Abstract

INTRODUCTION: Cervical spine procedures represent a major proportion of all spine surgery. Mitigating the revision rate following cervical procedures requires careful patient selection. While complication risk has successfully been predicted, revision risk has proven more challenging. This is likely due to the absence of granular variables in claims databases. The objective of this study was to develop a state-of-the-art of revision prediction of cervical spine surgery using laboratory and operative variables.METHODS: Using the Stanford Research Repository, patients undergoing a cervical spine procedure between 2016-2022 were identified (N=3151) and recent laboratory values were collected. Patients were classified into separate cohorts by revision outcome and timeframe. Machine and deep learning models were trained to predict each revision outcome from laboratory and operative variables.RESULTS: Red blood cell count, Hemoglobin, Hematocrit, Mean Corpuscular Hemoglobin Concentration, Red Blood Cell Distribution Width, Platelet Count, CO2, Anion Gap, and Calcium were all significantly associated with one or more revision cohorts. For the prediction of 3-month revision, the deep neural network achieved AUC of 0.833. The model demonstrated increased performance for anterior than posterior and arthrodesis than decompression procedures.CONCLUSIONS: Our deep learning approach successfully predicted 3-month revision outcomes from demographic variables, standard laboratory values, and operative variables, in a cervical spine surgery cohort. This work introduces standard laboratory values and operative codes as meaningful predictive variables for revision outcome prediction. The increased performance on certain procedures evidences the need for careful development and validation of "one-size-fits-all" risk scores for spine procedures.

View details for DOI 10.1016/j.wneu.2024.02.112

View details for PubMedID 38408699