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

The application of machine learning for treatment selection of unruptured brain arteriovenous malformations: A secondary analysis of the ARUBA trial data

OBJECTIVE

To build a supervised machine learning (ML) model that selects the best first-line treatment strategy for unruptured bAVMs.

METHODS

A Randomized Trial of Unruptured Brain Arteriovenous Malformations (ARUBA) trial data was obtained from the National Institute of Neurological Disorders and Stroke (NINDS). A team of five clinicians examined the demographic, clinical, and radiological details of each patient at baseline and reached a consensus on the best first-line treatment for bAVMs. Their treatment choice was used to train an automated supervised ML (autoML) model to select treatments for bAVMs for the training dataset. The accuracy and AUC of the algorithm in selecting the treatment strategy were measured for the test dataset, and feature importance scores of the included variables were calculated.

RESULTS

Among the 100,000 combinations of supervised ML algorithms and their hyperparameters attempted by autoML, gradient boosting classifier had the best predictive performance with an overall accuracy of 0.74 and an area under the curve (AUC) of 0.88. The treatment-specific accuracies were 0.96, 0.85, 0.84, and 0.82; and AUCs were 0.75, 0.95, 0.80, and 0.88 for medical management, surgery, endovascular embolization, and gamma-knife radiosurgery, respectively. Spetzler-Martin score, followed by eloquent AVM location and AVM size, were the three most important features in determining treatments.

CONCLUSION

ML could reliably select the best first-line treatment strategy for bAVMs as per multidisciplinary expert consensus. This study can be replicated for larger population-based AVM registries, with the inclusion of outcome data, thus helping address the bias involved in the management of unruptured bAVMs.

Machine learning for predicting poor outcomes in aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysis involving 8445 participants

Early prediction of poor outcomes in patients impacted with aneurysmal subarachnoid hemorrhage (aSAH) is crucial for timely intervention and effective management. This systematic review and meta-analysis aimed to evaluate the performance of machine learning (ML) algorithms in predicting poor outcomes in patients with aSAH, assessing their sensitivity, specificity, and other algorithm metrics. A comprehensive search of PubMed, Scopus, Embase, Web of science and Cochrane library conducted to identify eligible studies. We extracted data on sensitivity, specificity, accuracy, precision, F1score and area under the curve (AUC) from the included studies. Out of 2238 studies screened, 12 met our eligibility criteria involving ML approaches in patients with confirmed aSAH. ML algorithms, particularly XGBoost and CatBoost, offer promising performance for predicting poor outcomes in aSAH patients. Meta-analysis was performed on 12 studies resulted in a pooled sensitivity of 0.88 [95 % CI: 0.76-0.94], specificity of 0.78 [95 % CI 0.66-0.86], positive DLR of 3.91 [95 % CI: 2.42-6.30], negative DLR of 0.16 [95 % CI: 0.07-0.34], diagnostic odds ratio of 24.9 [95 % CI: 7.97-77.82], the diagnostic score of 3.21[95 % CI: 2.08-4.35], and the area AUC was 0.82, indicating substantial diagnostic performance. However, conventional LR showed slightly superior predictive function compared to ML algorithms. These findings underscore the potential of ML algorithms to significantly advance the predictability of poor outcomes in patients with aSAH, suggesting that ML can play a critical role in enhancing clinical decision-making.

Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis

BACKGROUND

Delayed cerebral ischemia (DCI) is a primary contributor to death after subarachnoid hemorrhage (SAH), with significant incidence. Therefore, early determination of the risk of DCI is an urgent need. Machine learning (ML) has received much attention in clinical practice. Recently, some studies have attempted to apply ML models for early noninvasive prediction of DCI. However, systematic evidence for its predictive accuracy is still lacking.

OBJECTIVE

The aim of this study was to synthesize the prediction accuracy of ML models for DCI to provide evidence for the development or updating of intelligent detection tools.

METHODS

PubMed, Cochrane, Embase, and Web of Science databases were systematically searched up to May 18, 2023. The risk of bias in the included studies was assessed using PROBAST (Prediction Model Risk of Bias Assessment Tool). During the analysis, we discussed the performance of different models in the training and validation sets.

RESULTS

We finally included 48 studies containing 16,294 patients with SAH and 71 ML models with logistic regression as the main model type. In the training set, the pooled concordance index (C index), sensitivity, and specificity of all the models were 0.786 (95% CI 0.737-0.835), 0.77 (95% CI 0.69-0.84), and 0.83 (95% CI 0.75-0.89), respectively, while those of the logistic regression models were 0.770 (95% CI 0.724-0.817), 0.75 (95% CI 0.67-0.82), and 0.71 (95% CI 0.63-0.78), respectively. In the validation set, the pooled C index, sensitivity, and specificity of all the models were 0.767 (95% CI 0.741-0.793), 0.66 (95% CI 0.53-0.77), and 0.78 (95% CI 0.71-0.84), respectively, while those of the logistic regression models were 0.757 (95% CI 0.715-0.800), 0.59 (95% CI 0.57-0.80), and 0.80 (95% CI 0.71-0.87), respectively.

CONCLUSIONS

ML models appear to have relatively desirable power for early noninvasive prediction of DCI after SAH. However, enhancing the prediction sensitivity of these models is challenging. Therefore, efficient, noninvasive, or minimally invasive low-cost predictors should be further explored in future studies to improve the prediction accuracy of ML models.

TRIAL REGISTRATION

PROSPERO (CRD42023438399); https://tinyurl.com/yfuuudde.

Leveraging machine learning algorithms to forecast delayed cerebral ischemia following subarachnoid hemorrhage: a systematic review and meta-analysis of 5,115 participants

It is feasible to predict delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) using Artificial intelligence (AI) algorithms, which may offer significant improvements in early diagnosis and patient management. This systematic review and meta-analysis evaluate the efficacy of machine learning (ML) in predicting DCI, aiming to integrate complex clinical data to enhance diagnostic accuracy. We searched PubMed, Scopus, Web of science, and Embase databases without restrictions until June 2024, applying PRISMA guidelines. Out of 1498 studies screened, 10 met our eligibility criteria involving ML approaches in patients with confirmed aSAH. The studies employed various ML algorithms and reported differential ML metrics outcomes. Meta-analysis was performed on eight studies, which resulted in a pooled sensitivity of 0.79 [95% CI: 0.63-0.89], specificity of 0.78[95% CI: 0.68-0.85], positive DLR of 3.54 [95% CI: 2.22-5.64] and the negative DLR of 0.28 [95% CI: 0.15-0.52], diagnostic odds ratio of 12.82 [95% CI: 4.66-35.28], the diagnostic score of 2.55 [95% CI: 1.54-3.56], and the area under the curve (AUC) of 0.85. These findings show significant diagnostic accuracy and demonstrate the potential of ML algorithms to significantly improve the predictability of DCI, implying that ML could impart a significant role on improving clinical decision making. However, variability in methodological approaches across studies shows a need for standardization to realize the full benefits of ML in clinical settings.

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.