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The dynamics of hematoma surface regularity and hematoma expansion in acute intracerebral hemorrhage
Journal of Clinical Neuroscience xxx (xxxx) xxx
Contents lists available at ScienceDirect
Journal of Clinical Neuroscience journal homepage: www.elsevier.com/locate/jocn
Clinical study
The dynamics of hematoma surface regularity and hematoma expansion in acute intracerebral hemorrhage Dogan Dinc Oge, Mehmet Akif Topcuoglu, Rahsan Gocmen, Ethem Murat Arsava ⇑ Department of Neurology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
a r t i c l e
i n f o
Article history: Received 27 November 2019 Accepted 27 January 2020 Available online xxxx Keywords: Intracerebral hemorrhage ICH expansion Spot sign Island sign 3D modeling Prediction bio-marker
a b s t r a c t The clarification of factors that contribute to hematoma expansion in the setting of intracerebral hemorrhage (ICH) and the relevant physical dynamics are implemental for development of management strategies. Herein, we assessed the interplay between hematoma expansion and surface regularity of intracerebral bleeds. To do so, hematoma contours were outlined on admission and follow-up computed tomography (CT) studies using semi-automated thresholding algorithms in 133 ICH patients. Hematoma p v olume Þ, ranging from 0 (very irregular surface) volume, surface area and surface regularity [SR=6 pðpffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3 ðsurfaceareaÞ
to 1 (perfectly regular surface suggestive of 3D spherical structure)] were determined by 3D Slicer software (www.slicer.org). Hematoma growth was defined as 33% relative growth, or 6 mL absolute growth. Our results are as follows: The median (IQR) hematoma volume was 14.2 (6.0–34.9) mL on admission CT obtained 2.4 (1.5–4.4) hours after symptom onset; the mean ± SD SR value was calculated as 0.62 ± 0.14. Patients who underwent imaging at earlier time points were more likely to have higher SR (r = 0.18; p = 0.035). The median hematoma volume at follow-up, 35 (21–47) hours after the initial scan, was 19.7 (6.9–44.4) mL. The regularity index decreased significantly at this time point to 0.58 ± 0.13 (p < 0.001) and corresponding increase of surface irregularity was independent of change in hematoma volume. Baseline hematoma volume, INR, and time to initial imaging were significant predictors of hematoma expansion. In conclusion, our findings suggest that hematomas evolve into more irregular 3D shapes during follow-up. These observations are consistent with the ‘domino’ hypothesis put forward for ICH expansion. Ó 2020 Elsevier Ltd. All rights reserved.
1. Introduction The size of the hematoma at the time of admission, and the amount of expansion over the ensuing hours or days are considered as the major determinants of outcome in the setting of intracerebral hemorrhage (ICH) [1–3]. As it is practically not possible to intervene with the initial degree of insult, current therapeutic efforts like blood pressure management or correction of underlying coagulopathy, are directed to alter the fate regarding hematoma expansion [4]. Therefore, clarification of factors that contribute to hematoma expansion, and understanding relevant physical dynamics are important for development of new management strategies. In 1977, Fisher, based on his observation in pathology specimens, has suggested that additional shearing of blood vessels surrounding the initial site of hemorrhage lead to foci of secondary bleeds, and thereby contribute to expansion in the peripheral ⇑ Corresponding author at: Department of Neurology, Faculty of Medicine, Hacettepe University, 06100 Sihhiye, Ankara, Turkey. E-mail address: [email protected] (E.M. Arsava).
region of the hematoma [5]. This observation, named as domino or avalanche effect, was supported by recent imaging studies showing bimodal volumetric distributions of hematoma volumes in ICH patients, which can be considered to reflect the presence of certain volume thresholds, over which hematomas continue to expand in a feed-forward manner [6,7]. The eccentric location of the ‘spot sign’ within the hematoma on imaging studies and its colocalization with disruptions in vessel wall integrity on pathology specimens, are considered as additional evidence for the domino model of hematoma expansion [8–10]. On the basis of the domino-effect hypothesis and observations from radiologic studies suggestive of hematoma growth in a nonuniform fashion [8], various authors have focused on the prognostic role of hematoma shape in hematoma expansion. Some of these studies, primarily using visual assessment scales for categorizing hematomas in terms of shape [3,11–14], have highlighted the contribution of hematoma margin irregularity to hematoma expansion, and thereby to poor outcome. A recent addition to this armamentarium has been the description of island sign, which has been shown as a reliable predictor of unfavorable radiologic
https://doi.org/10.1016/j.jocn.2020.01.081 0967-5868/Ó 2020 Elsevier Ltd. All rights reserved.
