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

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.

Keywords: Acute febrile; Acute necrotizing encephalopathy; Children; Complications; Encephalitis; Influenza; Predictive model.

Fig. 1

Flowchart of study population. IAE: influenza virus-associated encephalitis; ANE: acute necrotizing encephalopathy

Fig. 2

Brain MRI of a 13-year-old boy with acute necrotizing encephalopathy. A Axial view of T2-weighted image (T2WI) show swelling and hyperintense signals (arrow) in both thalami; (B) Axial view of T2 fluid-attenuated inversion recovery sequence (T2-FLAIR) show swelling and hyperintense signals (arrow) in both thalami; (C) equal or slightly low signals on T1-weighted image (T1WI) with internal hemorrhage (arrow) and necrosis (star); (D) contrast-enhanced T1WI shows no enhancing brain lesions

Fig. 3

Variable importance of top 15 variables identified by random forest (RF) model. Procalcitonin, PCT; γ-glutamyltransferase, γ-GT; aspartate aminotransferase, AST; α-hydroxybutyric dehydrogenase, α-HBDH; alanine aminotransferase, ALT; alkaline phosphatase, ALP; c-reactive protein, CRP; lactate dehydrogenase, LDH; oxygen partial pressure, OPP; prothrombin time, PT

Fig. 4

Relationship between the cross-validation error and the number of variables

Fig. 5

Influence of the selected variables calculated by the random forest method on the seizure. PCT: procalcitonin; γ-GT: γ-glutamyltransferase; AST: aspartate aminotransferase; A/G: albumin/globulin ratio; α-HBDH: α-hydroxybutyric dehydrogenase; ALT: alanine aminotransferase; ALP: alkaline phosphatase; CRP: C-reactive protein

Fig. 6

ROC curves on random forest model in validation set. A, ROC curve of classifying seizure from the other two classes. B, ROC curve of classifying mild IAE from the other two classes. C, ROC curve of classifying INE from the other two classes