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Predicting pain recovery in patients with acute low back pain: Updating and validation of a clinical prediction model
Author(s) -
Silva Tatiane,
Macaskill Petra,
Kongsted Alice,
Mills Kathryn,
Maher Chris G.,
Hancock Mark J.
Publication year - 2019
Publication title -
european journal of pain
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.305
H-Index - 109
eISSN - 1532-2149
pISSN - 1090-3801
DOI - 10.1002/ejp.1308
Subject(s) - statistic , medicine , cross validation , calibration , sample (material) , statistics , mathematics , chromatography , chemistry
Background The prognosis of acute low back pain ( LBP ) is typically good; however, there is substantial variation in individual patient's outcomes. We recently developed a prediction model that was able to predict the likelihood of pain recovery in patients with acute LBP who continue to have pain approximately 1 week after initially seeking care. The aims of the current study were to (a) re‐categorize the variables in the developmental dataset to be able to validate the model in the validation dataset; (b) refit the existing model in the developmental dataset; and (c) validate the model in the validation dataset. Methods The validation study sample comprised 737 patients with acute LBP , with a pain score of ≥2/10, 1 week after initially seeking care and with duration of current episode of ≤4 weeks. The primary outcome measure was days to pain recovery. Some of the variables from the development dataset were re‐categorized prior to refitting the existing model in the developmental dataset using Cox regression. The performance (calibration and discrimination) of the prediction model was then tested in the validation dataset. Results Three variables of the development dataset were re‐categorized. The performance of the prediction model with re‐categorized variables in the development dataset was good (C‐statistic = 0.76, 95% CI 0.70–0.82). The discrimination of the model using the validation dataset resulted in a C‐statistic of 0.71 (95% CI 0.63–0.78). The calibration for the validation sample was acceptable at 1 month. However, at 1 week the predicted proportions within quintiles tended to overestimate the observed recovery proportions, and at 3 months, the predicted proportions tended to underestimate the observed recovery proportions. Conclusions The updated prediction model demonstrated reasonably good external validity and may be useful in practice, but further validation and impact studies in relevant populations should be conducted. Significance A clinical prediction model based on five easily collected variables demonstrated reasonable external validity. The prediction model has the potential to inform patients and clinicians of the likely prognosis of individuals with acute LBP but requires impact studies to assess its clinical usefulness.