{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T01:21:32Z","timestamp":1790299292324,"version":"4.1.0"},"reference-count":34,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,1]]},"abstract":"<jats:sec>\n<jats:title>OBJECTIVE<\/jats:title>\n<jats:p>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\u2019 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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>METHODS<\/jats:title>\n<jats:p>This was a retrospective study based on data from the National Surgical Quality Improvement Program\u2013Pediatric 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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>RESULTS<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>CONCLUSIONS<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>","DOI":"10.3171\/2024.2.peds23523","type":"journal-article","created":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T02:24:40Z","timestamp":1712975080000},"page":"57-65","source":"Crossref","is-referenced-by-count":8,"title":["Machine learning for enhanced prognostication: predicting 30-day outcomes following posterior fossa decompression surgery for Chiari malformation type I in a pediatric cohort"],"prefix":"10.3171","volume":"34","author":[{"given":"Victor Gabriel","family":"El-Hajj","sequence":"first","affiliation":[{"name":"Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota;"},{"name":"Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul Karim","family":"Ghaith","sequence":"additional","affiliation":[{"name":"Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota;"},{"name":"Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Elmi-Terander","sequence":"additional","affiliation":[{"name":"Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward S.","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David J.","family":"Daniels","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamad","family":"Bydon","sequence":"additional","affiliation":[{"name":"Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota;"},{"name":"Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota; and"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"issue":"6","key":"b1","series-title":"Pediatr Neurol","first-page":"449","article-title":"Chiari type I malformation in a pediatric population","volume":"40","author":"Aitken LA","year":"2009","unstructured":"Aitken LA, Lindan CE, Sidney S,  Chiari type I malformation in a pediatric population. 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