2023
DOI: 10.1097/bsd.0000000000001512
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Developing Mixed-effects Models to Optimize Prediction of Postoperative Outcomes in a Modern Sample of Over 450,000 Patients Undergoing Elective Cervical Spine Fusion Surgery

Shane Shahrestani,
Nolan J. Brown,
John K. Yue
et al.

Abstract: Study Design: A retrospective cohort. Objective: We utilize big data and modeling techniques to create optimized comorbidity indices for predicting postoperative outcomes following cervical spine fusion surgery. Summary of Background Data: Cervical spine decompression and fusion surgery are commonly used to treat degenerative cervical spine pathologies. However, there is a paucity of high-quality data defini… Show more

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“…Hospital identifiers were included as random effects. [26][27][28][29] Model performance was assessed using the area under the curve (AUC) of each ROC curve, acting as a proxy. An AUC of 0.50 indicates a random guess, whereas values exceeding 0.70 are considered acceptable.…”
Section: Discussionmentioning
confidence: 99%
“…Hospital identifiers were included as random effects. [26][27][28][29] Model performance was assessed using the area under the curve (AUC) of each ROC curve, acting as a proxy. An AUC of 0.50 indicates a random guess, whereas values exceeding 0.70 are considered acceptable.…”
Section: Discussionmentioning
confidence: 99%