The evaluation value of disturbance coefficient combined with bedside continuous video electroencephalogram for the short-term prognosis of severe encephalitis in children

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Methods: This study retrospectively analyzed the clinical data of 136 children diagnosed with severe encephalitis in the Intensive Care Medicine Department of Hebei Children's Hospital from January 2021 to July 2024. According to the prognosis at discharge, patients were divided into a poor prognosis group (54 cases) and a good prognosis group (82 cases). Compare the clinical manifestations, modified GCS scores, early warning scores, clinical indicators monitored after admission, DC values, and video electroencephalography between two groups of children. Logistic regression was used to analyze the risk factors for poor prognosis, and receiver operating characteristic (ROC) curves were used to analyze the predictive value of DC, video EEG, and their combined evaluation of prognosis. Results: There was a statistically significant difference ( P <0.05) in the modified GCS score, early warning score, lactate dehydrogenase, blood ammonia, and abnormal cranial imaging between the two groups of patients. The DC values monitored at admission in the poor prognosis group were significantly lower than those in the good group, with 22 cases (40.7%) having severe EEG abnormalities, and the difference was statistically significant ( P <0.05). GCS score, early warning score DC、 Severe abnormal video electroencephalogram is a risk factor for poor prognosis in children with severe encephalitis. The area under the ROC curve for predicting the prognosis of children with severe encephalitis using DC values is 0.734, with an optimal cutoff value of 75, a sensitivity of 74.1%, and a specificity of 63.4%. The area under the ROC curve for predicting prognosis using video electroencephalogram is 0.701, with a sensitivity of 53.7% and specificity of 86.6%. The area under the combined prediction curve is 0.822, with a sensitivity of 77.8% and specificity of 75.6%. Conclusion: Severe encephalitis is the result of the interaction of multiple factors, and clinical attention should be paid to GCS score, early warning score, DC, and video electroencephalogram. The combined application of DC and bedside video EEG can significantly improve the evaluation efficiency of short-term prognosis in children with severe encephalitis. Classification number: R725.9 Disturbance coefficient Video electroencephalogram Severe encephalitis in children Prognosis Non invasive monitoring Figures Figure 1 Figure 2 1 Introduction Encephalitis is an inflammatory disease of the brain parenchyma characterized by neuronal damage, often leading to neurological dysfunction. Most children with encephalitis have a good prognosis, but about 20% may develop severe encephalitis [ 1 ] . Severe encephalitis is one of the most critical neurological diseases in children, with a dangerous condition that progresses rapidly and can lead to serious neurological sequelae, even death. In the Pediatric Intensive Care Unit (PICU), approximately 10% -25% of children die from severe encephalitis. In addition, more than half (up to 55%) of surviving children will experience varying degrees of neurodevelopmental disorders. These long-term and persistent neurological and cognitive sequelae impose a heavy disease burden on the affected children themselves, their families, and society [ 2 – 3 ] . Therefore, early assessment of the prognosis of such children and targeted treatment are particularly crucial [ 4 ] . Prognostic assessment is not only related to the selection and optimization of treatment plans, but also affects the management of family expectations and the formulation of rehabilitation strategies. At present, the prognosis assessment of severe encephalitis in children mainly relies on a series of clinical indicators, such as Glasgow Coma Scale (GCS) score, neuroimaging examination, and traditional EEG pattern interpretation. However, these methods all have certain limitations. In recent years, with the advancement of follow-up neurological intensive care technology, continuous video electroencephalography (CVEEG) monitoring has become increasingly popular in PICU applications. It can record EEG activity and clinical behavior for a long time and dynamically, providing an irreplaceable tool for discovering non convulsive seizures and the evolution of EEG background activity [ 5 ] . At the same time, the disturbance coefficient of the non-invasive brain edema dynamic monitor has important value in monitoring changes in intracranial pressure, disease assessment, and prognosis judgment in children with traumatic brain injury [ 6 ] . However, current domestic and foreign research mostly focuses on the predictive value of a single technology for prognosis. This article aims to systematically combine the disturbance coefficient, an objective quantitative indicator, with the dynamic visualization information of bedside continuous video electroencephalography, in order to provide a more objective and accurate theoretical basis for early clinical intervention and personalized prognosis judgment. 2 Subjects and methods 2.1Research Object : This study retrospectively analyzed the clinical data of 136 children diagnosed with severe encephalitis in the Intensive Care Medicine Department of Hebei Children's Hospital from January 2021 to July 2024, including 65 boys and 71 girls with an average age of 5 (3–7) years. According to the prognosis at discharge, the patients were divided into a good prognosis group and a poor prognosis group. There were 82 cases in the good group, including 40 boys and 42 girls with an average age of 4 (3–7) years, and 54 cases in the poor group, including 25 boys and 29 girls with an average age of 5 (3–7) years. See Fig. 1 for details. 2.1.1 Inclusion criteria : All enrolled children meet the diagnostic criteria for encephalitis in the International Encephalitis Federation [ 7 ] , as shown in Table 1 , and meet one of the following conditions [ 1 ] : (1) consciousness disorders, such as agitation, coma, etc; (2) Frequent convulsions or persistent state of convulsions; (3) There are manifestations of cortical damage, such as mental disorders, behavioral abnormalities, limb movement disorders, etc; (4) Persistent or severe intracranial hypertension, brain herniation formation, irregular respiratory rhythm, etc; (5) Multiple organ dysfunction. 2.1.2 Exclusion criteria : (1) Incomplete clinical data; (2) Admitted within 24 hours; (3) Having a history of epilepsy, genetic metabolic history, or delayed growth and development in the past; (4) Individuals with a history of central nervous system infections and residual sequelae. 2.2 Research Methods 2.2.1 General information collection : Record the medical history of the child, including the presence of headache, vomiting, fever, status epilepticus, modified GCS score and early warning score upon admission, and monitor clinical indicators after admission, including cerebrospinal fluid cell count, cerebrospinal fluid protein, cerebrospinal fluid pressure, lactate dehydrogenase, blood ammonia, cranial imaging, etiology, and pathogen. Table 1 Diagnostic criteria for encephalopathy and encephalitis encephalopathy (1) Symptoms persist for more than 24 hours (2) Changes in mental state: such as drowsiness, restlessness, personality changes, abnormal behavior, etc., without other illnesses Because it can be explained Encephalitis (infection or autoimmune) Brain disease + at least 2 of the following (1) Heating ≥ 38 ℃ (2) Epileptic seizures (3) Localized neurological deficit (4) Increased number of cerebrospinal fluid cells (≥ 5) (5) Neuroimaging shows new onset (acute phase appearance) indicating brain parenchymal abnormalities in encephalitis (6) Abnormal electroencephalogram consistent with encephalitis, with no other etiology to explain The improved GCS score [ 8 ] includes: open eyes, best movement, and best language response, with a maximum score of 15 points, divided into 3 levels: (1) mild consciousness impairment: ≥ 12 points; (2) Moderate consciousness disorder: 9–11 points; (3) Severe consciousness disorders: ≤ 8 points, see Table 2 for details. The above scoring process was completed by two attending physicians with more than 5 years of clinical experience. The early warning score [ 9 ] includes three aspects: consciousness, cardiovascular system, and respiratory system. The higher the score, the higher the level of danger and the more severe the condition. Please refer to Table 3 for specific details. The above scoring process was completed by two attending physicians with more than 5 years of clinical experience. Table 3 Early Warning Score for Children project 0 points 1 point 2 points 3 points consciousness Lively/Moderate asleep irritated Sleepiness/blurred consciousness or reduced response to pain cardiovascular Red skin color or capillary refill time 1–2 seconds Pale or dull skin tone or capillary refill time of 3 seconds Skin tone is pale gray or purple, or capillary refill time is 4 seconds, or heart rate is 20 beats per minute faster than normal Skin tone that is pale gray or purple, flower spots, or capillary refill time > 5 seconds, or heart rate that is 30 beats per minute faster than normal, or bradycardia breathe Within the normal range, the inspiratory three concave sign is negative The respiratory rate increased by 10 times/min compared to the normal value, and the auxiliary respiratory muscle work increased by FiO 2 30% or oxygen flow rate 4 L/min The respiratory rate increased by 20 times/min compared to the normal value, the inspiratory three concave sign was positive, FiO 2 40% or oxygen flow rate was 6 L/min Slow breathing 5 times/min compared to normal, accompanied by sternum depression or groaning FiO 2 50% or oxygen flow rate 8 L/min Note: FiO 2 : Volume fraction of inhaled oxygen; Additional: If nebulized inhalation therapy is required every 15 minutes or if there is persistent vomiting after surgery, an additional 2 points will be added each. FiO 2 : fraction of inspiration O 2 ; additional item༚score 2 extra for one quarter hourly nebulizers or persistent vomiting following surgery. 