Cerebralcavernous malformations (CCMs) are vascular lesions composed of low-flow, cluster-organized capillaries that account for 5%–10% of total cerebral vascular pathologies.1Even if a precise measure is difficult, since the majority are clinically silent, the estimated annual incidence in the general population is between 0.4% and 1.0%2. Up to 40% are discovered incidentally with an increasing trend over time,3so follow-up timing and monitoring strategies have a fundamental role in the clinical and surgical management of these patients.
Although histologically benign in nature, these lesions are prone to bleeding rupture with an estimated rate of 2%–6% per year.4When located in eloquent areas, ruptures are associated with significant morbidity and mortality.5,6Factors associated with bleeding are not completely understood, but previous studies have found that a single, large infratentorial lesion is most commonly associated with a symptomatic CCM7and that a previous history of hemorrhages and associated developmental venous anomaly increase the risk of rupture.8,9
Notably, several drugs, commonly used for different pathologies, have been studied to assess their role in the clinical and radiological evolution of CCMs. Antithrombotic medications have been associated with a reduced risk of bleeding in different studies,1,3,10,11thought to be due to the inhibition of the inflammatory mechanism causing rupture. Beta-blockers, specifically propranolol, have been proposed to have a therapeutic effect in causing regression of the CCM and stopping recurrent bleeding.12A randomized controlled trial (RCT) was initiated on the use of propranolol for familial CCMs,13but recent studies have found conflicting results regarding a protective role of propranolol in sporadic CCMs.1,14Statins have well-known vessel wall stabilization effects, and a synergistic protective effect has been found when combined with antithrombotic drugs15but not when used alone.1,11,14
Indeed, the role of hemorrhagic factors in general, such as renal or liver disease, labile international normalized ratio (INR), prior major bleeding or predisposition to bleeding, is also not clear. The aim of this study was to evaluate possible predictive factors for bleeding, in particular regarding medications and already validated hemorrhagic scales such as the HAS-BLED (hypertension, abnormal renal/liver function, stroke, bleeding history or predisposition, labile INR, elderly, drugs/alcohol concomitantly).16
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这是一个全国多中心case-con nonfundedtrol study with retrospective data collection. The minimum follow-up time was 28 days. The study was approved by the local ethics committee. Inclusion criteria were patients older than 18 years with a previous or new diagnosis of CCM, including single, multiple, and familial CCMs, based on neuroimaging findings.
We collected data on patient demographics (sex and age), CCM characteristics (median time from diagnosis, number, Zabramski type, familial CCM mutations, anatomical lesion site, side, symptomatic lesions, and epilepsy), major cardiovascular events (MACE), alcohol or smoking use, comorbidities (diabetes, obesity and BMI, hypertension, and menopause), HAS-BLED score for hemorrhagic risk stratification (a score that estimates the risk of major bleeding for patients on anticoagulation therapy to assess risks and benefits in atrial fibrillation care), modified Rankin Scale (mRS) and Glasgow Outcome Scale (GOS) scores, and surgical treatment.
For MACE, we included history of stroke, acute myocardial infarction, cardiac arrhythmias, deep vein thrombosis, and pulmonary embolism. We included current and former smokers (defined as adults who smoked at least 100 cigarettes in his or her lifetime but who had quit smoking at the time of the interview). We considered alcohol use disorders as defined by criteria in theDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition.
Volumes were measured by tracing areas of the T2-hypointense hemosiderin ring on consecutive MRI slices. Lesion volumes were divided into the following quartiles: < 11.9 mm3, 11.9–79 mm3, 80–299 mm3, and ≥ 300 mm3.
All medications (antiplatelets, anticoagulants, beta-blockers, autonomic nervous system–acting drugs, central nervous system–acting drugs, cardiovascular agents, smooth muscle–acting agents, lipid modifying/antigout agents, antirheumatic drugs, endocrine drugs, chemotherapy drugs, and others) were collected.
Bleeding was defined as a hemorrhage in the extracapsular zone with a volumetric increase of the lesion of at least 20% on MRI. Size was measured as the maximum diameter including surrounding hemosiderin on T2-weighted 1.5T MRI.
