An Appraisal of the Quality of Development and Reporting of Predictive Models in Spine Surgery

Study DesignLiterature review.ObjectiveThe Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement was developed to improve the generalizability of predictive models. This study systematically evaluated the quality of predictive models related to spine procedures and assessed their compliance with the TRIPOD guidelines.MethodsA systematic search was conducted on PubMed to identify original research articles published between January 1st, 2018, and February 1st, 2023 reporting prediction models in the top six spine journals ranked by Scimago Journal Ranking (SJR): Journal of Bone and Joint Surgery, Spine, Journal of Orthopaedic Trauma, Journal of Neurosurgery: Spine, Neurosurgery, and Neurosurgical Focus. We assessed article adherence to the TRIPOD criteria using a standardized checklist.Results72 articles were included and analyzed with the TRIPOD checklist. Median compliance with the TRIPOD criteria was 57.14% (IQR: 48.33-64.95%). Compliance varied significantly across journals (P < 0.05). Among the TRIPOD criteria, the lowest compliance was observed in blinding the assessment of predictors (n = 8, 16.00%), fully presenting the model for use (n = 12, 17.91%), and providing sufficient information to allow for the external validation of results (n = 13, 19.70%).ConclusionsPublished machine learning models predicting outcomes in spine surgery often do not meet the established guidelines for their development, validation, and reporting outlined by TRIPOD. This lack of compliance may suggest that these models have not been adequately validated externally or adopted into routine clinical practice in spine surgery.