2.2.2 Disturbance coefficient monitoring : Within 24 hours of admission, all pediatric patients should be monitored using the BORN-BE-IVA non-invasive brain edema monitor (Chongqing Boenfuk Medical Equipment Co., Ltd.). The nurse removes the hair above the temporal area of the patient's ear screen to ensure a smooth scalp in the spare skin area at the wing point. Connect the electrode pads by a physician: Use alcohol or disinfectant wipes to degrease and disinfect the electrode bonding area 2–3 times. Connect the lead wires to the device and fasten the electrode pads in brown, green, white, and black order. The four electrode pads are symmetrical on both sides, and the center (button) of the rear electrode pad is aligned with the highest point of the auricle above the external auditory canal. The lower edge of the electrode pad overlaps with the extension line of the outer corner of the eye. The front electrode pad is closely attached to the rear electrode pad and placed side by side, using the "meter" method for bonding. The measurement time is 30 minutes each time, recorded every 2 seconds, and the average value of the most stable segment observed during the measurement process for 15 minutes is the measurement DC. 2.2.3 VEEG monitoring : Within 24 hours of admission, all children were monitored using a Japanese made EEG9100/920016 lead video EEG monitor. The electrodes were placed according to the international 10–20 system, with the average electrode as the reference electrode. The resistance impedance was ≤ 5k Ω, the amplitude was 100 µ V/cm, the paper feed speed was 10mm/s, and the monitoring time was > 4 hours. During the monitoring period, the status of the children and clinical events were recorded, and the final results of the EEG were jointly interpreted by PICU physicians and neurophysiologists. According to the Clinical EEG Training Course, it can be divided into [ 10 ] : (1) Mild abnormalities: slowed background rhythm; (2) Moderate abnormality: Diffuse high amplitude slow wave paroxysmal appearance or focal epileptic discharge; (3) Severe abnormality: There are many diffuse high amplitude slow waves or explosive suppression phenomena, as well as widespread low voltage. 2.2.4 Prognostic assessment of brain function : The Glasgow Outcome Scale (GOS) [ 11 ] was used to evaluate the prognosis of all patients upon discharge. Its content includes: 1. Death; 2 points for plant survival (with only minimal response, able to open eyes during sleep/wakefulness cycles); 3 points are classified as severe disability (unable to live independently in daily life and requiring care); 4 is classified as mild disability (able to live independently and work under protection); 5 points for good recovery (returning to normal life, although with some minor defects). 1–3 points are classified as poor prognosis group, and 4–5 points are classified as good prognosis group. 2.3 Statistical methods : SPSS 22.0 statistical software was used to perform statistical processing on the data. Measurement data that conform to normal distribution are represented by mean ± standard deviation ( \(\:\stackrel{-}{\text{x}}\) ±s), while measurement data that do not conform to normal distribution are represented by median and interquartile range [M (P 25 -P 75 )]. The t-test is used for comparing continuous variables that follow a normal distribution, while the rank sum test is used for comparing continuous variables that do not follow a normal distribution; Count data is presented in terms of the number of cases (%), and comparison between groups is performed using the chi square test or Fisher's exact test. The analysis of influencing factors was conducted using a multiple factor logistic regression model, and receiver operating characteristic (ROC) curves were plotted to evaluate prognosis for individual and combined indicators. P < 0.05 is considered statistically significant. 3 Results 3.1 General conditions of the two groups of children. There are 82 cases in the good prognosis group, of which 25 cases (30.5%) have headaches, 28 cases (34.1%) have vomiting, and 76 cases (92.7%) have fever symptoms. There were 54 cases in the poor prognosis group, of which 17 cases (31.5%) had headaches, 20 cases (37.0%) had vomiting, and 50 cases (92.6%) had fever symptoms. There was no statistically significant difference ( P > 0.05) in gender, age, and clinical manifestations (headache, vomiting, fever) between the two groups of children. 35 cases (64.8%) in the poor prognosis group had status epilepticus, while 11 cases (13.4%) in the good group, and the difference was statistically significant ( P < 0.05), as shown in Table 4 . Table 4 Comparison of general conditions between the two groups of children indicator Good prognosis group (N = 82), n (%) Poor prognosis group (N = 54), n (%) χ༒ /Z P Gender (Example) Male 40 (48.8) Female 42 (51.2) Male 25 (46.3) Female 29 (53.7) 0.081 0.780 Age [M (P 25 -P 75 ), years] 4(3–7) 5(3–7) −0.246 0.806 headache have none 25(30.5) 57(69.5) 17(31.5) 37(68.5) 0.015 0.902 vomit have none 28(34.1) 54(65.9) 20(37.0) 34(63.0) 0.119 0.730 fever have none 76(92.7) 6(7.3) 50(92.6) 4(7.4) 0.100 0.752 Status epilepticus have none 11(13.4) 71(86.6) 35(64.8) 19(35.2) 38.431 <0.001 3.2 The scores of the two groups of children at admission. The early warning scores of the poor prevention group were higher than those of the good group upon admission, and the difference was statistically significant ( P < 0.05). The modified GCS score of 136 children with severe encephalitis: 72 cases (52.9%) scored ≤ 8, 48 cases (35.3%) scored 9–11, and 16 cases (11.8%) scored ≥ 12. The degree of consciousness impairment in the poor prognosis group was significantly abnormal, and the difference was statistically significant compared with the good prognosis group ( P < 0.05), as shown in Table 5 . Table 5 Admission scores of the two groups of children rating Good prognosis group (N = 82), n (%) Poor prognosis group (N = 54), n (%) t /Z P Early warning score (± s, points) \(\:\stackrel{\text{-}}{\text{x}}\) 4.96 ± 1.22 6.22 ± 1.49 5.466 <0.001 Improved GCS Score ≤ 8 points 9–11 points ≥ 12 points 33(40.2) 11(13.4) 38(46.3) 39(72.2) 5(9.3) 10(18.5) 3.706 <0.001 3.3 Comparison of clinical indicators between the two groups of children. 87 cases (64.0%) had abnormal cerebrospinal fluid, 110 cases (80.9%) had elevated cerebrospinal fluid pressure, and 111 cases (81.6%) had elevated cerebrospinal fluid protein. There was no statistically significant difference between the two groups of children in terms of cerebrospinal fluid cell count, cerebrospinal fluid protein, and cerebrospinal fluid pressure ( P > 0.05). The lactate dehydrogenase and blood ammonia indicators in the poor prognosis group were significantly higher than those in the good group, and the differences were statistically significant ( P < 0.05). Among all the patients, 85 cases (62.5%) had abnormal cranial imaging, with 48 cases (88.9%) in the poor prognosis group, significantly higher than the good group, and the difference was statistically significant (P < 0.05). Among all the children, there were 7 cases (5.1%) of encephalitis caused by bacterial infection, 37 cases (27.2%) caused by viral infection, 17 cases (12.5%) due to immune factors, and 75 cases (55.1%) with unknown causes. There was no statistically significant difference in the distribution of causes between the two groups ( P > 0.05), as shown in Table 6 . Table 6 Comparison of clinical indicators between the two groups of children indicator Good prognosis group (N = 82), n (%) Poor prognosis group (N = 54), n (%) test value P Cerebrospinal fluid ( \(\:\stackrel{\text{-}}{\text{x}}\) ± s) Cell count (× 106/L) Protein (g/L) Pressure value (mmH2O) 6.74 ± 4.10 1.12 ± 0.31 239.45 ± 29.47 8.06 ± 4.23 1.23 ± 0.78 248.34 ± 28.45 1.814 1.148 1.745 0.072 0.252 0.083 Lactate dehydrogenase (± s, U/L) \(\:\stackrel{\text{-}}{\text{x}}\) 334.15 ± 20.67 479.98 ± 24.09 37.676 <0.001 Blood ammonia (± s, umol/L) \(\:\stackrel{\text{-}}{\text{x}}\) 78.34 ± 10.41 114.28 ± 13.47 17.504 <0.001 Head Imaging (Example) abnormal normal 37(45.1) 45(54.9) 48(88.9) 6(11.1) 26.61 <0.001 Cause (Example) bacteria virus immunity unknown 4(4.9) 25(30.5) 11(13.4) 42(51.2) 3(5.6) 12(22.2) 6(11.1) 33(61.1) 1.171 0.242 3.4 Comparison of disturbance coefficient and video electroencephalogram between two groups of children. The DC values monitored at admission in the poor prognosis group were significantly lower than those in the good group, and the difference was statistically significant ( P < 0.05). There were 22 cases (40.7%) with severe abnormal EEG in the poor prognosis group, which was significantly higher than that in the good group, and the difference was statistically significant ( P < 0.05), as shown in Table 7 . Table 7 Comparison of DC and CVEEG between the two groups of children indicator Good prognosis group (N = 82), n (%) Poor prognosis group (N = 54), n (%) t /Z P Disturbance coefficient (± s) \(\:\stackrel{\text{-}}{\text{x}}\) 79.34 ± 14.98 63.20 ± 13.65 6.365 <0.001 Video electroencephalogram (example) Mild abnormality Moderate abnormality Severe abnormality 20(24.4) 48(58.5) 14(17.1) 5(9.3) 27(50.0) 22(40.7) 3.336 <0.001 3.5 Multivariate logistic analysis. The indicators with P < 0.05 in univariate analysis were included in the binary logistic regression model for multivariate analysis. The results showed that the modified Glasgow Coma Scale (GCS) score, early warning score, DC, and severe abnormality on video electroencephalogram (VEEG) were independent risk factors for poor prognosis in children with severe encephalitis, as detailed in Tables 8 and 9 . Table 8 Multivariate logistic regression analysis of prognostic factors in children with severe encephalitis factor variable name Assignment instructions Status epilepticus X1 0 = None, 1 = Yes Improved GCS Score X2 0 = > 8 points, 1 = ≤ 8 points Head Imaging X3 0 = normal, 1 = abnormal CVEEG X4 0 = mild to moderate abnormality, 1 = severe abnormality Table 9 Logistic analysis of factors influencing brain function in severe Encephalitis influencing factors B S.E, Wals P Exp (B) 95%CI lower limit upper limit Status epilepticus 0.214 1.082 0.039 0.843 1.239 0.149 10.327 Improved GCS Score −0.518 0.155 11.207 0.001 0.595 0.44 0.807 Early Warning Score −0.822 0.334 6.049 0.014 0.44 0.228 0.846 LDH −0.005 0.004 1.788 0.181 0.995 0.988 1.002 blood ammonia −0.012 0.011 1.124 0.289 0.988 0.966 1.01 Head Imaging 0.851 0.651 1.705 0.192 2.341 0.653 8.391 DC 0.049 0.018 7.654 0.006 1.05 1.014 1.087 Video electroencephalogram 1.545 0.774 3.986 0.046 4.686 1.029 21.343 3.6Predictive value of DC, CVEEG and Combined. The area under the ROC curve for DC value prediction of prognosis was 0.734, with the optimal cutoff value at 75, sensitivity of 74.1%, and specificity of 63.4%. The area under the ROC curve for video EEG prediction of prognosis was 0.701, with sensitivity of 53.7% and specificity of 86.6%. Both indicators demonstrated good predictive value for adverse outcomes of brain function. When these two indicators were combined for prediction, the area under the curve was 0.822, with sensitivity of 77.8% and specificity of 75.6%, indicating higher accuracy. See Fig. 2 and Table 10 for details. Table 10 Clinical value of each indicator in prognosis assessment indicator AUC 95%CI P value Sensitivity (%) Specificity (%) DC 0.734 (0.649–0.819) < 0.001 74.1 63.9 Video electroencephalogram 0.701 (0.607–0.795) < 0.001 53.7 86.6 joint indicator 0.822 (0.749–0.895) < 0.001 77.8 75.6 4 Discussion In the pediatric intensive care unit, severe encephalitis has the characteristics of rapid disease development, fast progression, and high disability