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Updated prediction model for low back pain recovery evaluation

Updated prediction model for low back pain recovery evaluation Updated prediction model for low back pain recovery evaluation
Updated prediction model for low back pain recovery evaluation Updated prediction model for low back pain recovery evaluation

What's new?

A recently updated detection model for low back pain was found to be significantly efficient in predicting the possible recovery of patients with acute low back pain.

Evident from the findings of a recently published study in the European Journal of Pain, a clinical prediction model with five easily collected variables present a potential efficacy in the possible prognosis of low back pain (LBP).


A total of 737 participants with a pain score of ≥2/10 and duration of current episode of ≤4 weeks were selected for the investigation. Days to pain recovery were considered as the primary outcome. Before refitting the current model, some of the variables from the development dataset were re-classified. The calibration and discrimination of the prediction model were later examined in the validation dataset. The predictor variables involved in the analysis were the number of previous episodes, pain intensity change over the first week, duration of current episode, pain intensity, and depression.


Three variables went through the re-classification and performed well in the development dataset. In one month, the calibration for the validation sample was accepted. The predicted proportions in the quintiles led to overestimate the noticed recovery proportions at one week and underestimate at three months. The renewed prediction model illustrated good external validity and beneficial in practice; however, further impact studies and validation in related populations is required.


Source:

The European Journal of Pain

Article:

Predicting pain recovery in patients with acute low back pain: Updating and validation of a clinical prediction model

Authors:

T da Silva et al.

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