Introduction
Influenza in children is a major cause of morbidity and mortality
worldwide 1-3. Annual epidemics in adults and children
are associated with an estimated 3-5 million cases of severe illness,
and about 290,000-650,000 deaths 2. Influenza
infection is seasonal in temperate countries, with peaks during the
winter months, but it has sustained activity throughout the year in
tropical climates 1-3. Infection can be caused by
subtypes A, B, and C; influenza A and B are the dominant circulating
viruses 3-5. Minor genetic variations (antigenic
drift) are the cause of seasonal variation, and major large-scale
reassortments generate novel strains (antigenic shift) with little or no
pre-existing immunity in the human population, leading to pandemic
strains 1,3,5. Transmission occurs via respiratory
droplets and fomites. The incubation period is 1-4 days1,3.
Populations with increased risk for complicated or severe disease course
include all children aged 6-59 months, children who have chronic
pulmonary or cardiovascular, renal, hepatic, neurologic, hematologic, or
metabolic disorders, children with immunosuppression due to medications
or disease, children and adolescents who are receiving long-term aspirin
therapy, and children with morbid obesity 4. The major
complications include acute otitis media, primary viral pneumonia, and
influenza-associated bacterial pneumonia, myositis/rhabdomyolysis,
myocarditis, pericarditis, and central nervous system diseases. Among
the neurological complications, seizures, acute influenza
virus-associated encephalitis (IAE), and acute necrotizing
encephalopathy (ANE) indicate critical influenza infection and are
associated with significant morbidity and mortality. Among the patients
with IAE, 81.8% are 1-5 years of age 6. IAE presents
as convulsions, acute disturbance of consciousness, and coma7-9. ANE typically occurs in children <5
years of age and is characterized by rapid progression to
encephalopathy, coma, or death within 1-2 days from onset7,8,10. The mortality rate of ANE is about 70%11.
Because these neurological complications can be managed to some extent,
and prognosis can be improved if early treatment is undertaken11, predicting the occurrence of neurological
complications if influenza is of clinical importance. Some previous
studies suggested laboratory indicators, signs, and symptoms that are
associated with ANE 12-15. Yamamoto et al.16 suggested a severity score for ANE that is able to
identify the patients at high-risk of ANE but did not include seizures
and IAE.
Therefore, the aim of the present study was to establish and validate an
early prediction model to discriminate among neurological complications
such as seizures, IAE, and ANE in children with influenza. Such a system
could allow identifying the children at high risk of neurological
complications and in whom early treatment should be performed in order
to improve prognosis.
Methods
Study design and patients
This was a retrospective single-center case-control study conducted at
Guangzhou Women and Children Medical Center, China, from November 2012
to January 2020. This study was approved by the Ethics Committee of
Guangzhou Women and Children Medical Center. All patients signed an
informed consent form upon admission.
The inclusion criteria were: 1) children (<18 years); 2)
admitted to the hospital with influenza virus infection; and 3)
neurological manifestations during hospitalization. The exclusion
criteria were: 1) admission >7 days after onset; 2)
co-infected with other pathogens; 3) comorbidities like brain trauma,
sequelae of viral encephalitis, or metabolic diseases; 4) missing data
>30%; or 5) neurological complications other than
seizures, IAE, or ANE.
Influenza with seizures was defined as convulsive seizures during fever,
consciousness after the seizures, a maximum of two seizure events, and
no abnormalities in the cerebrospinal fluid examination and head
imaging. IAE was defined as convulsions, acute cognitive impairment,
acute disturbance of consciousness, and coma 7-9,
without specific biochemistry and imaging changes 11.
ANE was defined as acute fever, frequent convulsions, acute disturbance
of consciousness, coma, and multiple organ failure, with a risk of death7,8,10; biochemistry changes are not specific11, but imaging shows brain edema and necrosis of
thalamus and other deep brain structures 11,17.
Observation indexes
Detailed demographic, clinical characteristics at admission, and
biochemistry and hematologic indicators of the included patients were
extracted from the structured electronic medical records system (EMRS).
The earliest value of hematologic indicators within 48 h after admission
was used.