rate. It is prone to intellectual and motor developmental disorders, epilepsy, paralysis, etc [ 12 ] , and individual prognosis varies greatly. Early and accurate evaluation of its short-term prognosis is crucial for guiding clinical stratification management, optimizing treatment strategies, and improving the long-term neurological function of children. In clinical practice, appropriate monitoring strategies and evaluation tools are used to predict the brain function of such children, reducing waste of medical resources and lowering disability and mortality rates. The prognosis of children with severe encephalitis is closely related to the severity of brain parenchymal damage, and the main pathological and physiological manifestations are: firstly, structural and perfusion damage caused by cerebral edema and increased intracranial hypertension; The second is electrophysiological dysfunction caused by abnormal excitability and synchronization disorders of neurons themselves. Related studies have shown that clinical manifestations of severe encephalitis in children, such as fever, limb movement disorders, and cerebrospinal fluid examination results, cannot be used as supporting points for evaluating prognosis [ 13 ] . This study found that there was no statistically significant difference in clinical manifestations such as fever, headache, and vomiting between the good prognosis group and the poor prognosis group, and there was no significant difference in cerebrospinal fluid examination results between the two groups of children. Although cerebrospinal fluid is an important diagnostic indicator in children with severe encephalitis, its abnormal rate is low and cannot be used as a prognostic indicator. In clinical practice, the prognosis is still evaluated based on clinical signs such as pupil reflex, corneal reflex, depth reflex, as well as serum and cerebrospinal fluid neuron specific enolase detection and cranial imaging examination [ 14 ] . Children with severe encephalitis are in critical condition and should not be transported out. Head imaging examinations cannot be evaluated in real-time and continuously, and there are many limiting factors. Serum biomarker testing is limited in primary hospitals due to laboratory conditions, so more convenient, practical, non-invasive, and continuous monitoring methods are needed to evaluate prognosis. In recent years, non-invasive brain edema monitoring devices have been widely used in clinical practice, and their bedside, non-invasive, and dynamic real-time capabilities have been recognized by clinical physicians. They have important value in monitoring changes in intracranial pressure, evaluating the condition, and predicting prognosis in children with brain injuries [15] . Its working principle is based on biological electromagnetic fields and electrical impedance imaging technology. Normal brain tissue, as a stable state, is disturbed by conditions such as edema or bleeding. When electromagnetic waves pass through the brain, different signals can be detected and converted into disturbance coefficients. By monitoring DC values, quantitative data on brain edema can be provided to clinical physicians. This study found that the DC values monitored at admission in the poor prognosis group were significantly lower than those in the good prognosis group, and the difference was statistically significant. As a risk factor for poor prognosis in severe encephalitis, the area under the ROC curve of DC value is 0.734, which has certain clinical value. Therefore, DC dynamic monitoring provides a real-time and non-invasive quantitative window for clinical work. Bedside video EEG can directly monitor the "electrical activity status" of neurons. Children with severe encephalitis often present with convulsive or non convulsive status epilepticus, severe suppression or disintegration of background activity, which are direct signs of severe cortical dysfunction and are clearly related to long-term neurodevelopmental outcomes [ 16 ] . The degree of abnormality in electroencephalogram can reflect the severity of brain function impairment. The video EEG of children with severe encephalitis in this study mainly showed diffuse, focal, and paroxysmal high or low amplitude delta activity; Accompanied or not by epileptic wave release; There is a phenomenon of explosion suppression or widespread low voltage. There are studies [ 17 ] showing that abnormal video EEG is roughly parallel to clinical and prognosis. The more severe the EEG manifestation, the more severe the injury, the more severe the clinical symptoms, and the worse the prognosis. This study found that abnormal video electroencephalogram is a risk factor for poor prognosis, with an area under the ROC curve of 0.701, which is consistent with it. Severe cerebral edema and intracranial hypertension can directly lead to neuronal ischemia and metabolic failure, thereby triggering and exacerbating inhibition or abnormal excitation of brain electrical activity. Conversely, frequent epileptic activity can significantly increase brain metabolic demand, exacerbate cerebral edema, and form a vicious cycle. Therefore, combining the two indicators can reveal more complex clinical manifestations and provide more accurate clinical value for evaluating prognosis. This is consistent with the AUC of 0.822 found in this study for the combined evaluation of prognosis, indicating that the combination of the two can improve the predictive value of prognosis evaluation. This study also found that improved GCS scores and early warning scores are risk factors for poor prognosis. GCS score is an indicator reflecting the degree of consciousness disorders, with over 1/4 of children in PICU having consciousness disorders, and 31.4% of these children being caused by central nervous system infections [ 18 ] . Multiple studies have confirmed the correlation between GCS score and mortality and poor prognosis [ 19 ] , which is consistent with our research finding that the improved GCS score is a risk factor for poor prognosis in children with severe encephalitis. The early warning score for children is used to assess the severity of the condition of admitted children [ 20 ] . It is simple to operate and time-consuming. The higher the score, the more critical the condition is. If the risk is predicted and early intervention is carried out before the condition changes, it can buy more time for subsequent treatment and improve the prognosis of the child. Related studies have shown that [ 21 ] early warning scoring can help clinical physicians identify the severity of neurological inpatients, distinguish between mild and severe encephalitis, and provide certain clinical value for subsequent treatment and prognosis evaluation. There are also related studies that have found different prognostic risk factors for severe encephalitis of different etiologies [ 22 – 23 ] . This article did not conduct a stratified study on severe encephalitis of different etiologies, which is a direction for future research. The limitations of this study include: firstly, it is a single center retrospective study, which may have selection bias and provide lower levels of evidence than prospective studies. Secondly, we only evaluated the short-term outcomes at discharge, which cannot represent the changes in cognitive and social function of the patient after discharge. Long term neurocognitive prognosis requires long-term follow-up. The DC value may be affected by factors such as age and skull thickness, and reference thresholds need to be established in more populations. Future multicenter prospective studies are needed to validate the universality of this combination model and gain a deeper understanding of the long-term prognosis of brain function. In summary, non-invasive brain edema monitoring combined with video electroencephalography provides value in evaluating severe encephalitis brain injury from different core perspectives. The combination of the two can reveal the severity and evolution mechanism of the disease more early and comprehensively, and has synergistic evaluation value for the short-term prognosis of children. Declarations Ethics approval and consent to participate The studies involving humans were approved by Ethics Committee of Hebei Children's Hospital(Approval Number: 202407-88) and was conducted in accordance with the Declaration of Helsinki. All methods were performed in accordance with the relevant guidelines and regulations.Informed consent to participate was obtained from the parents or legal guardians of any participant under the age of 16. Conflicts of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Funding The author declares that this study and/or article has received financial support for publication. This study was funded by the Clinical Medicine Excellent Talent Training Project sponsored by the Hebei Provincial Government (ZF2024181). Author Contribution S.L. performed the experiments and wrote the article. L.H.and X.Z. performed the experiments. M.X. revised the article. Z.W. designed the study and reviewed the article. All authors read and approved the final manuscript as submitted. Data Availability The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. References Jin Mei, Geng Wenjin, Yue Ling, etc Early evaluation of brain function prognosis in children with severe encephalitis using amplitude integrated electroencephalography [J]. Chinese Journal of Physical Medicine and Rehabilitation, 2019, 41 (9): 692-695. PALMAS G,DUKE T.Severe encephalitis: aetiology,management and outcomes over 10 years in a pediatric intensive care unit[J]. Arch Dis Child,2023,108(11):922-928. HON K L,TASNG Y C,CHAN L C,et al.Outcome of Encephalitis in Pediatric Intensive Care Unit[J]. Indian J Pediatr,2016,83(10):1098-103. ZHAO J ,WANG Z,LI S,et al.The efficacy of haemoperfusion combined with continuous venovenous haemodiafiltration in the treatment of severe viral encephalitis in children[J]. Ital J Pediatr,2023,49(1):21. Liu Xiaoyan Application of electroencephalogram in pediatric intensive care [J]. Chinese Journal of Pediatric Emergency Medicine, 2018, 25 (12): 907-912. [1] Lin Jie, He Minglian, Zou Yongjie, etc Meta analysis of the diagnostic value of non-invasive brain edema dynamic monitoring instrument for acute brain injury [J]. Chinese Journal of Brain Diseases and Rehabilitation (Electronic Edition), 2020, 10 (3): 132-138. DOI: 10.3877/cma.j.issn.2095-123X.2020.03.002. BRITTON P N,EASTWOOD K,PATERSON B,et al.Consensus guidelines for the investigation and management of encephalitis in adults and children in Australia and New Zealand[J]. Intern Med,2015,45(5):563-576. Wang Quan, Qian Suyun Common evaluation methods for children's consciousness level and brain dysfunction [J]. Chinese Journal of Practical Pediatrics, 2013, 28 (18): 1367-1370. Zhu Bichen, Lu Guoping Early Warning Score for Children [J]. Chinese Journal of Practical Pediatrics, 2018, 33 (06): 432-437. Chinese Anti Epilepsy