V. G. El-Hajj, A. Ghaith, Adrian Elmi-Terander, Edward S Ahn, David J Daniels, Mohamad Bydon
{"title":"增强预后的机器学习:预测小儿队列中I型Chiari畸形后窝减压手术后30天的预后。","authors":"V. G. El-Hajj, A. Ghaith, Adrian Elmi-Terander, Edward S Ahn, David J Daniels, Mohamad Bydon","doi":"10.3171/2024.2.PEDS23523","DOIUrl":null,"url":null,"abstract":"OBJECTIVE\nChiari malformation type I (CM-I) is a congenital disorder occurring in 0.1% of the population. In symptomatic cases, surgery with posterior fossa decompression (PFD) is the treatment of choice. Surgery is, however, associated with peri- and postoperative complications that may require readmission or renewed surgical intervention. Given the associated financial costs and the impact on patients' well-being, there is a need for predictive tools that can assess the likelihood of such adverse events. The aim of this study was therefore to leverage machine learning algorithms to develop a predictive model for 30-day readmissions and reoperations after PFD in pediatric patients with CM-I.\n\n\nMETHODS\nThis was a retrospective study based on data from the National Surgical Quality Improvement Program-Pediatric database. Eligible patients were those undergoing PFD (Current Procedural Terminology code 61343) for CM-I between 2012 and 2021. Patients undergoing surgery for tumors or vascular lesions were excluded. Unplanned 30-day readmission and unplanned 30-day reoperation were the main study outcomes. Additional outcome data considered included the length of hospital stay, 30-day complications, discharge disposition, and 30-day mortality. Training and testing samples were randomly generated (80:20) to study the 30-day readmission and reoperation using logistic regression, decision tree, random forest (RF), K-nearest neighbors, and Gaussian naive Bayes algorithms.\n\n\nRESULTS\nA total of 7106 pediatric patients undergoing PFD were included. The median age was 9.2 years (IQR 4.7, 14.2 years). Most of the patients were female (56%). The 30-day readmission and reoperation rates were 7.5% and 3.4%, respectively. Headaches (32%) and wound-related complications (30%) were the most common reasons for 30-day readmission, while wound revisions and evacuation of fluid or blood (62%), followed by CSF diversion-related procedures (28%), were the most common reasons for 30-day reoperation. RF classifiers had the highest predictive accuracy for both 30-day readmissions (area under the curve [AUC] 0.960) and reoperations (AUC 0.990) compared with the other models. On feature importance analysis, sex, developmental delay, ethnicity, respiratory disease, premature birth, hydrocephalus, and congenital/genetic anomaly were some of the variables contributing the most to both RF models.\n\n\nCONCLUSIONS\nUsing a large-scale nationwide dataset, machine learning models for the prediction of both 30-day readmissions and reoperations were developed and achieved high accuracy. This highlights the utility of machine learning in risk stratification and surgical decision-making for pediatric CM-I.","PeriodicalId":16549,"journal":{"name":"Journal of neurosurgery. Pediatrics","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning for enhanced prognostication: predicting 30-day outcomes following posterior fossa decompression surgery for Chiari malformation type I in a pediatric cohort.\",\"authors\":\"V. G. El-Hajj, A. Ghaith, Adrian Elmi-Terander, Edward S Ahn, David J Daniels, Mohamad Bydon\",\"doi\":\"10.3171/2024.2.PEDS23523\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"OBJECTIVE\\nChiari malformation type I (CM-I) is a congenital disorder occurring in 0.1% of the population. In symptomatic cases, surgery with posterior fossa decompression (PFD) is the treatment of choice. Surgery is, however, associated with peri- and postoperative complications that may require readmission or renewed surgical intervention. Given the associated financial costs and the impact on patients' well-being, there is a need for predictive tools that can assess the likelihood of such adverse events. The aim of this study was therefore to leverage machine learning algorithms to develop a predictive model for 30-day readmissions and reoperations after PFD in pediatric patients with CM-I.\\n\\n\\nMETHODS\\nThis was a retrospective study based on data from the National Surgical Quality Improvement Program-Pediatric database. Eligible patients were those undergoing PFD (Current Procedural Terminology code 61343) for CM-I between 2012 and 2021. Patients undergoing surgery for tumors or vascular lesions were excluded. Unplanned 30-day readmission and unplanned 30-day reoperation were the main study outcomes. Additional outcome data considered included the length of hospital stay, 30-day complications, discharge disposition, and 30-day mortality. Training and testing samples were randomly generated (80:20) to study the 30-day readmission and reoperation using logistic regression, decision tree, random forest (RF), K-nearest neighbors, and Gaussian naive Bayes algorithms.