Clinical evaluation of acute necrotizing encephalopathy in children

Fan, Xin; Huang, Li; Li, Suyun; Yang, Sen; Song, Yongling; Chen, Qinglian; Xiong, Ye; Peng, Qiuyan; Ma, Wencheng; Hu, Dandan; Li, Peiqing

doi:10.3389/fped.2022.947693

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Further studies should consider the possibility of adding imaging variables to refine the present model. Indeed, the presence of brain imaging features is usually associated with a poor prognosis [39][40][41][42][43]. Furthermore, CSF examination in patients with ANE usually reveals increased amounts of proteins [30].…”

Section: Discussionmentioning

confidence: 99%

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

Li,

Xiao,

et al. 2024

BMC Pediatr

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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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Section: Discussionmentioning

confidence: 99%

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

Li,

Xiao,

et al. 2024

BMC Pediatr

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“…Near-term infants have progressive fibrosis of the WM fibers, which inhibits water diffusion. However, the inflammatory state of the brain leads to abnormal myelination, which reduces diffusion restriction ( 26 ). The FWM develops in the late period and is easily damaged by an inflammatory reaction ( 27 ).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of white matter microstructural alterations in premature infants with necrotizing enterocolitis

Zhang,

Zhao,

Zhu

et al. 2023

Quant Imaging Med Surg

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Background Preterm infants with necrotizing enterocolitis (NEC) are at high risk of adverse neurodevelopmental outcomes. The aim of this study was to explore the value of diffusion tensor imaging (DTI) combined with serum C-reactive protein (CRP) and procalcitonin (PCT) in evaluating alterations of white matter (WM) microstructure in preterm infants with NEC. Methods A retrospective cross-sectional study was conducted in which all participants were consecutively enrolled at The Third Affiliated Hospital of Zhengzhou University from June 2017 and October 2021. Data from 30 preterm infants with NEC [mean gestational age at birth 31.41±1.15 weeks; mean age at magnetic resonance imaging (MRI) 37.53±3.08 weeks] and 40 healthy preterm infants with no NEC were recorded (mean gestational age at birth 32.27±2.09 weeks; mean age at MRI 37.15±3.23 weeks). WM was used to obtain the fractional anisotropy (FA) and mean diffusivity (MD) values of the regions of interest (ROIs). Additionally, serum levels of CRP and PCT were determined. Spearman correlation analysis was performed between the WM-derived parameters, CRP level, and the PCT serum index. Results Preterm infants with NEC had reduced FA values and elevated MD values in WM regions [posterior limbs of the internal capsule (PLIC), lentiform nucleus (LN), frontal white matter (FWM)] compared to the control group (P<0.05). Additionally, the FA of the PLIC was negatively correlated with serum CRP (r=−0.846; P<0.05) and PCT (r=−0.843; P<0.05). Meanwhile, the MD of PLIC was positively correlated with serum CRP (r=0.743; P<0.05) and PCT (r=0.743; P<0.05, respectively). The area under the curve (AUC) of FA and MD combined with CRP and PCT in the diagnosis of WM microstructure alterations with NEC was 0.968, representing a considerable improvement in predicted efficacy over single indicators, including FA [AUC: 0.938; 95% confidence interval (CI): 0.840–0.950], MD (AUC: 0.807; 95% CI: 0.722–0.838), CRP (AUC: 0.867; 95% CI: 0.822–0.889), and PCT (AUC: 0.706; 95% CI: 0.701–0.758). Conclusions WM can noninvasively and quantitatively assess the WM microstructure alterations in preterm infants with NEC. WM combined with serum CRP and PCT demonstrated superior performance in detecting and evaluating WM microstructure alterations in preterm infants with NEC.

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