Prediction model development
The prediction model was developed based on random forests (RF), which
is an ensemble of decision trees 18-20. RF is good at
describing the relationship between independent and dependent variables
with high flexibility and sufficient accuracy 18. The
two main parameters in RF are mtry (the number of random variables used
in each tree) and ntree (the number of trees used in the forest). In
this model, the mtry value was the square root of the number of
predictors, and the ntree value was 500. The missing values were
replaced by the median of each group.
The study patients were split into two separate data sets using 5-fold
cross-validation by the RF method: 80% of them were in the training set
(the algorithm creation group), and the remaining 20% were in the
validation set to obtain unbiased estimates of correct classification
rates and variable importance. The equation of the correct
classification rate was:
\begin{equation}
Correct\ classification\ rate=\frac{True\ Seizures+True\ IAE+True\ ANE}{\text{Number\ of\ patients\ in\ the\ data\ set}}\nonumber \\
\end{equation}Statistical analysis
Categorical variables were presented as counts and percentages, and the
differences were analyzed using the chi-square test. Continuous
variables were tested for normal distribution using the Shapiro-Wilk
test. The continuous variables were presented as medians and
interquartile ranges (IQR) based on non-normal distribution, and the
differences between groups were analyzed using the Kruskal-Wallis test.
All probability values were 2-sided when applicable, and P-values
<0.05 were considered statistically significant. Analyses were
performed using SAS 9.4 for Windows (SAS Institute, Inc., Cary, NC, USA)
and R software (version 3.2.5).
Results
Characteristics of the patients
Figure 1 presents the enrollment flowchart. There were 433 patients
included in the analysis. Of the 433 patients (294 males, 139 females;
median age 2.8 (1.7,4.8) years) with neurological manifestations that
occurred during influenza infection, 278 (64.2%) were ultimately
diagnosed as seizure, 106 (24.5%) as IAE, and 49 (11.3%) as ANE. The
78 variables, including demographic characteristics, clinical symptoms,
and biochemical and hematologic indicators collected for each patient
are shown in Table 1. The incidences of in-hospital death of the three
groups were 0.4% (1/278; this patient had a chromosomal abnormality),
0% (0/106), and 32.7% (16/49) in the seizure, IAE, and ANE groups,
respectively.
Variable selection
Variable selection was carried out using the different feature subsets
RF method. The top 15 variables selected by importance are shown in
Figure 2. The larger the importance number is, the more important the
variable is. Figure 3 shows the relationship between the
cross-validation error and the number of variables. When the number of
variables increases to 10, the error achieves a minimum of 0.16. With
the number of variables increasing gradually to 78, the error increases.
Thus, the final model included 10 features for IAE and ANE prediction:
convulsions, procalcitonin (PCT), urea, γ-glutamyl transferase (γ-GT),
aspartate aminotransferase (AST), albumin/globulin ratio (A/G),
α-hydroxybutyric dehydrogenase (HBD), alanine aminotransferase (ALT),
alkaline phosphatase (ALP), and C-reactive protein (CRP).
Model development
The influences of the selected variables calculated by the random forest
on the seizure are shown in Figure 4. When the number of convulsions was
only 1-2 at admission, confidence could be high that the child will only
have seizures, but the number of convulsions in children with IAE or ANE
could be ≥3 or 0. With increases in PCT, urea, γ-GT, α-HBDH, ALT, and
AST levels, the children were less likely to have only seizures. With
the decreases of A/G and ALP, the children were less likely to have only
seizures.
Model validation
The prediction accuracy of the model was internally evaluated by 5-fold
cross-validation. The basic information comparing the train and test
sets is shown in Table 2, and all the differences in the characteristics
of the two data sets were not significantly different.
The prediction model gave a prediction accuracy of 84.2%. In order to
examine the performance of the newly developed model, the training model
was tested based on the validation set containing 85 patients. The
external validation achieved 88.2% accuracy. Based on Table 3, it was
less likely that seizure was wrongly classified, but IAE (22.7%, 5/22)
was prone to be misdiagnosed as seizure, 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 IAE, and none were misdiagnosed
as seizures.
Discussion
Neurological complications of influenza are associated with high
morbidity and mortality in children 11. The prognosis
could be improved if early treatments are undertaken11. Therefore, this study aimed to establish and
validate an early prediction model to discriminate among neurological
complications such as seizures, IAE, and ANE in children with influenza.
