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Predicting hemorrhagic transformation after large vessel occlusion stroke in the era of mechanical thrombectomy

  • Takanori Iwamoto ,

    Contributed equally to this work with: Takanori Iwamoto, Takaya Kitano

    Roles Data curation, Funding acquisition, Investigation, Writing – review & editing

    Affiliation Department of Stroke Medicine, Kawasaki Medical School, Okayama, Japan

  • Takaya Kitano ,

    Contributed equally to this work with: Takanori Iwamoto, Takaya Kitano

    Roles Conceptualization, Data curation, Formal analysis, Writing – original draft

    takayakitano@gmail.com

    Affiliations Department of Stroke Medicine, Kawasaki Medical School, Okayama, Japan, Department of Neurology, Osaka University Graduate School of Medicine, Osaka Japan, Department of Neurology, Toyonaka Municipal Hospital, Osaka, Japan

  • Naoki Oyama,

    Roles Data curation, Investigation, Supervision, Writing – review & editing

    Affiliation Department of Stroke Medicine, Kawasaki Medical School, Okayama, Japan

  • Yoshiki Yagita

    Roles Conceptualization, Data curation, Project administration, Writing – review & editing

    Affiliation Department of Stroke Medicine, Kawasaki Medical School, Okayama, Japan

Abstract

Serum biomarkers are associated with hemorrhagic transformation and brain edema after cerebral infarction. However, whether serum biomarkers predict hemorrhagic transformation in large vessel occlusion stroke even after mechanical thrombectomy, which has become widely used, remains uncertain. In this prospective study, we enrolled patients with large vessel occlusion stroke in the anterior circulation. We analyzed 91 patients with serum samples obtained on admission. The levels of matrix metalloproteinase-9 (MMP-9), amyloid precursor protein (APP) 770, endothelin-1, S100B, and claudin-5 were measured. We examined the association between serum biomarkers and hemorrhagic transformation within one week. Fifty-four patients underwent mechanical thrombectomy, and 17 patients developed relevant hemorrhagic transformation (rHT, defined as hemorrhagic changes ≥ hemorrhagic infarction type 2). Neither MMP-9 (no rHT: 46 ± 48 vs. rHT: 15 ± 4 ng/mL, P = 0.30), APP770 (80 ± 31 vs. 85 ± 8 ng/mL, P = 0.53), endothelin-1 (7.0 ± 25.7 vs. 2.0 ± 2.1 pg/mL, P = 0.42), S100B (13 ± 42 vs. 12 ± 15 pg/mL, P = 0.97), nor claudin-5 (1.7 ± 2.3 vs. 1.9 ± 1.5 ng/mL, P = 0.68) levels on admission were associated with subsequent rHT. When limited to patients who underwent mechanical thrombectomy, the level of claudin-5 was higher in patients with rHT than in those without (1.2 ± 1.0 vs. 2.1 ± 1.7 ng/mL, P = 0.0181). APP770 levels were marginally higher in patients with a midline shift ≥ 5 mm than in those without (79 ± 29 vs. 97 ± 41 ng/mL, P = 0.084). The predictive role of serum biomarkers has to be reexamined in the mechanical thrombectomy era because some previously reported serum biomarkers may not predict hemorrhagic transformation, whereas the level of APP770 may be useful for predicting brain edema.

Introduction

Reperfusion therapy such as intravenous recombinant tissue plasminogen activator (rt-PA) and mechanical thrombectomy improves the outcome of patients with acute ischemic stroke [13]. However, it is also known that reperfusion therapies may worsen the hemorrhagic transformation [3]. In addition, reperfusion injury may augment brain edema [4, 5], although several studies on the association between brain edema and reperfusion show conflicting results [6, 7]. Once severe hemorrhagic transformation or edema has developed, the prognosis is often not favorable [8, 9]. Therefore, the ability to rapidly identify patients who are at higher risk of hemorrhagic transformation and brain edema is necessary.

Hemorrhagic transformation and edema after cerebral infarction mainly result from disruption of the blood brain barrier (BBB) and increased vascular permeability due to injury and/or remodeling of cerebral vessels [10]. Several studies have previously revealed that biomarkers using peripheral blood are useful for predicting hemorrhagic transformation and brain edema [1116]. For example, elevated matrix metalloproteinase-9 (MMP-9) levels are associated with hemorrhagic transformation [13], and elevated levels of endothelin-1 predict severe cerebral edema in stroke patients treated with rt-PA. However, these findings were obtained before mechanical thrombectomy became widely used, and whether these biomarkers predict hemorrhagic transformation and brain edema in the era of mechanical thrombectomy remains unclear.

