Machine learning-based identification of determinants for rehabilitation success and future healthcare use prevention in patients with high-grade, chronic, nonspecific low back pain: an individual data 7-year follow-up analysis on 154,167 individuals

Author:

Niederer Daniel1ORCID,Schiller Joerg2,Groneberg David A.3,Behringer Michael4,Wolfarth Bernd5,Gabrys Lars6

Affiliation:

1. Department of Sports Medicine and Exercise Physiology, Institute of Occupational, Social and Environmental Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany

2. Department of Rehabilitation Medicine, Hannover Medical School, Hannover, Germany

3. Institute of Occupational, Social and Environmental Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany

4. Department of Sports Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany

5. Department of Sports Medicine, Humboldt University and Charité University School of Medicine, Berlin, Germany

6. University of Applied Sciences for Sports and Management, Potsdam, Germany

Abstract

Abstract To individually prescribe rehabilitation contents, it is of importance to know and quantify factors for rehabilitation success and the risk for a future healthcare use. The objective of our multivariable prediction model was to determine factors of rehabilitation success and the risk for a future healthcare use in patients with high-grade, chronic low back pain. We included members of the German pension fund who participated from 2012 to 2019 in multimodal medical rehabilitation with physical and psychological treatment strategies because of low back pain (ICD10:M54.5). Candidate prognostic factors for rehabilitation success and for a future healthcare use were identified using Gradient Boosting Machines and Random Forest algorithms in the R-package caret on a 70% training and a 30% test set. We analysed data from 154,167 patients; 8015 with a second medical rehabilitation measure and 5161 who retired because of low back pain within the study period. The root-mean-square errors ranged between 494 (recurrent rehabilitation) and 523 (retirement) days (R 2 = 0.183-0.229), whereas the prediction accuracy ranged between 81.9% for the prediction of the rehabilitation outcome, and 94.8% for the future healthcare use prediction model. Many modifiable prognostic factors (such as duration of the rehabilitation [inverted u-shaped], type of the rehabilitation, and aftercare measure), nonmodifiable prognostic factors (such as sex and age), and disease-specific factors (such as sick leave days before the rehabilitation [linear positive] together with the pain grades) for rehabilitation success were identified. Inpatient medical rehabilitation programmes (3 weeks) may be more effective in preventing a second rehabilitation measure and/or early retirement because of low back pain compared with outpatient rehabilitation programs. Subsequent implementation of additional exercise programmes, cognitive behavioural aftercare treatment, and following scheduled aftercare are likely to be beneficial.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Anesthesiology and Pain Medicine,Neurology (clinical),Neurology

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