Statistical Analysis
Potential differences in the recorded clinical characteristics among patients with bleeding versus nonbleeding CCMs were first evaluated using the chi-square test for categorical variables and the t-test and Kruskal-Wallis test for normally distributed and nonnormally distributed continuous variables, respectively, The Shapiro-Wilk test was used to assess the distribution of the continuous variables.
The potential independent predictors of bleeding CCMs were then evaluated using multivariate logistic regression. Covariates were selected for inclusion in the final model using a stepwise forward process with the following inclusion criteria: clinical relevance, p < 0.15 on univariate analysis, hypertension, and antiplatelet and beta-blocker use. Lesion volume was included in the analyses both in its original (continuous) form and after categorization, exploring different possible cutoffs. Quintiles and quartiles of lesion volumes were separately evaluated in two different models (with all other covariates remaining stable), with no significant differences. Given the higher R2of the model including quartiles, quartiles were retained in the final model, and four categories were identified (< 11.9 mm3, 11.9–79 mm3, 80–299 mm3, and ≥ 300 mm3). A minimum events-to-variable ratio of 10 was maintained in multivariate modeling to avoid overfitting. The goodness-of-fit was checked using the Hosmer-Lemeshow test, and the predictive power assessed through C-statistics (area under the receiving operating characteristic [ROC] curve [AUC]).
Additionally, to estimate the potential of increasing lesion volumes to predict bleeding, we computed the sensitivity, specificity, positive and negative predictive values, and the AUC for each quartile of lesion volume; 95% CIs were computed according to the efficient-score method (corrected for continuity) described by Newcombe.17Statistical significance was defined as a two-sided p value < 0.05, and all analyses were carried out using Stata version 13.1 (StataCorp).
Results
Clinical and radiological data of 257 patients were collected. Sixty-five (25.3%) patients presented with a bleeding CCM. All results are reported inTables 1and2.
Characteristics in patients with bleeding versus nonbleeding CCMs
| Variable | Overall Sample | Bleeding CCM | Nonbleeding CCM | p Value* |
|---|---|---|---|---|
| No. of patients | 257 | 65 | 192 | |
| Male sex, % | 46.7 | 46.2 | 46.9 | 0.9 |
| Mean age, yrs | 43.4 | 37.4 | 45.4 | 0.004 |
| Median time from diagnosis to op, mos (IQR) | 16.6 (52.1) | 12.2 (33.3) | 16.6 (70.9) | 0.4 |
| Familial CCM mutations, % | ||||
| CCM1 | 5.9 | 1.5 | 7.3 | 0.09 |
| CCM2 | 0.0 | 0.0 | 0.0 | |
| CCM3 | 0.0 | 0.0 | 0.0 | |
| Smoking status, % | ||||
| Current | 7.1 | 9.2 | 6.3 | 0.02 |
| Former* | 8.6 | 1.6 | 11.1 | 0.2 |
| Never | 84.3 | 89.2 | 82.6 | 0.4 |
| Alcohol use, % | 2.0 | 3.1 | 1.6 | 0.5 |
| Diabetes, % | 3.9 | 4.6 | 3.7 | 0.7 |
| Hypertension, % | 25.8 | 9.2 | 31.4 | <0.001 |
| Mean BMI (SD) | 25.3 (3.4) | 25.1 (3.5) | 25.4 (3.4) | 0.6 |