In this prospective study, we examined whether these serum biomarkers were associated with the development of hemorrhagic transformation or brain edema in patients with large-vessel occlusion stroke in the anterior circulation. Furthermore, we investigated the association between biomarkers and functional outcomes.

Materials and methods

This study complied with the Declaration of Helsinki, and the retrospective study protocol was approved by the institutional ethics committee of Kawasaki Medical School.

Subjects

This was a single-center, prospective, observational study. We enrolled patients with acute ischemic stroke with internal carotid artery (ICA) or middle cerebral artery (MCA) occlusion who were admitted to Kawasaki Medical School Hospital within 24 hours of onset since June 2016. Patients aged 20 years or older who provided written informed consent were registered, and there was no upper age limit or minimum Alberta Stroke Program Early CT Score (ASPECTS) [17]. The study aimed to enroll 100 patients, and recruitment was censored in February 2020.

Data collection

Data, including age, sex, medical history, pre-stroke modified Rankin scale (mRS) score [18], stroke subtype [19], National Institutes of Health Stroke Scale (NIHSS) score, sites of occluded vessels, laboratory findings, and ASPECTS [17], which were determined based on computed tomography (CT) and magnetic resonance imaging (MRI) at admission, and the use of intravenous recombinant tissue plasminogen activator (rt-PA), were acquired from the patients’ medical records. If both CT and MRI were performed, MRI findings were prioritized to determine the ASPECTS value.

Reperfusion was evaluated at the end of the endovascular procedures and/or 24 ± 12 h after admission using magnetic resonance angiography (MRA). Successful angiographic reperfusion was identified based on grade 2b or greater using the expanded thrombolysis in cerebral infarction (eTICI) system in digital subtraction angiography [20], and grade 2 or greater using modified Mori grade in MRA [21].

Mechanical thrombectomy was defined as a procedure with arterial catheterization with a stent retriever or catheter aspiration, or both, with or without the delivery of a thrombolytic agent. The decision on whether to perform endovascular treatment and the type of treatment for each patient was left to the discretion of the attending physicians. The total number of device passes attempted before angiographic reperfusion or at the end of the procedure was reviewed for each patient. We classified the techniques as follows: “catheter aspiration,” “stent retriever,” and “combined”.

Outcomes

The primary outcome of this study was hemorrhagic transformation within a week of admission. The secondary outcomes were the development of malignant edema, neurological deterioration, and functional outcomes at three months after stroke onset. CT or MRI scans were performed 72 ± 12 h after admission to evaluate hemorrhagic transformation and edema. In addition, evaluations were performed in accordance with clinical necessity. According to the European-Australasian Acute Stroke Study II definitions [22], hemorrhagic transformation was classified as hemorrhagic infarction type 1 (HI1) or type 2 (HI2), and parenchymal hematoma, as type 1, type 2, or remote parenchymal hematoma. As reported previously, relevant hemorrhagic transformation was defined as HI2 and any type of PH [13]. Malignant edema was defined as the presence of midline shift ≥ 5 mm [23, 24]. All brain images were evaluated by an experienced neurologist (T.K.) who was blinded to the levels of serum biomarkers and functional outcomes. Neurological deterioration was defined as an increase of ≥4 points in the NIHSS score within a week of admission. Functional outcome scores based on mRS were collected three months after stroke onset. The patient follow-up method has been described previously [25].

Serum biomarker measurement

Serum samples were taken immediately upon admission at the emergency department before rt-PA administration or endovascular therapy and stored at < -80°C. Serum biomarker levels were determined using commercially available quantitative ELISA kits as follows: MMP-9, human ELISA kits for MMP-9 (PK-EL-64106, PromoCell, Heidelberg, Germany), amyloid precursor protein (APP) 770, Human ELISA kit of APP770 (#27736, Immuno-Biological Laboratories, Fujioka, Japan); endothelin-1, endothelin-1 ELISA kit (ADI-900-020A, EnzoLifescience, Lausen, Switzerland); S100B, Human ELISA kit of S100B (DY1820-05, DRG MedTek, Warsaw, Poland); and claudin-5, human claudin-5 ELISA kit (NBP2-75332, Novus Biologicals, CO, USA).

