Predicting therapy success and costs for personalized treatment recommendations using baseline characteristics: Data-driven analysis

Vincent Bremer, Dennis Becker, Spyros Kolovos, Burkhardt Funk, Ward Van Breda, Mark Hoogendoorn, Heleen Riper

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Article numbere10275
Pages (from-to)1-11
Number of pages11
JournalJournal of Medical Internet Research
Volume20
Issue number8
DOIs
Publication statusPublished - 21 Aug 2018

Fingerprint

Health Care Costs
Costs and Cost Analysis
Therapeutics
Cost-Benefit Analysis
Decision Making
Quality-Adjusted Life Years
Depressive Disorder
Administrative Personnel
Internet
Uncertainty
Anxiety
Randomized Controlled Trials
Depression
Psychology
Delivery of Health Care

Keywords

  • Cost effectiveness
  • Machine learning
  • Mental health
  • Treatment recommendation

Cite this

@article{ac453c8712d74580b8846ebce2c3a03c,
title = "Predicting therapy success and costs for personalized treatment recommendations using baseline characteristics: Data-driven analysis",
abstract = "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.",
keywords = "Cost effectiveness, Machine learning, Mental health, Treatment recommendation",
author = "Vincent Bremer and Dennis Becker and Spyros Kolovos and Burkhardt Funk and {Van Breda}, Ward and Mark Hoogendoorn and Heleen Riper",
year = "2018",
month = "8",
day = "21",
doi = "10.2196/10275",
language = "English",
volume = "20",
pages = "1--11",
journal = "Journal of Medical Internet Research",
issn = "1438-8871",
publisher = "Journal of medical Internet Research",
number = "8",

}

Predicting therapy success and costs for personalized treatment recommendations using baseline characteristics : Data-driven analysis. / Bremer, Vincent; Becker, Dennis; Kolovos, Spyros; Funk, Burkhardt; Van Breda, Ward; Hoogendoorn, Mark; Riper, Heleen.

In: Journal of Medical Internet Research, Vol. 20, No. 8, e10275, 21.08.2018, p. 1-11.

Research output: Contribution to JournalArticleAcademicpeer-review

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, Spyros

AU - Funk, Burkhardt

AU - Van Breda, Ward

AU - Hoogendoorn, Mark

AU - Riper, Heleen

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 - Cost effectiveness

KW - Machine learning

KW - Mental health

KW - Treatment recommendation

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

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

U2 - 10.2196/10275

DO - 10.2196/10275

M3 - Article

VL - 20

SP - 1

EP - 11

JO - Journal of Medical Internet Research

JF - Journal of Medical Internet Research

SN - 1438-8871

IS - 8

M1 - e10275

ER -