Please cite this article as: D. D. Oge, M. A. Topcuoglu, R. Gocmen et al., The dynamics of hematoma surface regularity and hematoma expansion in acute intracerebral hemorrhage, Journal of Clinical Neuroscience, https://doi.org/10.1016/j.jocn.2020.01.081
2
D.D. Oge et al. / Journal of Clinical Neuroscience xxx (xxxx) xxx
and clinical outcome in patients with ICH [15]. Despite their practical utility as a bed-side prognosticator, these shape-based evaluations lack mathematical validation. In this study, using a mathematically defined surface regularity metric, we specifically evaluated the interplay between surface regularity, hematoma expansion and various neuro-imaging predictors of expansion. 2. Methods We retrospectively identified patients admitted with a diagnosis of ICH from our prospectively collected stroke patient database. Out of a total of 306 ICH patients admitted between the period of September 2009 and July 2018, we evaluated a total of 140 patients who had an initial brain computed tomography (CT) study within the first 12 h of symptom onset and a follow-up CT within 72 h, obtained per the discretion of the treating physician. We further excluded 7 patients where reliable outlining of hematoma contours could not be performed due to extensive motion artefacts or presence of additional pathologies like tumor or metal clips. Therefore, the final analyses were restricted to 133 patients. The study was approved by the local ethics committee. We extracted baseline demographic and stroke-pertinent clinical features (vascular risk factors, admission National Institutes of Health Stroke Scale (NIHSS) score, admission blood pressure, admission international normalized ratio (INR), admission Glasgow Coma Scale, time to initial and follow-up CT) from the database. Hematoma contours were outlined by semi-automated thresholding using the MRIcro software (University of Nottingham, UK) on admission and follow-up non-contrast CT images obtained on a multi-detector row scanner (Sensation 16, Siemens, Germany; image acquisition parameters: sequential mode, slice thickness 5 mm, 120–130 kV and 200 mA). The masks were then transferred to 3D Slicer 4.10.1 (www.slicer.org), which provided volume and surface area information of the 3D hematoma masks. These values were then used to calculate the surface regularity (SR) index ranging from 0 (very irregular surface) to 1 (perfectly regular surface suggestive of 3D spherical structure) according to the following formula [16]:
0
1
p B v olume C SR ¼ 6 p@qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiA 3 ðsurfaceareaÞ Morphologic features of hematomas on admission CT images were also visually evaluated regarding surface regularity (with a score of 3 signifying irregular hematoma) [11], and presence of island sign [15]. In patients having separated hemorrhage islands, virtual bridges were created during the outlining step to connect the scattered hematomas to the main hemorrhage, for the purpose of calculating SR values. Presence or absence of spot-sign was determined in the subset of patients who had a CT-angiography at the initial stage of evaluation. Hematoma growth was defined as 33% relative growth, or 6 mL absolute growth on followup with respect to admission CT [17]. 2.1. Statistical analysis Categorical variables are presented as n (%), and continuous variables as mean ± standard deviation (SD) or medianinterquartile range (IQR). Kolmogorov-Smirnov test was performed to assess the normality of continuous variables. Chi-square test, Pearson’s and Spearman’s correlation, and independent samples t-test were used for bivariate analyses, appropriately. The significance of change in SR values over time was evaluated by paired sample t-test and repeated measures general linear models (GLM). Logistic regression analysis was performed to identify inde-
pendent predictors of hematoma expansion. A p value of < 0.05 was considered statistically significant. All analyses were performed by SPSS version 16.0. 3. Results The study population was comprised of 56 female and 77 male patients, with a mean ± SD age of 66 ± 14 years. Baseline clinical characteristics of the study population are summarized in Table 1. The median (IQR) hematoma volume was 14.2 (6.0–34.9) mL on admission CT obtained 2.4 (1.5–4.4) hours after symptom onset. Sixty-one (46%) patients had an irregular hematoma per the definition of Barras et al. [11], while island sign [15] was considered to be present in 48 (36%) patients. Among the 59 patients, who underwent CT-angiography, a spot sign was evident in 10 (17%). At the time of admission, mean ± SD SR value was calculated as 0.62 ± 0.14. Patients who underwent imaging at earlier time points were more likely to have higher SR (r = 0.18; p = 0.035). Expectedly, patients with irregular contours per visual inspection [0.55 ± 0.13 vs. 0.68 ± 0.12; p < 0.001] and those with an island sign [0.55 ± 0.13 vs. 0.66 ± 0.13; p < 0.001] had significantly lower SR values. Although SR value was numerically lower in spot sign positive cases, this was not statistically significant (p = 0.217). We were not able to find any significant relationship between clinical features and SR, except for lower SR values in patients with higher NIHSS and lower GCS, as a reflection of larger hematoma volumes in such patients. Follow-up imaging obtained 35 (21–47) hours after initial CT, revealed a median (IQR) hematoma volume of 19.7 (6.9–44.4) mL. With respect to baseline, there was a significant decrease in regularity index to 0.58 ± 0.13 (p < 0.001) (Fig. 1), and the significance persisted when adjusted for the change in hematoma volume (F = 18.45; p < 0.001; GLM repeated measures). Hematoma expansion was observed in 35 (26%) patients per the 6 mL absolute growth definition and in 32 (24%) patients per the 33% relative growth definition. The decrease in SR values was significant regardless of the presence or absence of hematoma expansion (Table 2). Regarding the morphological features of interest to the current study, island sign was associated with absolute hematoma growth (p = 0.028), but not with relative growth, visual irregularity was associated with neither, while spot sign was positively associated with both absolute (p = 0.011) and relative (p = 0.032) growth. No significant relationship was observed with baseline SR values and hematoma expansion. In multivariate models, baseline hematoma volume, higher INR, and shorter time to initial imaging were the only significant predictors of hematoma expansion (Table 3); in the subgroup of patients with CT-angiography, spot sign dominated the models as a significant predictor of hematoma enlargement (Table 4). Table 1 Baseline characteristics of the study population. Age Female gender Hypertension Diabetes Mellitus Hyperlipidemia Coronary Artery Disease Atrial fibrillation Admission NIHSS score Admission GCS Admission systolic blood pressure Admission diastolic blood pressure Anticoagulant therapy Admission INR > 1.4
66 ± 14 years 56 (42%) 89 (67%) 31 (23%) 29 (22%) 32 (24%) 16 (12%) 12 (5–18) 13 (10–15) 179 ± 38 mmHg 99 ± 23 mmHg 25 (19%) 17 (13%)
Categorical variables are presented as n (%), continuous variables are presented as mean ± SD or median (IQR).
