Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning

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

Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning

Pediatr Neurosurg. 2023;58(4):206-214. doi: 10.1159/000531754. Epub 2023 Jun 30.

Authors

Affiliations

¹ Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
² Department of Medical Engineering, California Institute of Technology, Pasadena, California, USA.
³ Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
⁴ Department of Biomedical Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA.
⁵ School of Medicine, University of California, Irvine, California, USA.
⁶ Department of Neurological Surgery, University of California, Irvine, California, USA.
⁷ School of Medicine, Mercer University, Macon, Georgia, USA.
⁸ School of Medicine, Mercer University, Savannah, Georgia, USA, npierzchajlo@gmail.com.
⁹ Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland, USA.

Abstract

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.

Keywords: Diagnoses; Hydrocephalus; Machine learning surgical outcome; Neurosurgery; Pediatric patients; Risk factor; Shunt.

MeSH terms

Child
Comorbidity
Female
Humans
Hydrocephalus* / etiology
Neurosurgical Procedures / methods
Retrospective Studies
Ventriculoperitoneal Shunt* / adverse effects
Ventriculoperitoneal Shunt* / methods

Grants and funding

There are no disclosures of funding for this study.