| Menopause status, % (n = 137/35/102)† | 24.8 | 14.3 | 28.4 | 0.10 |
| History of MACE, % (n = 78/4/74)† | 51.3 | 100 | 48.7 | 0.045 |
| MACE type, % (n = 40/4/36)† | ||||
| Stroke | 5.0 | 0.0 | 5.6 | |
| AMI | 2.5 | 0.0 | 2.8 | |
| Arrhythmias | 22.5 | 0.0 | 25.0 | |
| DVT/PE | 12.5 | 50.0 | 8.3 | 0.02 |
| Other | 57.5 | 50.0 | 58.3 | 0.8 |
| Antiplatelet drug use, % | 16.8 | 4.6 | 20.9 | 0.002 |
| Anticoagulant drug use, % | ||||
| None | 97.7 | 98.5 | 97.4 | 0.9 |
| DOAC | 1.5 | 0.0 | 2.1 | 0.4 |
| NOAC | 0.8 | 1.5 | 0.5 | 0.9 |
| Beta-blocker use, % | 14.1 | 4.6 | 17.3 | 0.011 |
| Other pharmacological treatment, % | ||||
| ANS-acting drugs | 0.0 | 0.0 | 0.0 | |
| CNS-acting drugs | 29.5 | 50.0 | 28.4 | 0.4 |
| Cardiovascular agents | 67.9 | 50.0 | 68.9 | 0.4 |
| Smooth muscle–acting agents | 6.4 | 25.0 | 5.4 | 0.12 |
| Lipid modifying/antigout agents | 11.5 | 25.0 | 10.8 | 0.3 |
| Antirheumatic drugs | 12.8 | 0.0 | 13.5 | 0.4 |
| Endocrine drugs | 20.5 | 50.0 | 18.9 | 0.13 |
| Chemotherapy drugs | 0.0 | 0.0 | 0.0 | — |
| Other drug classes | 25.6 | 50.0 | 24.3 | 0.3 |
| Zabramski classification, % (n = 177/61/116)† | ||||
| Accidental | 1.1 | 1.6 | 0.9 | 0.9 |
| Type I | 50.3 | 68.9 | 40.5 | <0.001 |
| Type II | 39.5 | 26.2 | 46.5 | 0.009 |
| Type III | 2.3 | 1.6 | 2.6 | 0.7 |
| Type IV | 6.8 | 1.6 | 9.5 | 0.046 |
| Presence of multiple lesions, % | 37.5 | 20.0 | 43.5 | 0.001 |
| Anatomic lesion site, % (n = 253/65/188)† | 0.8 | |||
| Frontal | 30.0 | 29.2 | 30.3 | |
| Parietal | 11.5 | 10.8 | 11.7 | |
| Temporal | 21.3 | 16.9 | 22.9 | |
| Insular | 1.6 | 1.5 | 1.6 | |
| Occipital | 7.1 | 4.6 | 8.0 | |
| Brainstem | 12.7 | 16.9 | 11.2 | |
| Cerebellum | 12.3 | 16.9 | 10.6 | |
| Other | 3.5 | 3.2 | 3.7 | |
| Lesion side, % | 0.3 | |||
| Midline | 16.8 | 18.5 | 16.2 | |
| Rt | 32.8 | 24.6 | 35.6 | |
| Lt | 50.4 | 56.9 | 48.2 | |
| Lesion vol (n = 230/60/170)† | ||||
| Median vol, mm3(IQR) | 800 (2881) | 1050 (4560) | 523 (2000) | <0.001 |
| By vol quartile, % | ||||
| <11.9 mm3 | 25.2 | 10.0 | 30.6 | 0.002 |
| 11.9–79 mm3 | 24.4 | 25.0 | 24.1 | 0.9 |
| 80–299 mm3 | 24.8 | 28.3 | 23.5 | 0.5 |
| ≥300 mm3 | 25.6 | 36.7 | 21.8 | 0.02 |
| Symptomatic lesion, % | 83.6 | 98.5 | 78.5 | <0.001 |
| Epilepsy, % | 34.8 | 49.2 | 29.8 | 0.005 |
| HAS-BLED score, % | 0.13 | |||
| 0 | 57.0 | 69.2 | 52.9 | |
| 1 | 23.1 | 23.1 | 23.0 | |
| 2 | 11.3 | 3.1 | 14.1 | |
| 3 | 6.3 | 4.6 | 6.8 | |
| 4 | 1.5 | 0.0 | 2.1 | |
| ≥5 | 0.8 | 0.0 | 1.0 | |
| GOS score, % | 0.5 | |||
| 1 | 0.8 | 1.5 | 0.5 | |
| 2 | 0.0 | 0.0 | 0.0 | |
| 3 | 12.5 | 16.9 | 11.0 | |
| 4 | 16.0 | 15.4 | 16.2 | |
| 5 | 70.7 | 66.2 | 72.3 | |
| mRS score, % | 0.4 | |||
| 0 | 46.1 | 36.9 | 49.2 | |
| 1 | 36.7 | 38.5 | 36.1 | |
| 2 | 8.6 | 13.8 | 6.8 | |
| 3 | 6.3 | 6.2 | 6.3 | |
| 4 | 0.8 | 1.5 | 0.5 | |
| 5 | 0.8 | 1.5 | 0.5 | |
| 6 | 0.8 | 1.5 | 0.5 | |
| 治疗ed w/ surgery, % | 71.6 | 89.2 | 65.6 | <0.001 |
AMI =急性心肌梗塞;ANS =自主nervous system; CNS = central nervous system; DOAC = direct-acting oral anticoagulants; DVT = deep vein thrombosis; NOAC = non–vitamin K antagonist oral anticoagulants; PE = pulmonary embolism.
