Dataset Information

Predicting Risk of Perioperative Ischemic Optic Neuropathy in Spine Fusion Surgery: A Cohort Study Using the National Inpatient Sample.

ABSTRACT:

Background

Ischemic optic neuropathy (ION) is a rare complication of anesthesia and surgery that causes vision loss in spine fusion. We sought to develop a predictive model based on known preoperative risk factors for perioperative ION to guide patient and physician preoperative decision-making.

Methods

In the National Inpatient Sample (NIS) for 1998-2012, discharges for posterior thoracic, lumbar, and sacral spine fusion were identified and classified by ION status. Variables were selected without weighting via variable clustering using Principal Component Analysis of Mixed Data (PCA-MIX). Hierarchical clustering with 4 clusters was performed, and the variable with largest squared loading in each cluster was chosen. By splitting our sample into a training and testing data set, we developed and internally validated a predictive model. The final model using variables known preoperatively was constructed to allow determination of relative and absolute risk of developing perioperative ION and was tested for calibration and discrimination.

Results

The final predictive model based on hierarchical clustering contained 3 preoperative factors, age, male or female sex, and the presence of obstructive sleep apnea (OSA). The predictive model based on these factors had an area under the receiver operating characteristic curve (AUC) of 0.65 and good calibration. A score cutoff of >1 had 100% sensitivity, while score of 3 had 96.5% specificity. The highest estimated absolute risk (844.5/million) and relative risk of ION (46.40) was for a man, age 40-64 years, with OSA.

Conclusions

The predictive model could enable screening for patients at higher risk of ION to provide more accurate risk assessment and surgical and anesthetic planning for perioperative ION in spine fusion.

SUBMITTER: Shah SH

PROVIDER: S-EPMC8098669 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background contextPerioperative ischemic optic neuropathy (ION) is a devastating complication of spinal fusion surgery.PurposeTo develop predictive models of this blinding condition using a longitudinal medical administrative claims database, which provides temporal sequence of perioperative ischemic optic neuropathy and potential risk factors.DesignNested case control study.Patient sampleParticipants in Cliniformatics Data Mart medical claims database (2007-2017) with hospitalization involving lumbar or thoracic spinal fusion surgery and no history of ION.Outcome measuresPerioperative ION (or not) during hospitalization for lumbar or thoracic spinal fusion surgery.MethodsSixty-five ION cases and 106,871 controls were identified. Matched controls (n=211) were selected based on year of surgery and zip code. Chronic and perioperative variables were assigned based on medical claims codes. Least absolute shrinkage and selection (LASSO) penalized conditional logistic regression with 10-fold cross validation was used to select variables for the optimal predictive model from the subset of variables with p<.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.ResultsThe 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. Area under ROC curve was 0.75 (95% confidence interval [CI]: 0.68, 0.82) for the matched sample and 0.72 (95% CI: 0.66, 0.78) for the full sample.ConclusionsThis 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 International Classification of Disease-10 codes and study of ophthalmic conditions as risk factors. Similar to other studies of this condition the multivariable model included age, male gender, perioperative organ damage and perioperative hemorrhage. Hypertension, chronic anemia and carotid artery stenosis were new predictive factors identified by this study.

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