Dynamic prediction of psychological treatment outcomes: development and validation of a prediction model using routinely collected symptom data

The Lancet Digital Health - Tập 3 - Trang e231-e240 - 2021
Claire Bone1, Melanie Simmonds-Buckley1, Richard Thwaites2, David Sandford3, Mariia Merzhvynska4, Julian Rubel5, Anne-Katharina Deisenhofer6, Wolfgang Lutz6, Jaime Delgadillo1
1Department of Psychology, Clinical Psychology Unit, University of Sheffield, Sheffield, UK
2Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, Penrith, UK
3Lancashire and South Cumbria NHS Foundation Trust, Preston, UK
4Department of Psychology, University of Zurich, Zurich, Switzerland
5Department of Psychology, Justus Liebig University Giessen, Giessen, Germany
6Department of Psychology, University of Trier, Trier, Germany

Tài liệu tham khảo

Delgadillo, 2020, A development pathway towards precision mental health care, JAMA Psychiatry, 77, 889, 10.1001/jamapsychiatry.2020.1048 Lutz, 1999, Patient profiling: an application of random coefficient regression models to depicting the response of a patient to outpatient psychotherapy, J Consult Clin Psychol, 67, 571, 10.1037/0022-006X.67.4.571 Finch, 2001, Psychotherapy quality control: the statistical generation of expected recovery curves for integration into an early warning system, Clin Psychol Psychother, 8, 231, 10.1002/cpp.286 Howard, 1996, Evaluation of psychotherapy. Efficacy, effectiveness, and patient progress, Am Psychol, 51, 1059, 10.1037/0003-066X.51.10.1059 Krause, 1998, Exploring individual change, J Consult Clin Psychol, 66, 838, 10.1037/0022-006X.66.5.838 Garfield, 1996, Some problems associated with “validated” forms of psychotherapy, Clin Psychol Sci Pract, 3, 218, 10.1111/j.1468-2850.1996.tb00073.x Lutz, 2005, Predicting change for individual psychotherapy clients on the basis of their nearest neighbors, J Consult Clin Psychol, 73, 904, 10.1037/0022-006X.73.5.904 Saunders, 2016, Predicting treatment outcome in psychological treatment services by identifying latent profiles of patients, J Affect Disord, 197, 107, 10.1016/j.jad.2016.03.011 Delgadillo, 2016, Different people respond differently to therapy: a demonstration using patient profiling and risk stratification, Behav Res Ther, 79, 15, 10.1016/j.brat.2016.02.003 Delgadillo, 2017, Case complexity as a guide for psychological treatment selection, J Consult Clin Psychol, 85, 835, 10.1037/ccp0000231 Lambert, 2015, Progress feedback and the OQ-system: the past and the future, Psychotherapy, 52, 381, 10.1037/pst0000027 Duncan, 2012, The Partners for Change Outcome Management System (PCOMS): the heart and soul of change project, Can Psychol, 53, 93, 10.1037/a0027762 Delgadillo, 2018, Feedback-informed treatment versus usual psychological treatment for depression and anxiety: a multisite, open-label, cluster randomised controlled trial, Lancet Psychiatry, 5, 564, 10.1016/S2215-0366(18)30162-7 Lutz, 2019, Towards integrating personalized feedback research into clinical practice: development of the Trier Treatment Navigator (TTN), Behav Res Ther, 120, 10.1016/j.brat.2019.103438 Shimokawa, 2010, Enhancing treatment outcome of patients at risk of treatment failure: meta-analytic and mega-analytic review of a psychotherapy quality assurance system, J Consult Clin Psychol, 78, 298, 10.1037/a0019247 Kendrick, 2016, Routine use of patient reported outcome measures (PROMs) for improving treatment of common mental health disorders in adults, Cochrane Database Syst Rev, 7 Lambert, 2001, Patient-focused research: using patient outcome data to enhance treatment effects, J Consult Clin Psychol, 69, 159, 10.1037/0022-006X.69.2.159 Clark, 2011, Implementing NICE guidelines for the psychological treatment of depression and anxiety disorders: the IAPT experience, Int Rev Psychiatry, 23, 318, 10.3109/09540261.2011.606803 Richards, 2009 Roth, 2008, Using an evidence-based methodology to identify the competences required to deliver effective cognitive and behavioural therapy for depression and anxiety disorders, Behav Cogn Psychother, 36, 129, 10.1017/S1352465808004141 Kroenke, 2001, The PHQ-9: validity of a brief depression severity measure, J Gen Intern Med, 16, 606, 10.1046/j.1525-1497.2001.016009606.x Richards, 2011, Implementation of psychological therapies for anxiety and depression in routine practice: two year prospective cohort study, J Affect Disord, 133, 51, 10.1016/j.jad.2011.03.024 Kroenke, 2007, Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection, Ann Intern Med, 146, 317, 10.7326/0003-4819-146-5-200703060-00004 Jacobson, 1991, Clinical significance: a statistical approach to defining meaningful change in psychotherapy research, J Consult Clin Psychol, 59, 12, 10.1037/0022-006X.59.1.12 Delgadillo, 2014, Early changes, attrition, and dose-response in low intensity psychological interventions, Br J Clin Psychol, 53, 114, 10.1111/bjc.12031 Hsieh, 1989, Sample size tables for logistic regression, Stat Med, 8, 795, 10.1002/sim.4780080704 Robinson, 2020, Dose-response patterns in low and high intensity cognitive behavioral therapy for common mental health problems, Depress Anxiety, 37, 285, 10.1002/da.22999 Nagelkerke, 1991, A note on a general definition of the coefficient of determination, Biometrika, 78, 691, 10.1093/biomet/78.3.691 Swets, 1988, Measuring the accuracy of diagnostic systems, Science, 240, 1285, 10.1126/science.3287615 West, 2006 Zou, 2005, Regularization and variable selection via the elastic net, J R Stat Soc B, 67, 301, 10.1111/j.1467-9868.2005.00503.x Chen T, Guestrin C. Xgboost: a scalable tree boosting system. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; San Fransisco, CA; Aug 13–17, 2016. Platt, 1999, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, Advances in Large Margin Classifiers, 10, 61 Fine, 1999 Beard, 2019, Early response to psychological therapy as a predictor of depression and anxiety treatment outcomes: a systematic review and meta-analysis, Depress Anxiety, 36, 866, 10.1002/da.22931 Aderka, 2012, Sudden gains during psychological treatments of anxiety and depression: a meta-analysis, J Consult Clin Psychol, 80, 93, 10.1037/a0026455 Colombet, 2000, Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression, Proc AMIA Symp, 156 Lynam, 2020, Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults, Diagn Progn Res, 4, 6, 10.1186/s41512-020-00075-2 Christodoulou, 2019, A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models, J Clin Epidemiol, 110, 12, 10.1016/j.jclinepi.2019.02.004