Please cite this article as: D. D. Oge, M. A. Topcuoglu, R. Gocmen et al., The dynamics of hematoma surface regularity and hematoma expansion in acute intracerebral hemorrhage, Journal of Clinical Neuroscience, https://doi.org/10.1016/j.jocn.2020.01.081
D.D. Oge et al. / Journal of Clinical Neuroscience xxx (xxxx) xxx
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4. Discussion
Fig. 1. A representative figure showing the change in hematoma volume and surface regularity in a sample patient. Top rows show axial computed tomography images at admission and follow-up, with insets illustrating 3D images of hematomas in a similar orientation with CT. The 3D images are presented in a closer view and different orientation in the bottom rows.
Table 2 SR values on admission and follow-up CT’s in patients with and without hematoma expansion. SR on admission
SR on follow-up
Absolute growth 6 cc Present (n = 35) Absent (n = 98)
0.59 ± 0.14 0.63 ± 0.14
0.53 ± 0.12 0.60 ± 0.14
p 0.007 <0.001
Relative growth 33% Present (n = 33) Absent (n = 100)
0.66 ± 0.14 0.61 ± 0.14
0.58 ± 0.12 0.58 ± 0.14
0.001 <0.001
Table 3 Multivariate predictors of hematoma growth. Dependent variable: growth 6 cc
OR (95%CI)
p
Admission hematoma volume INR > 1.4 Time to admission CT
1.02 (1.01–1.04) 8.25 (2.40–28.33) 0.78 (0.63–0.95)
0.009 0.001 0.014
Dependent variable: growth 33% Admission hematoma volume INR > 1.4 Time to admission CT
0.98 (0.95–1.00) 10.68 (2.73–41.82) 0.80 (0.64–0.99)
0.032 0.001 0.036
Independent variables that were eliminated during backward selection included island sign, SR index, hematoma irregularity per visual assessment, admission NIHSS score.
Table 4 Multivariate predictors of hematoma growth in patients with CT angiography. Dependent variable: growth 6 cc
OR (95%CI)
p
Spot sign
6.00 (1.37–26.24)
0.017
Dependent variable: growth 33% Admission hematoma volume Spot sign
0.92 (0.85–0.99) 8.42 (1.47–48.31)
0.035 0.017
The domino model raised by Fisher, suggests that hematoma growth is caused by shearing of blood vessels around the initial site of hemorrhage, which in turn contribute to additional bleedings and culminate in asymmetric hematoma growth [5]. Later studies performed in patients with cerebral amyloid angiopathy, highlighting a bimodal volumetric distribution model to discriminate micro- and macrobleeds, was considered not only as an evidence of different pathophysiological mechanisms, but also a nonuniform growth pattern in the setting of ICH [6–8]. Complementing these clinical observations, the experimental study by Liu et al. has shown the asymmetric nature of hematoma growth both at the time of initial hemorrhage and during secondary expansion [18]. This present study adds to the current literature by evaluating the tempo of a mathematically modeled surface regularity index during initial and follow-up CT’s. Our results show that the SR index decreases approximately by 6% on follow-up images, which means that hematomas evolve into more irregular 3D structures as time passes on. This increase in irregularity is independent of the change observed in hematoma volume or presence of hematoma growth, and is again consistent with the domino hypothesis put forward for ICH expansion. Despite the consistency in the literature regarding the asymmetric nature of hematoma expansion, contradictory findings have been reported regarding the prognostic value of hematoma shape and regularity (Supplemental Table I). Former studies focusing on the role of hematoma shape on growth primarily used one of the two visual assessment methods developed by Fujii et al. or Barras et al. for classifying hematoma shapes [11,12], and differed substantially with respect to covariates included into multivariate models. The quantitative expression of irregularity by the SR index used in our study was in concordance with the qualitative assessments performed visually, however was not significantly related to hematoma growth, similar to some observations in the literature (Supplemental Table I) [11,13,19–21]. On the other hand, in studies reporting a significant association between hematoma irregularity and growth after adjustment for confounders, the sensitivity of hematoma margin irregularity for predicting hematoma expansion was mediocre at best and ranged between 49% and 69% [13,22]. This level of sensitivity probably contributes to the conflicting reports in the literature, and explains why studies failing to report significant associations, similar to ours, had smaller sample sizes in comparison to the positive results in larger cohorts. Another factor that might have contributed to the heterogeneity of observations might have been the variability in hematoma growth definitions used. In this regard, island sign, a marker closely related to surface irregularity, was associated with absolute hematoma growth, but not with relative growth, in our cohort. Some limitations of our study merit consideration. The retrospective design introduces an inherent selection bias. The slice thickness on the axial CT was 5 mm, per our standard imaging protocol; the resulting anisotropy of the voxels with longer dimensions in the z-axis with respect to x-axis and y-axis, might have led to an overestimation of irregularity in our calculations. However, we believe that the margin of error was similar in both the admission and follow-up CT images, as they were obtained with the same imaging parameters, and our results would not have changed dramatically if CT imaging was performed by thin slice sections. The relatively small size of our patient cohort and lack of CT angiography studies for assessment of spot-sign in approximately half of our patients are other important limitations of the study. In conclusion, we have shown that surface regularity of intracerebral bleeds can be expressed in a quantitative fashion and its
