Unmet Needs and Challenges in Clinical Research of Intracerebral Hemorrhage

Selim, Magdy; Hanley, Daniel F.; Broderick, Joseph P.; Goldstein, Joshua N.; Gregson, Barbara; Falcione, Guido; Gonzales, Nicole R.; Gurol, Edip M; Kersten, Jocelyn; Lewkowicz, Henry; Mendelow, AD; Muehlschlegel, Susanne; Neuman, Richey; Palesch, Yuko Y.; Rosenblum, Michael; Sheth, Kevin N; Singh, Vineeta; Ziai, Wendy; Keep, Richard F.; Aronowski, Jaroslaw; Genstler, Curtis; James, Michael L.; Ratan, Rajiv R.; Sansing, Lauren H; Youd, Anna; Xi, Guohua; Zille, Marietta; Anderson, Craig S.; Awad, Issam A.; Bastings, E.; Bednar, Martin M.; Coon, Alexander L.; Gottesman, Rebecca; Katz, Bryan; Khan, Saima; Koenig, James I.; Koroshetz, Walter J.; Ling, Shari M.; Loftus, Christopher M.; Lockhardt, John; Louis, Thomas A.; Marler, John R.; Moy, Claudia S.; Peña, Carlos; Pollack, Charles V.; Omert, Laurel; Shah, Monica; Shoamanesh, Ashkan; Singer, Michael S.; Steiner, Thorsten; Torbey, Michel T.; Tymianski, Mike; Wakhloo, Ajay K.; Vespa, Paul; Zuccarello, Mario; Zheng, Xiaolin

doi:10.1161/strokeaha.117.019541

Cited by 41 publications

(12 citation statements)

References 28 publications

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“…In contrast to recent advances in interventional treatments of patients with ischemic stroke, beneficial effects of medical treatment and surgical intervention on the mortality and functional outcome of ICH patients were not observed in recent trials [ 4 , 5 ]. Accurate stratification of ICH prognosis is highly desired regardless of the therapeutic options that are available and remains a clinical research priority [ 6 ]. Therefore, several prognostic tools have been proposed for the prediction of mortality and functional outcome in spontaneous ICH [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

Nawabi

Kniep

Elsayed

et al. 2021

Transl. Stroke Res.

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We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional outcome at mRS 2, 3, and 4. Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach. Receiver-operating-characteristic area under the curve (ROC AUC) of the machine learning classifier using image features only was 0.80 (95% CI [0.77; 0.82]) for predicting mRS ≤ 2, 0.80 (95% CI [0.78; 0.81]) for mRS ≤ 3, and 0.79 (95% CI [0.77; 0.80]) for mRS ≤ 4. Trained on survival prediction (mRS ≤ 5), the classifier reached an AUC of 0.80 (95% CI [0.78; 0.82]) which was equivalent to results of the ICH Score. If combined, the integrated model showed a significantly higher AUC of 0.84 (95% CI [0.83; 0.86], P value <0.05). Accordingly, sensitivities were significantly higher at Youden Index maximum cut-offs (77% vs. 74% sensitivity at 76% specificity, P value <0.05). Machine learning–based evaluation of quantitative high-end image features provided the same discriminatory power in predicting functional outcome as multidimensional clinical scoring systems. The integration of conventional scores and image features had synergistic effects with a statistically significant increase in AUC.

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Section: Introductionmentioning

confidence: 99%

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

Nawabi

Kniep

Elsayed

et al. 2021

Transl. Stroke Res.

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“…), along with addressing multiple issues in translational research (use of several insult severities, multiple endpoints, etc.) [ 52 , 53 ]. Thus, these data should be considered in the broader context as a significant failure to find benefit in “confirmatory studies”, and when sufficient data exist, they should be included in meta-analyses to help determine the true benefit of glibenclamide and whether there are certain contexts in which it provides biologically meaningful benefit [ 54 ].…”

