New insights for predicting surgery outcome in patients with temporal lobe epilepsy. A systematic review.

Resective surgery is the treatment of choice for one-third of adult patients with focal, drug-resistant epilepsy. This procedure is associated with substantial clinical and cognitive risks. In clinical practice, there is no validated model for epilepsy surgery outcome prediction (ESOP). Meta-analyses on ESOP studies assessing prognostic factors report discrepancies in terms of study design. Our review aims to systematically investigate methodological and analytical aspects of studies predicting clinical and cognitive outcomes after temporal lobe epilepsy surgery. A systematic review of ESOP studies published between 2000 and 2022 from three databases (MEDLINE, Web of Science, and PsycINFO) was completed by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. It yielded 4867 articles. Among them, 21 corresponded to our inclusion criteria and were therefore retained in the final review. The risk of bias was assessed using A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST). Data extracted from the 21 studies were analyzed using narrative synthesis and descriptive statistics. Our findings show an increase in the use of multimodal datasets and machine learning analyses in recent ESOP studies, although regression remained the most frequently used approach. We also identified a more frequent use of network notions in recent ESOP studies. Nevertheless, several methodological issues were noted, such as small sample sizes, lack of information on the follow-up period, variability in seizure outcome, and the definition of neuropsychological postoperative change. Of 21 studies, only one provided a clinical tool to anticipate the cognitive outcome after epilepsy surgery. We conclude that methodological issues should be overcome before we move towards more complete models to better predict clinical and cognitive outcomes after epilepsy surgery. Recommendations for future studies to harness the possibilities of multimodal datasets and data fusion, are provided. A stronger bridge between fundamental and clinical research may result in developing accessible clinical tools.

A machine learning approach to explore cognitive signatures in patients with temporo-mesial epilepsy.

We aimed to identify cognitive signatures (phenotypes) of patients suffering from mesial temporal lobe epilepsy (mTLE) with respect to their epilepsy lateralization (left or right), through the use of SVM (Support Vector Machine) and XGBoost (eXtreme Gradient Boosting) machine learning (ML) algorithms. Specifically, we explored the ability of the two algorithms to identify the most significant scores (features, in ML terms) that segregate the left from the right mTLE patients. We had two versions of our dataset which consisted of neuropsychological test scores: a "reduced and working" version (n = 46 patients) without any missing data, and another one "original" (n = 57) with missing data but useful for testing the robustness of results obtained with the working dataset. The emphasis was placed on a precautionary machine learning (ML) approach for classification, with reproducible and generalizable results. The effects of several clinical medical variables were also studied. We obtained excellent predictive classification performances (>75%) of left and right mTLE with both versions of the dataset. The most segregating features were four language and memory tests, with a remarkable stability close to 100%. Thus, these cognitive tests appear to be highly relevant for neuropsychological assessment of patients. Moreover, clinical variables such as structural asymmetry between hippocampal gyri, the age of patients and the number of anti-epileptic drugs, influenced the cognitive phenotype. This exploratory study represents an in-depth analysis of cognitive scores and allows observing interesting interactions between language and memory performance. We discuss implications of these findings in terms of clinical and theoretical applications and perspectives in the field of neuropsychology.

Interrater reliability of 2 sedation scales in a medical intensive care unit: a preliminary report.

BACKGROUND: Critical care nurses must assess the effectiveness of sedatives and analgesic agents in order to titrate doses. OBJECTIVES: To measure the interrater reliability of 2 sedation scales used to assess patients in medical intensive care units. METHODS: The interrater reliabilities of the Motor Activity Assessment Scale and the Luer sedation scale were compared prospectively in 31 patients receiving mechanical ventilation in an 18-bed medical intensive care unit of a tertiary care institution. Three registered nurses, 1 clinical pharmacist, and 1 physician simultaneously and independently followed a standardized procedure to rate each patient by using the 2 scales. Scales were randomly ordered to counteract ordering effect. Analysis of variance with post hoc Duncan multiple range tests was used to detect bias; a correlation coefficient matrix was used to examine degree of association among raters; and the intraclass correlation coefficient was measured to control for multiple raters. RESULTS: No significant bias was detected with either scale. The Motor Activity Assessment Scale had less variation (Pearson r = 0.75-0.92) than did the Luer scale (Pearson r = 0.37-0.94) and had a stronger intraclass correlation coefficient (0.81 vs 0.79). CONCLUSIONS: The Motor Activity Assessment Scale showed the highest consistency among raters.