Defined as an adult who has smoked at least 100 cigarettes in his or her lifetime but who had quit smoking at the time of interview.
Values expressed as n indicate the number of overall, bleeding CCM, and nonbleeding CCM patients with data for the variable, respectively.
Logistic regression model evaluating the potential predictors of bleeding CCMs
| Bleeding, % | Adjusted OR (95% CI) | p Value | |
|---|---|---|---|
| Age, 10-yr increase | 0.91 (0.74–1.11) | 0.3 | |
| Hypertension | |||
| No | 31.1 | 1 (ref) | |
| Yes | 9.1 | 0.42 (0.11–1.54) | 0.2 |
| Diabetes | |||
| No | 25.2 | 1 (ref) | |
| Yes | 30.0 | 1.38 (0.23–8.50) | 0.7 |
| Antiplatelet drug use | |||
| No | 29.1 | 1 (ref) | |
| Yes | 7.0 | 0.39 (0.07–2.12) | 0.3 |
| Beta-blocker drug use | |||
| No | 28.2 | 1 (ref) | |
| Yes | 8.3 | 1.24 (0.60–2.57) | 0.6 |
| Lesion vol* | |||
| Model A: by quartile | |||
| <11.9 mm3 | 10.3 | 1 (ref) | |
| 11.9–79 mm3 | 26.8 | 2.82 (0.95–8.37) | 0.06 |
| 80–299 mm3 | 29.8 | 2.09 (0.71–6.17) | 0.2 |
| ≥300 mm3 | 37.3 | 3.11 (1.09–8.86) | 0.034 |
| Model B: 10-mm3increase | 1.00 (0.99–1.00) | 0.4 |
原始的百分比指帕特的比例ients with bleeding lesions in each category of exposed and unexposed subjects (e.g., the percentage of bleeding lesions among those with and without hypertension). The final model is based on 230 observations, with 60 successes.
Two separate models were fit. Model A included lesion volume categorized into quartiles. Model B included the same variable in its original (continuous) form, with all other covariates remaining stable. Model A: AUC 0.70; Hosmer-Lemeshow test for goodness-of-fit, p = 0.99. Model B: AUC 0.66; Hosmer-Lemeshow test for goodness-of-fit, p = 0.99.
Demographic Characteristics of the Population
Male patients represented 46.7% of the overall population (46.2% in the bleeding group and 46.9% in the nonbleeding group). The average age was 43.4 years (range 18–68 years) in the overall population (37.4 years in the bleeding group and 45.4 years in the nonbleeding group, p = 0.004), The mean follow-up for the nonbleeding group was 1329 days (3.6 years, range 30–8136 days) and that for the bleeding group was 1442 days (3.9 years, range 28–8124 days).
Clinical Risk Factors for CCM Bleeding
CCM1mutation was present in 5.9% of patients (1.5% in the bleeding group and in 7.3% in the nonbleeding group). Results regarding smoking history and alcohol use are reported inTable 1. Diabetes was present in 3.9% of patients (4.6% in the bleeding group and 3.7% in the nonbleeding group). Hypertension was present in 25.8% of patients (9.2% in the bleeding group and 31.4% in the nonbleeding group, p < 0.0001). Results regarding BMI, menopause status, MACE are reported inTable 1.