Association, EEG and Neurophysiology Branch, Clinical EEG Training Course Writing Group Clinical EEG Training Course [M]. Beijing: People's Health Press, 2013:235-241. LAX PERICALL M T,TAYLOR E.Family function and its relationship to injury severity and psychiatric outcome in children with acquired brain injury：a systematized review[J]. Dev With Child Neurol,2014,56(1:19-30.DOI:10.1111/dmcn.12237. DE BLAUW D,BRUNING AHL,BUSCH CBE,et al.Epidemiology and Etiology of Severe Childhood Encephalitis in The Netherlands[J]. Pediatr Infect Dis,2020,39(4):267-272. Hu Wenjing, Yang Liming, Liao Hongmei, etc Clinical characteristics, prognosis, and related factors analysis of severe viral encephalitis in children [J]. Chinese Journal of Infection Control, 2018, 17 (3): 241-246. DOI: 10.3969/j.issn.1671-9638.2018.03.012. Chen Feng, Zhang Furong, Sun Jimin, etc The effect of mild hypothermia on serum and cerebrospinal fluid NSE and S100B protein expression in children with severe viral encephalitis [J]. Journal of Huazhong University of Science and Technology: Medical Edition, 2017, 46 (3): 291-294. DOI: 10.3870/j.issn. 1672-0741. March 10, 2017. LYMPEROPOULOS G, LYMPEROPOULOS P, ALIKARI V, et al. Applications for electrical impedance tomography (EIT) and electrical properties of the human body[J]. Adv Exp Med Biol, 2017, 989: 109-117. FAN TH,PREMRAJ L,ROBERTS J,et al.In-Hospital Neurologic Complications, Neuromonitoring, and Long-Term Neurologic Outcomes in Patients With Sepsis:A Systematic Review and Meta-Analysis[J]. Crit Care Med,2024,52(3):452-463. MILSHTEIN N Y,PARET G,REIF S,et al.Acute childhood encephalitis at 2 tertiary care children hospitals in Israel:etiology and clinical characteristics[J]. Pediatr Emerg Care,2016,32(2): 82-86.DOI:10.1097/PEC.0000000000000468. DUYU M,KARAKAYA ALTUN Z,YILDIZ S. Nontraumatic coma in the pediatric intensive care unit: etiology, clinical characteristics and outcome[J]. Turk J Med Sci,2021,51(1):214-223. KHOLIFIA A,RUSMAWATININGTYAS D,MAKRUFARDI F, et al.Factors associated with mortality in intracranial infection patients admitted to pediatric intensive care unit:A retrospective cohort study[J]. Ann Med Surg (Lond),2021,70:102884. HANSEN G,HOCHMAN J,GARNER M,et al.Pediatric early warning score and deteriorating ward patients on high-flow therapy[J]. Pediatr Int,2019,61(3):278-283. Li Huina, An Hong, Gao Jielin, Zhao Yingmian, Wang Xiaoxue The effectiveness of improving early warning scores for children in the diagnosis of viral encephalitis [J]. Journal of Clinical and Pathological Sciences, 2020, 40 (07): 1740-1743. SINGF T D,FUGATE J E,RABINSTEIN A.The spectrum of acute encephalitis: causes,management,and,predictors,of outcome[J]. Neurology,2015,84(4): 359-366. GONG Z,LAO D,HUANG F,et al.Risk Factors and Prognosis in Anti-NMDA Receptor Encephalitis Patients with Disturbance of Consciousness[J]. Patient Relat Outcome Meas,2023,14:181-192. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8636447","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591149223,"identity":"32ecf4a3-b8b8-47d0-89ea-57c72e41f5b9","order_by":0,"name":"shuai liu","email":"","orcid":"","institution":"Hebei Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"shuai","middleName":"","lastName":"liu","suffix":""},{"id":591149224,"identity":"20ae05a2-a992-4cfb-8f16-0d94b2f61b8b","order_by":1,"name":"lihong hu","email":"","orcid":"","institution":"Hebei Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"lihong","middleName":"","lastName":"hu","suffix":""},{"id":591149225,"identity":"056db501-035a-4fc3-ba6d-a936e81af7f5","order_by":2,"name":"meixian xu","email":"","orcid":"","institution":"Hebei Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"meixian","middleName":"","lastName":"xu","suffix":""},{"id":591149226,"identity":"dd089672-0ef4-403e-aa32-8cc50b5e249a","order_by":3,"name":"xin zhao","email":"","orcid":"","institution":"Hebei Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"xin","middleName":"","lastName":"zhao","suffix":""},{"id":591149227,"identity":"122b2390-1c0a-4878-ad6d-420cfb413708","order_by":4,"name":"zexi wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACNvnzDwwk//zjYWNmSHyQUFFDWAufBA9DgWXDATl+9obHBg/OHCOsRQ6o5UNlwwFjyZ6DzyQftjAT4TDp3oMbbu64k7jhRnJaRWIDGwN/e3cCfi0y55INZ555BtSSlnYjcYcMg8SZsxvwa2FIMDOWYGMGaskBajnDxmAgkUtQi/nvP2At+d8KEtuYidAikWNgINl2GOj9A2kMxGnhOZZgIHEmDRTIyRIJZ47xEPSLfHvzAQOJChtwVH78UVEjx9/ei18LBuAhTfkoGAWjYBSMAqwAAMMTT+Tg9VmsAAAAAElFTkSuQmCC","orcid":"","institution":"Hebei Children's Hospital","correspondingAuthor":true,"prefix":"","firstName":"zexi","middleName":"","lastName":"wang","suffix":""}],"badges":[],"createdAt":"2026-01-19 08:33:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8636447/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8636447/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12887-026-06892-6","type":"published","date":"2026-04-22T15:59:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102940513,"identity":"ea34c4d4-07d5-4bab-b217-4069742515d5","added_by":"auto","created_at":"2026-02-18 17:06:43","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":410426,"visible":true,"origin":"","legend":"\u003cp\u003eScreening Process Diagram\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8636447/v1/5c785513d50bb700ff6d365e.jpeg"},{"id":102940512,"identity":"63734b2c-4d49-438e-b1dd-fd051de8e1a8","added_by":"auto","created_at":"2026-02-18 17:06:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111571,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for prediction of prognosis for each indicator\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8636447/v1/8e7944ede9865e64f03f5047.png"},{"id":107928456,"identity":"8f6dd64c-317d-468e-9342-4ae3ba771977","added_by":"auto","created_at":"2026-04-27 16:10:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1052660,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8636447/v1/d86f8399-7d1f-468d-b852-19758a721356.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The evaluation value of disturbance coefficient combined with bedside continuous video electroencephalogram for the short-term prognosis of severe encephalitis in children","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eEncephalitis is an inflammatory disease of the brain parenchyma characterized by neuronal damage, often leading to neurological dysfunction. Most children with encephalitis have a good prognosis, but about 20% may develop severe encephalitis\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Severe encephalitis is one of the most critical neurological diseases in children, with a dangerous condition that progresses rapidly and can lead to serious neurological sequelae, even death. In the Pediatric Intensive Care Unit (PICU), approximately 10% -25% of children die from severe encephalitis. In addition, more than half (up to 55%) of surviving children will experience varying degrees of neurodevelopmental disorders. These long-term and persistent neurological and cognitive sequelae impose a heavy disease burden on the affected children themselves, their families, and society \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Therefore, early assessment of the prognosis of such children and targeted treatment are particularly crucial \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Prognostic assessment is not only related to the selection and optimization of treatment plans, but also affects the management of family expectations and the formulation of rehabilitation strategies. At present, the prognosis assessment of severe encephalitis in children mainly relies on a series of clinical indicators, such as Glasgow Coma Scale (GCS) score, neuroimaging examination, and traditional EEG pattern interpretation. However, these methods all have certain limitations. In recent years, with the advancement of follow-up neurological intensive care technology, continuous video electroencephalography (CVEEG) monitoring has become increasingly popular in PICU applications. It can record EEG activity and clinical behavior for a long time and dynamically, providing an irreplaceable tool for discovering non convulsive seizures and the evolution of EEG background activity\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. At the same time, the disturbance coefficient of the non-invasive brain edema dynamic monitor has important value in monitoring changes in intracranial pressure, disease assessment, and prognosis judgment in children with traumatic brain injury \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. However, current domestic and foreign research mostly focuses on the predictive value of a single technology for prognosis. This article aims to systematically combine the disturbance coefficient, an objective quantitative indicator, with the dynamic visualization information of bedside continuous video electroencephalography, in order to provide a more objective and accurate theoretical basis for early clinical intervention and personalized prognosis judgment.\u003c/p\u003e"},{"header":"2 Subjects and methods","content":"\u003cp\u003e \u003cb\u003e2.1Research Object\u003c/b\u003e: This study retrospectively analyzed the clinical data of 136 children diagnosed with severe encephalitis in the Intensive Care Medicine Department of Hebei Children's Hospital from January 2021 to July 2024, including 65 boys and 71 girls with an average age of 5 (3\u0026ndash;7) years. According to the prognosis at discharge, the patients were divided into a good prognosis group and a poor prognosis group. There were 82 cases in the good group, including 40 boys and 42 girls with an average age of 4 (3\u0026ndash;7) years, and 54 cases in the poor group, including 25 boys and 29 girls with an average age of 5 (3\u0026ndash;7) years. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.1.1 Inclusion criteria\u003c/b\u003e: All enrolled children meet the diagnostic criteria for encephalitis in the International Encephalitis Federation \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and meet one of the following conditions \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e: (1) consciousness disorders, such as agitation, coma, etc; (2) Frequent convulsions or persistent state of convulsions; (3) There are manifestations of cortical damage, such as mental disorders, behavioral abnormalities, limb movement disorders, etc; (4) Persistent or severe intracranial hypertension, brain herniation formation, irregular respiratory rhythm, etc; (5) Multiple organ dysfunction.\u003c/p\u003e\u003cp\u003e \u003cb\u003e2.1.2 Exclusion criteria\u003c/b\u003e: (1) Incomplete clinical data; (2) Admitted within 24 hours; (3) Having a history of epilepsy, genetic metabolic history, or delayed growth and development in the past; (4) Individuals with a history of central nervous system infections and residual sequelae.