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dc.contributor.authorBerg, Bjørnar
dc.contributor.authorGorosito, Martin A.
dc.contributor.authorFjeld, Olaf
dc.contributor.authorHaugerud, Hårek
dc.contributor.authorStorheim, Kjersti
dc.contributor.authorSolberg, Tore K.
dc.contributor.authorGrotle, Margreth
dc.date.accessioned2024-09-05T09:50:19Z
dc.date.available2024-09-05T09:50:19Z
dc.date.issued2024-02-07
dc.description.abstractIMPORTANCE Lumber disc herniation surgery can reduce pain and disability. However, a sizable minority of individuals experience minimal benefit, necessitating the development of accurate prediction models.<p> <p>OBJECTIVE To develop and validate prediction models for disability and pain 12 months after lumbar disc herniation surgery. <p>DESIGN, SETTING, AND PARTICIPANTS A prospective, multicenter, registry-based prognostic study was conducted on a cohort of individuals undergoing lumbar disc herniation surgery from January 1, 2007, to May 31, 2021. Patients in the Norwegian Registry for Spine Surgery from all public and private hospitals in Norway performing spine surgery were included. Data analysis was performed from January to June 2023. <p>EXPOSURES Microdiscectomy or open discectomy. <p>MAIN OUTCOMES AND MEASURES Treatment success at 12 months, defined as improvement in Oswestry Disability Index (ODI) of 22 points or more; Numeric Rating Scale (NRS) back pain improvement of 2 or more points, and NRS leg pain improvement of 4 or more points. Machine learning models were trained for model development and internal-external cross-validation applied over geographic regions to validate the models. Model performance was assessed through discrimination (C statistic) and calibration (slope and intercept). RESULTS Analysis included 22 707 surgical cases (21 161 patients) (ODI model) (mean [SD] age, 47.0 [14.0] years; 12 952 [57.0%] males). Treatment nonsuccess was experienced by 33% (ODI), 27% (NRS back pain), and 31% (NRS leg pain) of the patients. In internal-external cross-validation, the selected machine learning models showed consistent discrimination and calibration across all 5 regions. The C statistic ranged from 0.81 to 0.84 (pooled random-effects meta-analysis estimate, 0.82; 95% CI, 0.81-0.84) for the ODI model. Calibration slopes (point estimates, 0.94-1.03; pooled estimate, 0.99; 95% CI, 0.93-1.06) and calibration intercepts (point estimates, −0.05 to 0.11; pooled estimate, 0.01; 95% CI, −0.07 to 0.10) were also consistent across regions. For NRS back pain, the C statistic ranged from 0.75 to 0.80 (pooled estimate, 0.77; 95% CI, 0.75-0.79); for NRS leg pain, the C statistic ranged from 0.74 to 0.77 (pooled estimate, 0.75; 95% CI, 0.74-0.76). Only minor heterogeneity was found in calibration slopes and intercepts. <p>CONCLUSION The findings of this study suggest that the models developed can inform patients and clinicians about individual prognosis and aid in surgical decision-making.en_US
dc.identifier.citationBerg, Gorosito, Fjeld, Haugerud, Storheim, Solberg, Grotle. Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery. JAMA Network Open. 2024;7(2):e2355024en_US
dc.identifier.cristinIDFRIDAID 2249670
dc.identifier.doi10.1001/jamanetworkopen.2023.55024
dc.identifier.issn2574-3805
dc.identifier.urihttps://hdl.handle.net/10037/34532
dc.language.isoengen_US
dc.publisherAmerican Medical Associationen_US
dc.relation.journalJAMA Network Open
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleMachine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgeryen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)