Kinesiophobia is not required to predict chronic low back pain in workers: a decision curve analysis

A M Panken,M W Heymans,J B Staal

doi:10.1186/s12891-020-3186-8

Abstract

BackgroundCurrently used performance measures for discrimination were not informative to determine the clinical benefit of predictor variables. The purpose was to evaluate if a former relevant predictor, kinesiophobia, remained clinically relevant to predict chronic occupational low back pain (LBP) in the light of a novel discriminative performance measure, Decision Curve Analysis (DCA), using the Net Benefit (NB).MethodsProspective cohort data (n = 170) of two merged randomized trials with workers with LBP on sickleave, treated with Usual Care (UC) were used for the analyses. An existing prediction model for chronic LBP with the variables ‘a clinically relevant change in pain intensity and disability status in the first 3 months’, ‘baseline measured pain intensity’ and ‘kinesiophobia’ was compared with the same model without the variable ‘kinesiophobia’ using the NB and DCA.ResultsBoth prediction models showed an equal performance according to the DCA and NB. Between 10 and 95% probability thresholds of chronic LBP risk, both models were of clinically benefit. There were virtually no differences between both models in the improved classification of true positive (TP) patients.ConclusionsThis study showed that the variable kinesiophobia, which was originally included in a prediction model for chronic LBP, was not informative to predict chronic LBP by using DCA. DCA and NB have to be used more often to develop clinically beneficial prediction models in workers because they are more sensitive to evaluate the discriminate ability of prediction models.

Highlights

The development of prediction models has grown in popularity in Low Back Pain (LBP) research [1,2,3]
A recent literature review showed that most prediction models do not use performance measures that evaluate the clinical usefulness of the models well [5]
A novel performance measure to evaluate the discriminative ability of a prediction model is Decision Curve Analysis (DCA) and the Net Benefit (NB) [9]

Summary

Introduction

The development of prediction models has grown in popularity in Low Back Pain (LBP) research [1,2,3]. A novel performance measure to evaluate the discriminative ability of a prediction model is Decision Curve Analysis (DCA) and the Net Benefit (NB) [9] This method is able to identify the number of patients that are better classified and incorporate clinical consequences of using a model, which is useful for clinicians [7]. The objective of the current study is to evaluate if kinesiophobia is a clinically relevant predictor of chronic LBP in the light of the novel discriminative performance measure, Decision Curve Analysis (DCA), using the Net Benefit (NB) because this measure is more suitable to test the predictive performance of separate predictor variables. The purpose was to evaluate if a former relevant predictor, kinesiophobia, remained clinically relevant to predict chronic occupational low back pain (LBP) in the light of a novel discriminative performance measure, Decision Curve Analysis (DCA), using the Net Benefit (NB)

Objectives

Methods

Results

Conclusion