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

Author:

Vigdal Ørjan Nesse1ORCID,Storheim Kjersti12ORCID,Killingmo Rikke Munk1ORCID,Rysstad Tarjei1ORCID,Pripp Are Hugo1ORCID,van der Gaag Wendelien3ORCID,Chiarotto Alessandro3ORCID,Koes Bart34ORCID,Grotle Margreth12ORCID

Affiliation:

1. Department of Rehabilitation Science and Health Technology, Faculty of Health Science, OsloMet—Oslo Metropolitan University, Oslo, Norway

2. Research and Communication Unit for Musculoskeletal Health (FORMI), Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway

3. Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands

4. Center for Muscle and Health, University of Southern Denmark, Odense, Denmark

Abstract

Abstract 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, R 2 ≥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 R 2, 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 R 2 was 0.23. The disability model demonstrated excellent discrimination (AUC 0.81, 95% confidence interval: 0.76-0.85), and R 2 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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Anesthesiology and Pain Medicine,Neurology (clinical),Neurology

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