\\n\\n\\nRESULTS\\nA total of 7106 pediatric patients undergoing PFD were included. The median age was 9.2 years (IQR 4.7, 14.2 years). Most of the patients were female (56%). The 30-day readmission and reoperation rates were 7.5% and 3.4%, respectively. Headaches (32%) and wound-related complications (30%) were the most common reasons for 30-day readmission, while wound revisions and evacuation of fluid or blood (62%), followed by CSF diversion-related procedures (28%), were the most common reasons for 30-day reoperation. RF classifiers had the highest predictive accuracy for both 30-day readmissions (area under the curve [AUC] 0.960) and reoperations (AUC 0.990) compared with the other models. On feature importance analysis, sex, developmental delay, ethnicity, respiratory disease, premature birth, hydrocephalus, and congenital/genetic anomaly were some of the variables contributing the most to both RF models.\\n\\n\\nCONCLUSIONS\\nUsing a large-scale nationwide dataset, machine learning models for the prediction of both 30-day readmissions and reoperations were developed and achieved high accuracy. This highlights the utility of machine learning in risk stratification and surgical decision-making for pediatric CM-I.\",\"PeriodicalId\":16549,\"journal\":{\"name\":\"Journal of neurosurgery. Pediatrics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of neurosurgery. 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Machine learning for enhanced prognostication: predicting 30-day outcomes following posterior fossa decompression surgery for Chiari malformation type I in a pediatric cohort.
OBJECTIVE
Chiari malformation type I (CM-I) is a congenital disorder occurring in 0.1% of the population. In symptomatic cases, surgery with posterior fossa decompression (PFD) is the treatment of choice. Surgery is, however, associated with peri- and postoperative complications that may require readmission or renewed surgical intervention. Given the associated financial costs and the impact on patients' well-being, there is a need for predictive tools that can assess the likelihood of such adverse events. The aim of this study was therefore to leverage machine learning algorithms to develop a predictive model for 30-day readmissions and reoperations after PFD in pediatric patients with CM-I.
METHODS
This was a retrospective study based on data from the National Surgical Quality Improvement Program-Pediatric database. Eligible patients were those undergoing PFD (Current Procedural Terminology code 61343) for CM-I between 2012 and 2021. Patients undergoing surgery for tumors or vascular lesions were excluded. Unplanned 30-day readmission and unplanned 30-day reoperation were the main study outcomes. Additional outcome data considered included the length of hospital stay, 30-day complications, discharge disposition, and 30-day mortality. Training and testing samples were randomly generated (80:20) to study the 30-day readmission and reoperation using logistic regression, decision tree, random forest (RF), K-nearest neighbors, and Gaussian naive Bayes algorithms.
RESULTS
A total of 7106 pediatric patients undergoing PFD were included. The median age was 9.2 years (IQR 4.7, 14.2 years). Most of the patients were female (56%). The 30-day readmission and reoperation rates were 7.5% and 3.4%, respectively. Headaches (32%) and wound-related complications (30%) were the most common reasons for 30-day readmission, while wound revisions and evacuation of fluid or blood (62%), followed by CSF diversion-related procedures (28%), were the most common reasons for 30-day reoperation. RF classifiers had the highest predictive accuracy for both 30-day readmissions (area under the curve [AUC] 0.960) and reoperations (AUC 0.990) compared with the other models. On feature importance analysis, sex, developmental delay, ethnicity, respiratory disease, premature birth, hydrocephalus, and congenital/genetic anomaly were some of the variables contributing the most to both RF models.
CONCLUSIONS
Using a large-scale nationwide dataset, machine learning models for the prediction of both 30-day readmissions and reoperations were developed and achieved high accuracy. This highlights the utility of machine learning in risk stratification and surgical decision-making for pediatric CM-I.