Whole-Brain Permeability Analysis on Admission Improves Prediction of Delayed Cerebral Ischemia Following Aneurysmal Subarachnoid Hemorrhage

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.

Early and delayed blood-brain barrier permeability predicts delayed cerebral ischemia and outcomes following aneurysmal subarachnoid hemorrhage

OBJECTIVES

This study aimed to monitor blood-brain barrier permeability within 24 h and during the delayed cerebral ischemia (DCI) time window (DCITW) spanning 4-14 days after aneurysmal subarachnoid hemorrhage (aSAH) and to investigate its correlation with both DCI occurrence and outcomes at three months.

METHODS

A total of 128 patients were stratified based on the DCI occurrence and three-month modified Rankin scale scores. Comparison of Ktrans at admission (admission Ktrans) and during DCITW (DCITW Ktrans) was conducted between DCI and non-DCI groups, as well as between groups with good and poor outcomes. Changes in Ktrans were also analyzed. Multivariate logistic regression analysis was performed to identify independent predictors of DCI and poor outcomes.

RESULTS

Admission Ktrans (0.58 ± 0.18 vs 0.47 ± 0.12, p = 0.002) and DCITW Ktrans (0.54 ± 0.19 vs 0.41 ± 0.14, p < 0.001) were significantly higher in the DCI group compared with the non-DCI group. Although both were higher in the poor outcome group than the good outcome group, the difference was not statistically significant at admission (0.53 ± 0.18 vs 0.49 ± 0.14, p = 0.198). Ktrans in the non-DCI group (0.47 ± 0.12 vs 0.41 ± 0.14, p = 0.004) and good outcome group (0.49 ± 0.14 vs 0.41 ± 0.14, p < 0.001) decreased significantly from the admission to DCITW. Multivariate analysis identified DCITW Ktrans and admission Ktrans as independent predictors of poor outcomes (OR = 1.73, 95%CI: 1.24-2.43, p = 0.001) and DCI (OR = 1.75, 95%CI: 1.25-2.44, p = 0.001), respectively.

CONCLUSION

Elevated Ktrans at admission is associated with the occurrence of DCI. Continuous monitoring of Ktrans from admission to DCITW can accurately identify reversible and irreversible changes and can predict outcomes at 3 months.

CLINICAL RELEVANCE STATEMENT

Ktrans measured with CT perfusion is a valuable tool for predicting both delayed cerebral ischemia and three-month outcomes following aneurysmal subarachnoid hemorrhage. Monitoring changes in Ktrans from admission to time window of delayed cerebral ischemia can guide treatment and management decisions for aneurysmal subarachnoid hemorrhage patients.

KEY POINTS

• Ktrans measured at admission and during the delayed cerebral ischemia time window (4-14 days) holds distinct clinical significance following aneurysmal subarachnoid hemorrhage. • Admission Ktrans serves as a predictor for delayed cerebral ischemia, while continuous assessment of Ktrans from admission to the delayed cerebral ischemia time window can predict three-month outcomes. • Monitoring Ktrans at different stages improves instrumental in enhancing decision-making and treatment planning for patients with aneurysmal subarachnoid hemorrhage.

Early Metabolic Disruption and Predictive Biomarkers of Delayed-Cerebral Ischemia in Aneurysmal Subarachnoid Hemorrhage

Delayed cerebral ischemia (DCI) following aneurysmal subarachnoid hemorrhage (aSAH) is a major cause of complications and death. Here, we set out to identify high-performance predictive biomarkers of DCI and its underlying metabolic disruptions using metabolomics and lipidomics approaches. This single-center prospective observational study enrolled 61 consecutive patients with severe aSAH; among them, 22 experienced a DCI. Nine patients without aSAH were included as validation controls. Blood and cerebrospinal fluid (CSF) were sampled within the first 24 h after admission. We identified a panel of 20 metabolites that, together, showed high predictive performance for DCI. This panel of metabolites included lactate, cotinine, salicylate, 6 phosphatidylcholines, and 4 sphingomyelins. The interplay of the metabolome and the lipidome found between CSF and plasma in our patients underscores that aSAH and its associated DCI complications can extend beyond cerebral implications, with a peripheral dimension as well. As an illustration, early biological disruptions that might explain the subsequent DCI found systemic hypoxia driven mainly by higher blood lactate, arginine, and proline metabolism likely associated with vascular NO and disrupted ceramide/sphingolipid metabolism. We conclude that targeting early peripheral hypoxia preceding DCI could provide an interesting strategy for the prevention of vascular dysfunction.

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.

Basis and current state of computed tomography perfusion imaging: a review

Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.