Statistical analysis

Serum biomarker levels were compared according to hemorrhagic transformation type, presence or absence of midline shift, and neurologic deterioration. In addition, the association between serum biomarker levels and baseline characteristics such as age, NIHSS score, and ASPECTS was evaluated. Finally, we evaluated the association between serum biomarker levels and functional outcomes. Additionally, we analyzed the association between serum biomarkers and favorable outcome (mRS ≤ 2).

Continuous variables were reported as means and standard deviations or medians and interquartile ranges, while categorical variables were reported as numbers and percentages. Continuous variables were compared using t-tests. Correlations were tested using the Pearson correlation coefficient. Statistical significance was set at P <0.05. All analyses were performed using SAS on demand (SAS 9.4, SAS Institute Inc., Cary, NC, USA).

Results

Patients characteristics

A total of 258 patients underwent screening. Among them, 96 patients with serum samples provided written informed consent. Four patients were excluded because they did not have ICA or MCA occlusion but other vessel occlusion, and one patient was excluded because of poor imaging quality. As a result, 91 patients were included in this study (Fig 1).

Baseline patient characteristics are shown in Table 1. The mean age was 77 years, and 60% of the patients were men. The mean NIHSS score was 18. The most frequently occluded vessel was the horizontal segment of the MCA (62%), followed by the intracranial ICA (27%) and the insular segment of the MCA (6%). Intravenous rt-PA was administered to 28 (31%) patients, and 54 (59%) patients underwent mechanical thrombectomy. As the first attempt, stent retriever was used in 18 (33%) patients, catheter aspiration in 20 (37%), and the combination of both in 16 (30%). Reperfusion was obtained within 36 h of admission in 67 patients (77%).

Primary outcome

Relevant hemorrhagic transformation was observed in 17 (19%) patients, and parenchymal hematoma, 4 (4%) patients. The levels of serum biomarkers according to the hemorrhagic transformation type are shown in Fig 2. MMP-9 (no hemorrhagic transformation or HI1: 46 ± 48 vs. relevant hemorrhagic transformation: 15 ± 4 ng/mL, P = 0.30), APP770 (80 ± 31 vs. 85 ± 8 ng/mL, P = 0.53), endothelin-1 (7.0 ± 25.7 vs. 2.0 ± 2.1 pg/mL, P = 0.42), S100B (13 ± 42 vs. 12 ± 15 pg/mL, P = 0.97), and claudin-5 (1.7 ± 2.3 vs. 1.9 ± 1.5 ng/mL, P = 0.68) levels on admission were not associated with the development of relevant hemorrhagic transformation. Moreover, neither MMP-9 (44 ± 44 vs. 33 ± 12 ng/mL, P = 0.62), APP770 (81 ± 32 vs. 93 ± 8 ng/mL, P = 0.44), endothelin-1 (6.3 ± 23.7 vs. 0.4 ± 0.6 pg/mL, P = 0.62), S100B (13 ± 39 vs. 14 ± 20 pg/mL, P = 0.96), nor claudin-5 (1.7 ± 2.2 vs. 1.5 ± 1.1 ng/mL, P = 0.82) levels were associated with parenchymal hematoma. In patients who underwent mechanical thrombectomy, the level of claudin-5 was higher in patients with relevant hemorrhagic transformation than those without (1.2 ± 1.0 vs. 2.1 ± 1.7 ng/mL, P = 0.0181, S1 Fig).

thumbnail
Fig 2. Association between hemorrhagic transformation and biomarkers.

There was no significant association between hemorrhagic transformation type and serum biomarker levels. APP, amyloid precursor protein; HI, hemorrhagic infarction; HT, hemorrhagic transformation; MMP-9, matrix metalloproteinase-9; PH, parenchymal hematoma; RHT, relevant hemorrhagic transformation.

https://doi.org/10.1371/journal.pone.0256170.g002

There was no significant association between the distribution of the techniques used as the first pass and hemorrhagic transformation (P = 0.64). On the contrary, patients who underwent ≥3 passes had more relevant hemorrhagic transformation than those with <3 passes (44% vs. 8%, P = 0.004, S2 Fig).