Pharmacological Risk Factors for CCM Bleeding
Antiplatelets were used in 16.8% of all patients (4.6% in the bleeding group and 20.9% in the nonbleeding group, p = 0.0002), while only 2.3% of patients took anticoagulants (direct acting oral anticoagulants or non–vitamin K oral anticoagulants) (1.5% in the bleeding group and 2.6% in the nonbleeding group). Beta-blockers were used in 16.8% of patients (4.6% in the bleeding group and 17.3% in the nonbleeding group, p = 0.011). Results of other pharmacological treatments are reported inTable 1.
Clinical Presentation and Outcome After CCM Bleeding
In all patients, the most common Zabramski types were I (50.3%) and II (39.5%): 68.9% and 26.2%, respectively, in the bleeding group and 26.2% and 46.5%, respectively, in the nonbleeding group (seeTable 1for further details). Multiple lesions were reported in 37.5% of patients (20.0% in the bleeding group and 43.5% in the nonbleeding group, p = 0.001). Results regarding anatomical site and side are reported inTable 1. The overall median volume was 800 mm3. Volume quartiles are reported inTable 1.
Epilepsy was reported in 34.8% of patients (49.2% in the bleeding group and 29.8% in the nonbleeding group). The HAS-BLED score was 0 or 1 (low bleeding risk) in most patients (57.0% and 23.1%, respectively) with the same trend in patients in the bleeding and nonbleeding groups (Table 1). Most patients (70.7%) had a favorable outcome with a GOS score of 5, with the same trend in the bleeding (66.2%) and in the nonbleeding (72.3%) groups. Similar results were recorded for the mRS score (Table 1). Surgery was performed in 71.6% of patients (89.2% in the bleeding group and 65.6% in the nonbleeding group).
Potential Predictors of Bleeding
We performed a logistic regression model evaluating the potential predictors of bleeding (Table 2). Results showed that the only statistically significant predictor for bleeding was volume ≥ 300 mm3. We also evaluated the diagnostic accuracy of each quartile of lesion volume to predict bleeding (Table 3,Fig. 1).
Diagnostic accuracy of each quartile of lesion volume to predict bleeding
| Vol Quartile | AUC (95% CI) |
|---|---|
| <11.9 mm3 | 0.40 (0.35–0.45) |
| 11.9–79 mm3 | 0.50 (0.44–0.57) |
| 80–299 mm3 | 0.52 (0.46–0.59) |
| ≥300 mm3 | 0.57 (0.51–0.64) |
Lesion volume ≥ 300 mm3: sensitivity: 36.7% (95% CI 24.6%–50.0%); specificity: 78.2% (95% CI 71.3%–84.2%); positive predictive value: 37.3% (95% CI 25.0%–50.9%); negative predictive value: 77.8% (95% CI 70.8%–83.8%).
ROC curves showing the diagnostic performance of each lesion volume in predicting lesion bleeding. Ref. = reference.
When translating these findings into diagnostic models, all lesion volumes had similarly limited diagnostic accuracy in predicting bleeding (AUC 0.40, 95% CI 0.35–0.45; AUC 0.50, 95% CI 0.44–0.57; AUC 0.52, 95% CI 0.46–0.59; and AUC 0.57, 95% CI 0.51–0.64) for lesions < 11.9 mm3, 11.9–79 mm3, 80–299 mm3, and ≥ 300 mm3, respectively).
When we explored the diagnostic accuracy of the different volume thresholds, lesions ≥ 300 mm3showed a limited sensitivity (36.7%, 95% CI 24.6–50.1) but a high specificity (78.2% (95% CI 71.3%–84.2%), with an AUC of 0.57 (95% CI 0.51–0.64).
