Machine learning predicts risk of cerebrospinal fluid shunt failure in children: a study from the hydrocephalus clinical research network



While conventional statistical approaches have been used to identify risk factors for cerebrospinal fluid (CSF) shunt failure, these methods may not fully capture the complex contribution of clinical, radiologic, surgical, and shunt-specific variables influencing this outcome. Using prospectively collected data from the Hydrocephalus Clinical Research Network (HCRN) patient registry, we applied machine learning (ML) approaches to create a predictive model of CSF shunt failure.


Pediatric patients (age < 19 years) undergoing first-time CSF shunt placement at six HCRN centers were included. CSF shunt failure was defined as a composite outcome including requirement for shunt revision, endoscopic third ventriculostomy, or shunt infection within 5 years of initial surgery. Performance of conventional statistical and 4 ML models were compared.


Our cohort consisted of 1036 children undergoing CSF shunt placement, of whom 344 (33.2%) experienced shunt failure. Thirty-eight clinical, radiologic, surgical, and shunt-design variables were included in the ML analyses. Of all ML algorithms tested, the artificial neural network (ANN) had the strongest performance with an area under the receiver operator curve (AUC) of 0.71. The ANN had a specificity of 90% and a sensitivity of 68%, meaning that the ANN can effectively rule-in patients most likely to experience CSF shunt failure (i.e., high specificity) and moderately effective as a tool to rule-out patients at high risk of CSF shunt failure (i.e., moderately sensitive). The ANN was independently validated in 155 patients (prospectively collected, retrospectively analyzed).


These data suggest that the ANN, or future iterations thereof, can provide an evidence-based tool to assist in prognostication and patient-counseling immediately after CSF shunt placement.

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The authors would like to thank their colleagues for their past and ongoing support of HCRN:

D Brockmeyer, M Walker, R Bollo, S Cheshier, J Blount, J Johnston, B Rocque, L Acakpo-Satchivi, WJ Oakes, J Rutka, M Taylor, P Dirks, D Curry, G Aldave, R Dauser, A Jea, S Lam, H Weiner, T Luerssen, R Ellenbogen, J Ojemann, A Lee, A Avellino, S Greene, E Tyler-Kabara, T Abel, TS Park, J Strahle, S McEvoy, M Smyth, N Tulipan, A Singhal, P Steinbok, D Cochrane, W Hader, C Gallagher, M Benour, E Kiehna, JG McComb, P Chiarelli, A Robison, A Alexander, M Handler, B O’Neill, C Wilkinson, L Governale, A Drapeau, J Leonard, E Sribnick, A Shaikhouni, E Ahn, A Cohen, M Groves, S Robinson, and CM Bonfield.

In addition, our work would not be possible without the outstanding support of the dedicated personnel at each clinical site and the data coordinating center. Special thanks go to the following: L Holman, J Clawson, P Martello, N Tattersall, T Bach (Salt Lake City); A Arynchyna, A Bey (Birmingham); H Ashrafpour, M Lamberti-Pasculli, L O’Connor (Toronto); E Sanchez, E Santisbon, S Martinez, S Ryan (Houston); A Anderson, G Bowen (Seattle); K Diamond, A Luther (Pittsburgh); A Morgan, H Botteron, D Morales, M Gabir, D Berger, D Mercer (St. Louis); A Wiseman, J Stoll, D Dawson, S Gannon (Nashville); A Cheong, R Hengel (British Columbia); R Rashid, S Ahmed (Calgary); M Alrefaie, R Daniel, A Loudermilk (Baltimore); N Rea, C Cook (Los Angeles); S Staulcup (Colorado); A Sheline (Columbus); and N Nunn, M Langley, V Wall, D Austin, B Conley, V Freimann, L Herrera, B Miller (Utah Data Coordinating Center).


The HCRN has been funded by National Institute of Neurological Disorders and Stroke (NINDS grant nos. 1U01NS107486-01A1 and 1RC1NS068943-01), Patient Centered Outcome Research Institute (PCORI grant no. CER-1403-13857), The Gerber Foundation (reference no. 1692-3638), private philanthropy, and the Hydrocephalus Association. A.T.H. is supported by the National Institutes of Health (F30HL143826) and Vanderbilt University Medical Scientist Training Program (5T32GM007347). None of the sponsors participated in design and conduct of this study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of this manuscript. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the sponsors.

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Correspondence to Andrew T. Hale.

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Previously presented as a platform talk at the AANS/CNS Pediatric Section Annual Meeting, Scottsdale AZ, December 8th,2019.

Appendix. Hydrocephalus Clinical Research Network

Appendix. Hydrocephalus Clinical Research Network


The HCRN currently consists of the following clinical centers and investigators: Primary Children’s Hospital, University of Utah (J Kestle); Children’s Hospital of Alabama, University of Alabama at Birmingham (C Rozzelle); Hospital for Sick Children, University of Toronto (J Drake, A Kulkarni); Texas Children’s Hospital, Baylor College of Medicine (W Whitehead); Seattle Children’s Hospital, University of Washington (S Browd, T Simon, J Hauptman); Children’s Hospital of Pittsburgh, University of Pittsburgh (I Pollack); St. Louis Children’s Hospital, Washington University in St. Louis (D Limbrick); Monroe Carell Jr. Children’s Hospital at Vanderbilt, Vanderbilt University Medical Center (J Wellons, R Naftel, C Shannon); British Columbia Children’s Hospital, University of British Columbia (M Tamber, P McDonald); Alberta Children’s Hospital, University of Calgary (J Riva-Cambrin); The Johns Hopkins Hospital (E Jackson); Children’s Hospital of Los Angeles (M Krieger, J Chu); Children’s Hospital Colorado (T Hankinson); Nationwide Children’s Hospital (J Pindrik); HCRN Data Coordinating Center, Department of Pediatrics, University of Utah (R Holubkov).

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Hale, A.T., Riva-Cambrin, J., Wellons, J.C. et al. Machine learning predicts risk of cerebrospinal fluid shunt failure in children: a study from the hydrocephalus clinical research network. Childs Nerv Syst (2021).

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  • Hydrocephalus
  • CSF shunt failure
  • HCRN
  • Machine learning
  • Artificial intelligence