Secondary outcomes

Malignant edema, defined as the presence of midline shift ≥ 5 mm, was observed in 10 (11%) patients. The levels of serum biomarkers according to the presence or absence of a midline shift are shown in Fig 3. Neither MMP-9 (without midline shift: 44 ± 45 vs. with midline shift: 46 ± 29 ng/mL, P = 0.92), endothelin-1 (6.5 ± 2.6 vs. 2.4 ± 3.1 pg/mL, P = 0.59), S100B (13 ± 40 vs. 12 ± 16 pg/mL, P = 0.93), nor claudin-5 (1.8 ± 2.3 vs. 1.2 ± 0.9 ng/mL, P = 0.45) levels on admission predicted the development of midline shift, while the APP770 level was marginally higher in patients with midline shift than those without (79 ± 29 vs. 97 ± 41 ng/mL, P = 0.084). Among patients who achieved successful reperfusion, the levels of APP770 were higher in patients with midline shift than in those without (75 ± 26 vs. 118 ± 41 ng/mL, P = 0.003, S3 Fig). The association between baseline characteristics and APP770 levels is shown in S4 Fig. The levels of APP770 were negatively correlated with age (r = -0.32, P = 0.002); however, there was no significant association between the levels of APP770 and NIHSS score (r = -0.05, P = 0.62) or ASPECTS (r = -0.05, P = 0.63).

thumbnail
Fig 3. Association between brain edema and biomarkers.

APP770 levels were marginally higher in patients with a midline shift ≥ 5 mm than in those without (79 ± 29 vs. 97 ± 41 ng/mL, P = 0.084). APP for amyloid precursor protein; MMP-9, matrix metalloproteinase-9.

https://doi.org/10.1371/journal.pone.0256170.g003

Neurological deterioration was observed in 8 (9%) patients. The levels of serum biomarkers according to the presence or absence of neurological deterioration are shown in Fig 4. Neither MMP-9 (45 ± 45 vs. 36 ± 21 ng/mL, P = 0.61), APP770 (82 ± 32 vs. 76 ± 28 ng/mL, P = 0.59), endothelin-1 (6.4 ± 24.2 vs. 3.1 ± 5.2 pg/mL, P = 0.70), S100B (13 ± 40 vs. 7 ± 15 pg/mL, P = 0.69), nor claudin-5 (1.8 ± 2.3 vs. 1.1 ± 1.3 ng/mL, P = 0.45) levels on admission was associated with neurological deterioration.

thumbnail
Fig 4. Association between neurological deterioration and biomarkers.

There was no significant association between neurological deterioration and the levels of serum biomarkers. APP for amyloid precursor protein; MMP-9, matrix metalloproteinase-9.

https://doi.org/10.1371/journal.pone.0256170.g004

The association between the modified Rankin Scale score and serum biomarkers is shown in Fig 5. Neither MMP-9 (r = 0.09, P = 0.42), APP770 (r = -0.06, P = 0.60), endothelin-1 (r = 0.07, P = 0.51), S100B (r = 0.07, P = 0.52), nor claudin-5 (r = 0.04, P = 0.75) levels on admission were associated with functional outcomes. There was no significant association between serum biomarkers and favorable outcome (S1 Table).

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Fig 5. Association between functional outcome and biomarkers.

There was no significant association between the modified Rankin Scale score at three months after stroke and serum biomarker levels.

https://doi.org/10.1371/journal.pone.0256170.g005

Discussion

In this single-center prospective study, we examined whether the levels of serum MMP-9, APP770, endothelin-1, S100B, and claudin-5 predict the development of hemorrhagic transformation after large vessel occlusion stroke. Contrary to our expectation, none of these biomarkers were significantly associated with hemorrhagic transformation in our entire cohort, except that the level of claudin-5 was higher in patients with relevant hemorrhagic transformation than in those without when limited to patients who underwent mechanical thrombectomy. The levels of APP770 were marginally associated with severe brain edema. Our study also revealed that none of these biomarkers was associated with functional outcomes.

The usefulness of serum biomarkers for predicting hemorrhagic transformation and edema has been previously reported [1116]. MMP-9 is a proteolytic enzyme that degrades the endothelial basal lamina and plays a key role in producing edema and hemorrhagic transformation [2628]. The overexpression of the vasoconstrictor endothelin-1 leads to brain edema, and is a possible biomarker of BBB disruption [15, 29]. S100B is a calcium-binding protein, and it is known that the elevation of serum S100B level reflects BBB damage because the concentration of the protein is much lower than that of cerebrospinal fluid [30, 31]. Claudin-5 is a tight junction protein, and is one of the structural components of the BBB [11]. It seems reasonable that these biomarkers are associated with the course after cerebral infarction. However, except for Claudin-5, we failed to demonstrate the predictive role of these serum biomarkers for hemorrhagic transformation in the era of mechanical thrombectomy. There are several reasons for this. Among patients who underwent mechanical thrombectomy, the degree of direct vessel wall damage by endovascular procedures may be more important than BBB damage due to ischemia in this context; more than three passes of stent retriever predict parenchymal hematoma [32], and the rate of symptomatic intracranial hemorrhage or parenchymal hematoma exponentially increases with the elapsed time after puncture [33]. Also, the relatively low number of patients who received intravenous rt-PA may have affected the results, because rt-PA is known to activate MMP-9 [34].