\u003c/p\u003e\u003cp\u003e \u003cb\u003e2.2 Research Methods\u003c/b\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003e2.2.1 General information collection\u003c/b\u003e: Record the medical history of the child, including the presence of headache, vomiting, fever, status epilepticus, modified GCS score and early warning score upon admission, and monitor clinical indicators after admission, including cerebrospinal fluid cell count, cerebrospinal fluid protein, cerebrospinal fluid pressure, lactate dehydrogenase, blood ammonia, cranial imaging, etiology, and pathogen.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic criteria for encephalopathy and encephalitis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eencephalopathy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1) Symptoms persist for more than 24 hours\u003c/p\u003e \u003cp\u003e(2) Changes in mental state: such as drowsiness, restlessness, personality changes, abnormal behavior, etc., without other illnesses\u003c/p\u003e \u003cp\u003eBecause it can be explained\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEncephalitis (infection or autoimmune)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain disease\u0026thinsp;+\u0026thinsp;at least 2 of the following\u003c/p\u003e \u003cp\u003e(1) Heating\u0026thinsp;\u0026ge;\u0026thinsp;38 ℃\u003c/p\u003e \u003cp\u003e(2) Epileptic seizures\u003c/p\u003e \u003cp\u003e(3) Localized neurological deficit\u003c/p\u003e \u003cp\u003e(4) Increased number of cerebrospinal fluid cells (\u0026ge;\u0026thinsp;5)\u003c/p\u003e \u003cp\u003e(5) Neuroimaging shows new onset (acute phase appearance) indicating brain parenchymal abnormalities in encephalitis\u003c/p\u003e \u003cp\u003e(6) Abnormal electroencephalogram consistent with encephalitis, with no other etiology to explain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe improved GCS score \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e includes: open eyes, best movement, and best language response, with a maximum score of 15 points, divided into 3 levels: (1) mild consciousness impairment: \u0026ge; 12 points; (2) Moderate consciousness disorder: 9\u0026ndash;11 points; (3) Severe consciousness disorders: \u0026le; 8 points, see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details. The above scoring process was completed by two attending physicians with more than 5 years of clinical experience.\u003c/p\u003e \u003cp\u003eThe early warning score \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e includes three aspects: consciousness, cardiovascular system, and respiratory system. The higher the score, the higher the level of danger and the more severe the condition. Please refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for specific details. The above scoring process was completed by two attending physicians with more than 5 years of clinical experience.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"714\" height=\"643\"\u003e\u003c/p\u003e\n \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEarly Warning Score for Children\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eproject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 points\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 points\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 points\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econsciousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLively/Moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003easleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eirritated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSleepiness/blurred consciousness or reduced response to pain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecardiovascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed skin color or capillary refill time 1\u0026ndash;2 seconds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePale or dull skin tone or capillary refill time of 3 seconds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkin tone is pale gray or purple, or capillary refill time is 4 seconds, or heart rate is 20 beats per minute faster than normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSkin tone that is pale gray or purple, flower spots, or capillary refill time\u0026thinsp;\u0026gt;\u0026thinsp;5 seconds, or heart rate that is 30 beats per minute faster than normal, or bradycardia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebreathe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin the normal range, the inspiratory three concave sign is negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe respiratory rate increased by 10 times/min compared to the normal value, and the auxiliary respiratory muscle work increased by FiO 2 30% or oxygen flow rate 4 L/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe respiratory rate increased by 20 times/min compared to the normal value, the inspiratory three concave sign was positive, FiO 2 40% or oxygen flow rate was 6 L/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSlow breathing 5 times/min compared to normal, accompanied by sternum depression or groaning FiO 2 50% or oxygen flow rate 8 L/min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: FiO\u003csub\u003e2\u003c/sub\u003e: Volume fraction of inhaled oxygen; Additional: If nebulized inhalation therapy is required every 15 minutes or if there is persistent vomiting after surgery, an additional 2 points will be added each.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFiO \u003csub\u003e2\u003c/sub\u003e: fraction of inspiration O\u003csub\u003e2\u003c/sub\u003e; additional item༚score 2 extra for one quarter hourly nebulizers or persistent vomiting following surgery.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2.2 Disturbance coefficient monitoring\u003c/b\u003e: Within 24 hours of admission, all pediatric patients should be monitored using the BORN-BE-IVA non-invasive brain edema monitor (Chongqing Boenfuk Medical Equipment Co., Ltd.). The nurse removes the hair above the temporal area of the patient's ear screen to ensure a smooth scalp in the spare skin area at the wing point. Connect the electrode pads by a physician: Use alcohol or disinfectant wipes to degrease and disinfect the electrode bonding area 2\u0026ndash;3 times. Connect the lead wires to the device and fasten the electrode pads in brown, green, white, and black order. The four electrode pads are symmetrical on both sides, and the center (button) of the rear electrode pad is aligned with the highest point of the auricle above the external auditory canal. The lower edge of the electrode pad overlaps with the extension line of the outer corner of the eye. The front electrode pad is closely attached to the rear electrode pad and placed side by side, using the \"meter\" method for bonding. The measurement time is 30 minutes each time, recorded every 2 seconds, and the average value of the most stable segment observed during the measurement process for 15 minutes is the measurement DC.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2.3 VEEG monitoring\u003c/b\u003e: Within 24 hours of admission, all children were monitored using a Japanese made EEG9100/920016 lead video EEG monitor. The electrodes were placed according to the international 10\u0026ndash;20 system, with the average electrode as the reference electrode. The resistance impedance was \u0026le;\u0026thinsp;5k Ω, the amplitude was 100 \u0026micro; V/cm, the paper feed speed was 10mm/s, and the monitoring time was \u0026gt;\u0026thinsp;4 hours. During the monitoring period, the status of the children and clinical events were recorded, and the final results of the EEG were jointly interpreted by PICU physicians and neurophysiologists. According to the Clinical EEG Training Course, it can be divided into\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e: (1) Mild abnormalities: slowed background rhythm; (2) Moderate abnormality: Diffuse high amplitude slow wave paroxysmal appearance or focal epileptic discharge; (3) Severe abnormality: There are many diffuse high amplitude slow waves or explosive suppression phenomena, as well as widespread low voltage.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2.4 Prognostic assessment of brain function\u003c/b\u003e: The Glasgow Outcome Scale (GOS) \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003ewas used to evaluate the prognosis of all patients upon discharge. Its content includes: 1. Death; 2 points for plant survival (with only minimal response, able to open eyes during sleep/wakefulness cycles); 3 points are classified as severe disability (unable to live independently in daily life and requiring care); 4 is classified as mild disability (able to live independently and work under protection); 5 points for good recovery (returning to normal life, although with some minor defects). 1\u0026ndash;3 points are classified as poor prognosis group, and 4\u0026ndash;5 points are classified as good prognosis group.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 Statistical methods\u003c/b\u003e: SPSS 22.0 statistical software was used to perform statistical processing on the data. Measurement data that conform to normal distribution are represented by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s), while measurement data that do not conform to normal distribution are represented by median and interquartile range [M (P\u003csub\u003e25\u003c/sub\u003e-P\u003csub\u003e75\u003c/sub\u003e)]. The t-test is used for comparing continuous variables that follow a normal distribution, while the rank sum test is used for comparing continuous variables that do not follow a normal distribution; Count data is presented in terms of the number of cases (%), and comparison between groups is performed using the chi square test or Fisher's exact test. The analysis of influencing factors was conducted using a multiple factor logistic regression model, and receiver operating characteristic (ROC) curves were plotted to evaluate prognosis for individual and combined indicators.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered statistically significant.\u003c/p\u003e "},{"header":"3 Results","content":"\u003cp\u003e \u003cb\u003e3.1 General conditions of the two groups of children.\u003c/b\u003e There are 82 cases in the good prognosis group, of which 25 cases (30.5%) have headaches, 28 cases (34.1%) have vomiting, and 76 cases (92.7%) have fever symptoms. There were 54 cases in the poor prognosis group, of which 17 cases (31.5%) had headaches, 20 cases (37.0%) had vomiting, and 50 cases (92.6%) had fever symptoms. There was no statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in gender, age, and clinical manifestations (headache, vomiting, fever) between the two groups of children. 