External Validation of an Extreme Gradient Boosting Model for Prediction of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.

Advances in biomarkers for vasospasm - Towards a future blood-based diagnostic test

Objective

Cerebral vasospasm and the resultant delayed cerebral infarction is a significant source of mortality following aneurysmal SAH. Vasospasm is currently detected using invasive or expensive imaging at regular intervals in patients following SAH, thus posing a risk of complications following the procedure and financial burden on these patients. Currently, there is no blood-based test to detect vasospasm.

Methods

PubMed, Web of Science, and Embase databases were systematically searched to retrieve studies related to cerebral vasospasm, aneurysm rupture, and biomarkers. The study search dated from 1997 to 2022. Data from eligible studies was extracted and then summarized.

Results

Out of the 632 citations screened, only 217 abstracts were selected for further review. Out of those, only 59 full text articles met eligibility and another 13 were excluded.

Conclusions

We summarize the current literature on the mechanism of cerebral vasospasm and delayed cerebral ischemia, specifically studies relating to inflammation, and provide a rationale and commentary on a hypothetical future bloodbased test to detect vasospasm. Efforts should be focused on clinical-translational approaches to create such a test to improve treatment timing and prediction of vasospasm to reduce the incidence of delayed cerebral infarction.

Prediction of delayed cerebral ischemia followed aneurysmal subarachnoid hemorrhage. A machine-learning based study

INTRODUCTION

Delayed Cerebral Ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH) that can lead to poor outcomes. Machine learning techniques have shown promise in predicting DCI and improving risk stratification.

METHODS

In this study, we aimed to develop machine learning models to predict the occurrence of DCI in patients with aSAH. Patient data, including various clinical variables and co-factors, were collected. Six different machine learning models, including logistic regression, multilayer perceptron, decision tree, random forest, gradient boosting machine, and extreme gradient boosting (XGB), were trained and evaluated using performance metrics such as accuracy, area under the curve (AUC), precision, recall, and F1 score.

RESULTS

After data augmentation, the random forest model demonstrated the best performance, with an AUC of 0.85. The multilayer perceptron neural network model achieved an accuracy of 0.93 and an F1 score of 0.85, making it the best performing model. The presence of positive clinical vasospasm was identified as the most important feature for predicting DCI.

CONCLUSIONS

Our study highlights the potential of machine learning models in predicting the occurrence of DCI in patients with aSAH. The multilayer perceptron model showed excellent performance, indicating its utility in risk stratification and clinical decision-making. However, further validation and refinement of the models are necessary to ensure their generalizability and applicability in real-world settings. Machine learning techniques have the potential to enhance patient care and improve outcomes in aSAH, but their implementation should be accompanied by careful evaluation and validation.