In the present study, the levels of APP770 were marginally higher among patients with a midline shift than in those without. APP770 is a different APP isoform than the neuronal APP695. APP770 is secreted by inflamed endothelial cells and activated platelets [35]. Among patients with small subcortical infarcts, we have previously reported that APP770 levels are higher in patients with progressive neurological deficits than in those without, suggesting that APP770 might be a biomarker of cerebral small vessel disease [36]. We measured APP770 levels in patients with large vessel occlusion with the assumption that they reflect endothelial dysfunction. Our findings suggest that the levels of APP770 on admission may predict the extent of reperfusion injury, as the association between APP770 and brain edema was stronger in patients who achieved successful reperfusion than in those who did not.

Space-occupying cerebral edema subsequent to a large infarction can lead to neurologic deterioration and brain herniation. The natural course in patients with malignant edema is disastrous, with mortality rates up to 80% [37]. Reperfusion, especially when late, may augment brain edema [4, 5]. Therefore, predicting brain edema may help to decide who should not undergo reperfusion therapy. In addition, as early decompressive surgery has been shown to reduce mortality and disability in patients with malignant edema, it is important to identify patients who are at risk for developing malignant edema. Further studies are needed to determine the predictive value of serum APP770 levels for brain edema in patients with large vessel occlusion stroke.

Some serum biomarkers have been reported in patients who do not undergo mechanical thrombectomy, but they were not evaluated in the present study. Cellular fibronectin and platelet-derived growth factor-CC are known predictors of hemorrhagic transformation [13, 38]. However, we were unable to measure them appropriately. A high serum homocysteine level and low Caveolin-1 level have been reported as independent predictors of hemorrhagic transformation [39]. Furthermore, some non-serologic biomarkers have been reported in patients who undergo mechanical thrombectomy. For example, a high admission neutrophil-to-lymphocyte ratio has been reported as an independent predictor of symptomatic intracranial hemorrhage after mechanical thrombectomy [40, 41]. In addition, patient age, smoking, ASPECTS, general anesthesia, and embolization in a new territory are reportedly associated with hemorrhagic transformation [9]. Poor collateral status is also associated with hemorrhagic transformation [9, 42]. Using serum biomarkers in combination with these biomarkers may contribute to accurate prediction of patients who are at a higher risk of hemorrhagic transformation.

This study has several limitations. First, the number of patients from whom informed consent was obtained was lower than to those who were screened. This is due to the difficulty in obtaining written informed consent in hyperacute situations. Selection bias may have affected our findings. Second, we did not evaluate the correlation between serum biomarkers and perfusion imaging, although perfusion status is reported to be associated with hemorrhagic transformation [43]. Finally, this study may be underpowered, and multivariate analysis could not be performed due to the limited sample size.

Conclusions

Previously reported serum biomarkers did not predict hemorrhagic transformation in this cohort of patients with large vessel occlusion stroke, except that the level of claudin-5 was higher in patients with relevant hemorrhagic transformation than in those without in the subpopulation who underwent mechanical thrombectomy. On the other hand, the levels of APP770 were marginally associated with brain edema. Further studies are needed to identify the best biomarker for hemorrhagic transformation in the era of mechanical thrombectomy.

Supporting information

S1 Fig. Association between hemorrhagic transformation and biomarkers only in patients who underwent mechanical thrombectomy.

The level of claudin-5 was higher in patients with relevant hemorrhagic transformation than those without (1.2 ± 1.0 vs. 2.1 ± 1.7 ng/mL; P = 0.0181). APP, amyloid precursor protein; HI, hemorrhagic infarction; HT, hemorrhagic transformation; MMP-9, matrix metalloproteinase-9; PH, parenchymal hematoma; RHT, relevant hemorrhagic transformation.

https://doi.org/10.1371/journal.pone.0256170.s001

(DOCX)

S2 Fig. Association between hemorrhagic transformation and number of device passes.

