Next Article in Journal
Clinical Evaluation of Sleep Disorders in Parkinson’s Disease
Previous Article in Journal
Gender and Nightshift Work: A Cross Sectional Study on Sleep Quality and Daytime Somnolence
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Brain Sciences
Volume 13
Issue 4
10.3390/brainsci13040608
brainsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Noncontrast Computed Tomography Markers Associated with Hematoma Expansion: Analysis of a Multicenter Retrospective Study

by
Lianghong Yu
1,2,3,4,†,
Mingpei Zhao
1,2,†,
Yuanxiang Lin
1,2,3,4,†,
Jiateng Zeng
1,2,
Qiu He
1,2,
Yan Zheng
1,2,
Ke Ma
5,
Fuxin Lin
1,2,3,4,5,* and
Dezhi Kang
1,2,3,4,5,*
1
Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
2
Department of Neurosurgery, Binhai Branch of National Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
3
Fujian Institute for Brain Disorders and Brain Science, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
4
Fujian Provincial Clinical Research Center for Neurological Diseases, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
5
Clinical Research and Translation Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Brain Sci. 2023, 13(4), 608; https://doi.org/10.3390/brainsci13040608
Submission received: 7 February 2023 / Revised: 19 March 2023 / Accepted: 24 March 2023 / Published: 3 April 2023
(This article belongs to the Section Neuropathology)
Browse Figure
Versions Notes

Abstract

:
Background: Hematoma expansion (HE) is a significant predictor of poor outcomes in patients with intracerebral hemorrhage (ICH). Non-contrast computed tomography (NCCT) markers in ICH are promising predictors of HE. We aimed to determine the association of the NCCT markers with HE by using different temporal HE definitions. Methods: We utilized Risa-MIS-ICH trial data (risk stratification and minimally invasive surgery in acute intracerebral hemorrhage). We defined four HE types based on the time to baseline CT (BCT) and the time to follow-up CT (FCT). Hematoma volume was measured by software with a semi-automatic edge detection tool. HE was defined as a follow-up CT hematoma volume increase of >6 mL or a 33% hematoma volume increase relative to the baseline CT. Multivariable regression analyses were used to determine the HE parameters. The prediction potential of indicators for HE was evaluated using receiver-operating characteristic analysis. Results: The study enrolled 158 patients in total. The time to baseline CT was independently associated with HE in one type (odds ratio (OR) 0.234, 95% confidence interval (CI) 0.077–0.712, p = 0.011), and the blend sign was independently associated with HE in two types (OR, 6.203–6.985, both p < 0.05). Heterogeneous density was independently associated with HE in all types (OR, 6.465–88.445, all p < 0.05) and was the optimal type for prediction, with an area under the curve of 0.674 (p = 0.004), a sensitivity of 38.9%, and specificity of 96.0%. Conclusion: In specific subtypes, the time to baseline CT, blend sign, and heterogeneous density were independently associated with HE. The association between NCCT markers and HE is influenced by the temporal definition of HE. Heterogeneous density is a stable and robust predictor of HE in different subtypes of hematoma expansion.

1. Introduction

Acute spontaneous cerebral hemorrhage is the most severe and incurable type of stroke, with one-third of patients dying within the first month [1]. Hematoma expansion (HE) is a strong predictor of poor outcomes in patients with intracerebral hemorrhage (ICH) [2,3,4]. Markers that are predictive of HE can help in selecting patients for hemostatic treatment [5]. Therefore, the early identification of patients at high risk of HE is critical. The spot sign on computed tomographic angiography (CTA) has been shown to have a high predictive value for increased hematoma [5,6,7,8,9].
Furthermore, leakage and iodine signs are also sensitive predictors of HE [10,11]. However, in some countries and regions, the low popularity rate of CTA limits the use of these markers. In contrast to CTA, non-contrast computed tomography (NCCT) markers have the potential to be a low-cost and widely used tool for cerebral hemorrhage risk stratification [12]. NCCT markers can be classified as density and shape markers [13]. Swirl signs, heterogeneous density, black hole signs, blend signs, and blood-fluid levels are widely used as qualitative hematoma heterogeneity markers to predict HE [14,15,16,17,18]. Currently, there is no standardized definition of HE, and the relationship between predictors and HE varies according to different volume growth definitions of HE [16,19,20,21]. However, there is a lack of studies examining the relationship between predictors and HE under different temporal definitions. Consequently, we sought to determine the associations between NCCT markers and hematoma expansion under various temporal definitions and to identify stable markers for predicting hematoma expansion.

