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Papers of the Week

Papers: 22 Jul 2023 - 28 Jul 2023


Human Studies, Neurobiology

Musculoskeletal Pain

2023 Jul 24



External validation and updating of prognostic prediction models for nonrecovery among older adults seeking primary care for back pain.


Vigdal ØN, Storheim K, Killingmo RM, Rysstad T, Pripp AH, van der Gaag W, Chiarotto A, Koes B, Grotle M


Prognostic prediction models for 3 different definitions of nonrecovery were developed in the Back Complaints in the Elders study in the Netherlands. The models’ performance was good (optimism-adjusted area under receiver operating characteristics [AUC] curve ≥0.77, R2 ≥0.3). This study aimed to assess the external validity of the 3 prognostic prediction models in the Norwegian Back Complaints in the Elders study. We conducted a prospective cohort study, including 452 patients aged ≥55 years, seeking primary care for a new episode of back pain. Nonrecovery was defined for 2 outcomes, combining 6- and 12-month follow-up data: Persistent back pain (≥3/10 on numeric rating scale) and persistent disability (≥4/24 on Roland-Morris Disability Questionnaire). We could not assess the third model (self-reported nonrecovery) because of substantial missing data (>50%). The models consisted of biopsychosocial prognostic factors. First, we assessed Nagelkerke R2, discrimination (AUC) and calibration (calibration-in-the-large [CITL], slope, and calibration plot). Step 2 was to recalibrate the models based on CITL and slope. Step 3 was to reestimate the model coefficients and assess if this improved performance. The back pain model demonstrated acceptable discrimination (AUC 0.74, 95% confidence interval: 0.69-0.79), and R2 was 0.23. The disability model demonstrated excellent discrimination (AUC 0.81, 95% confidence interval: 0.76-0.85), and R2 was 0.35. Both models had poor calibration (CITL <0, slope <1). Recalibration yielded acceptable calibration for both models, according to the calibration plots. Step 3 did not improve performance substantially. The recalibrated models may need further external validation, and the models’ clinical impact should be assessed.