Please cite this article as: D. D. Oge, M. A. Topcuoglu, R. Gocmen et al., The dynamics of hematoma surface regularity and hematoma expansion in acute intracerebral hemorrhage, Journal of Clinical Neuroscience, https://doi.org/10.1016/j.jocn.2020.01.081
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change over time is consistent with the asymmetric growth of hematomas as suggested by experimental and clinical studies. Future studies performed with larger number of patients could shed more light on the prognostic role and clinical utility of this measure. Conflicts of Interest Dr. Topcuoglu has received speaker honoraria from Boehringer Ingelheim, Nutricia, Sanofi and Abbott. He has served on scientific advisory boards for Boehringer Ingelheim and Pfizer. Dr. Arsava has received speaker honoraria from Boehringer Ingelheim, Pfizer, Sanofi, Abbott and Nutricia. He has served on scientific advisory boards for Pfizer, Boehringer Ingelheim, and Nutricia. Drs. Gocmen and Oge have nothing to disclose. Acknowledgements None. Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.jocn.2020.01.081. References [1] Broderick JP et al. Volume of intracerebral hemorrhage. A powerful and easyto-use predictor of 30-day mortality. Stroke 1993;24(7):987–93. [2] LoPresti MA et al. Hematoma volume as the major determinant of outcomes after intracerebral hemorrhage. J Neurol Sci 2014;345(1–2):3–7. [3] Wang CW et al. Hematoma shape, hematoma size, Glasgow coma scale score and ICH score: which predicts the 30-day mortality better for intracerebral hematoma?. PLoS One 2014;9(7):e102326. [4] Hemphill 3rd JC et al. Guidelines for the management of spontaneous intracerebral hemorrhage: a guideline for healthcare professionals from the American heart association/American stroke association. Stroke 2015;46 (7):2032–60.
[5] Fisher CM. Pathological observations in hypertensive cerebral hemorrhage. J Neuropathol Exp Neurol 1971;30(3):536–50. [6] Greenberg SM et al. Microbleeds versus macrobleeds: evidence for distinct entities. Stroke 2009;40(7):2382–6. [7] Greenberg CH et al. Modeling intracerebral hemorrhage growth and response to anticoagulation. PLoS One 2012;7(10):e48458. [8] Boulouis G et al. Anatomic pattern of intracerebral hemorrhage expansion: relation to CT angiography spot sign and hematoma center. Stroke 2014;45 (4):1154–6. [9] Brouwers HB et al. Clinical applications of the computed tomography angiography spot sign in acute intracerebral hemorrhage: a review. Stroke 2012;43(12):3427–32. [10] Huynh TJ, Keith J, Aviv RI. Histopathological characteristics of the ‘‘spot sign” in spontaneous intracerebral hemorrhage. Arch Neurol 2012;69(12):1654–5. [11] Barras CD et al. Density and shape as CT predictors of intracerebral hemorrhage growth. Stroke 2009;40(4):1325–31. [12] Fujii Y et al. Hematoma enlargement in spontaneous intracerebral hemorrhage. J Neurosurg 1994;80(1):51–7. [13] Blacquiere D et al. Intracerebral hematoma morphologic appearance on noncontrast computed tomography predicts significant hematoma expansion. Stroke 2015;46(11):3111–6. [14] Delcourt C et al. Significance of hematoma shape and density in intracerebral hemorrhage: the intensive blood pressure reduction in acute intracerebral hemorrhage trial study. Stroke 2016;47(5):1227–32. [15] Morotti A et al. Standards for detecting, interpreting, and reporting noncontrast computed tomographic markers of intracerebral hemorrhage expansion. Ann Neurol 2019. [16] Perez-Beteta J et al. Tumor surface regularity at mr imaging predicts survival and response to surgery in patients with glioblastoma. Radiology 2018;288 (1):218–25. [17] Dowlatshahi D et al. Defining hematoma expansion in intracerebral hemorrhage: relationship with patient outcomes. Neurology 2011;76 (14):1238–44. [18] Liu R et al. Modeling the pattern of contrast extravasation in acute intracerebral hemorrhage using dynamic contrast-enhanced MR. Neurocrit Care 2015;22(2):320–4. [19] Ji N et al. Imaging and clinical prognostic indicators for early hematoma enlargement after spontaneous intracerebral hemorrhage. Neurol Res 2009;31 (4):362–6. [20] Takeda R et al. A practical prediction model for early hematoma expansion in spontaneous deep ganglionic intracerebral hemorrhage. Clin Neurol Neurosurg 2013;115(7):1028–31. [21] Yao X et al. The HEP score: a nomogram-derived hematoma expansion prediction scale. Neurocrit Care 2015;23(2):179–87. [22] Morotti A et al. Blood pressure reduction and noncontrast CT markers of intracerebral hemorrhage expansion. Neurology 2017;89(6):548–54.
