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Open Access 01-12-2022 | Care | Research

Development and internal validation of a machine learning prediction model for low back pain non-recovery in patients with an acute episode consulting a physiotherapist in primary care

Authors: J. Knoop, W. van Lankveld, L. Beijer, F. J. B. Geerdink, M. W. Heymans, T. J. Hoogeboom, S. Hoppenbrouwers, E. van Overmeeren, R. Soer, C. Veenhof, K. C. P. Vissers, P. J. van der Wees, M. Sappelli, J. B. Staal

Published in: BMC Musculoskeletal Disorders | Issue 1/2022

Abstract

Background

While low back pain occurs in nearly everybody and is the leading cause of disability worldwide, we lack instruments to accurately predict persistence of acute low back pain. We aimed to develop and internally validate a machine learning model predicting non-recovery in acute low back pain and to compare this with current practice and ‘traditional’ prediction modeling.

Methods

Prognostic cohort-study in primary care physiotherapy. Patients (n = 247) with acute low back pain (≤ one month) consulting physiotherapists were included. Candidate predictors were assessed by questionnaire at baseline and (to capture early recovery) after one and two weeks. Primary outcome was non-recovery after three months, defined as at least mild pain (Numeric Rating Scale > 2/10). Machine learning models to predict non-recovery were developed and internally validated, and compared with two current practices in physiotherapy (STarT Back tool and physiotherapists’ expectation) and ‘traditional’ logistic regression analysis.

Results

Forty-seven percent of the participants did not recover at three months. The best performing machine learning model showed acceptable predictive performance (area under the curve: 0.66). Although this was no better than a’traditional’ logistic regression model, it outperformed current practice.

Conclusions

We developed two prognostic models containing partially different predictors, with acceptable performance for predicting (non-)recovery in patients with acute LBP, which was better than current practice. Our prognostic models have the potential of integration in a clinical decision support system to facilitate data-driven, personalized treatment of acute low back pain, but needs external validation first.

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Title: Development and internal validation of a machine learning prediction model for low back pain non-recovery in patients with an acute episode consulting a physiotherapist in primary care
Authors: J. Knoop
W. van Lankveld
L. Beijer
F. J. B. Geerdink
M. W. Heymans
T. J. Hoogeboom
S. Hoppenbrouwers
E. van Overmeeren
R. Soer
C. Veenhof
K. C. P. Vissers
P. J. van der Wees
M. Sappelli
J. B. Staal
Publication date: 01-12-2022
Publisher: BioMed Central
Keywords: Care
Back Pain
Back Pain
Back Pain
Published in: BMC Musculoskeletal Disorders / Issue 1/2022
Electronic ISSN: 1471-2474
DOI: https://doi.org/10.1186/s12891-022-05718-7

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Development and internal validation of a machine learning prediction model for low back pain non-recovery in patients with an acute episode consulting a physiotherapist in primary care

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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