Shane Shahrestani, Nathan Shlobin, Julian L Gendreau, Nolan J Brown, Alexander Himstead, Neal A Patel, Noah Pierzchajlo, Sachiv Chakravarti, Darrin Jason Lee, Peter A Chiarelli, Carli L Bullis, Jason Chu
{"title":"利用机器学习开发预测模型，预测33248例分流性脑积水患儿的分流并发症。","authors":"Shane Shahrestani, Nathan Shlobin, Julian L Gendreau, Nolan J Brown, Alexander Himstead, Neal A Patel, Noah Pierzchajlo, Sachiv Chakravarti, Darrin Jason Lee, Peter A Chiarelli, Carli L Bullis, Jason Chu","doi":"10.1159/000531754","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Hydrocephalus is a common pediatric neurosurgical pathology, typically treated with a ventricular shunt, yet approximately 30% of patients experience shunt failure within the first year after surgery. As a result, the objective of the present study was to validate a predictive model of pediatric shunt complications with data retrieved from the Healthcare Cost and Utilization Project (HCUP) National Readmissions Database (NRD).</p><p><strong>Methods: </strong>The HCUP NRD was queried from 2016 to 2017 for pediatric patients undergoing shunt placement using ICD-10 codes. Comorbidities present upon initial admission resulting in shunt placement, Johns Hopkins Adjusted Clinical Groups (JHACG) frailty-defining criteria, and Major Diagnostic Category (MDC) at admission classifications were obtained. The database was divided into training (n = 19,948), validation (n = 6,650), and testing (n = 6,650) datasets. Multivariable analysis was performed to identify significant predictors of shunt complications which were used to develop logistic regression models. Post hoc receiver operating characteristic (ROC) curves were created.</p><p><strong>Results: </strong>A total of 33,248 pediatric patients aged 6.9 ± 5.7 years were included. Number of diagnoses during primary admission (OR: 1.05, 95% CI: 1.04-1.07) and initial neurological admission diagnoses (OR: 3.83, 95% CI: 3.33-4.42) positively correlated with shunt complications. Female sex (OR: 0.87, 95% CI: 0.76-0.99) and elective admissions (OR: 0.62, 95% CI: 0.53-0.72) negatively correlated with shunt complications. ROC curve for the regression model utilizing all significant predictors of readmission demonstrated area under the curve of 0.733, suggesting these factors are possible predictors of shunt complications in pediatric hydrocephalus.</p><p><strong>Conclusion: </strong>Efficacious and safe treatment of pediatric hydrocephalus is of paramount importance. Our machine learning algorithm delineated possible variables predictive of shunt complications with good predictive value.</p>","PeriodicalId":54631,"journal":{"name":"Pediatric Neurosurgery","volume":" ","pages":"206-214"},"PeriodicalIF":0.9000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614444/pdf/","citationCount":"0","resultStr":"{\"title\":\"Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning.\",\"authors\":\"Shane Shahrestani, Nathan Shlobin, Julian L Gendreau, Nolan J Brown, Alexander Himstead, Neal A Patel, Noah Pierzchajlo, Sachiv Chakravarti, Darrin Jason Lee, Peter A Chiarelli, Carli L Bullis, Jason Chu\",\"doi\":\"10.1159/000531754\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Hydrocephalus is a common pediatric neurosurgical pathology, typically treated with a ventricular shunt, yet approximately 30% of patients experience shunt failure within the first year after surgery. As a result, the objective of the present study was to validate a predictive model of pediatric shunt complications with data retrieved from the Healthcare Cost and Utilization Project (HCUP) National Readmissions Database (NRD).</p><p><strong>Methods: </strong>The HCUP NRD was queried from 2016 to 2017 for pediatric patients undergoing shunt placement using ICD-10 codes. Comorbidities present upon initial admission resulting in shunt placement, Johns Hopkins Adjusted Clinical Groups (JHACG) frailty-defining criteria, and Major Diagnostic Category (MDC) at admission classifications were obtained. The database was divided into training (n = 19,948), validation (n = 6,650), and testing (n = 6,650) datasets. Multivariable analysis was performed to identify significant predictors of shunt complications which were used to develop logistic regression models. Post hoc receiver operating characteristic (ROC) curves were created.