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BackgroundIntracerebral hemorrhage (ICH) is the deadliest form of stroke. In observational studies, lower serum magnesium has been linked to more hematoma expansion (HE) and intracranial hemorrhage, implying that supplemental magnesium sulfate is a potential acute treatment for patients with ICH and could reduce HE. FAST-MAG (Field Administration of Stroke Therapy - Magnesium) was a clinical trial of magnesium sulfate started prehospital in patients with acute stroke within 2 hours of last known well enrolled. CT was not required prior to enrollment, and several hundred patients with acute ICH were enrolled. In this ancillary analysis, we assessed the effect of magnesium sulfate treatment upon HE in patients with acute ICH.MethodsWe retrospectively analyzed data that were prospectively collected in the FAST-MAG study. Patients received intravenous magnesium sulfate or matched placebo within 2 hours of onset. We compared HE among patients allocated to intravenous magnesium sulfate or placebo with a Mann-Whitney U. We used the same method to compare neurological deficit severity (National Institutes of Health Stroke Scale) and global disability (modified Rankin Scale) at 3 months.ResultsAmong 268 patients with ICH meeting study entry criteria, mean 65.4±13/4 years, 33% were female, and 211 (79%) had a history of hypertension. Initial deficit severities were median (interquartile range) of 4 (3-5) on the Los Angeles Motor Scale in the field and National Institutes of Health Stroke Scale score of 16 (9.5-25.5) early after hospital arrival. Follow-up brain imaging was performed a median of 17.1 (11.3-22.7) hours after first scan. The magnesium and placebo groups did not statistically differ in hematoma volume on arrival, 10.1 (5.6-28.7) versus 12.4 (5.6-28.7) mL (P=0.6), or HE, 2.0 (0.1-7.4) versus 1.5 (-0.2 to 8) mL (P=0.5). There was no difference in functional outcomes (modified Rankin Scale score of 3-6), 59% versus 50% (P=0.5).ConclusionsMagnesium sulfate did not reduce HE or improve functional outcomes at 90 days. A benefit for patients with initial hypomagnesemia was not addressed.RegistrationURL: https://www.Clinicaltrialsgov; Unique identifier: NCT00059332.

Objective: Hematoma expansion (HE) predicts disability and death after acute intracerebral hemorrhage (ICH). Aspirin and anticoagulants have been associated with HE. We tested the hypothesis that P2Y12 inhibitors predict subsequent HE in patients. We explored laboratory measures of P2Y12 inhibition and dual antiplatelet therapy with aspirin (DAPT). Methods: We prospectively identified patients with ICH. Platelet activity was measured with the VerifyNow‐P2Y12 assay. Hematoma volumes for initial and follow‐up CTs were calculated using a validated semi‐automated technique. HE was defined as the difference between hematoma volumes on the initial and follow‐up CT scans. Nonparametric statistics were performed with Kruskal–Wallis H, and correction for multiple comparisons performed with Dunn's test. Results: In 194 patients, 15 (7.7%) were known to take a P2Y12 inhibitor (clopidogrel in all but one). Patients taking a P2Y12 inhibitor had more HE compared to patients not taking a P2Y12 inhibitor (3.5 [1.2–11.9] vs. 0.1 [−0.8–1.4] mL, p = 0.004). Patients taking DAPT experienced the most HE (7.2 [2.6–13.8] vs. 0.0 [−1.0–1.1] mL, p = 0.04). The use of P2Y12 inhibitors was associated with less P2Y12 activity (178 [149–203] vs. 288 [246–319] P2Y12 reaction units, p = 0.005). Interpretation: Patients taking a P2Y12 inhibitor had more HE and less P2Y12 activity. The effect was most pronounced in patients on DAPT, suggesting a synergistic effect of P2Y12 inhibitors and aspirin with respect to HE. Acute reversal of P2Y12 inhibitors in acute ICH requires further study. [ABSTRACT FROM AUTHOR]

Background: Magnesium (Mg) is a neuroprotectant in preclinical models. Lower serum Mg levels have been associated with symptomatic hemorrhagic transformation (HT) in patients with ischemic stroke. Early treatment of acute ischemic stroke with Mg may reduce rates of symptomatic HT. Methods: In this post hoc study of the Field Administration of Stroke Therapy Magnesium (FAST-MAG) trial, 1,245 participants with a diagnosis of cerebral ischemia received 20 g of Mg or placebo initiated in the prehospital setting. Posttreatment serum Mg level was measured for 809 participants. Cases of clinical deterioration, defined as worsening by ≥4 points on the National Institute of Health Stroke Scale (NIHSS), were imaged and evaluated for etiology. Symptomatic HT was defined as deterioration with imaging showing new hemorrhage. Results: Clinical deterioration occurred in 187 and symptomatic HT in 46 of 1,245 cases of cerebral ischemia. Rates of deterioration and symptomatic HT were not significantly lower in those who received Mg (15.7% vs. 14.4%, p = 0.591; 2.8% vs. 4.6%, p = 0.281). In cases where serum Mg level was obtained posttreatment, lower serum Mg level (<1.7 mg/dL) was associated with significantly higher rates of deterioration and symptomatic HT (27.5% vs. 15.5%, p = 0.0261; 11.6% vs. 3.65%, p = 0.00819). Conclusions: Treatment with Mg did not significantly reduce rates of clinical deterioration or symptomatic HT. Future analysis should address whether treatment with Mg could have influenced the subgroup with low serum Mg at baseline. [ABSTRACT FROM AUTHOR]