Patients who underwent ≥3 passes had more relevant hemorrhagic transformation than those with <3 passes (44% vs. 8%, P = 0.004). HT, hemorrhagic transformation.

https://doi.org/10.1371/journal.pone.0256170.s002

(DOCX)

S3 Fig. Association between brain edema and serum APP770 according to the presence or absence of successful reperfusion.

Among patients who achieved successful reperfusion, the levels of APP770 were higher in patients with midline shift than in those without (75 ± 26 vs. 118 ± 41 ng/mL, P = 0.003).

https://doi.org/10.1371/journal.pone.0256170.s003

(DOCX)

S4 Fig. Association between the level of APP770 and patients’ characteristics.

The levels of APP770 were negatively correlated with age (r = -0.32, P = 0.002). Among patients who achieved successful reperfusion at 24 ± 12 hours after admission, the levels of APP770 were higher in patients with a midline shift ≥ 5 mm than in those without (75 ± 26 vs. 118 ± 41 ng/mL, P = 0.003).

https://doi.org/10.1371/journal.pone.0256170.s004

(DOCX)

S1 Table. Association between favorable outcome and biomarkers.

https://doi.org/10.1371/journal.pone.0256170.s005

(DOCX)

Acknowledgments

The authors thank M. Okamoto and M. Nishi for data collection.