Please cite this article as: D. D. Oge, M. A. Topcuoglu, R. Gocmen et al., The dynamics of hematoma surface regularity and hematoma expansion in acute intracerebral hemorrhage, Journal of Clinical Neuroscience, https://doi.org/10.1016/j.jocn.2020.01.081
Contents lists available at ScienceDirect
Journal of Clinical Neuroscience journal homepage: www.elsevier.com/locate/jocn
Clinical study
The dynamics of hematoma surface regularity and hematoma expansion in acute intracerebral hemorrhage Dogan Dinc Oge, Mehmet Akif Topcuoglu, Rahsan Gocmen, Ethem Murat Arsava ⇑ Department of Neurology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
a r t i c l e
i n f o
Article history: Received 27 November 2019 Accepted 27 January 2020 Available online xxxx Keywords: Intracerebral hemorrhage ICH expansion Spot sign Island sign 3D modeling Prediction bio-marker
a b s t r a c t The clarification of factors that contribute to hematoma expansion in the setting of intracerebral hemorrhage (ICH) and the relevant physical dynamics are implemental for development of management strategies. Herein, we assessed the interplay between hematoma expansion and surface regularity of intracerebral bleeds. To do so, hematoma contours were outlined on admission and follow-up computed tomography (CT) studies using semi-automated thresholding algorithms in 133 ICH patients. Hematoma p v olume Þ, ranging from 0 (very irregular surface) volume, surface area and surface regularity [SR=6 pðpffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3 ðsurfaceareaÞ
to 1 (perfectly regular surface suggestive of 3D spherical structure)] were determined by 3D Slicer software (www.slicer.org). Hematoma growth was defined as 33% relative growth, or 6 mL absolute growth. Our results are as follows: The median (IQR) hematoma volume was 14.2 (6.0–34.9) mL on admission CT obtained 2.4 (1.5–4.4) hours after symptom onset; the mean ± SD SR value was calculated as 0.62 ± 0.14. Patients who underwent imaging at earlier time points were more likely to have higher SR (r = 0.18; p = 0.035). The median hematoma volume at follow-up, 35 (21–47) hours after the initial scan, was 19.7 (6.9–44.4) mL. The regularity index decreased significantly at this time point to 0.58 ± 0.13 (p < 0.001) and corresponding increase of surface irregularity was independent of change in hematoma volume. Baseline hematoma volume, INR, and time to initial imaging were significant predictors of hematoma expansion. In conclusion, our findings suggest that hematomas evolve into more irregular 3D shapes during follow-up. These observations are consistent with the ‘domino’ hypothesis put forward for ICH expansion. Ó 2020 Elsevier Ltd. All rights reserved.
1. Introduction The size of the hematoma at the time of admission, and the amount of expansion over the ensuing hours or days are considered as the major determinants of outcome in the setting of intracerebral hemorrhage (ICH) [1–3]. As it is practically not possible to intervene with the initial degree of insult, current therapeutic efforts like blood pressure management or correction of underlying coagulopathy, are directed to alter the fate regarding hematoma expansion [4]. Therefore, clarification of factors that contribute to hematoma expansion, and understanding relevant physical dynamics are important for development of new management strategies. In 1977, Fisher, based on his observation in pathology specimens, has suggested that additional shearing of blood vessels surrounding the initial site of hemorrhage lead to foci of secondary bleeds, and thereby contribute to expansion in the peripheral ⇑ Corresponding author at: Department of Neurology, Faculty of Medicine, Hacettepe University, 06100 Sihhiye, Ankara, Turkey. E-mail address: [email protected] (E.M. Arsava).
