Subarachnoid hemorrhage in ten questions

An update of predictors of cerebral infarction after aneurysmal subarachnoid hemorrhage: systematic review and meta-analysis

INTRODUCTION

Evidence about predictors of poor outcomes such as cerebral infarction (CI) after aneurysmal subarachnoid hemorrhage (aSAH) has not been fully elucidated.

EVIDENCE ACQUISITION

We performed a systematic review and meta-analysis on studies with adults with aSAH considering RCT and non-RCT, prospective, and retrospective cohort studies describing clinical, imaging as well as angiographic studies in patients with aSAH.

EVIDENCE SYNTHESIS

After reviewing the complete text, 11 studies were considered eligible, out of which four were ruled out. Degree of clinical severity was the most predictive factor with a higher degree at the presentation on different severity scales being associated with a statistically significant increasing the risk of suffering a CI following aSAH (OR 2.49 [95% CI 1.38-4.49] P=0.0003). Aneurysm size increased the risk of CI (OR 1.49 [95% CI 1.20-1.85] P=0.0003; I²=4%). In six studies analyzed, it was found that an important factor for the subsequent development of CI is vasospasm (OR 7.62 [2.19, 26.54], P=0.0001).

CONCLUSIONS

The development of vasospasm is a risk factor for CI development after aSAH. In our review, three factors were associated with an increased risk of CI: clinical severity at presentation, vasospasm, and aneurysm size. The major limitation of this meta-analysis is that included studies were conducted retrospectively or were post hoc analyses of a prospective trial.

Can novel CT-and MR-based neuroimaging biomarkers further improve the etiological diagnosis of lobar intra-cerebral hemorrhage?

Lobar hematomas represent around half of all supratentorial hemorrhages and have high mortality and morbidity. Their management depends on the underlying cause. Apart from local causes such as vascular malformation, which are rare and can usually be easily excluded thanks to imaging, the vast majority of lobar hematomas equally frequently result from either hypertensive arteriolopathy (HA) or cerebral amyloid angiopathy (CAA). Distinguishing between CAA and HA is important for prognostication (risk of recurrence nearly sevenfold higher in the former), for decision-making regarding, e.g., antithrombotic therapies (for other indications) and for clinical trials of new therapies. Currently, a non-invasive diagnosis of probable CAA can be made using the MR-based modified Boston criteria, which have excellent specificity but moderate sensitivity against histopathological reference, leading to the clinically largely irrelevant diagnosis of "possible CAA". Furthermore, the Boston criteria cannot be applied when both lobar and deep MRI hemorrhagic markers are present, a not uncommon situation. Here we propose to test whether new CT and MR-based imaging biomarkers, namely finger-like projections of the hematoma and adjacent subarachnoid hemorrhage on acute-stage CT or MRI, and remote punctate diffusion-weighted imaging ischemic lesions on acute or subacute-stage MRI, have the potential to improve the performance of the Boston criteria. Furthermore, we also propose to test whether clinical-radiological biomarkers may also allow a positive diagnosis of HA to be made in lobar hematomas, which, if feasible, would not only further reduce the prevalence of "possible CAA" but also permit a diagnosis of HA and/or CAA to be made in the presence of mixed deep and lobar MRI hemorrhagic markers.

DSA Image Analysis of Clinical Features and Nursing Care of Cerebral Aneurysm Patients Based on the Deep Learning Algorithm

OBJECTIVE

A deep learning algorithm was developed for automatic detection and localization of intracranial aneurysms in DSA, and its clinical characteristics were analyzed, and targeted nursing measures were formulated.

METHODS

Using a retrospective multicenter study method based on radiology reports, DSA images of aneurysms were randomly divided into 75 cases in the training set, 20 cases in the internal test set, and 35 cases in the external test set. Using a computer-aided detection method based on the three-dimensional U-Net (3D U-Net), after preprocessing DSA images, automatic segmentation of intracranial blood vessels is performed to obtain regions of interest, and based on the segmentation results, physicians' annotations are introduced. The 3D U-Net network model is trained and adjusted, and the obtained model is used to automatically detect the cerebral aneurysm area.

RESULTS

Fivefold cross-validation was used for the training set and the internal test set, and a sensitivity of (94.4 ± 1.1)% was obtained. Automatic detection of aneurysms was performed on the external test set, and the average false positive rate was 0.86 FPs/case (false positives/case). The resulting sensitivity was 82.9%. The classification comparison of external test sets showed that the sensitivity of the method for detecting aneurysms with sizes of 5.00~<10.00 mm and ≥10.00 mm (88.2% and 100.0%) was higher than that for aneurysms with sizes of <3.00 mm and 3.00~<5.00 mm (50.0% and 72.7%). The sensitivity of patients aged 50-60 years and >60 years (90.0% and 87.5%) was higher than that of patients aged <50 years (66.7%), and there was little difference between different genders (84.6% in males and 81.8% in females).