2. Materials and Methods

2.1. Study Design and Subjects

Our study is based on the Risa-MIS-ICH trial retrospective study database (Clinical Trials Identifier: NCT03862729, https://www.clinicaltrials.gov, accessed on 1 December 2021). As of November 2021, the retrospective study had enrolled 1040 patients with spontaneous supratentorial cerebral hemorrhage and 225 patients with time to baseline CT within 12 h and a follow-up CT within 72 h. The time to baseline CT (BCT) was defined as the time from symptom onset to CT scan immediately after admission to the hospital. The exclusion criteria were as follows: (1) the CT was not NCCT at baseline; (2) the surgical intervention was conducted before the follow-up CT; and (3) the CT image quality was not optimal. The Ethics Committee at Fujian Medical University’s First Affiliated Hospital approved the study (Ethical Approval Number: MRCTA, ECFAH of FMU [2018] 082-1).

2.2. Treatment

The patients in this study were treated according to the following two guidelines: Guidelines for the Management of Spontaneous Intracerebral Hemorrhage: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association (2015) [22] and Chinese multidisciplinary expert consensus: Diagnosis and Treatment of Spontaneous Cerebral Hemorrhage (2015).

2.3. Imaging Evaluation

Admission and follow-up NCCT scans were obtained according to the standard clinical protocols with axial 5-mm section thickness. The swirl sign, black hole sign, blend sign, blood-fluid level, and ultraearly hematoma growth (uHG) are defined based on the previous literature [3,13,14,15,16,17,18]. Heterogeneous density is defined as at least three foci of hypoattenuation compared to the surrounding hematoma, as assessed on the axial NCCT slice with the most ICH. (Barras density scale = III, IV, or V) [16]. Supplementary Figure S1 shows images of several representative NCCT markers. Brainlab iPlan CMF 3.0 software (Brainlab, Heimstetten, Germany) assessed the volume of the hematoma using semi-automatic edge detection technology. Two experienced neurosurgeons independently reviewed all images and evaluated the presence or absence of various NCCT markers based strictly on the definitions. They were unaware of clinical information about the patient before the imaging evaluation. A reviewer was added to the panel in case of discrepancies, and the three resolved the issue by consensus. According to commonly used growth criteria, hematoma expansion was defined as an absolute growth volume of more than 6 mL or a relative growth of more than 33% [5,23,24,25]. Four types of hematoma growth were defined according to BCT < 6 or 12 h and FCT < 24 or 72 h.

2.4. Statistical Analysis

SPSS version 27.0 (SPSS Inc., Chicago, IL, USA) and MedCalc software (MedCalc 20.0, MedCalc, Mariakerke, Belgium) were used for the analysis. A two-tailed Student’s t-test was used to compare the continuous variables with a normal distribution reported as the mean (SD). Continuous variables with non-normal distributions were represented by the median (25th–75th quartile) and analyzed using the Mann–Whitney U test. Categorical variables were expressed as numbers (%), and distribution differences between groups were determined using the chi-square test or Fisher’s test. We used univariate analysis to find potential essential factors that could influence HE. Elements with p < 0.05 in the univariate analysis were forced into the multiple regression model, adjusting for potential predictors of p < 0.05 in other types. Receiver-operating characteristic curves were drawn to assess independent predictors for predicting HE. The inter-rater and intra-rater reliability was calculated through the intraclass correlation coefficient (ICC) in the measurement of hematoma volume. For an ICC, moderate agreement was considered as 0.41 to 0.60, substantial agreement as 0.61 to 0.80, and excellent as 0.81 to 1.00. All tests were 2-tailed, and p < 0.05 was considered statistically significant.