region of the hematoma [5]. This observation, named as domino or avalanche effect, was supported by recent imaging studies showing bimodal volumetric distributions of hematoma volumes in ICH patients, which can be considered to reflect the presence of certain volume thresholds, over which hematomas continue to expand in a feed-forward manner [6,7]. The eccentric location of the ‘spot sign’ within the hematoma on imaging studies and its colocalization with disruptions in vessel wall integrity on pathology specimens, are considered as additional evidence for the domino model of hematoma expansion [8–10]. On the basis of the domino-effect hypothesis and observations from radiologic studies suggestive of hematoma growth in a nonuniform fashion [8], various authors have focused on the prognostic role of hematoma shape in hematoma expansion. Some of these studies, primarily using visual assessment scales for categorizing hematomas in terms of shape [3,11–14], have highlighted the contribution of hematoma margin irregularity to hematoma expansion, and thereby to poor outcome. A recent addition to this armamentarium has been the description of island sign, which has been shown as a reliable predictor of unfavorable radiologic
https://doi.org/10.1016/j.jocn.2020.01.081 0967-5868/Ó 2020 Elsevier Ltd. All rights reserved.
Please cite this article as: D. D. Oge, M. A. Topcuoglu, R. Gocmen et al., The dynamics of hematoma surface regularity and hematoma expansion in acute intracerebral hemorrhage, Journal of Clinical Neuroscience, https://doi.org/10.1016/j.jocn.2020.01.081
2
D.D. Oge et al. / Journal of Clinical Neuroscience xxx (xxxx) xxx
and clinical outcome in patients with ICH [15]. Despite their practical utility as a bed-side prognosticator, these shape-based evaluations lack mathematical validation. In this study, using a mathematically defined surface regularity metric, we specifically evaluated the interplay between surface regularity, hematoma expansion and various neuro-imaging predictors of expansion. 2. Methods We retrospectively identified patients admitted with a diagnosis of ICH from our prospectively collected stroke patient database. Out of a total of 306 ICH patients admitted between the period of September 2009 and July 2018, we evaluated a total of 140 patients who had an initial brain computed tomography (CT) study within the first 12 h of symptom onset and a follow-up CT within 72 h, obtained per the discretion of the treating physician. We further excluded 7 patients where reliable outlining of hematoma contours could not be performed due to extensive motion artefacts or presence of additional pathologies like tumor or metal clips. Therefore, the final analyses were restricted to 133 patients. The study was approved by the local ethics committee. We extracted baseline demographic and stroke-pertinent clinical features (vascular risk factors, admission National Institutes of Health Stroke Scale (NIHSS) score, admission blood pressure, admission international normalized ratio (INR), admission Glasgow Coma Scale, time to initial and follow-up CT) from the database. Hematoma contours were outlined by semi-automated thresholding using the MRIcro software (University of Nottingham, UK) on admission and follow-up non-contrast CT images obtained on a multi-detector row scanner (Sensation 16, Siemens, Germany; image acquisition parameters: sequential mode, slice thickness 5 mm, 120–130 kV and 200 mA). The masks were then transferred to 3D Slicer 4.10.1 (www.slicer.org), which provided volume and surface area information of the 3D hematoma masks. These values were then used to calculate the surface regularity (SR) index ranging from 0 (very irregular surface) to 1 (perfectly regular surface suggestive of 3D spherical structure) according to the following formula [16]:
0
1
p B v olume C SR ¼ 6 p@qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiA 3 ðsurfaceareaÞ Morphologic features of hematomas on admission CT images were also visually evaluated regarding surface regularity (with a score of 3 signifying irregular hematoma) [11], and presence of island sign [15]. In patients having separated hemorrhage islands, virtual bridges were created during the outlining step to connect the scattered hematomas to the main hemorrhage, for the purpose of calculating SR values. Presence or absence of spot-sign was determined in the subset of patients who had a CT-angiography at the initial stage of evaluation. Hematoma growth was defined as 33% relative growth, or 6 mL absolute growth on followup with respect to admission CT [17]. 2.1. Statistical analysis Categorical variables are presented as n (%), and continuous variables as mean ± standard deviation (SD) or medianinterquartile range (IQR). Kolmogorov-Smirnov test was performed to assess the normality of continuous variables. Chi-square test, Pearson’s and Spearman’s correlation, and independent samples t-test were used for bivariate analyses, appropriately. The significance of change in SR values over time was evaluated by paired sample t-test and repeated measures general linear models (GLM). Logistic regression analysis was performed to identify inde-
