{"title":"Progression to refractory status epilepticus: A machine learning analysis by means of classification and regression tree analysis","authors":"","doi":"10.1016/j.yebeh.2024.110005","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and Objectives</h3><div>to identify predictors of progression to refractory status epilepticus (RSE) using a machine learning technique.</div></div><div><h3>Methods</h3><div>Consecutive patients aged ≥ 14 years with SE registered in a 9-years period at Modena Academic Hospital were included in the analysis. We evaluated the risk of progression to RSE using logistic regression and a machine learning analysis by means of classification and regression tree analysis (CART) to develop a predictive model of progression to RSE.</div></div><div><h3>Results</h3><div>705 patients with SE were included in the study; of those, 33 % (233/705) evolved to RSE. The progression to RSE was an independent risk factor for 30-day mortality, with an OR adjusted for previously identified possible univariate confounders of 4.086 (CI 95 % 2.390–6.985; p < 0.001). According to CART the most important variable predicting evolution to RSE was the impaired consciousness before treatment, followed by acute symptomatic hypoxic etiology and periodic EEG patterns. The decision tree identified 14 nodes with a risk of evolution to RSE ranging from 1.5 % to 90.8 %. The overall percentage of success in classifying patients of the decision tree was 79.4 %; the percentage of accurate prediction was high, 94.1 %, for those patients not progressing to RSE and moderate, 49.8 %, for patients evolving to RSE.</div></div><div><h3>Conclusions</h3><div>Decision-tree analysis provided a meaningful risk stratification based on few variables that are easily obtained at SE first evaluation: consciousness before treatment, etiology, and severe EEG patterns. CART models must be viewed as potential new method for the stratification RSE at single subject level deserving further exploration and validation.</div></div>","PeriodicalId":11847,"journal":{"name":"Epilepsy & Behavior","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S152550502400386X/pdfft?md5=64fb06f453f1a384d1ba397575f0b2c0&pid=1-s2.0-S152550502400386X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epilepsy & Behavior","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S152550502400386X","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Background and Objectives
to identify predictors of progression to refractory status epilepticus (RSE) using a machine learning technique.
Methods
Consecutive patients aged ≥ 14 years with SE registered in a 9-years period at Modena Academic Hospital were included in the analysis. We evaluated the risk of progression to RSE using logistic regression and a machine learning analysis by means of classification and regression tree analysis (CART) to develop a predictive model of progression to RSE.
Results
705 patients with SE were included in the study; of those, 33 % (233/705) evolved to RSE. The progression to RSE was an independent risk factor for 30-day mortality, with an OR adjusted for previously identified possible univariate confounders of 4.086 (CI 95 % 2.390–6.985; p < 0.001). According to CART the most important variable predicting evolution to RSE was the impaired consciousness before treatment, followed by acute symptomatic hypoxic etiology and periodic EEG patterns. The decision tree identified 14 nodes with a risk of evolution to RSE ranging from 1.5 % to 90.8 %. The overall percentage of success in classifying patients of the decision tree was 79.4 %; the percentage of accurate prediction was high, 94.1 %, for those patients not progressing to RSE and moderate, 49.8 %, for patients evolving to RSE.
Conclusions
Decision-tree analysis provided a meaningful risk stratification based on few variables that are easily obtained at SE first evaluation: consciousness before treatment, etiology, and severe EEG patterns. CART models must be viewed as potential new method for the stratification RSE at single subject level deserving further exploration and validation.
期刊介绍:
Epilepsy & Behavior is the fastest-growing international journal uniquely devoted to the rapid dissemination of the most current information available on the behavioral aspects of seizures and epilepsy.
Epilepsy & Behavior presents original peer-reviewed articles based on laboratory and clinical research. Topics are drawn from a variety of fields, including clinical neurology, neurosurgery, neuropsychiatry, neuropsychology, neurophysiology, neuropharmacology, and neuroimaging.
From September 2012 Epilepsy & Behavior stopped accepting Case Reports for publication in the journal. From this date authors who submit to Epilepsy & Behavior will be offered a transfer or asked to resubmit their Case Reports to its new sister journal, Epilepsy & Behavior Case Reports.