![]() ![]() 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. A novel performance measure to evaluate the discriminative ability of a prediction model is Decision Curve Analysis (DCA) and the Net Benefit (NB). However, these measures have shown to be less sensitive in evaluating the added discriminative performance of a predictor and do not provide direct feedback on the number of chronic LBP patients that are correctly classified and are therefore less clinically useful. Performance measures as Receiver Operating Characteristic Curve (ROC curve), the Area Under this Curve (AUC), sensitivity and specificity, in combination with a low and high risk cutoff point can be used to determine the clinical feasibility of a developed prediction model. This probability estimate may be an indication for the clinician to adjust the treatment goals to the patient needs.Ī recent literature review showed that most prediction models (developed for physical therapists) do not use performance measures that evaluate the clinical usefulness of the models well. Prediction models can help clinicians as physical therapists in daily practice in making a prognosis and provide an estimate of the probability of persisting symptoms for individual patients. The development of prediction models has grown in popularity in Low Back Pain (LBP) research. ![]() 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. This 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. There were virtually no differences between both models in the improved classification of true positive (TP) patients. Between 10 and 95% probability thresholds of chronic LBP risk, both models were of clinically benefit. Resultsīoth prediction models showed an equal performance according to the DCA and NB. 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. Prospective 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. 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). Currently used performance measures for discrimination were not informative to determine the clinical benefit of predictor variables. ![]()
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