title: Predicting ventriculoperitoneal shunt infection in children with hydrocephalus using artificial neural network.
abstract: OBJECTIVE
  The relationships between shunt infection and predictive factors have not been previously investigated using Artificial Neural Network (ANN) model. The aim of this study was to develop an ANN model to predict shunt infection in a group of children with shunted hydrocephalus.
  
  
  METHODS
  Among more than 800 ventriculoperitoneal shunt procedures which had been performed between April 2000 and April 2011, 68 patients with shunt infection and 80 controls that fulfilled a set of meticulous inclusion/exclusion criteria were consecutively enrolled. Univariate analysis was performed for a long list of risk factors, and those with p value < 0.2 were used to create ANN and logistic regression (LR) models.
  
  
  RESULTS
  Five variables including birth weight, age at the first shunting, shunt revision, prematurity, and myelomeningocele were significantly associated with shunt infection via univariate analysis, and two other variables (intraventricular hemorrhage and coincided infections) had a p value of less than 0.2. Using these seven input variables, ANN and LR models predicted shunt infection with an accuracy of 83.1 % (AUC; 91.98 %, 95 % CI) and 55.7 % (AUC; 76.5, 95 % CI), respectively. The contribution of the factors in the predictive performance of ANN in descending order was history of shunt revision, low birth weight (under 2000 g), history of prematurity, the age at the first shunt procedure, history of intraventricular hemorrhage, history of myelomeningocele, and coinfection.
  
  
  CONCLUSIONS
  The findings show that artificial neural networks can predict shunt infection with a high level of accuracy in children with shunted hydrocephalus. Also, the contribution of different risk factors in the prediction of shunt infection can be determined using the trained network.
authors.affiliation: Department of Neurosurgery, Children's Hospital Medical Center, Tehran University of Medical Science, Gharib street, Tehran, 141557854, Iran.
authors.forename: Zohreh
authors.initials: Z
authors.lastname: Habibi
authors.affiliation: Department of Neurosurgery, Imam Khomeini Hospital, Tehran University of Medical Science, Tehran, Iran.
authors.forename: Abolhasan
authors.initials: A
authors.lastname: Ertiaei
authors.affiliation: Department of Neurosurgery, Children's Hospital Medical Center, Tehran University of Medical Science, Gharib street, Tehran, 141557854, Iran.
authors.forename: Mohammad Sadegh
authors.initials: MS
authors.lastname: Nikdad
authors.affiliation: Department of Neurosurgery, Children's Hospital Medical Center, Tehran University of Medical Science, Gharib street, Tehran, 141557854, Iran.
authors.forename: Atefeh Sadat
authors.initials: AS
authors.lastname: Mirmohseni
authors.affiliation: Department of Neurosurgery, Children's Hospital Medical Center, Tehran University of Medical Science, Gharib street, Tehran, 141557854, Iran.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Afarideh
authors.affiliation: Department of Neurosurgery, Children's Hospital Medical Center, Tehran University of Medical Science, Gharib street, Tehran, 141557854, Iran.
authors.forename: Vahid
authors.initials: V
authors.lastname: Heidari
authors.affiliation: Department of Neurosurgery, Imam Khomeini Hospital, Tehran University of Medical Science, Tehran, Iran.
authors.forename: Hooshang
authors.initials: H
authors.lastname: Saberi
authors.affiliation: Department of Neurosurgery, Imam Khomeini Hospital, Tehran University of Medical Science, Tehran, Iran.
authors.forename: Abdolreza Sheikh
authors.initials: AS
authors.lastname: Rezaei
authors.affiliation: Department of Neurosurgery, Children's Hospital Medical Center, Tehran University of Medical Science, Gharib street, Tehran, 141557854, Iran. nejat@sina.tums.ac.ir.
authors.forename: Farideh
authors.initials: F
authors.lastname: Nejat
mesh_terms: D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D006849:Hydrocephalus / Q000601:surgery; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D016571:Neural Networks, Computer; D012307:Risk Factors; D013530:Surgical Wound Infection / Q000209:etiology*; D017287:Ventriculoperitoneal Shunt / Q000009:adverse effects*
keywords: Artificial neural network; Hydrocephalus; Shunt infection; Ventriculoperitoneal shunt