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A Piglet Model of Neonatal Hypoxic-Ischemic Encephalopathy
Published on: May 16, 2015
Prognostic factor identification and model construction in pediatric influenza-associated encephalopathy: A
Yue Hou1, Wenqin Xie1, Xin Liu1
1Department of Pediatrics, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Insights
Identifying risk factors for poor outcomes in pediatric infectious encephalopathy (IAE) is crucial. Elevated blood markers like AST and CK-MB can predict severe outcomes and mortality in children with IAE, guiding early intervention.
Area of Science:
- Pediatric Neurology
- Infectious Diseases
- Biomarkers Research
Background:
- Infectious encephalopathy (IAE) poses a significant risk for poor outcomes in children.
- Identifying early diagnostic and prognostic indicators for IAE is essential for timely intervention.
Purpose of the Study:
- To identify risk factors associated with poor outcomes in pediatric IAE.
- To develop a predictive model for stratifying patients based on outcome severity.
Main Methods:
- Retrospective collection of clinical, laboratory, imaging, and treatment data from pediatric IAE patients.
- Univariate and survival analyses to identify prognostic factors.
- Development of a predictive model using key biomarkers.
Main Results:
- 16 out of 42 children (38.1%) experienced poor outcomes, including 6 deaths (14.3%).
- Poor outcomes correlated with deep coma, structural brain damage on MRI, and EEG abnormalities.
- Elevated CSF IL-10, CSF pro, blood ALT, AST, CK-MB, and LDL-H were significant discriminators.
- AST and CK-MB showed strong predictive value, with CK-MB being an independent mortality risk factor.
Conclusions:
- Elevated biomarkers (CSF IL-10, CSF pro, AST, ALT, CK-MB, LDL-H) at admission are linked to poor outcomes in pediatric IAE.
- Specific cutoffs for AST (≥132 IU/L) and CK-MB (≥136 IU/L) can serve as early warning indicators for severe outcomes.
- Timely recognition and intervention are critical for high-risk pediatric IAE patients.
Background:
To identify risk factors for poor outcomes in IAE and to guide early diagnosis and intervention.
Methods:
We retrospectively collected clinical, laboratory, imaging, treatment, and outcome data of children with IAE. Univariate and survival analyses identified prognostic factors and supported the development of a prediction model.
Results:
Among 42 children with IAE, 16 (38.1 %) had poor outcomes, including 6 (14.3 %) in-hospital deaths. Poor outcomes were associated with deep coma (75 %), structural brain damage in MRI (86.7 %), and EEG abnormalities (100 %). Univariate analysis revealed that CSF IL-10, CSF pro, blood ALT, AST, CK-MB, and LDL-H were significant discriminators between the good outcome and poor outcome groups (all P < 0.001). Subsequent survival analysis showed AST had the best predictive value, while CK-MB was identified as the only independent risk factor of mortality. A predictive model using optimal cutoffs for AST (>132 IU/L) and CK-MB (>136 IU/L) was developed to stratify patients into good outcome, severe sequelae, and death groups, achieving prediction accuracies of 92.3 %, 100 %, and 66.7 %, respectively.
Conclusion:
Our findings indicate that elevated levels of CSF IL-10, CSF pro, and blood AST, ALT, CK-MB, and LDL-H at admission are significantly associated with poor outcomes in pediatric IAE patients. Specifically, AST ≥132 IU/L and CK-MB ≥136 IU/L may serve as early warning indicators of poor outcomes, highlighting the need for timely recognition and intervention in high-risk patients.
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