3. Results

3.1. Characteristics of Study Patients

A total of 158 ICH patients were included in this study and were divided into four types according to different CT time definitions (Supplementary Figure S2). HE was observed in 19.1% to 21.2% of ICH patients. The average age of the patients was 61.0 ± 12.5 years; 81.0% were male, 69.6% suffered from hypertension, and 18.3% suffered from diabetes mellitus. At admission, the NCCT scan revealed that 82.3% of patients had deep ICH, 29.7% had combined IVH, and 12.0% had combined hydrocephalus. The two raters’ ICH volumes at the baseline CT and the follow-up CT were the same (inter-rater ICC [95% CI]:0.99 [0.99–1.00]). Moreover, there was no difference between intra-rater assessments (intra-rater ICC [95% CI]:0.99 [0.99–1.00]). Detailed clinical and imaging information for the patients is shown in Table 1.

3.2. Association between NCCT Markers and Hematoma Expansion for Different Definitions

In the definition type of BCT within 6 h and FCT within 24 h, patients with HE were more likely to present as heterogeneous (p < 0.001) and presented with a shorter time to baseline CT (p = 0.005) and a larger uHG (p = 0.022) than those patients without HE (Table 2). The univariate analysis demonstrated that “heterogeneous” (p < 0.001, OR (95% CI), 15.061 (3.380–67.106)), time to baseline CT (p = 0.006, OR (95% CI), 0.504 (0.309–0.821)), and uHG (p = 0.017, OR (95% CI), 1.048 (1.008–1.090)) were associated with HE (Table 3). Multivariable analyses showed that “heterogeneous” (p < 0.001, 40.536 (5.366–306.223)) and the time to baseline CT (p = 0.009, 0.278 (0.106–0.729)) were the independent risk factors, and the association remained significant after adjustment (Table 4). In the definition type of BCT within 6 h and FCT within 72 h, patients with HE were more likely to present as “heterogeneous” (p < 0.001), blend sign (p = 0.001), and presented with a shorter time to baseline CT (p = 0.023) and a larger uHG (p = 0.047) than those patients without HE (Table 2). The univariate analysis demonstrated that heterogeneous (p < 0.001, OR (95% CI), 10.471 (2.832–38.711)), blend sign (p = 0.001, OR (95% CI), 8.653 (2.291–32.677)), time to baseline CT (p = 0.025, OR (95% CI), 0.665 (0.465–0.949)), uHG (p = 0.016, OR (95% CI), and 1.044 (1.008–1.081)) were associated with HE (Table 3). Multivariable analyses showed that heterogeneous (p = 0.002, 8.833 (2.165–36.037)) and blend sign (p = 0.008, 7.121 (1.651–30.709)) were the independent risk factors, and the association remained significant after adjustment (Table 4). In the definition type of BCT within 12 h and FCT within 24 h, patients with HE were more likely to show “heterogeneous” (p < 0.001) and a black hole sign (p = 0.030), and to have a shorter time to baseline CT (p = 0.030) and a larger uHG (p = 0.010) than patients without HE (Table 5). The univariate analysis demonstrated that heterogeneous (p < 0.001, OR (95% CI), 12.714 (3.376–47.878)), black hole sign (p = 0.016, OR (95% CI), 5.176 (1.350–19.847)), uHG (p = 0.012, OR (95% CI), and 1.047 (1.010–1.086)) were associated with HE (Table 6). Multivariable analyses showed that heterogeneous (p = 0.008, 11.259 (1.873–67.687)) and uHG (p = 0.036, 1.044 (1.003–1.087)) were the independent risk factors, but only heterogeneous remains significant after adjustment. (Table 4). In the definition type of BCT within 12 h and FCT within 72 h, patients with HE were more likely to have diabetes mellitus (p < 0.001), “heterogeneous” (p < 0.001), a black hole sign (p = 0.024), and a blend sign (p < 0.001) were less likely to have intraventricular hemorrhage (IVH) (p = 0.022), and presented with a shorter time to baseline CT (p = 0.018) and a larger uHG (p = 0.022) than those patients without HE (Table 5). The univariate analysis demonstrated that diabetes mellitus (p = 0.045, OR (95% CI), 3.147 (1.027–9.639)), IVH (p = 0.029, OR (95% CI), 0.289 (0.095–0.880)), heterogeneous (p < 0.001, OR (95% CI), 9.603 (3.155–29.231)), black hole sign (p = 0.045, OR (95% CI), 3.147 (1.027–9.639)), blend sign (p < 0.001, OR (95% CI), 10.696 (2.973–38.474)), uHG (p = 0.007, OR (95% CI), and 1.045 (1.012–1.079)) were associated with HE (Table 6). Multivariable analyses showed that heterogeneous (p = 0.009, 6.473 (1.579–26.541)) and uHG (p = 0.015, 6.197 (1.428–26.886)) were the independent risk factors, and the association remained significant after adjustment (Table 4).

