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

Ørjan Nesse Vigdal*, Kjersti Storheim, Rikke Munk Killingmo, Tarjei Rysstad, Are Hugo Pripp, Wendelien van der Gaag, Alessandro Chiarotto, Bart Koes, Margreth Grotle

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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

Original languageEnglish
Pages (from-to)2759-2768
Number of pages10
JournalPain
Volume164
Issue number12
Early online date24 Jul 2023
DOIs
Publication statusPublished - 1 Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 Lippincott Williams and Wilkins. All rights reserved.

Fingerprint

Dive into the research topics of 'External validation and updating of prognostic prediction models for nonrecovery among older adults seeking primary care for back pain'. Together they form a unique fingerprint.

Cite this