External validation of a prediction model for disability and pain after lumbar disc herniation surgery: a prospective international registry-based cohort study.
Background and purpose — We aimed to externally validate machine learning models developed in Norway by evaluating their predictive outcome of disability and pain 12 months after lumbar disc herniation surgery in a Swedish and Danish cohort. Methods — Data was extracted for patients undergoing micro...
| Publicado en: | Acta Orthopaedica Vol. 96; pp. 512 - 521 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Medical Journals Sweden AB
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191461399&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191461399 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17453674 1BVL jtl: Acta Orthopaedica issn: 17453674 maglogo: N pubinfo: dt: 2025 vid: 96 pid: 59195 pub: Medical Journals Sweden AB artinfo: ui: 191461399 191461399 191461399 10.2340/17453674.2025.44251 191461399 ppf: 512 ppct: 9 formats: fmt: @attributes: type: P tig: atl: External validation of a prediction model for disability and pain after lumbar disc herniation surgery: a prospective international registry-based cohort study. aug: au: ABBOTT, Allan PEDERSEN, Casper Friis HEDEVIK, Henrik PARAI, Catharina GOROSITO, Martin A. ANDERSEN, Mikkel INGEBRIGTSEN, Tor SOLBERG, Tore K. GROTLE, Margreth BERG, Bjørnar affil: Unit of Physiotherapy, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden. sug: subj: Intervertebral Disk Displacement Surgery Diskectomy Methods Prediction Models Evaluation Machine Learning Postoperative Pain Postoperative Complications Disability Evaluation Human Male Female Middle Age Descriptive Statistics Prospective Studies Norway Registries, Disease Scales Validation Studies Artificial Intelligence Pain Measurement Data Analysis Software Middle Aged: 45-64 years Male Female ab: Background and purpose — We aimed to externally validate machine learning models developed in Norway by evaluating their predictive outcome of disability and pain 12 months after lumbar disc herniation surgery in a Swedish and Danish cohort. Methods — Data was extracted for patients undergoing microdiscectomy or open discectomy for lumbar disc herniation in the NORspine, SweSpine and DaneSpine national registries. Outcome of interest was changes in Oswestry disability index (ODI) (≥ 22 points), Numeric Rating Scale (NRS) for back pain (≥ 2 points), and NRS for leg pain (≥ 4 points). Model performance was evaluated by discrimination (C-statistic), calibration, overall fit, and net benefit. Results — For the ODI model, the NORspine cohort included 22,529 patients, the SweSpine cohort included 10,129 patients, and DaneSpine 5,670 patients. The ODI model’s C-statistic varied between 0.76 and 0.81 and calibration slope point estimates varied between 0.84 and 0.99. The C-statistic for NRS back pain varied between 0.70 and 0.76, and calibration slopes varied between 0.79 and 1.03. The C-statistic for NRS leg pain varied between 0.71 and 0.74, and calibration slopes varied between 0.90 and 1.02. There was acceptable overall fit and calibration metrics with minor–modest but explainable heterogeneity observed in the calibration plots. Decision curve analyses displayed clear potential net benefit in treatment in accordance with the prediction models compared with treating all patients or none. Conclusion — Predictive performance of machine learning models for treatment success/non-success in disability and pain at 12 months post-surgery for lumbar disc herniation showed acceptable discrimination ability, calibration, overall fit, and net benefit reproducible in similar international contexts. Future clinical impact studies are required. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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