Development and internal validation of prognostic models for recovery in patients with non-specific neck pain presenting in primary care

Roel W. Wingbermühle, Alessandro Chiarotto, Emiel van Trijffel, Bart Koes, Arianne P. Verhagen, Martijn W. Heymans

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

Objectives: Development and internal validation of prognostic models for post-treatment and 1-year recovery in patients with neck pain in primary care. Design: Prospective cohort study. Setting: Primary care manual therapy practices. Participants: Patients with non-specific neck pain of any duration (n = 1193). Intervention: Usual care manual therapy. Outcome measures: Recovery defined in terms of pain intensity, disability, and global perceived improvement directly post-treatment and at 1-year follow-up. Results: All post-treatment models exhibited acceptable discriminative performance after derivation (AUC ≥ 0.7). The developed post-treatment disability model exhibited the best overall performance (R2 = 0.24; IQR, 0.22–0.26), discrimination (AUC = 0.75; 95% CI, 0.63–0.84), and calibration (slope 0.92; IQR, 0.91–0.93). After internal validation and penalization, this model retained acceptable discriminative performance (AUC = 0.74). The five other models, including those predicting 1-year recovery, did not reach acceptable discriminative performance after internal validation. Baseline pain duration, disability, and pain intensity were consistent predictors across models. Conclusion: A post-treatment prognostic model for disability was successfully developed and internally validated. This model has potential to inform primary care clinicians about a patient's individual prognosis after treatment, but external validation is required before clinical use can be recommended.
Original languageEnglish
Pages (from-to)61-72
Number of pages12
JournalPhysiotherapy
Volume113
DOIs
Publication statusPublished - 1 Dec 2021

Keywords

  • Model development
  • Neck pain
  • Prognostic factors
  • Prognostic models
  • Recovery

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