Discussion
For patients with CCMs, the knowledge of risk factors for bleeding and the possibility of positively influencing them is particularly crucial. No pharmacological treatment is at present available to inhibit the formation of new malformations, to stabilize the existing ones, and to stop their progression. To date, the standard of care is represented by treatment of CCM-associated clinical manifestations, such as headache and epilepsy, and consists of antiepileptic drugs or drugs for recurrent headache.13,18Neurosurgical excision is considered in patients with intractable seizures, recurrent hemorrhage, or mass effect. Risk factor assessment therefore represents an important issue in decision-making in patients with asymptomatic lesions. The 5-year risk of intracerebral hemorrhage in individuals with CCMs ranges from 3.8% to 30.8%.13
Results of our study demonstrated that age, diabetes, and nidus volume ≥ 300 mm3are possible potential predictors of bleeding, while a history of hypertension and use of antiplatelet and beta-blocker agents could have a protective effect. However, logistic regression analysis confirmed a predictive role only for lesion volume, most likely because some risk factors lose sensitivity due to the relatively small sample size examined.
These results are similar to those recently published by Rauscher et al.,19发现没有一个可以改变的血管risk factors showed a strong indication for influencing hemorrhage risk. Their findings may only suggest a more aggressive course in patients with active nicotine abuse or diabetes.
Beta-blockers, more specifically, propranolol, have already been studied as a potential medical treatment for CCMs.12Propranolol use in fact has been shown in RCTs to have a positive effect for the treatment of infantile hemangiomas, another common vascular lesion affecting the skin.20–23
Common precursor cells for propranolol-sensitive vascular tumors are CD15-positive cells that are usually found in the placental vessels.24The assumption of beta-blockers might then stabilize the vascular lesions, although there might be some other factors and drugs implied in the underlying mechanism. A recent randomized, open-label, blinded-endpoint, phase 2 pilot trial on symptomatic familial CCMs demonstrated that propranolol was safe and well tolerated in this population.25Propranolol might be beneficial for reducing the incidence of clinical events in individuals with symptomatic familial CCMs and might also reduce the number of new CCMs over 2 years, although the trial was not designed to be adequately powered to investigate efficacy.13However, the mechanism of action of propranolol for CCMs remains poorly understood.
This molecule has a pleiotropic effect on vascular permeability and angiogenesis and was found to rescue the function of the endothelium and to reduce de novo CCM formation in preclinical models, although propranolol did not significantly reduce the incidence of intracerebral hemorrhage in murine models.13,26
Our study also showed a potential protective effect of antiplatelet agents in the univariate analysis. This is in agreement with the report of Schneble et al., which found that long-term antithrombotic treatment with antiplatelet drugs or warfarin did not increase the frequency of CCM-related hemorrhage in their prospective cohort study of 87 patients.27Moreover, in their systematic review and meta-analysis of 1342 patients from 6 cohort studies, Zuurbier et al. reported that antithrombotic therapy (including both anticoagulant and antiplatelet drugs) is associated with a lower risk of intracranial hemorrhage or focal neurological deficit from CCMs compared with the avoidance of antithrombotic therapy (incidence rate ratio 0.25, 95% CI 0.13–0.51; p < 0.0001).10
Another study by Marques et al. showed that antiplatelet medication alone and in combination with statins was associated with a lower risk of hemorrhage at CCM diagnosis.15The underlying proposed pathophysiological mechanism is that the bleeding event might be triggered by thrombus formation in the dilated caverns of CCMs, where the blood flow is slow, and by the associated inflammatory response. The mechanism of thrombus formation can be divided into four steps: platelet tethering, activation and firm adhesion, aggregation and platelet recruitment, and thrombus stabilization;28血小板血栓fo中发挥关键作用rmation but also in the inflammatory response due to the cocktail of molecules in their granules, which are inhibited by the same antiplatelet agents.
The cohort study by Marques et al.15reported robust data regarding bleeding risk of CCMs, but they are limited to the role of antiplatelets and statins. However, our study also collected data not only about anticoagulants, but also regarding other kinds of drugs, such as anti-inflammatory drugs, antirheumatics, and beta-blockers as well as other systemic conditions that can be considered as hemorrhagic risk factors.
