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An early prediction model to identify neurological complications of childhood influenza: a random forest model
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  • Suyun Li,
  • Weiqiang Xiao,
  • Huixian Li,
  • Dandan Hu,
  • Kuanrong Li,
  • Qinglian Chen,
  • Guangming Liu,
  • Haomei Yang,
  • Yongling Song,
  • Qiuyan Peng,
  • Qiang Wang,
  • Shuyao Ning,
  • Yumei Xiong,
  • Wencheng Ma,
  • Jun Shen,
  • Kelu Zheng,
  • Yan Hong,
  • Peiqing Li,
  • Sida Yang
Weiqiang Xiao
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Huixian Li
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Kuanrong Li
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Qinglian Chen
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Guangming Liu
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Haomei Yang
Guangzhou Women and Children's Medical Center
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Yongling Song
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Qiuyan Peng
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Qiang Wang
Guangzhou Women and Children's Medical Center
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Shuyao Ning
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Yumei Xiong
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Wencheng Ma
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Kelu Zheng
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Yan Hong
Guangzhou Women and Children's Medical Center
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Peiqing Li
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Abstract

Background: NeurologicalEarly prediction model for seizures in influenza complications of influenza are associated with high morbidity and mortality in children. The prognosis could be improved if early treatments are undertaken. Objective: To establish and validate an early prediction model to discriminate among neurological complications such as seizures, acute influenza virus-associated encephalitis (IAE), and acute necrotizing encephalopathy (ANE) in children with influenza. Methods: This was a retrospective single-center case-control study conducted at the Guangzhou Women and Children’s Medical Center in Guangzhou (GWCMC), China, from November 2012 to January 2020. The random forest model was used to screen the characteristics and construct an early prediction model for convulsions, IAE, and ANE. Results: Of the 433 patients (294 male, 139 female; median age 2.8 (1.7,4.8) years), 278 (64.2%) had seizures, 106 (24.5%) had IAE, and 49 (11.3%) had ANE; 348 patients were in the training set and 85 in the validation set. When 10 variables were included, the cross-validation error was minimal; convulsions, procalcitonin, urea, γ-glutamyltransferase, aspartate aminotransferase, albumin/globulin ratio, α-hydroxybutyric dehydrogenase, alanine aminotransferase, alkaline phosphatase, and C-reactive protein were included. The likelihood of having only seizures decreased with increasing procalcitonin, urea, γ-glutamyltransferase, α-hydroxybutyric dehydrogenase, alanine aminotransferase, and aspartate aminotransferase, and with decreasing albumin/globulin ratio and alkaline phosphatase. The prediction model gave a prediction accuracy of 84.2%. Conclusion: This model can distinguish the seizures from IAE and from ANE. This could allow for the early management of children with influenza in order to prevent morbidity and mortality. The biochemical/hematologic markers lacked specificity.