The results suggest that this model can distinguish the seizures from
IAE and from ANE on patients hospitalized within 7 days of onset. This
could allow for the early management of children with influenza in order
to prevent morbidity and mortality. The biochemical/hematologic markers
lacked specificity.
In this study, the first measurements of biochemical and hematologic
indicators within 48 h after admission were evaluated using the RF
method, avoiding the problem of model overfitting caused by correlations
among the variables. The model has a good ability to distinguish
seizures from IAE and ANE early after admission, but for children with
IAE, the early hematologic indicators might be misleading, suggesting
that the early blood biochemical indicators lack specificity for IAE. In
addition, for children with ANE, which has a high mortality rate,
attention should be paid to the number of convulsion events before
admission and then the changes in biochemical and hematologic
indicators.
These results are globally supported by previous studies and by the
natural history of IAE and ANE. Indeed, ANE is characterized by frequent
convulsions 8,10,21, as observed in the present study.
A previous study showed that a combination of age <4 years,
repeated seizures, altered consciousness, and positive Babinski’s sign
were the high-risk factors for ANE 21. On the other
hand, the literature suggests that there is no specific laboratory
marker for ANE 9,22,23, but elevated AST, elevated
glucose, hematuria, proteinuria, and RANBP2 mutations could be
associated with ANE 8. Elevated serum transaminases
could also be associated with ANE 9. In the present
study, the likelihood of seizures decreased with the increasing levels
of AST, and many studies showed that AST levels increase when
soft-tissue necrosis occurs 24-26. Early research
showed that brain dehydration was maximal 30 min after urea injection
and resulted in an improvement of cerebral circulation27; the increase in urea might be related to the
reactive regulation of early cerebral edema. The endothelial cells of
the capillaries of the cerebral cortex in rats showed high γ-GT activity28, suggesting the possibility of cerebrovascular
involvement in early ANE. Activities of α-HBDH were measured in rats
after intermittent exposure to aerogenic hypoxia but no effects on
adults 29 and were also associated with edema,
ischemic and hemorrhagic changes 30. In the early
stages of influenza, increases in these factors could be a high risk for
ANE.
In the present study, the imaging parameters could not be included in
the analyses, mainly because of the too wide variety of examination
types and examination protocols performed. Future studies about the
refinement of the present model should examine the possibility of
including imaging variables. Indeed, most cases of ANE display brain
edema early in the course of the disease 17, before
the appearance of thalamus necrosis, which is usually at about 3 days
after onset 31,32. The presence of brain imaging
features is usually associated with poor prognosis12-15. Furthermore, cerebrospinal fluid examination in
patients with ANE usually reveals increased amounts of proteins9, but the cerebrospinal fluid examination was not
performed in all patients. A predictive model for ANE severity included
imaging and cerebrospinal features such as brain stem lesions and
cerebrospinal proteins 16. In the present study, older
children were more prone to ANE, but age ranked 12th in importance, and
only the first 10 variables were included, based on the cross-validation
error minimum principle.
Regarding IAE, the accuracy was lower than for ANE. This could be
because IAE is, based on the symptoms, an intermediate condition between
seizures and ANE 11. Indeed, as for ANE, AIE is
characterized by convulsions, disturbed consciousness, and coma7-9, but it does not progress to death11, and no specific biochemical, hematological, and
imaging markers can be identified 11. Nevertheless,
vascular injuries in the brain could be found in cadaveric studies33,34, but without progression to brain necrosis, as
seen in ANE.
This study has limitations. The correct classification rate of seizure
is high in our study, but the ability to early diagnosis IAE and ANE
still needs to be improved. This could be improved by including imaging
characteristics. In addition, for the biochemical and hematological
indicators, only the value in the first 48 h was considered, and the
eventual changes in those indicators were not considered. Since ANE is a
rapidly progressing condition, the exact timing of the evaluations can
affect the results. In addition, the study was performed at a single
hospital, and the sample size was therefore limited.
Conclusion
In conclusion, this RF model that was developed in the present study can
distinguish seizures from IAE and ANE, with high accuracy. This tool
could allow the early and correct discrimination between seizures and
ANE, allowing the initiation of early treatments in the identified
patients. Nevertheless, biochemical and hematologic characteristics
lacked specificity. Additional study is still necessary to refine this
model.