Predicting high health care use in patients with spinal disorder in secondary care : model development and validation
(2026) In Pain Reports 11(1).- Abstract
Abstract – Introduction: – Spinal disorders are among the biggest contributors to health care utilization (HCU).Objective: – To develop and externally validate a prediction model for high all-cause HCU (75th percentile during 1 year after the index date) among patients with spinal disorders visiting multidisciplinary secondary care clinics.Methods: – We developed and internally validated the model using the Norwegian Neck and Back Registry, including patients registered between January 1, 2016, and December 31, 2020, linked with national health registries (N = 9092). For external validation, we used data from the Danish SpineData Registry, linked with national registries, for the same period (N = 34, 853). We assessed Nagelkerke... (More)
Abstract – Introduction: – Spinal disorders are among the biggest contributors to health care utilization (HCU).Objective: – To develop and externally validate a prediction model for high all-cause HCU (75th percentile during 1 year after the index date) among patients with spinal disorders visiting multidisciplinary secondary care clinics.Methods: – We developed and internally validated the model using the Norwegian Neck and Back Registry, including patients registered between January 1, 2016, and December 31, 2020, linked with national health registries (N = 9092). For external validation, we used data from the Danish SpineData Registry, linked with national registries, for the same period (N = 34, 853). We assessed Nagelkerke R2, discrimination (area under receiver operating characteristics curve [AUC]), and calibration (calibration-in-the-large [CITL], slope, and calibration plot). Results: – The final model included sex, nationality, education, physical activity, smoking, prior HCU, work status, disability, health-related quality of life, medicine use, diagnosis, kinesiophobia, and comorbidity. It demonstrated acceptable discrimination (AUC 0.78, 95% confidence interval [CI], 0.77–0.78), an R2 of 0.26, and good calibration after internal validation. Upon external validation, the model demonstrated excellent discrimination (AUC 0.81, 95% CI 0.80–0.81) and an R2 of 0.31. The calibration slope was 1.08 (95% CI 1.06–1.11) and CITL was 0.16 (95% CI 0.12–0.19). Predicted probabilities closely matched observed probabilities across all deciles in internal validation, with slight underestimation of high HCU in the top 3 deciles during external validation.Conclusion: – Overall, the model shows promise in predicting high HCU in patients with spinal disorders referred to secondary care but requires further testing and validation in implementation settings before recommendation.
(Less)
- author
- organization
- publishing date
- 2026-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Epidemiology, High health care utilization, Prediction model, Registry data, Spinal disorders, Validation
- in
- Pain Reports
- volume
- 11
- issue
- 1
- article number
- e1382
- publisher
- Lippincott Williams & Wilkins
- external identifiers
-
- scopus:105036027700
- pmid:41562120
- ISSN
- 2471-2531
- DOI
- 10.1097/PR9.0000000000001382
- language
- English
- LU publication?
- yes
- id
- fa69f958-7269-48b4-9f78-9a5a4600ba53
- date added to LUP
- 2026-05-21 14:46:06
- date last changed
- 2026-06-04 15:43:49
@article{fa69f958-7269-48b4-9f78-9a5a4600ba53,
abstract = {{<p>Abstract – Introduction: – Spinal disorders are among the biggest contributors to health care utilization (HCU).Objective: – To develop and externally validate a prediction model for high all-cause HCU (75th percentile during 1 year after the index date) among patients with spinal disorders visiting multidisciplinary secondary care clinics.Methods: – We developed and internally validated the model using the Norwegian Neck and Back Registry, including patients registered between January 1, 2016, and December 31, 2020, linked with national health registries (N = 9092). For external validation, we used data from the Danish SpineData Registry, linked with national registries, for the same period (N = 34, 853). We assessed Nagelkerke R<sup>2</sup>, discrimination (area under receiver operating characteristics curve [AUC]), and calibration (calibration-in-the-large [CITL], slope, and calibration plot). Results: – The final model included sex, nationality, education, physical activity, smoking, prior HCU, work status, disability, health-related quality of life, medicine use, diagnosis, kinesiophobia, and comorbidity. It demonstrated acceptable discrimination (AUC 0.78, 95% confidence interval [CI], 0.77–0.78), an R<sup>2</sup> of 0.26, and good calibration after internal validation. Upon external validation, the model demonstrated excellent discrimination (AUC 0.81, 95% CI 0.80–0.81) and an R<sup>2</sup> of 0.31. The calibration slope was 1.08 (95% CI 1.06–1.11) and CITL was 0.16 (95% CI 0.12–0.19). Predicted probabilities closely matched observed probabilities across all deciles in internal validation, with slight underestimation of high HCU in the top 3 deciles during external validation.Conclusion: – Overall, the model shows promise in predicting high HCU in patients with spinal disorders referred to secondary care but requires further testing and validation in implementation settings before recommendation.</p>}},
author = {{Kjønø, Lise Grethe and Magnusson, Karin and Johnsen, Marianne Bakke and Storheim, Kjersti and Clausen, Stine Haugaard and Wilhelmsen, Maja and O'Neill, Søren and Pripp, Are Hugo and Berg, Bjørnar and Skovsgaard, Christian Volmar and Hartvigsen, Jan and Grotle, Margreth and Vigdal, Ørjan Nesse}},
issn = {{2471-2531}},
keywords = {{Epidemiology; High health care utilization; Prediction model; Registry data; Spinal disorders; Validation}},
language = {{eng}},
number = {{1}},
publisher = {{Lippincott Williams & Wilkins}},
series = {{Pain Reports}},
title = {{Predicting high health care use in patients with spinal disorder in secondary care : model development and validation}},
url = {{http://dx.doi.org/10.1097/PR9.0000000000001382}},
doi = {{10.1097/PR9.0000000000001382}},
volume = {{11}},
year = {{2026}},
}