J E Weeda, S M J van Kuijk, M P van den Berg, C H G Bastiaenen, H E Borst, L W van Rhijn, R A de Bie
{"title":"Identification of Predictors for Progression of Scoliosis in Rett Syndrome.","authors":"J E Weeda, S M J van Kuijk, M P van den Berg, C H G Bastiaenen, H E Borst, L W van Rhijn, R A de Bie","doi":"10.1080/17518423.2024.2365794","DOIUrl":null,"url":null,"abstract":"<p><p>Rett syndrome is a neurodevelopmental disorder in which scoliosis is a common orthopedic complication. This explorative study aims to identify predictors for rapid progression of scoliosis in Rett syndrome to enable variable selection for future prediction model development. A univariable logistic regression model was used to identify variables that discriminate between individuals with and without rapid progression of scoliosis (>10<math><msup><mi> </mi><mo>∘</mo></msup></math>Cobb angle/6 months) based on multi-center data. Predictors were identified using univariable logistic regression with OR (95% CI) and AUC (95% CI). Age at inclusion, Cobb angle at baseline and epilepsy have the highest discriminative ability for rapid progression of scoliosis in Rett syndrome.</p>","PeriodicalId":93976,"journal":{"name":"Developmental neurorehabilitation","volume":" ","pages":"126-133"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Developmental neurorehabilitation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17518423.2024.2365794","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/22 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Rett syndrome is a neurodevelopmental disorder in which scoliosis is a common orthopedic complication. This explorative study aims to identify predictors for rapid progression of scoliosis in Rett syndrome to enable variable selection for future prediction model development. A univariable logistic regression model was used to identify variables that discriminate between individuals with and without rapid progression of scoliosis (>10Cobb angle/6 months) based on multi-center data. Predictors were identified using univariable logistic regression with OR (95% CI) and AUC (95% CI). Age at inclusion, Cobb angle at baseline and epilepsy have the highest discriminative ability for rapid progression of scoliosis in Rett syndrome.