Introduction: Intracerebral hemorrhage is the deadliest form of stroke. Hematoma expansion, growth of the hematoma between the baseline computed tomography scan and a follow-up computed tomography scan at 24 ± 6 h, predicts long-term disability or death. Recombinant factor VIIa (rFVIIa) has reduced hematoma expansion in previous clinical trials with a variable effect on clinical outcomes, with the greatest impact on hematoma expansion and potential benefit when administered within 2 h of symptom onset. Methods: Factor VIIa for Hemorrhagic Stroke Treatment at Earliest Possible Time (FASTEST, NCT03496883) is a randomized controlled trial that will enroll 860 patients at ∼100 emergency departments and mobile stroke units in five countries. Patients are eligible for enrollment if they have acute intracerebral hemorrhage within 2 h of symptom onset confirmed by computed tomography, a hematoma volume of 2 to 60 mL, no or small volumes of intraventricular hemorrhage, do not take anticoagulant medications or concurrent heparin/heparinoids (antiplatelet medications are permissible), and are not deeply comatose. Enrolled patients will receive rFVIIa 80 µg/kg or placebo intravenously over 2 min. The primary outcome measure is the distribution of the ordinal modified Rankin Scale at 180 days. FASTEST is monitored by a Data Safety Monitoring Board. Safety endpoints include thrombotic events (e.g. myocardial infarction). Human subjects research is monitored by an external Institutional Review Board in participating countries. Discussion: In the US, FASTEST will be first NIH StrokeNet Trial with an Exception from Informed Consent which allows enrollment of non-communicative patients without an immediately identifiable proxy. [ABSTRACT FROM AUTHOR]

Purpose To develop a highly generalizable weakly supervised model to automatically detect and localize image-level intracranial hemorrhage (ICH) by using study-level labels. Materials and Methods In this retrospective study, the proposed model was pretrained on the image-level Radiological Society of North America dataset and fine-tuned on a local dataset by using attention-based bidirectional long short-term memory networks. This local training dataset included 10 699 noncontrast head CT scans in 7469 patients, with ICH study-level labels extracted from radiology reports. Model performance was compared with that of two senior neuroradiologists on 100 random test scans using the McNemar test, and its generalizability was evaluated on an external independent dataset. Results The model achieved a positive predictive value (PPV) of 85.7% (95% CI: 84.0, 87.4) and an area under the receiver operating characteristic curve of 0.96 (95% CI: 0.96, 0.97) on the held-out local test set ( n = 7243, 3721 female) and 89.3% (95% CI: 87.8, 90.7) and 0.96 (95% CI: 0.96, 0.97), respectively, on the external test set ( n = 491, 178 female). For 100 randomly selected samples, the model achieved performance on par with two neuroradiologists, but with a significantly faster ( P < .05) diagnostic time of 5.04 seconds per scan (vs 86 seconds and 22.2 seconds for the two neuroradiologists, respectively). The model's attention weights and heatmaps visually aligned with neuroradiologists' interpretations. Conclusion The proposed model demonstrated high generalizability and high PPVs, offering a valuable tool for expedited ICH detection and prioritization while reducing false-positive interruptions in radiologists' workflows. Keywords: Computer-Aided Diagnosis (CAD), Brain/Brain Stem, Hemorrhage, Convolutional Neural Network (CNN), Transfer Learning Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Akinci D'Antonoli and Rudie in this issue.

During the first COVID surge, multiple changes in nurse staffing and workflows were made to support care delivery in a resource-constrained environment. We hypothesized that there was a higher rate of inpatient falls during the COVID surge. Furthermore, we predicted that an automated predictive analytic algorithm would perform as well as the Johns Hopkins Fall Risk Assessment. A retrospective review of falls for 3 months before and the first 3 months of the first COVID surge was conducted. We determined the total number of falls and the overall fall rate and examined the distribution of scores and accuracy of fall predictive models for both groups. There was a statistically significant increase in fall rate during the first 3 months of the COVID surge compared with the 3 prior months (2.48/1000 patient-days vs 1.89/1000 patient-days respectively; P = .041). The Johns Hopkins instrument had a greater sensitivity of 78.9% compared with 57.0% for the predictive analytic model. Specificity and accuracy of the predictive analytic model were higher than the Johns Hopkins instrument (71.3% vs 54.1% and 71.2% vs 54.3%, respectively). These findings suggest that the automated predictive analytic model could be used in a resource-constrained environment to accurately classify patients' risk of fall., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)