Section: Discussionmentioning

confidence: 99%

Glibenclamide does not improve outcome following severe collagenase-induced intracerebral hemorrhage in rats

et al. 2021

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Intracerebral hemorrhage (ICH) is a devastating insult with few effective treatments. Edema and raised intracranial pressure contribute to poor outcome after ICH. Glibenclamide blocks the sulfonylurea 1 transient receptor potential melastatin 4 (Sur1-Trpm4) channel implicated in edema formation. While glibenclamide has been found to improve outcome and reduce mortality in animal models of severe ischemic stroke, in ICH the effects are less clear. In our previous study, we found no benefit after a moderate-sized bleed, while others have reported benefit. Here we tested the hypothesis that glibenclamide may only be effective in severe ICH, where edema is an important contributor to outcome. Glibenclamide (10 μg/kg loading dose, 200 ng/h continuous infusion) was administered 2 hours post-ICH induced by collagenase injection into the striatum of adult rats. A survival period of 24 hours was maintained for experiments 1–3, and 72 hours for experiment 4. Glibenclamide did not affect hematoma volume (~81 μL) or other safety endpoints (e.g., glucose levels), suggesting the drug is safe. However, glibenclamide did not lessen striatal edema (~83% brain water content), ionic dyshomeostasis (Na+, K+), or functional impairment (e.g., neurological deficits (median = 10 out of 14), etc.) at 24 hours. It also did not affect edema at 72 h (~86% brain water content), or overall mortality rates (25% and 29.4% overall in vehicle vs. glibenclamide-treated severe strokes). Furthermore, glibenclamide appears to worsen cytotoxic edema in the peri-hematoma region (cell bodies were 46% larger at 24 h, p = 0.0017), but no effect on cell volume or density was noted elsewhere. Overall, these findings refute our hypothesis, as glibenclamide produced no favorable effects following severe ICH.

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“…The delay of specific antidotes approval 10 has led to the off-label use of PCC for the reversal of oral Xa inhibitors but without showing beneficial effects on hematoma expansion 11 . To date, no medical or surgical clinical trial has improved patient outcome after ICH 12 , therefore, remaining a challenging unsolved clinical and public health problem 13 .…”

Section: Accepted Manuscriptmentioning

confidence: 99%

CM-352 Efficacy in a Mouse Model of Anticoagulant-Associated Intracranial Hemorrhage

et al. 2022

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Background: Intracranial haemorrhage (ICH) is one of the major devastating complications of anticoagulation. Matrix metalloproteinases (MMPs) inhibition has been proposed as a novel pharmacological approach for ICH treatment. Objectives: We evaluated the effects of CM-352 (MMPs-fibrinolysis inhibitor) in an experimental ICH model associated with oral anticoagulants as compared with clinically used prothrombin concentrate complex (PCC). Methods: ICH was induced by collagenase injection into the striatum of WT (C57BL/6J) anticoagulated mice (warfarin or rivaroxaban) and Mmp10 -/- mice. Hematoma volume and neurological deficits were measured 24h later by diaminobenzidine staining and different behavioural test. Circulating plasminogen activator inhibitor-1 (PAI-1) activity and interleukin-6 (IL-6) were measured in plasma samples and local inflammation was assessed by neutrophil infiltration. Finally, fibrinolytic effects of MMP-10 and rivaroxaban were evaluated by thromboelastometry and thrombin-activatable fibrinolysis inhibitor (TAFI) activation assays. Results: Only PCC reduced haemorrhage volume and improved functional outcome in warfarin-ICH, but both, PCC and CM-352 treatments, diminished haemorrhage volume (46%, p<0.01 and 64%, p<0.001, respectively) and ameliorated functional outcome in rivaroxaban-ICH. We further demonstrated that CM-352, but not PCC decreased neutrophil infiltration in the haemorrhage area at 24h. The effect of CM-352 could be related to MMP-10 inhibition since Mmp10-/- mice showed lower haemorrhage volume, better neurological score, reduced IL-6 levels and neutrophil infiltration, and increased PAI-1 after experimental ICH. Finally, we found that CM-352 reduced MMP-10 and rivaroxaban-related fibrinolytic effects in thromboelastometry and TAFI activation. Conclusions: CM-352 treatment, by diminishing MMPs and rivaroxaban-associated fibrinolytic effects, might be a novel antihaemorrhagic strategy for rivaroxaban-associated ICH.

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Unmet Needs and Challenges in Clinical Research of Intracerebral Hemorrhage

Cited by 41 publications

References 28 publications

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

Glibenclamide does not improve outcome following severe collagenase-induced intracerebral hemorrhage in rats

CM-352 Efficacy in a Mouse Model of Anticoagulant-Associated Intracranial Hemorrhage

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