pendent predictors of hematoma expansion. A p value of < 0.05 was considered statistically significant. All analyses were performed by SPSS version 16.0. 3. Results The study population was comprised of 56 female and 77 male patients, with a mean ± SD age of 66 ± 14 years. Baseline clinical characteristics of the study population are summarized in Table 1. The median (IQR) hematoma volume was 14.2 (6.0–34.9) mL on admission CT obtained 2.4 (1.5–4.4) hours after symptom onset. Sixty-one (46%) patients had an irregular hematoma per the definition of Barras et al. [11], while island sign [15] was considered to be present in 48 (36%) patients. Among the 59 patients, who underwent CT-angiography, a spot sign was evident in 10 (17%). At the time of admission, mean ± SD SR value was calculated as 0.62 ± 0.14. Patients who underwent imaging at earlier time points were more likely to have higher SR (r = 0.18; p = 0.035). Expectedly, patients with irregular contours per visual inspection [0.55 ± 0.13 vs. 0.68 ± 0.12; p < 0.001] and those with an island sign [0.55 ± 0.13 vs. 0.66 ± 0.13; p < 0.001] had significantly lower SR values. Although SR value was numerically lower in spot sign positive cases, this was not statistically significant (p = 0.217). We were not able to find any significant relationship between clinical features and SR, except for lower SR values in patients with higher NIHSS and lower GCS, as a reflection of larger hematoma volumes in such patients. Follow-up imaging obtained 35 (21–47) hours after initial CT, revealed a median (IQR) hematoma volume of 19.7 (6.9–44.4) mL. With respect to baseline, there was a significant decrease in regularity index to 0.58 ± 0.13 (p < 0.001) (Fig. 1), and the significance persisted when adjusted for the change in hematoma volume (F = 18.45; p < 0.001; GLM repeated measures). Hematoma expansion was observed in 35 (26%) patients per the 6 mL absolute growth definition and in 32 (24%) patients per the 33% relative growth definition. The decrease in SR values was significant regardless of the presence or absence of hematoma expansion (Table 2). Regarding the morphological features of interest to the current study, island sign was associated with absolute hematoma growth (p = 0.028), but not with relative growth, visual irregularity was associated with neither, while spot sign was positively associated with both absolute (p = 0.011) and relative (p = 0.032) growth. No significant relationship was observed with baseline SR values and hematoma expansion. In multivariate models, baseline hematoma volume, higher INR, and shorter time to initial imaging were the only significant predictors of hematoma expansion (Table 3); in the subgroup of patients with CT-angiography, spot sign dominated the models as a significant predictor of hematoma enlargement (Table 4). Table 1 Baseline characteristics of the study population. Age Female gender Hypertension Diabetes Mellitus Hyperlipidemia Coronary Artery Disease Atrial fibrillation Admission NIHSS score Admission GCS Admission systolic blood pressure Admission diastolic blood pressure Anticoagulant therapy Admission INR > 1.4
66 ± 14 years 56 (42%) 89 (67%) 31 (23%) 29 (22%) 32 (24%) 16 (12%) 12 (5–18) 13 (10–15) 179 ± 38 mmHg 99 ± 23 mmHg 25 (19%) 17 (13%)
Categorical variables are presented as n (%), continuous variables are presented as mean ± SD or median (IQR).
Please cite this article as: D. D. Oge, M. A. Topcuoglu, R. Gocmen et al., The dynamics of hematoma surface regularity and hematoma expansion in acute intracerebral hemorrhage, Journal of Clinical Neuroscience, https://doi.org/10.1016/j.jocn.2020.01.081
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4. Discussion
Fig. 1. A representative figure showing the change in hematoma volume and surface regularity in a sample patient. Top rows show axial computed tomography images at admission and follow-up, with insets illustrating 3D images of hematomas in a similar orientation with CT. The 3D images are presented in a closer view and different orientation in the bottom rows.
Table 2 SR values on admission and follow-up CT’s in patients with and without hematoma expansion. SR on admission
SR on follow-up
Absolute growth 6 cc Present (n = 35) Absent (n = 98)
0.59 ± 0.14 0.63 ± 0.14
0.53 ± 0.12 0.60 ± 0.14
p 0.007 <0.001
Relative growth 33% Present (n = 33) Absent (n = 100)
0.66 ± 0.14 0.61 ± 0.14
0.58 ± 0.12 0.58 ± 0.14
0.001 <0.001
Table 3 Multivariate predictors of hematoma growth. Dependent variable: growth 6 cc
OR (95%CI)
p
Admission hematoma volume INR > 1.4 Time to admission CT
1.02 (1.01–1.04) 8.25 (2.40–28.33) 0.78 (0.63–0.95)
0.009 0.001 0.014
Dependent variable: growth 33% Admission hematoma volume INR > 1.4 Time to admission CT
0.98 (0.95–1.00) 10.68 (2.73–41.82) 0.80 (0.64–0.99)
0.032 0.001 0.036
Independent variables that were eliminated during backward selection included island sign, SR index, hematoma irregularity per visual assessment, admission NIHSS score.
