Predicting optimal treatment outcomes using the Personalized Advantage Index for patients with persistent somatic symptoms.
Psychother Res
; 32(2): 165-178, 2022 02.
Article
in En
| MEDLINE
| ID: mdl-33910487
ABSTRACT
Objective:
Because individual patients with persistent somatic symptoms (PSS) respond differently to treatments, a better understanding of the factors that predict therapy outcomes are of high importance. Aggregating a wide selection of information into the treatment-decision process is a challenge for clinicians. Using the Personalized Advantage Index (PAI) this study aims to deal with this.Methods:
Data from a multicentre RCT comparing CBT (N = 128) versus CBT enriched with emotion regulation training (ENCERT) (N = 126) for patients diagnosed with somatic symptom disorder were used to identify based on two machine learning approaches predictors of therapy outcomes. The identified predictors were used to calculate the PAI.Results:
Five treatment unspecific predictors (pre-treatment somatic symptom severity, depression, symptom disability, health-related quality of life, age) and five treatment specific moderators (global functioning, early childhood traumatic events, gender, health anxiety, emotion regulation skills) were identified. Individuals assigned to their PAI-indicated optimal treatment had significantly lower somatic symptom severity at the end of therapy compared to those randomised to their non-optimal condition.Conclusion:
Allowing patients to choose a personalised treatment seems to be meaningful. This could help to improve outcomes for PSS and reduce its high costs to the health care system.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cognitive Behavioral Therapy
/
Medically Unexplained Symptoms
Type of study:
Clinical_trials
/
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Aspects:
Patient_preference
Limits:
Child, preschool
/
Humans
Language:
En
Journal:
Psychother Res
Journal subject:
PSICOLOGIA
/
PSIQUIATRIA
Year:
2022
Document type:
Article
Affiliation country:
Germany