TY - JOUR

T1 - Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics

T2 - Data-Driven Analysis

AU - Bremer, Vincent

AU - Becker, Dennis

AU - Kolovos, S.

AU - Funk, Burkhardt

AU - van Breda, Ward

AU - Hoogendoorn, Mark

AU - Riper, Heleen

N1 - Funding Information: This study has been conducted in the context of the European Union FP7 project E-COMPARED (project number 603098) and is based on the collected dataset in February 2017. We therefore thank the European Union for funding and the E-COMPARED consortium for the fantastic cooperation. Publisher Copyright: © Vincent Bremer, Dennis Becker, Spyros Kolovos, Burkhardt Funk, Ward van Breda, Mark Hoogendoorn, Heleen Riper. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.08.2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License.

PY - 2018/8/21

Y1 - 2018/8/21

N2 - Background: Different treatment alternatives exist for psychological disorders. Both clinical and cost effectiveness of treatment are crucial aspects for policy makers, therapists, and patients and thus play major roles for healthcare decision-making. At the start of an intervention, it is often not clear which specific individuals benefit most from a particular intervention alternative or how costs will be distributed on an individual patient level.Objective: This study aimed at predicting the individual outcome and costs for patients before the start of an internet-based intervention. Based on these predictions, individualized treatment recommendations can be provided. Thus, we expand the discussion of personalized treatment recommendation.Methods: Outcomes and costs were predicted based on baseline data of 350 patients from a two-arm randomized controlled trial that compared treatment as usual and blended therapy for depressive disorders. For this purpose, we evaluated various machine learning techniques, compared the predictive accuracy of these techniques, and revealed features that contributed most to the prediction performance. We then combined these predictions and utilized an incremental cost-effectiveness ratio in order to derive individual treatment recommendations before the start of treatment.Results: Predicting clinical outcomes and costs is a challenging task that comes with high uncertainty when only utilizing baseline information. However, we were able to generate predictions that were more accurate than a predefined reference measure in the shape of mean outcome and cost values. Questionnaires that include anxiety or depression items and questions regarding the mobility of individuals and their energy levels contributed to the prediction performance. We then described how patients can be individually allocated to the most appropriate treatment type. For an incremental cost-effectiveness threshold of 25,000 €/quality-adjusted life year, we demonstrated that our recommendations would have led to slightly worse outcomes (1.98%), but with decreased cost (5.42%).Conclusions: Our results indicate that it was feasible to provide personalized treatment recommendations at baseline and thus allocate patients to the most beneficial treatment type. This could potentially lead to improved decision-making, better outcomes for individuals, and reduced health care costs.

AB - Background: Different treatment alternatives exist for psychological disorders. Both clinical and cost effectiveness of treatment are crucial aspects for policy makers, therapists, and patients and thus play major roles for healthcare decision-making. At the start of an intervention, it is often not clear which specific individuals benefit most from a particular intervention alternative or how costs will be distributed on an individual patient level.Objective: This study aimed at predicting the individual outcome and costs for patients before the start of an internet-based intervention. Based on these predictions, individualized treatment recommendations can be provided. Thus, we expand the discussion of personalized treatment recommendation.Methods: Outcomes and costs were predicted based on baseline data of 350 patients from a two-arm randomized controlled trial that compared treatment as usual and blended therapy for depressive disorders. For this purpose, we evaluated various machine learning techniques, compared the predictive accuracy of these techniques, and revealed features that contributed most to the prediction performance. We then combined these predictions and utilized an incremental cost-effectiveness ratio in order to derive individual treatment recommendations before the start of treatment.Results: Predicting clinical outcomes and costs is a challenging task that comes with high uncertainty when only utilizing baseline information. However, we were able to generate predictions that were more accurate than a predefined reference measure in the shape of mean outcome and cost values. Questionnaires that include anxiety or depression items and questions regarding the mobility of individuals and their energy levels contributed to the prediction performance. We then described how patients can be individually allocated to the most appropriate treatment type. For an incremental cost-effectiveness threshold of 25,000 €/quality-adjusted life year, we demonstrated that our recommendations would have led to slightly worse outcomes (1.98%), but with decreased cost (5.42%).Conclusions: Our results indicate that it was feasible to provide personalized treatment recommendations at baseline and thus allocate patients to the most beneficial treatment type. This could potentially lead to improved decision-making, better outcomes for individuals, and reduced health care costs.

KW - Business informatics

KW - treatment recommendation, cost effectiveness,mental health,machine learning

KW - treatment recommendation

KW - cost effectiveness

KW - mental health

KW - machine learning

UR - http://www.jmir.org/2018/8/e10275/

UR - http://www.scopus.com/inward/record.url?scp=85052857433&partnerID=8YFLogxK

U2 - 10.2196/10275

DO - 10.2196/10275

M3 - Journal articles

C2 - 30131318

VL - 20

JO - Journal of Medical Internet Research

JF - Journal of Medical Internet Research

SN - 1439-4456

IS - 8

M1 - e10275

ER -