References

  1. 1. Jansen IGH, Mulder M, Goldhoorn RB. Endovascular treatment for acute ischaemic stroke in routine clinical practice: prospective, observational cohort study (MR CLEAN Registry). BMJ (Clinical research ed). 2018;360:k949.
  2. 2. Goyal M, Menon BK, van Zwam WH, Dippel DW, Mitchell PJ, Demchuk AM, et al. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet (London, England). 2016;387(10029):1723–31. pmid:26898852
  3. 3. Tissue plasminogen activator for acute ischemic stroke. N Engl J Med. 1995;333(24):1581–7. pmid:7477192
  4. 4. Pillai DR, Dittmar MS, Baldaranov D, Heidemann RM, Henning EC, Schuierer G, et al. Cerebral ischemia-reperfusion injury in rats—a 3 T MRI study on biphasic blood-brain barrier opening and the dynamics of edema formation. J Cereb Blood Flow Metab. 2009;29(11):1846–55. pmid:19654585
  5. 5. Bell BA, Symon L, Branston NM. CBF and time thresholds for the formation of ischemic cerebral edema, and effect of reperfusion in baboons. Journal of neurosurgery. 1985;62(1):31–41. pmid:3964854
  6. 6. Kimberly WT, Dutra BG, Boers AMM, Alves HCBR, Berkhemer OA, van den Berg L, et al. Association of Reperfusion With Brain Edema in Patients With Acute Ischemic Stroke: A Secondary Analysis of the MR CLEAN Trial. JAMA neurology. 2018;75(4):453–61. pmid:29365017
  7. 7. Broocks G, Hanning U, Flottmann F, Schonfeld M, Faizy TD, Sporns P, et al. Clinical benefit of thrombectomy in stroke patients with low ASPECTS is mediated by oedema reduction. Brain: a journal of neurology. 2019;142(5):1399–407. pmid:30859191
  8. 8. Bardutzky J, Schwab S. Antiedema Therapy in Ischemic Stroke. Stroke. 2007;38(11):3084–94. pmid:17901384
  9. 9. Boisseau W, Fahed R, Lapergue B, Desilles J-P, Zuber K, Khoury N, et al. Predictors of Parenchymal Hematoma After Mechanical Thrombectomy. Stroke. 2019;50(9):2364–70. pmid:31670928
  10. 10. Jickling GC, Liu D, Stamova B, Ander BP, Zhan X, Lu A, et al. Hemorrhagic transformation after ischemic stroke in animals and humans. J Cereb Blood Flow Metab. 2014;34(2):185–99. pmid:24281743
  11. 11. Li W, Pan R, Qi Z, Liu KJ. Current progress in searching for clinically useful biomarkers of blood-brain barrier damage following cerebral ischemia. Brain circulation. 2018;4(4):145–52. pmid:30693340
  12. 12. Kazmierski R, Michalak S, Wencel-Warot A, Nowinski WL. Serum tight-junction proteins predict hemorrhagic transformation in ischemic stroke patients. Neurology. 2012;79(16):1677–85. pmid:22993287
  13. 13. Castellanos M, Sobrino T, Millan M, Garcia M, Arenillas J, Nombela F, et al. Serum cellular fibronectin and matrix metalloproteinase-9 as screening biomarkers for the prediction of parenchymal hematoma after thrombolytic therapy in acute ischemic stroke: a multicenter confirmatory study. Stroke. 2007;38(6):1855–9. pmid:17478737
  14. 14. Foerch C, Wunderlich MT, Dvorak F, Humpich M, Kahles T, Goertler M, et al. Elevated serum S100B levels indicate a higher risk of hemorrhagic transformation after thrombolytic therapy in acute stroke. Stroke. 2007;38(9):2491–5. pmid:17673718
  15. 15. Moldes O, Sobrino T, Millan M, Castellanos M, Perez de la Ossa N, Leira R, et al. High serum levels of endothelin-1 predict severe cerebral edema in patients with acute ischemic stroke treated with t-PA. Stroke. 2008;39(7):2006–10. pmid:18436890
  16. 16. Serena J, Blanco M, Castellanos M, Silva Y, Vivancos J, Moro MA, et al. The prediction of malignant cerebral infarction by molecular brain barrier disruption markers. Stroke. 2005;36(9):1921–6. pmid:16100032
  17. 17. Barber PA, Demchuk AM, Zhang J, Buchan AM. Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. ASPECTS Study Group. Alberta Stroke Programme Early CT Score. Lancet (London, England). 2000;355(9216):1670–4.
  18. 18. van Swieten JC, Koudstaal PJ, Visser MC, Schouten HJ, van Gijn J. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19(5):604–7. pmid:3363593
  19. 19. Adams HP Jr., Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke. 1993;24(1):35–41. pmid:7678184
  20. 20. Liebeskind DS, Bracard S, Guillemin F, Jahan R, Jovin TG, Majoie CB, et al. eTICI reperfusion: defining success in endovascular stroke therapy. J Neurointerv Surg. 2019;11(5):433–8. pmid:30194109
  21. 21. Mori E, Minematsu K, Nakagawara J, Yamaguchi T, Sasaki M, Hirano T. Effects of 0.6 mg/kg Intravenous Alteplase on Vascular and Clinical Outcomes in Middle Cerebral Artery Occlusion. Stroke. 2010;41(3):461–5. pmid:20075341
  22. 22. Larrue V, von Kummer RR, Muller A, Bluhmki E. Risk factors for severe hemorrhagic transformation in ischemic stroke patients treated with recombinant tissue plasminogen activator: a secondary analysis of the European-Australasian Acute Stroke Study (ECASS II). Stroke. 2001;32(2):438–41. pmid:11157179
  23. 23. Shimoyama T, Kimura K, Uemura J, Yamashita S, Saji N, Shibazaki K, et al. The DASH score: a simple score to assess risk for development of malignant middle cerebral artery infarction. J Neurol Sci. 2014;338(1–2):102–6. pmid:24423583
