Habibi Zohreh, Ertiaei Abolhasan, Nikdad Mohammad Sadegh, Mirmohseni Atefeh Sadat, Afarideh Mohsen, Heidari Vahid, Saberi Hooshang, Rezaei Abdolreza Sheikh, Nejat Farideh
Department of Neurosurgery, Children's Hospital Medical Center, Tehran University of Medical Science, Gharib street, Tehran, 141557854, Iran.
Department of Neurosurgery, Imam Khomeini Hospital, Tehran University of Medical Science, Tehran, Iran.
Childs Nerv Syst. 2016 Nov;32(11):2143-2151. doi: 10.1007/s00381-016-3248-2. Epub 2016 Sep 14.
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.
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.
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.
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.
此前尚未使用人工神经网络（ANN）模型研究分流感染与预测因素之间的关系。本研究的目的是开发一种ANN模型，以预测一组患有脑积水分流术的儿童的分流感染情况。
在2000年4月至2011年4月期间进行的800多例脑室腹腔分流手术中，连续纳入了68例有分流感染的患者和80例符合一系列严格纳入/排除标准的对照。对一长串风险因素进行单因素分析，将p值<0.2的因素用于创建ANN和逻辑回归（LR）模型。
通过单因素分析，出生体重、首次分流时的年龄、分流修正、早产和脊髓脊膜膨出这五个变量与分流感染显著相关，另外两个变量（脑室内出血和合并感染）的p值小于0.2。使用这七个输入变量，ANN和LR模型预测分流感染的准确率分别为83.1%（AUC；91.98%，95%CI）和55.7%（AUC；76.5，95%CI）。这些因素在ANN预测性能中的贡献从高到低依次为分流修正史、低出生体重（低于2000g）、早产史、首次分流手术时的年龄、脑室内出血史、脊髓脊膜膨出史和合并感染。
研究结果表明，人工神经网络可以高精度地预测患有脑积水分流术的儿童的分流感染情况。此外，使用经过训练的网络可以确定不同风险因素在分流感染预测中的贡献。
