BMC Pediatrics (May 2024)

Identification of neurological complications in childhood influenza: a random forest model

  • 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,
  • Sida Yang,
  • Peiqing Li

DOI
https://doi.org/10.1186/s12887-024-04773-4
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 12

Abstract

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Abstract Background Among the neurological complications of influenza in children, the most severe is acute necrotizing encephalopathy (ANE), with a high mortality rate and neurological sequelae. ANE is characterized by rapid progression to death within 1–2 days from onset. However, the knowledge about the early diagnosis of ANE is limited, which is often misdiagnosed as simple seizures/convulsions or mild acute influenza-associated encephalopathy (IAE). Objective To develop and validate an early prediction model to discriminate the ANE from two common neurological complications, seizures/convulsions and mild IAE in children with influenza. Methods This retrospective case-control study included patients with ANE (median age 3.8 (2.3,5.4) years), seizures/convulsions alone (median age 2.6 (1.7,4.3) years), or mild IAE (median age 2.8 (1.5,6.1) years) at a tertiary pediatric medical center in China between November 2012 to January 2020. The random forest algorithm was used to screen the characteristics and construct a prediction model. Results Of the 433 patients, 278 (64.2%) had seizures/convulsions alone, 106 (24.5%) had mild IAE, and 49 (11.3%) had ANE. The discrimination performance of the model was satisfactory, with an accuracy above 0.80 from both model development (84.2%) and internal validation (88.2%). Seizures/convulsions were less likely to be wrongly classified (3.7%, 2/54), but mild IAE (22.7%, 5/22) was prone to be misdiagnosed as seizures/convulsions, and a small proportion (4.5%, 1/22) of them was prone to be misdiagnosed as ANE. Of the children with ANE, 22.2% (2/9) were misdiagnosed as mild IAE, and none were misdiagnosed as seizures/convulsions. Conclusion This model can distinguish the ANE from seizures/convulsions with high accuracy and from mild IAE close to 80% accuracy, providing valuable information for the early management of children with influenza.

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