This is the first study evaluating the possible correlation of the HAS-BLED score to a higher risk of bleeding. This score estimates the risk of major bleeding for patients on anticoagulation therapy to assess the risks and benefits in atrial fibrillation care. It includes the presence of systemic hypertension, renal or hepatic disease, history of stroke or other major bleeding, labile INR, age > 65 years, medication predisposing to bleeding, and alcohol use. Most of the patients with moderate- and high-risk scores (scores of 2–4) were in the nonbleeding group (Table 1). Although the differences were not statistically significant (p = 0.13), anticoagulation in general seems not to influence the bleeding risk. HAS-BLED scores have previously been applied and validated for estimating the risk of major bleeding in patients with several pathologies or those undergoing surgical procedures.29–31
Moreover, the HAS-BLED scale has good predictive value for intracranial bleeding, while other scales (e.g., ATRIA [anticoagulation and risk factors in atrial fibrillation]) are not predictive.32In a Swedish study on atrial fibrillation cohort, the rates of major bleeding (and intracranial bleeding) increased with increasing HAS-BLED scores.33Indeed, a high HAS-BLED score allows the clinician to flag patients at risk of serious bleeding in an informed manner rather than relying on guesswork. The HAS-BLED score also makes clinicians think about the potentially reversible risk factors for bleeding (e.g., uncontrolled blood pressure, labile INRs if on warfarin, and concomitant use of aspirin/nonsteroidal anti-inflammatory drugs). Moreover, medical personnel in other specialties (such as cardiologists) are asking neurosurgeons if patients who require anticoagulants and have unruptured cerebral vascular lesions (such as cavernomas, arteriovenous malformations, or aneurysms) can safely take these medications.
由于这些原因,我们决定也评估HAS-BLED score as a summary of several factors that could influence a general risk of intracranial bleeding. Of course, a formal validation for neurosurgical diseases, with a specific prospective study and a higher number of patients, should be performed. Volume ≥ 300 mm3seems to be the only factor that influences bleeding risk. However, although statistically significant, the AUC representing the diagnostic accuracy remains moderate (0.57, 95% CI 0.51–0.64).
Limitations
Our data were in part obtained retrospectively, which can lead to known information and selection biases. The number of patients in the bleeding group was much smaller than in the nonbleeding group. This could constitute a bias in the final interpretation of the data, and some variables did not reach statistical significance potentially because of the small number of patients in the bleeding group.
Conclusions
Our study seems to confirm several previous findings of a bleeding risk in proportion to the size of the CCM. Although with less sensitivity, cardiovascular risk factors in general and antithrombotic agents and beta-blockers could have a protective role against bleeding events. We did not find that a higher HAS-BLED score is associated with increased bleeding risk. However, to confirm these findings, larger studies of the natural history of these lesions in larger populations and pharmacological RCTs with prolonged follow-up also including patients with sporadic CCMs are needed.
Disclosures
The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.
Author Contributions
Conception and design: Scerrati, Travaglini, Bradaschia, De Bonis, Albanese, Sturiale. Acquisition of data: Travaglini, Bradaschia, Dones, Auricchio, Benato, Sturiale. Analysis and interpretation of data: Scerrati, Flacco. Drafting the article: Travaglini, Bradaschia, Flacco, Sturiale. Critically revising the article: Scerrati, Mantovani, Travaglini, De Bonis, Farneti, Flacco, Albanese, Sturiale. Reviewed submitted version of manuscript: Cavallo, Flacco, Albanese, Sturiale. Approved the final version of the manuscript on behalf of all authors: Scerrati. Statistical analysis: Flacco. Administrative/technical/material support: Auricchio. Study supervision: Scerrati, De Bonis, Cavallo.
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16 ↑
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18 ↑
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19 ↑
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20 ↑
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23 ↑
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24 ↑
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25 ↑
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27 ↑
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28 ↑
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31 ↑
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33 ↑
FribergL,RosenqvistM,LipGYH.Evaluation of risk stratification schemes for ischaemic stroke and bleeding in 182 678 patients with atrial fibrillation: the Swedish Atrial Fibrillation cohort study.Eur Heart J.2012;33(12):1500–1510.
