Abstract

Perioperative ischemic optic neuropathy (ION) is a devastating complication of spinal fusion surgery.To develop predictive models of this blinding condition using longitudinal medical administrative claims databases, which provide temporal sequence of perioperative ischemic optic neuropathy and potential risk factors.Nested case control study PATIENT SAMPLE: Participants in Cliniformatics® Data Mart medical claims database (2007-2017) with hospitalization involving lumbar or thoracic spinal fusion surgery and no history of ION.Peri-operative ION (or not) during hospitalization for lumbar or thoracic spinal fusion surgery.65 ION cases and 106,871 controls were identified. Matched controls (n=211) were selected based on year of surgery and zip code. Chronic and peri-operative variables were assigned based on medical claims codes. Least absolute shrinkage and selection (LASSO) penalized conditional logistic regression with ten-fold cross validation was used to select variables for the optimal predictive model from the subset of variables with p < 0.15 between cases and matched controls (unadjusted conditional logistic regression). Receiver operating characteristic (ROC) curves were generated for the strata-independent matched and full sample.The predictive model included age 57-65 years, male gender, diabetes with and without complications, chronic anemia, hypertension, heart failure, carotid stenosis, perioperative hemorrhage and perioperative organ damage in the predictive model. Area under ROC curve was 0.75 (95% CI: 0.68, 0.82) for the matched sample and 0.72 (95% CI: 0.66, 0.78) for the full sample.This predictive model for ION in spine fusion considering chronic conditions and perioperative conditions is unique to date in its use of longitudinal medical claims data, inclusion of ICD-10 codes and study of ophthalmic conditions as risk factors. Similar to other studies of this condition the multivariable model included age, male gender, peri-operative organ damage and peri-operative hemorrhage. Hypertension, chronic anemia and carotid artery stenosis were new predictive factors identified by this study.

View details for DOI 10.1016/j.spinee.2020.11.011

View details for PubMedID 33248270