CONCLUSION

The deep learning algorithm has high diagnostic performance in detecting intracranial aneurysms, which is verified by external datasets.

Incidental Unruptured Intracranial Aneurysms Do Not Impact Outcome in Patients With Acute Cerebral Infarction

Background: This study was to examine the patients with acute cerebral infarction (ACI) treated at a single center over 9 years and who underwent Unruptured intracranial aneurysm (UIA) screening by three-dimensional time-of-flight magnetic resonance angiography (3D-TOF-MRA), and to explore the factors associated with outcomes.

Methods: The outcome was the modified Rankin scale (mRS) score at 90 days after stroke onset. The outcome was classified into a good outcome (mRS score of 0–2 points) and poor outcome (mRS score of 3–6 points).

Results: UIAs were found in 260 (6.5%) of 4,033 patients with ACI; 2,543 (63.1%) had a good outcome, and 1,490 (36.9%) had a poor outcome. There was no difference in outcomes between the two groups (P = 0.785). The multivariable analysis showed that age (OR = 1.009, 95%CI: 1.003–1.014, P = 0.003), diabetes (OR = 1.179, 95%CI: 1.035–1.342, P = 0.013), ischemic stroke history (OR = 1.451, 95%CI: 1.256–1.677, P < 0.001), and baseline NIHSS score (OR = 1.034, 95%CI: 1.018–1.050, P < 0.001) were independently associated with the 90-day outcomes in patients with ACI. The presence of incidental UIA was not associated with outcomes after ACI.

Conclusions: Age, diabetes, ischemic stroke history, and baseline NIHSS score were independently associated with the early outcomes of patients with ACI.

Intracranial hypertension in patients with aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis

Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating and life-threatening condition with high mortality and morbidity. Even though there is an association with intracranial pressure (ICP) raise and aSAH, there is a lack of recommendations regarding the indications for ICP monitoring in patients with aSAH. Defining what patients are at a higher risk to develop intracranial hypertension and its role in the functional outcome and mortality in patients with aSAH will be the purpose of the following systematic review and meta-analysis. The primary endpoint is to determine the prevalence and impact on mortality of ICP in patients with aSAH. Secondary endpoints aim to describe the variables related to the development of ICP and the relationship between traumatic and aneurysmal etiology of intracranial hypertension. PubMed, Embase, Central Cochrane Registry of Controlled Trials, and research meeting abstracts were searched up to August 2019 for studies that performed ICP monitoring, assessed the prevalence of intracranial hypertension and the mortality, in adults. Newcastle Ottawa scale (NOS) was used to assess study quality. The statistical analysis was performed using the Mantel-Haenszel methodology for the prevalence and mortality of intracranial hypertension for reasons with a randomized effect analysis model. Heterogeneity was assessed by I². A total of 110 bibliographic citations were identified, 20 were considered potentially eligible, and after a review of the full text, 12 studies were considered eligible and 5 met the inclusion criteria for this review. One study obtained 7 points in the NOS, another obtained 6 points, and the rest obtained 5 points. Five studies were chosen for the final analysis, involving 793 patients. The rate of intracranial hypertension secondary to aSAH was 70.69% (95% CI 56.79-82.84%) showing high heterogeneity (I² = 92.48%, p = < 0.0001). The results of the meta-analysis of mortality rate associated with intracranial hypertension after aSAH found a total of four studies, which involved 385 patients. The mortality rate was 30.3% (95% CI: 14.79-48.57%). Heterogeneity was statistically significant (I² = 90.36%; p value for heterogeneity < 0.001). We found that in several studies, they reported that a high degree of clinical severity scale (Hunt and Hess or WNFS) and tomographic (Fisher) were significantly correlated with the increase in ICP above 20 mmHg (P < 0.05). The interpretation of the results could be underestimated for the design heterogeneity of the included studies. New protocols establishing the indications for ICP monitoring in aSAH are needed. Given the high heterogeneity of the studies included, we cannot provide clinical recommendations regarding this issue.

Unruptured intracranial aneurysms: An updated review of current concepts for risk factors, detection and management

The management of patients with unruptured intracranial aneurysms (UIAs) is a complex clinical challenge and constitutes an immense field of research. While a preponderant proportion of these aneurysms never rupture, the consequences of such an event are severe and represent an important healthcare problem. To date, however, the natural history of UIAs is not completely understood and there is no accurate means to discriminate the UIAs that will rupture from those that will not. Yet, a good understanding of the recent evidence and future perspectives is needed when advising a patient with IA to tailor any information to the given patient's level of risk and psychoaffective status. Thus, this review addresses the current concepts of epidemiology, risk factors, detection and management of UIAs.