35 cases (64.8%) in the poor prognosis group had status epilepticus, while 11 cases (13.4%) in the good group, and the difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of general conditions between the two groups of children\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood prognosis group (N\u0026thinsp;=\u0026thinsp;82), n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor prognosis group (N\u0026thinsp;=\u0026thinsp;54), n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ༒ /Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Example)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale 40 (48.8)\u003c/p\u003e \u003cp\u003eFemale 42 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale 25 (46.3)\u003c/p\u003e \u003cp\u003eFemale 29 (53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge [M (P\u003csub\u003e25\u003c/sub\u003e-P\u003csub\u003e75\u003c/sub\u003e), years]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(3\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(3\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheadache\u003c/p\u003e \u003cp\u003ehave\u003c/p\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(30.5)\u003c/p\u003e \u003cp\u003e57(69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(31.5)\u003c/p\u003e \u003cp\u003e37(68.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evomit\u003c/p\u003e \u003cp\u003ehave\u003c/p\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(34.1)\u003c/p\u003e \u003cp\u003e54(65.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(37.0)\u003c/p\u003e \u003cp\u003e34(63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efever\u003c/p\u003e \u003cp\u003ehave\u003c/p\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76(92.7)\u003c/p\u003e \u003cp\u003e6(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(92.6)\u003c/p\u003e \u003cp\u003e4(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatus epilepticus\u003c/p\u003e \u003cp\u003ehave\u003c/p\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(13.4)\u003c/p\u003e \u003cp\u003e71(86.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(64.8)\u003c/p\u003e \u003cp\u003e19(35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 The scores of the two groups of children at admission.\u003c/b\u003eThe early warning scores of the poor prevention group were higher than those of the good group upon admission, and the difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The modified GCS score of 136 children with severe encephalitis: 72 cases (52.9%) scored\u0026thinsp;\u0026le;\u0026thinsp;8, 48 cases (35.3%) scored 9\u0026ndash;11, and 16 cases (11.8%) scored\u0026thinsp;\u0026ge;\u0026thinsp;12. The degree of consciousness impairment in the poor prognosis group was significantly abnormal, and the difference was statistically significant compared with the good prognosis group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdmission scores of the two groups of children\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003erating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood prognosis group (N\u0026thinsp;=\u0026thinsp;82), n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor prognosis group (N\u0026thinsp;=\u0026thinsp;54), n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et /Z\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly warning score (\u0026plusmn;\u0026thinsp;s, points)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved GCS Score\u003c/p\u003e \u003cp\u003e\u0026le;\u0026thinsp;8 points\u003c/p\u003e \u003cp\u003e9\u0026ndash;11 points\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;12 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(40.2)\u003c/p\u003e \u003cp\u003e11(13.4)\u003c/p\u003e \u003cp\u003e38(46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(72.2)\u003c/p\u003e \u003cp\u003e5(9.3)\u003c/p\u003e \u003cp\u003e10(18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Comparison of clinical indicators between the two groups of children.\u003c/b\u003e87 cases (64.0%) had abnormal cerebrospinal fluid, 110 cases (80.9%) had elevated cerebrospinal fluid pressure, and 111 cases (81.6%) had elevated cerebrospinal fluid protein. There was no statistically significant difference between the two groups of children in terms of cerebrospinal fluid cell count, cerebrospinal fluid protein, and cerebrospinal fluid pressure (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe lactate dehydrogenase and blood ammonia indicators in the poor prognosis group were significantly higher than those in the good group, and the differences were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAmong all the patients, 85 cases (62.5%) had abnormal cranial imaging, with 48 cases (88.9%) in the poor prognosis group, significantly higher than the good group, and the difference was statistically significant \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAmong all the children, there were 7 cases (5.1%) of encephalitis caused by bacterial infection, 37 cases (27.2%) caused by viral infection, 17 cases (12.5%) due to immune factors, and 75 cases (55.1%) with unknown causes. There was no statistically significant difference in the distribution of causes between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical indicators between the two groups of children\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood prognosis group (N\u0026thinsp;=\u0026thinsp;82), n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor prognosis group (N\u0026thinsp;=\u0026thinsp;54), n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003etest value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrospinal fluid (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn; s)\u003c/p\u003e \u003cp\u003eCell count (\u0026times; 106/L)\u003c/p\u003e \u003cp\u003eProtein (g/L)\u003c/p\u003e \u003cp\u003ePressure value (mmH2O)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10\u003c/p\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003cp\u003e239.45\u0026thinsp;\u0026plusmn;\u0026thinsp;29.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.06\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003c/p\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003cp\u003e248.34\u0026thinsp;\u0026plusmn;\u0026thinsp;28.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.814\u003c/p\u003e \u003cp\u003e1.148\u003c/p\u003e \u003cp\u003e1.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003cp\u003e0.252\u003c/p\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate dehydrogenase (\u0026plusmn;\u0026thinsp;s, U/L)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334.15\u0026thinsp;\u0026plusmn;\u0026thinsp;20.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e479.98\u0026thinsp;\u0026plusmn;\u0026thinsp;24.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood ammonia (\u0026plusmn;\u0026thinsp;s, umol/L)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.34\u0026thinsp;\u0026plusmn;\u0026thinsp;10.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.28\u0026thinsp;\u0026plusmn;\u0026thinsp;13.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead Imaging (Example)\u003c/p\u003e \u003cp\u003eabnormal\u003c/p\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(45.1)\u003c/p\u003e \u003cp\u003e45(54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(88.9)\u003c/p\u003e \u003cp\u003e6(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCause (Example)\u003c/p\u003e \u003cp\u003ebacteria\u003c/p\u003e \u003cp\u003evirus\u003c/p\u003e \u003cp\u003eimmunity\u003c/p\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(4.9)\u003c/p\u003e \u003cp\u003e25(30.5)\u003c/p\u003e \u003cp\u003e11(13.4)\u003c/p\u003e \u003cp\u003e42(51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(5.6)\u003c/p\u003e \u003cp\u003e12(22.2)\u003c/p\u003e \u003cp\u003e6(11.1)\u003c/p\u003e \u003cp\u003e33(61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4 Comparison of disturbance coefficient and video electroencephalogram between two groups of children.\u003c/b\u003e The DC values monitored at admission in the poor prognosis group were significantly lower than those in the good group, and the difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were 22 cases (40.7%) with severe abnormal EEG in the poor prognosis group, which was significantly higher than that in the good group, and the difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of DC and CVEEG between the two groups of children\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood prognosis group (N\u0026thinsp;=\u0026thinsp;82), n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor prognosis group (N\u0026thinsp;=\u0026thinsp;54), n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et /Z\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisturbance coefficient (\u0026plusmn;\u0026thinsp;s)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.34\u0026thinsp;\u0026plusmn;\u0026thinsp;14.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.20\u0026thinsp;\u0026plusmn;\u0026thinsp;13.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVideo electroencephalogram (example)\u003c/p\u003e \u003cp\u003eMild abnormality\u003c/p\u003e \u003cp\u003eModerate abnormality\u003c/p\u003e \u003cp\u003eSevere abnormality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(24.4)\u003c/p\u003e \u003cp\u003e48(58.5)\u003c/p\u003e \u003cp\u003e14(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(9.3)\u003c/p\u003e \u003cp\u003e27(50.0)\u003c/p\u003e \u003cp\u003e22(40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5 Multivariate logistic analysis.\u003c/b\u003eThe indicators with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were included in the binary logistic regression model for multivariate analysis. The results showed that the modified Glasgow Coma Scale (GCS) score, early warning score, DC, and severe abnormality on video electroencephalogram (VEEG) were independent risk factors for poor prognosis in children with severe encephalitis, as detailed in Tables\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of prognostic factors in children with severe encephalitis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003efactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003evariable name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssignment instructions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatus epilepticus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;None, 1\u0026thinsp;=\u0026thinsp;Yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved GCS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;8 points, 1\u0026thinsp;=\u0026thinsp;\u0026le;\u0026thinsp;8 points\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead Imaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;normal, 1\u0026thinsp;=\u0026thinsp;abnormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVEEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;mild to moderate abnormality, 1\u0026thinsp;=\u0026thinsp;severe abnormality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic analysis of factors influencing brain function in severe Encephalitis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003einfluencing factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS.E,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExp (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003elower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eupper limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatus epilepticus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved GCS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly Warning Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eblood ammonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead Imaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVideo electroencephalogram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.6Predictive value of DC, CVEEG and Combined.