Characteristics of paediatric patients with altered conscious state attended by road ambulances in a metropolitan area — An 8 year observational study

Prediction Model for Unfavorable Outcome in Spontaneous Intracerebral Hemorrhage Based on Machine Learning

OBJECTIVE

The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML).

METHODS

Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR).

RESULTS

We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables.

CONCLUSION

The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.

Management of Neurological Emergencies in Children: An Updated Overview

Neurological emergencies account for about one-third of the highest severity codes attributed in emergency pediatric departments. About 75% of children with acute neurological symptoms presents with seizures, headache, or other paroxysmal events. Life-threatening conditions involve a minor proportion of patients (e.g., less than 15% of children with headache and less than 5% of children with febrile seizures). This review highlights updated insights about clinical features, diagnostic workup, and therapeutic management of pediatric neurological emergencies. Particularly, details will be provided about the most recent insights about headache, febrile seizures, status epilepticus, altered levels of consciousness, acute motor impairment, acute movement disorders, and functional disorders, as well as the role of diagnostic tools (e.g., neuroimaging, lumbar puncture, and electroencephalography), in the emergency setting. Moreover, the impact of the current novel coronavirus disease2019 (COVID-19) pandemic on the evaluation of pediatric neurologic emergencies will also be analyzed.