Table 4 Multivariate predictors of hematoma growth in patients with CT angiography. Dependent variable: growth 6 cc
OR (95%CI)
p
Spot sign
6.00 (1.37–26.24)
0.017
Dependent variable: growth 33% Admission hematoma volume Spot sign
0.92 (0.85–0.99) 8.42 (1.47–48.31)
0.035 0.017
The domino model raised by Fisher, suggests that hematoma growth is caused by shearing of blood vessels around the initial site of hemorrhage, which in turn contribute to additional bleedings and culminate in asymmetric hematoma growth [5]. Later studies performed in patients with cerebral amyloid angiopathy, highlighting a bimodal volumetric distribution model to discriminate micro- and macrobleeds, was considered not only as an evidence of different pathophysiological mechanisms, but also a nonuniform growth pattern in the setting of ICH [6–8]. Complementing these clinical observations, the experimental study by Liu et al. has shown the asymmetric nature of hematoma growth both at the time of initial hemorrhage and during secondary expansion [18]. This present study adds to the current literature by evaluating the tempo of a mathematically modeled surface regularity index during initial and follow-up CT’s. Our results show that the SR index decreases approximately by 6% on follow-up images, which means that hematomas evolve into more irregular 3D structures as time passes on. This increase in irregularity is independent of the change observed in hematoma volume or presence of hematoma growth, and is again consistent with the domino hypothesis put forward for ICH expansion. Despite the consistency in the literature regarding the asymmetric nature of hematoma expansion, contradictory findings have been reported regarding the prognostic value of hematoma shape and regularity (Supplemental Table I). Former studies focusing on the role of hematoma shape on growth primarily used one of the two visual assessment methods developed by Fujii et al. or Barras et al. for classifying hematoma shapes [11,12], and differed substantially with respect to covariates included into multivariate models. The quantitative expression of irregularity by the SR index used in our study was in concordance with the qualitative assessments performed visually, however was not significantly related to hematoma growth, similar to some observations in the literature (Supplemental Table I) [11,13,19–21]. On the other hand, in studies reporting a significant association between hematoma irregularity and growth after adjustment for confounders, the sensitivity of hematoma margin irregularity for predicting hematoma expansion was mediocre at best and ranged between 49% and 69% [13,22]. This level of sensitivity probably contributes to the conflicting reports in the literature, and explains why studies failing to report significant associations, similar to ours, had smaller sample sizes in comparison to the positive results in larger cohorts. Another factor that might have contributed to the heterogeneity of observations might have been the variability in hematoma growth definitions used. In this regard, island sign, a marker closely related to surface irregularity, was associated with absolute hematoma growth, but not with relative growth, in our cohort. Some limitations of our study merit consideration. The retrospective design introduces an inherent selection bias. The slice thickness on the axial CT was 5 mm, per our standard imaging protocol; the resulting anisotropy of the voxels with longer dimensions in the z-axis with respect to x-axis and y-axis, might have led to an overestimation of irregularity in our calculations. However, we believe that the margin of error was similar in both the admission and follow-up CT images, as they were obtained with the same imaging parameters, and our results would not have changed dramatically if CT imaging was performed by thin slice sections. The relatively small size of our patient cohort and lack of CT angiography studies for assessment of spot-sign in approximately half of our patients are other important limitations of the study. In conclusion, we have shown that surface regularity of intracerebral bleeds can be expressed in a quantitative fashion and its
Please cite this article as: D. D. Oge, M. A. Topcuoglu, R. Gocmen et al., The dynamics of hematoma surface regularity and hematoma expansion in acute intracerebral hemorrhage, Journal of Clinical Neuroscience, https://doi.org/10.1016/j.jocn.2020.01.081
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change over time is consistent with the asymmetric growth of hematomas as suggested by experimental and clinical studies. Future studies performed with larger number of patients could shed more light on the prognostic role and clinical utility of this measure. Conflicts of Interest Dr. Topcuoglu has received speaker honoraria from Boehringer Ingelheim, Nutricia, Sanofi and Abbott. He has served on scientific advisory boards for Boehringer Ingelheim and Pfizer. Dr. Arsava has received speaker honoraria from Boehringer Ingelheim, Pfizer, Sanofi, Abbott and Nutricia. He has served on scientific advisory boards for Pfizer, Boehringer Ingelheim, and Nutricia. Drs. Gocmen and Oge have nothing to disclose. Acknowledgements None. Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.jocn.2020.01.081. References [1] Broderick JP et al. Volume of intracerebral hemorrhage. A powerful and easyto-use predictor of 30-day mortality. Stroke 1993;24(7):987–93. [2] LoPresti MA et al. Hematoma volume as the major determinant of outcomes after intracerebral hemorrhage. J Neurol Sci 2014;345(1–2):3–7. [3] Wang CW et al. Hematoma shape, hematoma size, Glasgow coma scale score and ICH score: which predicts the 30-day mortality better for intracerebral hematoma?. PLoS One 2014;9(7):e102326. [4] Hemphill 3rd JC et al. Guidelines for the management of spontaneous intracerebral hemorrhage: a guideline for healthcare professionals from the American heart association/American stroke association. Stroke 2015;46 (7):2032–60.
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Please cite this article as: D. D. Oge, M. A. Topcuoglu, R. Gocmen et al., The dynamics of hematoma surface regularity and hematoma expansion in acute intracerebral hemorrhage, Journal of Clinical Neuroscience, https://doi.org/10.1016/j.jocn.2020.01.081