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- For Pain Patients and Professionals
Back pain is a leading cause of disability worldwide and is common in older adults. No clinical prediction models for poor long-term outcomes have been developed in older patients with back pain. This study aimed to develop and internally validate three clinical prediction models for non-recovery in this population. A prospective cohort study in general practice was conducted (BACE, Netherlands), including 675 patients >55 years with a new episode of care for back pain. Three definitions of non-recovery were used combining 6- and 12-month follow-up data: 1) persistent back pain; 2) persistent disability; 3) perceived non-recovery. Sample size calculation resulted in a maximum of 14 candidate predictors that were selected from back pain prognostic literature and clinical experience. Multivariable logistic regression was used to develop the models (backward selection procedure). Models' performance was evaluated with explained variance (Nagelkerke's R2), calibration (Hosmer-Lemeshow test), and discrimination (AUC) measures. The models were internally validated in 250 bootstrapped samples to correct for over-optimism. All three models displayed good overall performance during development and internal validation (i.e. R2>30%; AUC>0.77). The model predicting persistent disability performed best, showing good calibration, discrimination (AUC 0.86, 95%CI 0.83-0.89; optimism-adjusted AUC 0.85), and explained variance (R2 49%, optimism-adjusted R2 46%). Common predictors in all models were: age, chronic duration, disability, a recent back pain episode, and patients' recovery expectations. Spinal morning stiffness and pain during spinal rotation were included in two out of three models. These models should be externally validated before being used in a clinical primary care setting.