Cost-effectiveness analysis of a chronic back pain multidisciplinary biopsychosocial rehabilitation (MBR) compared to standard care for privately insured in Germany

The use of a biopsychosocial model in the treatment of patients with chronic

OBJECTIVE

To explore the available knowledge on the application of biopsychosocial model (BPS) in patients with chronic low back pain (CLBP).

METHODS

A scoping review, including 32 papers that used a BPS to manage adult patients with CLBP, published in any language. Six different databases were searched.

RESULTS

According to the description of the protocols, most of them use BPS for assessment of the patients. In this first stage the most often evaluated were pain and disability, kinesiophobia and quality of life, and work-related factors. The intervention most used associate psychological and physical domains using pain education or cognitive functional therapy and active exercise. Even though was not the most common, in the social domain occupational, behavioral and family interventions were observed.

CONCLUSION

BPS is more often used as combination of physical and psychological aspects, being the social domain restricted to work-related factors. Patien'ts perception of their health status is little explored, and it is suggested that the International Classification of Functioning, Disability and Health be used.

PRACTICE IMPLICATION

Patient perception as well as social factors in addition to occupational ones should be included in the clinical approach of the patient with CLBP and should be further explored in research.

Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment

Introduction

Chronic musculoskeletal pain is a prevalent condition impacting around 20% of people globally; resulting in patients living with pain, fatigue, restricted social and employment capacity, and reduced quality of life. Interdisciplinary multimodal pain treatment programs have been shown to provide positive outcomes by supporting patients modify their behavior and improve pain management through focusing attention on specific patient valued goals rather than fighting pain.

Methods

Given the complex nature of chronic pain there is no single clinical measure to assess outcomes from multimodal pain programs. Using Centre for Integral Rehabilitation data from 2019-2021 (n = 2,364), we developed a multidimensional machine learning framework of 13 outcome measures across 5 clinically relevant domains including activity/disability, pain, fatigue, coping and quality of life. Machine learning models for each endpoint were separately trained using the most important 30 of 55 demographic and baseline variables based on minimum redundancy maximum relevance feature selection. Five-fold cross validation identified best performing algorithms which were rerun on deidentified source data to verify prognostic accuracy.

Results

Individual algorithm performance ranged from 0.49 to 0.65 AUC reflecting characteristic outcome variation across patients, and unbalanced training data with high positive proportions of up to 86% for some measures. As expected, no single outcome provided a reliable indicator, however the complete set of algorithms established a stratified prognostic patient profile. Patient level validation achieved consistent prognostic assessment of outcomes for 75.3% of the study group (n = 1,953). Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.

Discussion

These results indicate that although no single algorithm was individually conclusive, the complete stratified profile consistently identified patient outcomes. Our predictive profile provides promising positive contribution for clinicians and patients to assist with personalized assessment and goal setting, program engagement and improved patient outcomes.