\u003c/b\u003eThe area under the ROC curve for DC value prediction of prognosis was 0.734, with the optimal cutoff value at 75, sensitivity of 74.1%, and specificity of 63.4%. The area under the ROC curve for video EEG prediction of prognosis was 0.701, with sensitivity of 53.7% and specificity of 86.6%. Both indicators demonstrated good predictive value for adverse outcomes of brain function. When these two indicators were combined for prediction, the area under the curve was 0.822, with sensitivity of 77.8% and specificity of 75.6%, indicating higher accuracy. See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical value of each indicator in prognosis assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.649\u0026ndash;0.819)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVideo electroencephalogram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.607\u0026ndash;0.795)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ejoint indicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.749\u0026ndash;0.895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn the pediatric intensive care unit, severe encephalitis has the characteristics of rapid disease development, fast progression, and high disability rate. It is prone to intellectual and motor developmental disorders, epilepsy, paralysis, etc\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, and individual prognosis varies greatly. Early and accurate evaluation of its short-term prognosis is crucial for guiding clinical stratification management, optimizing treatment strategies, and improving the long-term neurological function of children. In clinical practice, appropriate monitoring strategies and evaluation tools are used to predict the brain function of such children, reducing waste of medical resources and lowering disability and mortality rates.\u003c/p\u003e \u003cp\u003eThe prognosis of children with severe encephalitis is closely related to the severity of brain parenchymal damage, and the main pathological and physiological manifestations are: firstly, structural and perfusion damage caused by cerebral edema and increased intracranial hypertension; The second is electrophysiological dysfunction caused by abnormal excitability and synchronization disorders of neurons themselves. Related studies have shown that clinical manifestations of severe encephalitis in children, such as fever, limb movement disorders, and cerebrospinal fluid examination results, cannot be used as supporting points for evaluating prognosis\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. This study found that there was no statistically significant difference in clinical manifestations such as fever, headache, and vomiting between the good prognosis group and the poor prognosis group, and there was no significant difference in cerebrospinal fluid examination results between the two groups of children. Although cerebrospinal fluid is an important diagnostic indicator in children with severe encephalitis, its abnormal rate is low and cannot be used as a prognostic indicator. In clinical practice, the prognosis is still evaluated based on clinical signs such as pupil reflex, corneal reflex, depth reflex, as well as serum and cerebrospinal fluid neuron specific enolase detection and cranial imaging examination\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Children with severe encephalitis are in critical condition and should not be transported out. Head imaging examinations cannot be evaluated in real-time and continuously, and there are many limiting factors. Serum biomarker testing is limited in primary hospitals due to laboratory conditions, so more convenient, practical, non-invasive, and continuous monitoring methods are needed to evaluate prognosis. In recent years, non-invasive brain edema monitoring devices have been widely used in clinical practice, and their bedside, non-invasive, and dynamic real-time capabilities have been recognized by clinical physicians. They have important value in monitoring changes in intracranial pressure, evaluating the condition, and predicting prognosis in children with brain injuries\u003csup\u003e[15]\u003c/sup\u003e. Its working principle is based on biological electromagnetic fields and electrical impedance imaging technology. Normal brain tissue, as a stable state, is disturbed by conditions such as edema or bleeding. When electromagnetic waves pass through the brain, different signals can be detected and converted into disturbance coefficients. By monitoring DC values, quantitative data on brain edema can be provided to clinical physicians. This study found that the DC values monitored at admission in the poor prognosis group were significantly lower than those in the good prognosis group, and the difference was statistically significant. As a risk factor for poor prognosis in severe encephalitis, the area under the ROC curve of DC value is 0.734, which has certain clinical value. Therefore, DC dynamic monitoring provides a real-time and non-invasive quantitative window for clinical work.\u003c/p\u003e \u003cp\u003eBedside video EEG can directly monitor the \"electrical activity status\" of neurons. Children with severe encephalitis often present with convulsive or non convulsive status epilepticus, severe suppression or disintegration of background activity, which are direct signs of severe cortical dysfunction and are clearly related to long-term neurodevelopmental outcomes \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The degree of abnormality in electroencephalogram can reflect the severity of brain function impairment. The video EEG of children with severe encephalitis in this study mainly showed diffuse, focal, and paroxysmal high or low amplitude delta activity; Accompanied or not by epileptic wave release; There is a phenomenon of explosion suppression or widespread low voltage. There are studies\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e showing that abnormal video EEG is roughly parallel to clinical and prognosis. The more severe the EEG manifestation, the more severe the injury, the more severe the clinical symptoms, and the worse the prognosis. This study found that abnormal video electroencephalogram is a risk factor for poor prognosis, with an area under the ROC curve of 0.701, which is consistent with it. Severe cerebral edema and intracranial hypertension can directly lead to neuronal ischemia and metabolic failure, thereby triggering and exacerbating inhibition or abnormal excitation of brain electrical activity. Conversely, frequent epileptic activity can significantly increase brain metabolic demand, exacerbate cerebral edema, and form a vicious cycle. Therefore, combining the two indicators can reveal more complex clinical manifestations and provide more accurate clinical value for evaluating prognosis. This is consistent with the AUC of 0.822 found in this study for the combined evaluation of prognosis, indicating that the combination of the two can improve the predictive value of prognosis evaluation.\u003c/p\u003e\u003cp\u003eThis study also found that improved GCS scores and early warning scores are risk factors for poor prognosis. GCS score is an indicator reflecting the degree of consciousness disorders, with over 1/4 of children in PICU having consciousness disorders, and 31.4% of these children being caused by central nervous system infections\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Multiple studies have confirmed the correlation between GCS score and mortality and poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, which is consistent with our research finding that the improved GCS score is a risk factor for poor prognosis in children with severe encephalitis. The early warning score for children is used to assess the severity of the condition of admitted children \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. It is simple to operate and time-consuming. The higher the score, the more critical the condition is. If the risk is predicted and early intervention is carried out before the condition changes, it can buy more time for subsequent treatment and improve the prognosis of the child. Related studies have shown that \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e early warning scoring can help clinical physicians identify the severity of neurological inpatients, distinguish between mild and severe encephalitis, and provide certain clinical value for subsequent treatment and prognosis evaluation. There are also related studies that have found different prognostic risk factors for severe encephalitis of different etiologies \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. This article did not conduct a stratified study on severe encephalitis of different etiologies, which is a direction for future research. The limitations of this study include: firstly, it is a single center retrospective study, which may have selection bias and provide lower levels of evidence than prospective studies. Secondly, we only evaluated the short-term outcomes at discharge, which cannot represent the changes in cognitive and social function of the patient after discharge. Long term neurocognitive prognosis requires long-term follow-up. The DC value may be affected by factors such as age and skull thickness, and reference thresholds need to be established in more populations. Future multicenter prospective studies are needed to validate the universality of this combination model and gain a deeper understanding of the long-term prognosis of brain function.\u003c/p\u003e \u003cp\u003eIn summary, non-invasive brain edema monitoring combined with video electroencephalography provides value in evaluating severe encephalitis brain injury from different core perspectives. The combination of the two can reveal the severity and evolution mechanism of the disease more early and comprehensively, and has synergistic evaluation value for the short-term prognosis of children.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e The studies involving humans were approved by Ethics Committee of Hebei Children's Hospital(Approval Number: 202407-88) and was conducted in accordance with the Declaration of Helsinki. All methods were performed in accordance with the relevant guidelines and regulations.Informed consent to participate was obtained from the parents or legal guardians of any participant under the age of 16.