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Article

Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care

by
Nadine Friedl
1,*,
Tobias Krieger
1,
Karine Chevreul
2,
Jean Baptiste Hazo
3,
Jérôme Holtzmann
4,
Mark Hoogendoorn
5,
Annet Kleiboer
6,
Kim Mathiasen
7,8,
Antoine Urech
9,
Heleen Riper
10,11,12 and
Thomas Berger
1
1
Department of Clinical Psychology, University of Bern, 3012 Bern, Switzerland
2
URC Eco Ile-de-France (AP-HP), Hotel Dieu, 1, Place du Parvis Notre Dame, 75004 Paris, France
3
Eceve, Unit 1123, Inserm, University of Paris, Health Economics Research Unit, Assistance Publique-Hôpitaux de Paris, 75004 Paris, France
4
University Hospital Grenoble Alpes, Mood Disorders and Emotional Pathologies Unit, Pôle de Psychiatrie, Neurologie et Rééducation Neurologique, 38043 Grenoble, France
5
Department of Computer Science, VU University Amsterdam Faculty of Sciences, De Boelelaan 1081m, 1081 HV Amsterdam, The Netherlands
6
Section Clinical Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam and EMGO+ Institute for Health Care and Research, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
7
Department of Psychology, Faculty of Health Sciences, University of Southern Denmark, 5230 Odense M, Denmark
8
Center of Telepsychiatry, University of Southern Denmark, 5000 Odense, Denmark
9
INSELSPITAL, University Hospital Bern, University Clinic for Neurology, University Acute-Neurorehabilitation Center, 3010 Bern, Switzerland
10
Department of Psychiatry and the Amsterdam Public Health Research Institute, GGZ inGeest/Amsterdam UMC, Vrije Universiteit, de Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
11
Department of Research and Innovation, GGZ inGeest Specialized Mental Health Care, Oldenaller 1, 1081 HJ Amsterdam, The Netherlands
12
Department of Clinical, Neuro-and Developmental Psychology and the Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands
 
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*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(2), 490; https://doi.org/10.3390/jcm9020490
Submission received: 30 December 2019 / Revised: 4 February 2020 / Accepted: 5 February 2020 / Published: 11 February 2020
(This article belongs to the Special Issue Innovative Technology Based Interventions for Psychological Treatment of Common Mental Disorders)
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Abstract

A variety of effective psychotherapies for depression are available, but patients who suffer from depression vary in their treatment response. Combining face-to-face therapies with internet-based elements in the sense of blended treatment is a new approach to treatment for depression. The goal of this study was to answer the following research questions: (1) What are the most important predictors determining optimal treatment allocation to treatment as usual or blended treatment? and (2) Would model-determined treatment allocation using this predictive information and the personalized advantage index (PAI)-approach result in better treatment outcomes? Bayesian model averaging (BMA) was applied to the data of a randomized controlled trial (RCT) comparing the efficacy of treatment as usual and blended treatment in depressive outpatients. Pre-treatment symptomatology and treatment expectancy predicted outcomes irrespective of treatment condition, whereas different prescriptive predictors were found. A PAI of 2.33 PHQ-9 points was found, meaning that patients who would have received the treatment that is optimal for them would have had a post-treatment PHQ-9 score that is two points lower than if they had received the treatment that is suboptimal for them. For 29% of the sample, the PAI was five or greater, which means that a substantial difference between the two treatments was predicted. The use of the PAI approach for clinical practice must be further confirmed in prospective research; the current study supports the identification of specific interventions favorable for specific patients.
Keywords: personalized advantage index; depression; blended treatment; CBT; treatment selection; Bayesian model averaging personalized advantage index; depression; blended treatment; CBT; treatment selection; Bayesian model averaging

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MDPI and ACS Style

Friedl, N.; Krieger, T.; Chevreul, K.; Hazo, J.B.; Holtzmann, J.; Hoogendoorn, M.; Kleiboer, A.; Mathiasen, K.; Urech, A.; Riper, H.; et al. Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care. J. Clin. Med. 2020, 9, 490. https://doi.org/10.3390/jcm9020490

AMA Style

Friedl N, Krieger T, Chevreul K, Hazo JB, Holtzmann J, Hoogendoorn M, Kleiboer A, Mathiasen K, Urech A, Riper H, et al. Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care. Journal of Clinical Medicine. 2020; 9(2):490. https://doi.org/10.3390/jcm9020490

Chicago/Turabian Style

Friedl, Nadine, Tobias Krieger, Karine Chevreul, Jean Baptiste Hazo, Jérôme Holtzmann, Mark Hoogendoorn, Annet Kleiboer, Kim Mathiasen, Antoine Urech, Heleen Riper, and et al. 2020. "Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care" Journal of Clinical Medicine 9, no. 2: 490. https://doi.org/10.3390/jcm9020490

APA Style

Friedl, N., Krieger, T., Chevreul, K., Hazo, J. B., Holtzmann, J., Hoogendoorn, M., Kleiboer, A., Mathiasen, K., Urech, A., Riper, H., & Berger, T. (2020). Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care. Journal of Clinical Medicine, 9(2), 490. https://doi.org/10.3390/jcm9020490

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