  24. 24. Barber PA, Hill MD, Eliasziw M, Demchuk AM, Pexman JHW, Hudon ME, et al. Imaging of the brain in acute ischaemic stroke: comparison of computed tomography and magnetic resonance diffusion-weighted imaging. Journal of Neurology, Neurosurgery & Psychiatry. 2005;76(11):1528–33. pmid:16227545
  25. 25. Nezu T, Mukai T, Uemura J, Yamashita M, Kitano T, Wada Y, et al. Multiple Infarcts Are Associated With Long-Term Stroke Recurrence and All-Cause Mortality in Cryptogenic Stroke Patients. Stroke. 2016;47(9):2209–15. pmid:27462116
  26. 26. Castellanos M, Leira R, Serena J, Pumar JM, Lizasoain I, Castillo J, et al. Plasma metalloproteinase-9 concentration predicts hemorrhagic transformation in acute ischemic stroke. Stroke. 2003;34(1):40–6. pmid:12511748
  27. 27. Romanic AM, Madri JA. Extracellular matrix-degrading proteinases in the nervous system. Brain Pathol. 1994;4(2):145–56. pmid:8061860
  28. 28. Rosenberg GA, Mun-Bryce S, Wesley M, Kornfeld M. Collagenase-induced intracerebral hemorrhage in rats. Stroke. 1990;21(5):801–7. pmid:2160142
  29. 29. Lo AC, Chen AY, Hung VK, Yaw LP, Fung MK, Ho MC, et al. Endothelin-1 overexpression leads to further water accumulation and brain edema after middle cerebral artery occlusion via aquaporin 4 expression in astrocytic end-feet. J Cereb Blood Flow Metab. 2005;25(8):998–1011. pmid:15815585
  30. 30. Kanner AA, Marchi N, Fazio V, Mayberg MR, Koltz MT, Siomin V, et al. Serum S100beta: a noninvasive marker of blood-brain barrier function and brain lesions. Cancer. 2003;97(11):2806–13. pmid:12767094
  31. 31. Marchi N, Rasmussen P, Kapural M, Fazio V, Kight K, Mayberg MR, et al. Peripheral markers of brain damage and blood-brain barrier dysfunction. Restor Neurol Neurosci. 2003;21(3–4):109–21. pmid:14530574
  32. 32. Bourcier R, Saleme S, Labreuche J, Mazighi M, Fahed R, Blanc R, et al. More than three passes of stent retriever is an independent predictor of parenchymal hematoma in acute ischemic stroke. J Neurointerv Surg. 2019;11(7):625–9. pmid:30389897
  33. 33. Alawieh A, Vargas J, Fargen KM, Langley EF, Starke RM, De Leacy R, et al. Impact of Procedure Time on Outcomes of Thrombectomy for Stroke. J Am Coll Cardiol. 2019;73(8):879–90. pmid:30819354
  34. 34. Wang X, Lee SR, Arai K, Lee SR, Tsuji K, Rebeck GW, et al. Lipoprotein receptor-mediated induction of matrix metalloproteinase by tissue plasminogen activator. Nat Med. 2003;9(10):1313–7. pmid:12960961
  35. 35. Kitazume S, Yoshihisa A, Yamaki T, Oikawa M, Tachida Y, Ogawa K, et al. Soluble amyloid precursor protein 770 is released from inflamed endothelial cells and activated platelets: a novel biomarker for acute coronary syndrome. J Biol Chem. 2012;287(48):40817–25. pmid:23033480
  36. 36. Saji N, Tone S, Murotani K, Yagita Y, Kimura K, Sakurai T. Cilostazol May Decrease Plasma Inflammatory Biomarkers in Patients with Recent Small Subcortical Infarcts: A Pilot Study. J Stroke Cerebrovasc Dis. 2018;27(6):1639–45. pmid:29454567
  37. 37. Hacke W, Schwab S, Horn M, Spranger M, De Georgia M, von Kummer R. ’Malignant’ Middle Cerebral Artery Territory Infarction: Clinical Course and Prognostic Signs. Archives of neurology. 1996;53(4):309–15. pmid:8929152
  38. 38. Rodriguez-Gonzalez R, Blanco M, Rodriguez-Yanez M, Moldes O, Castillo J, Sobrino T. Platelet derived growth factor-CC isoform is associated with hemorrhagic transformation in ischemic stroke patients treated with tissue plasminogen activator. Atherosclerosis. 2013;226(1):165–71. pmid:23218119
  39. 39. Liu L, Teng J, Ma M, Guo L, Yang L, Gao J, et al. Serum homocysteine level is an independent predictor for hemorrhagic transformation within 24 h of intravenous thrombolysis in acute ischemic stroke. Journal of clinical neuroscience: official journal of the Neurosurgical Society of Australasia. 2020;82(Pt A):13–9. pmid:33317721
  40. 40. Goyal N, Tsivgoulis G, Chang JJ, Malhotra K, Pandhi A, Ishfaq MF, et al. Admission Neutrophil-to-Lymphocyte Ratio as a Prognostic Biomarker of Outcomes in Large Vessel Occlusion Strokes. Stroke. 2018;49(8):1985–7. pmid:30002151
  41. 41. Pikija S, Sztriha LK, Killer-Oberpfalzer M, Weymayr F, Hecker C, Ramesmayer C, et al. Neutrophil to lymphocyte ratio predicts intracranial hemorrhage after endovascular thrombectomy in acute ischemic stroke. J Neuroinflammation. 2018;15(1):319. pmid:30442159
  42. 42. Li X, Liu H, Zeng W, Liu X, Wen Y, Xiong Q, et al. The Value of Whole-Brain Perfusion Parameters Combined with Multiphase Computed Tomography Angiography in Predicting Hemorrhagic Transformation in Ischemic Stroke. J Stroke Cerebrovasc Dis. 2020;29(4):104690. pmid:32067854
  43. 43. Suh CH, Jung SC, Cho SJ, Kim D, Lee JB, Woo DC, et al. Perfusion CT for prediction of hemorrhagic transformation in acute ischemic stroke: a systematic review and meta-analysis. European radiology. 2019;29(8):4077–87. pmid:30617485