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003ePublisher's note\u003c/h2\u003e \u003cp\u003e All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe author declares that this study and/or article has received financial support for publication. This study was funded by the Clinical Medicine Excellent Talent Training Project sponsored by the Hebei Provincial Government (ZF2024181).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.L. performed the experiments and wrote the article. L.H.and X.Z. performed the experiments. M.X. revised the article. Z.W. designed the study and reviewed the article. All authors read and approved the final manuscript as submitted.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJin Mei, Geng Wenjin, Yue Ling, etc Early evaluation of brain function prognosis in children with severe encephalitis using amplitude integrated electroencephalography [J]. Chinese Journal of Physical Medicine and Rehabilitation, 2019, 41 (9): 692-695.\u003c/li\u003e\n \u003cli\u003ePALMAS G,DUKE T.Severe encephalitis: aetiology,management and outcomes over 10 years in a pediatric intensive care unit[J]. Arch Dis Child,2023,108(11):922-928.\u003c/li\u003e\n \u003cli\u003eHON K L,TASNG Y C,CHAN L C,et al.Outcome of Encephalitis in Pediatric Intensive Care Unit[J]. Indian J Pediatr,2016,83(10):1098-103.\u003c/li\u003e\n \u003cli\u003eZHAO J ,WANG Z,LI S,et al.The efficacy of haemoperfusion combined with continuous venovenous haemodiafiltration in the treatment of severe viral encephalitis in children[J]. Ital J Pediatr,2023,49(1):21.\u003c/li\u003e\n \u003cli\u003eLiu Xiaoyan Application of electroencephalogram in pediatric intensive care [J]. Chinese Journal of Pediatric Emergency Medicine, 2018, 25 (12): 907-912.\u003c/li\u003e\n \u003cli\u003e[1] Lin Jie, He Minglian, Zou Yongjie, etc Meta analysis of the diagnostic value of non-invasive brain edema dynamic monitoring instrument for acute brain injury [J]. Chinese Journal of Brain Diseases and Rehabilitation (Electronic Edition), 2020, 10 (3): 132-138. DOI: 10.3877/cma.j.issn.2095-123X.2020.03.002.\u003c/li\u003e\n \u003cli\u003eBRITTON P N,EASTWOOD K,PATERSON B,et al.Consensus guidelines for the investigation and management of encephalitis in adults and children in Australia and New Zealand[J]. Intern Med,2015,45(5):563-576.\u003c/li\u003e\n \u003cli\u003eWang Quan, Qian Suyun Common evaluation methods for children\u0026apos;s consciousness level and brain dysfunction [J]. Chinese Journal of Practical Pediatrics, 2013, 28 (18): 1367-1370.\u003c/li\u003e\n \u003cli\u003eZhu Bichen, Lu Guoping Early Warning Score for Children [J]. Chinese Journal of Practical Pediatrics, 2018, 33 (06): 432-437.\u003c/li\u003e\n \u003cli\u003eChinese Anti Epilepsy Association, EEG and Neurophysiology Branch, Clinical EEG Training Course Writing Group Clinical EEG Training Course [M]. Beijing: People\u0026apos;s Health Press, 2013:235-241.\u003c/li\u003e\n \u003cli\u003eLAX PERICALL M T,TAYLOR E.Family function and its relationship to injury severity and psychiatric outcome in children with acquired brain injury：a systematized review[J]. Dev With Child Neurol,2014,56(1:19-30.DOI:10.1111/dmcn.12237.\u003c/li\u003e\n \u003cli\u003eDE BLAUW D,BRUNING AHL,BUSCH CBE,et al.Epidemiology and Etiology of Severe Childhood Encephalitis in The Netherlands[J]. Pediatr Infect Dis,2020,39(4):267-272.\u003c/li\u003e\n \u003cli\u003eHu Wenjing, Yang Liming, Liao Hongmei, etc Clinical characteristics, prognosis, and related factors analysis of severe viral encephalitis in children [J]. Chinese Journal of Infection Control, 2018, 17 (3): 241-246. DOI: 10.3969/j.issn.1671-9638.2018.03.012.\u003c/li\u003e\n \u003cli\u003eChen Feng, Zhang Furong, Sun Jimin, etc The effect of mild hypothermia on serum and cerebrospinal fluid NSE and S100B protein expression in children with severe viral encephalitis [J]. Journal of Huazhong University of Science and Technology: Medical Edition, 2017, 46 (3): 291-294. DOI: 10.3870/j.issn. 1672-0741. March 10, 2017.\u003c/li\u003e\n \u003cli\u003eLYMPEROPOULOS G, LYMPEROPOULOS P, ALIKARI V, et al. Applications for electrical impedance tomography (EIT) and electrical properties of the human body[J]. Adv Exp Med Biol, \u0026nbsp;2017, 989: 109-117.\u003c/li\u003e\n \u003cli\u003eFAN TH,PREMRAJ L,ROBERTS J,et al.In-Hospital Neurologic Complications, Neuromonitoring, and Long-Term Neurologic Outcomes in Patients With Sepsis:A Systematic Review and Meta-Analysis[J]. Crit Care Med,2024,52(3):452-463.\u003c/li\u003e\n \u003cli\u003eMILSHTEIN N Y,PARET G,REIF S,et al.Acute childhood encephalitis at 2 tertiary care children hospitals in Israel:etiology and clinical characteristics[J]. Pediatr Emerg Care,2016,32(2): 82-86.DOI:10.1097/PEC.0000000000000468.\u003c/li\u003e\n \u003cli\u003eDUYU M,KARAKAYA ALTUN Z,YILDIZ S. Nontraumatic coma in the pediatric intensive care unit: etiology, clinical characteristics and outcome[J]. Turk J Med Sci,2021,51(1):214-223.\u003c/li\u003e\n \u003cli\u003eKHOLIFIA A,RUSMAWATININGTYAS D,MAKRUFARDI F, et al.Factors associated with mortality in intracranial infection patients admitted to pediatric intensive care unit:A retrospective cohort study[J]. Ann Med Surg (Lond),2021,70:102884.\u003c/li\u003e\n \u003cli\u003eHANSEN G,HOCHMAN J,GARNER M,et al.Pediatric early warning score and deteriorating ward patients on high-flow therapy[J]. Pediatr Int,2019,61(3):278-283.\u003c/li\u003e\n \u003cli\u003eLi Huina, An Hong, Gao Jielin, Zhao Yingmian, Wang Xiaoxue The effectiveness of improving early warning scores for children in the diagnosis of viral encephalitis [J]. Journal of Clinical and Pathological Sciences, 2020, 40 (07): 1740-1743.\u003c/li\u003e\n \u003cli\u003eSINGF T D,FUGATE J E,RABINSTEIN A.The spectrum of acute encephalitis: causes,management,and,predictors,of outcome[J]. Neurology,2015,84(4): 359-366.\u003c/li\u003e\n \u003cli\u003eGONG Z,LAO D,HUANG F,et al.Risk Factors and Prognosis in Anti-NMDA Receptor Encephalitis Patients with Disturbance of Consciousness[J]. Patient Relat Outcome Meas,2023,14:181-192.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Disturbance coefficient, Video electroencephalogram, Severe encephalitis in children, Prognosis, Non invasive monitoring","lastPublishedDoi":"10.21203/rs.3.rs-8636447/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8636447/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003eExploring the value of non-invasive brain edema monitoring combined with bedside video electroencephalography in evaluating the short-term prognosis of children with severe encephalitis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis study retrospectively analyzed the clinical data of 136 children diagnosed with severe encephalitis in the Intensive Care Medicine Department of Hebei Children's Hospital from January 2021 to July 2024. According to the prognosis at discharge, patients were divided into a poor prognosis group (54 cases) and a good prognosis group (82 cases). Compare the clinical manifestations, modified GCS scores, early warning scores, clinical indicators monitored after admission, DC values, and video electroencephalography between two groups of children. Logistic regression was used to analyze the risk factors for poor prognosis, and receiver operating characteristic (ROC) curves were used to analyze the predictive value of DC, video EEG, and their combined evaluation of prognosis.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eThere was a statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) in the modified GCS score, early warning score, lactate dehydrogenase, blood ammonia, and abnormal cranial imaging between the two groups of patients. The DC values monitored at admission in the poor prognosis group were significantly lower than those in the good group, with 22 cases (40.7%) having severe EEG abnormalities, and the difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). GCS score, early warning score DC、 Severe abnormal video electroencephalogram is a risk factor for poor prognosis in children with severe encephalitis. The area under the ROC curve for predicting the prognosis of children with severe encephalitis using DC values is 0.734, with an optimal cutoff value of 75, a sensitivity of 74.1%, and a specificity of 63.4%. The area under the ROC curve for predicting prognosis using video electroencephalogram is 0.701, with a sensitivity of 53.7% and specificity of 86.6%. The area under the combined prediction curve is 0.822, with a sensitivity of 77.8% and specificity of 75.6%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eSevere encephalitis is the result of the interaction of multiple factors, and clinical attention should be paid to GCS score, early warning score, DC, and video electroencephalogram. The combined application of DC and bedside video EEG can significantly improve the evaluation efficiency of short-term prognosis in children with severe encephalitis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassification number: \u003c/strong\u003eR725.9\u003c/p\u003e","manuscriptTitle":"The evaluation value of disturbance coefficient combined with bedside continuous video electroencephalogram for the short-term prognosis of severe encephalitis in children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 17:06:11","doi":"10.21203/rs.3.rs-8636447/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-05T12:44:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T08:41:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T04:56:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34398834711414547615623417101413113742","date":"2026-02-14T00:13:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231258856039199131026716400379477199107","date":"2026-02-13T02:54:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T02:30:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-27T05:03:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-24T01:22:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-24T01:22:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2026-01-19T07:56:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"896be5b6-aef8-480c-b125-8694c2c500d7","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:08:51+00:00","versionOfRecord":{"articleIdentity":"rs-8636447","link":"https://doi.org/10.1186/s12887-026-06892-6","journal":{"identity":"bmc-pediatrics","isVorOnly":false,"title":"BMC Pediatrics"},"publishedOn":"2026-04-22 15:59:44","publishedOnDateReadable":"April 22nd, 2026"},"versionCreatedAt":"2026-02-18 17:06:11","video":"","vorDoi":"10.1186/s12887-026-06892-6","vorDoiUrl":"https://doi.org/10.1186/s12887-026-06892-6","workflowStages":[]},"version":"v1","identity":"rs-8636447","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8636447","identity":"rs-8636447","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}