Nomograms to Predict Verbal Memory Decline After Temporal Lobe Resection in Adults With Epilepsy. (20th July 2021)

• Record Type: Journal Article
• Authors: Busch, Robyn M.; Hogue, Olivia; Miller, Margaret; Ferguson, Lisa; McAndrews, Mary Pat; Hamberger, Marla; Kim, Michelle; McDonald, Carrie R.; Reyes, Anny; Drane, Daniel L.; Hermann, Bruce P.; Bingaman, William; Najm, Imad M.; Kattan, Michael W.; Jehi, Lara
• Is Part Of: Neurology. Volume 97:Number 3(2021)
• Publication Date: 2021-07-20
• Subjects: Neurology -- Periodicals; Neurology -- Periodicals; Neurologie -- Périodiques; 616.8
• DOI: 10.1212/WNL.0000000000012221
• Languages: English
• ISSNs: 0028-3878

Abstract

Abstract : Objective: To develop and externally validate models to predict the probability of postoperative verbal memory decline in adults after temporal lobe resection (TLR) for epilepsy using easily accessible preoperative clinical predictors. Methods: Multivariable models were developed to predict delayed verbal memory outcome on 3 commonly used measures: Rey Auditory Verbal Learning Test (RAVLT) and Logical Memory (LM) and Verbal Paired Associates (VPA) subtests from Wechsler Memory Scale–Third Edition. With the use of the Harrell step-down procedure for variable selection, models were developed in 359 adults who underwent TLR at the Cleveland Clinic and validated in 290 adults at 1 of 5 epilepsy surgery centers in the United States or Canada. Results: Twenty-nine percent of the development cohort and 26% of the validation cohort demonstrated significant decline on at least 1 verbal memory measure. Initial models had good to excellent predictive accuracy (calibration [c] statistic range 0.77–0.80) in identifying patients with memory decline; however, models slightly underestimated decline in the validation cohort. Model coefficients were updated with data from both cohorts to improve stability. The model for RAVLT included surgery side, baseline memory score, and hippocampal resection. The models for LM and VPA included surgery side, baseline score, and education. Updated model performance was good to excellent (RAVLT c = 0.81, LM c = 0.76, VPA c = 0.78). Model calibration was very good, indicating no systematic overestimation or underestimation of risk. Conclusions: Nomograms are provided in 2 easy-to-use formats to assist clinicians in estimating the probability of verbal memory decline in adults considering TLR for treatment of epilepsy. Classification of Evidence: This study provides Class II evidence that multivariable prediction models accurately predict verbal memory decline after TLR for epilepsy in adults.