-compulsive disorder clinics in Sweden enrolled 152 children and adolescents aged 8 to 17 years with obsessive-compulsive disorder, mainly through clinician referrals (110 [72%]). Recruitment began October 6, 2017, and ended May 24, 2019. Follow-up ended April 14, 2020.InterventionsParticipants were randomly assigned to receive either guided internet-delivered cognitive behavioral therapy or in-person cognitive behavioral therapy during a 16-week period. At the 3-month follow-up, nonresponders in both groups were offered additional in-person cognitive behavior therapy sessions.Main outcomes and measuresHealth outcomes were treatment response rates (primary outcome), remission rates, and quality-adjusted life-years. Cost data were collected before treatment, after treatment, at 3-month follow-up, and at 6-month follow-up (primary end point) and are presented in 2020 US dollars. The differences in incremental costs and health outcomes were compared between the groups and presented from the health care professional, health care sector, and societal perspectives.ResultsA total of 152 participants (94 girls [62%]; mean [SD] age, 13.4 [2.5] years) were randomized; 151 (99%) completed the trial. At the 6-month follow-up, 50 of 74 participants (68%) in the stepped-care group and 52 of 77 participants (68%) in the in-person cognitive behavioral therapy group were classified as treatment responders (odds ratio, 1.00 [95% CI, 0.51-1.98]; P = .99). Health economic analyses showed that the stepped-care group used fewer therapist resources than the in-person cognitive behavioral therapy group, resulting in a mean cost savings of $2104 (95% CI, $1202-$3006) per participant for the full study period of 10 months, corresponding to a relative savings of 39%. The cost savings remained largely comparable when taking wider health care sector and societal perspectives.Conclusions and relevanceThis study suggests that, for young people with obsessive-compulsive disorder, a low-cost digital intervention followed by in-person treatment for nonresponders was cost-effective compared with in-person cognitive behavior therapy alone.

Dataset Information

Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

Publications

Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

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