</p><p><strong>Results: </strong>A total of 33,248 pediatric patients aged 6.9 ± 5.7 years were included. Number of diagnoses during primary admission (OR: 1.05, 95% CI: 1.04-1.07) and initial neurological admission diagnoses (OR: 3.83, 95% CI: 3.33-4.42) positively correlated with shunt complications. Female sex (OR: 0.87, 95% CI: 0.76-0.99) and elective admissions (OR: 0.62, 95% CI: 0.53-0.72) negatively correlated with shunt complications. ROC curve for the regression model utilizing all significant predictors of readmission demonstrated area under the curve of 0.733, suggesting these factors are possible predictors of shunt complications in pediatric hydrocephalus.</p><p><strong>Conclusion: </strong>Efficacious and safe treatment of pediatric hydrocephalus is of paramount importance. Our machine learning algorithm delineated possible variables predictive of shunt complications with good predictive value.</p>\",\"PeriodicalId\":54631,\"journal\":{\"name\":\"Pediatric Neurosurgery\",\"volume\":\" \",\"pages\":\"206-214\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614444/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pediatric Neurosurgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1159/000531754\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/6/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pediatric Neurosurgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000531754","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/6/30 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning.
Introduction: Hydrocephalus is a common pediatric neurosurgical pathology, typically treated with a ventricular shunt, yet approximately 30% of patients experience shunt failure within the first year after surgery. As a result, the objective of the present study was to validate a predictive model of pediatric shunt complications with data retrieved from the Healthcare Cost and Utilization Project (HCUP) National Readmissions Database (NRD).
Methods: The HCUP NRD was queried from 2016 to 2017 for pediatric patients undergoing shunt placement using ICD-10 codes. Comorbidities present upon initial admission resulting in shunt placement, Johns Hopkins Adjusted Clinical Groups (JHACG) frailty-defining criteria, and Major Diagnostic Category (MDC) at admission classifications were obtained. The database was divided into training (n = 19,948), validation (n = 6,650), and testing (n = 6,650) datasets. Multivariable analysis was performed to identify significant predictors of shunt complications which were used to develop logistic regression models. Post hoc receiver operating characteristic (ROC) curves were created.
Results: A total of 33,248 pediatric patients aged 6.9 ± 5.7 years were included. Number of diagnoses during primary admission (OR: 1.05, 95% CI: 1.04-1.07) and initial neurological admission diagnoses (OR: 3.83, 95% CI: 3.33-4.42) positively correlated with shunt complications. Female sex (OR: 0.87, 95% CI: 0.76-0.99) and elective admissions (OR: 0.62, 95% CI: 0.53-0.72) negatively correlated with shunt complications. ROC curve for the regression model utilizing all significant predictors of readmission demonstrated area under the curve of 0.733, suggesting these factors are possible predictors of shunt complications in pediatric hydrocephalus.
Conclusion: Efficacious and safe treatment of pediatric hydrocephalus is of paramount importance. Our machine learning algorithm delineated possible variables predictive of shunt complications with good predictive value.
期刊介绍：
Articles in ''Pediatric Neurosurgery'' strives to publish new information and observations in pediatric neurosurgery and the allied fields of neurology, neuroradiology and neuropathology as they relate to the etiology of neurologic diseases and the operative care of affected patients. In addition to experimental and clinical studies, the journal presents critical reviews which provide the reader with an update on selected topics as well as case histories and reports on advances in methodology and technique. This thought-provoking focus encourages dissemination of information from neurosurgeons and neuroscientists around the world that will be of interest to clinicians and researchers concerned with pediatric, congenital, and developmental diseases of the nervous system.
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