Sofia Ljunggren1, Lena Andersson-Roswall1, Henrik Imberg2,3, Hans Samuelsson1,4, Kristina Malmgren1. 1. Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden. 2. Statistiska Konsultgruppen, Gothenburg, Sweden. 3. Department of Mathematical Sciences, Chalmers University of Technology and the University of Gothenburg, Gothenburg, Sweden. 4. Department of Psychology, University of Gothenburg, Gothenburg, Sweden.
Abstract
OBJECTIVES: The aim of the study was to develop a prediction model for verbal memory decline after temporal lobe resection (TLR) for epilepsy. The model will be used in the preoperative counselling of patients to give individualized information about risk for verbal memory decline. MATERIALS AND METHODS: A sample of 110 consecutive patients who underwent TLR for epilepsy at Sahlgrenska University Hospital between 1987 and 2011 constituted the basis for the prediction model. They had all gone through a formal neuropsychological assessment before surgery and 2 years after. Penalized regression and 20 × 10-fold cross-validation were used in order to build a reliable model for predicting individual risks. RESULTS: The final model included four predictors: side of surgery; inclusion or not of the hippocampus in the resection; preoperative verbal memory function; and presence/absence of focal to bilateral tonic-clonic seizures (TCS) the last year prior to the presurgical investigation. The impact of a history of TCS is a new finding which we interpret as a sign of a more widespread network disease which influences neuropsychological function and the cognitive reserve. The model correctly identified 82% of patients with post-operative decline in verbal memory, and the overall accuracy was 70%-85% depending on choice of risk thresholds. CONCLUSIONS: The model makes it possible to provide patients with individualized prediction regarding the risk of verbal memory decline following TLR. This will help them make more informed decisions regarding treatment, and it will also enable the epilepsy surgery team to prepare them better for the rehabilitation process.
OBJECTIVES: The aim of the study was to develop a prediction model for verbal memory decline after temporal lobe resection (TLR) for epilepsy. The model will be used in the preoperative counselling of patients to give individualized information about risk for verbal memory decline. MATERIALS AND METHODS: A sample of 110 consecutive patients who underwent TLR for epilepsy at Sahlgrenska University Hospital between 1987 and 2011 constituted the basis for the prediction model. They had all gone through a formal neuropsychological assessment before surgery and 2 years after. Penalized regression and 20 × 10-fold cross-validation were used in order to build a reliable model for predicting individual risks. RESULTS: The final model included four predictors: side of surgery; inclusion or not of the hippocampus in the resection; preoperative verbal memory function; and presence/absence of focal to bilateral tonic-clonic seizures (TCS) the last year prior to the presurgical investigation. The impact of a history of TCS is a new finding which we interpret as a sign of a more widespread network disease which influences neuropsychological function and the cognitive reserve. The model correctly identified 82% of patients with post-operative decline in verbal memory, and the overall accuracy was 70%-85% depending on choice of risk thresholds. CONCLUSIONS: The model makes it possible to provide patients with individualized prediction regarding the risk of verbal memory decline following TLR. This will help them make more informed decisions regarding treatment, and it will also enable the epilepsy surgery team to prepare them better for the rehabilitation process.
Authors: Nadine Conradi; Friederike Rosenberg; Susanne Knake; Louise Biermann; Anja Haag; Iris Gorny; Anke Hermsen; Viola von Podewils; Marion Behrens; Marianna Gurschi; Richard du Mesnil de Rochemont; Katja Menzler; Sebastian Bauer; Susanne Schubert-Bast; Christopher Nimsky; Jürgen Konczalla; Felix Rosenow; Adam Strzelczyk Journal: Sci Rep Date: 2021-05-26 Impact factor: 4.379