3.3. Independent Predictive Value of Significant Predictors for HE

The receiver-operating characteristic (ROC) curve analysis revealed that the area under the curve (AUC) of the heterogeneous type was 0.674 (p = 0.004) and the time to baseline CT was 0.715 (p = 0.001) for the type with BCT within 6 h and FCT within 24 h (Figure 1). The heterogeneity was 0.638 (p = 0.005) and the blend sign was 0.618 (p = 0.012) in the type with BCT within 6 h and FCT within 72 h (Figure 1). The heterogeneity was 0.660 (p = 0.003) in the type with BCT within 12 h and FCT within 24 h; meanwhile, the heterogeneity was 0.638 (p = 0.002) and the blend sign was 0.613 (p = 0.005) in the type with BCT within 12 and FCT within 72 h (Figure 1). “Heterogeneous” demonstrates consistently sound predictive value for any definition, predicting HE with sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve, as detailed in Table 7.

4. Discussion

Our study showed that heterogeneous hematoma was independently associated with the risk of hematoma expansion. However, the association of other predictors with hematoma expansion varies depending on each definition of HE. The blend sign was independently associated with HE in half of the descriptions, while time to baseline CT was independently associated with HE in only 25% of the definitions.
No standard definition of hematoma expansion is currently available. Reports on hematoma expansion refer to the increase in volume and the corresponding time. In earlier studies, hematoma expansion was defined as an absolute increase of 20 mL or a relative increase of >40% [26], but this definition is now rarely used. Later, Brott et al. defined hematoma expansion as an increase in the relative volume of greater than 33% [27,28]; this definition is still used today [29,30]. Currently, hematoma expansion is mainly defined by a combination of relative and absolute growth, with >33% or >6 mL [3,9,31,32,33,34,35] and >33% or >12.5 mL [17,36,37] being the standard; in particular, >33% or >6 mL is widely used. Different definitions of volume growth affect the relationship between predictors and HE [16,19,20,21]. The time aspect of hematoma definitions is often ignored. Due to the relative instability of early hematomas, hematoma growth is more common among patients with early ICH. The activation of the coagulation system allows the hematoma to become progressively more stable as time passes. Consequently, a CT scan performed late after the onset of the disease may be less likely to detect an enlarged hematoma, and it is more accurate to say that the hematoma has grown to a stable state than that it has not grown. H. Bart Brouwers et al. confirmed that a shorter time to computed tomography (≤6 vs. >6 h) independently predicted HE [31], and patients with a baseline CT time < 6 h were included in a subsequent study of hematoma expansion [4,20,35]. Considering that some patients cannot reach the hospital and receive a CT scan in time, especially in remote areas, and 17% of hematoma expansion occurred 6 h after onset [38], we consider it reasonable to include baseline CT time < 12 h in the study. In addition, there is no uniformity in the definition of follow-up CT time, but it is generally within 72 h [35,39,40]. As a result, when studying the relationship between predictors and HE, the type of HE definition used should be noted.
Contrast extravasation observed on CTA is a significant indicator for identifying ongoing bleeding [5]. The spot sign is a strong predictor of HE [5]; this sign is characterized as one or more 1 to 2 mm foci of enhancement within the hematoma on CTA source pictures [6], and the enhancement foci are generally considered to be manifestations of the extravasation of the contrast medium from the blood vessels. Later, the leakage and iodine signs on CTA images were also considered risk factors for HE sensitivity [10,11]. CTA is an ideal screening technique for cerebral hemorrhage because it can detect the marker that predicts hematoma expansion and identify bleeding caused by vascular malformation. However, CTA tests are not readily accessible, limiting their extensive application in clinical practice.
In contrast to CTA, NCCT is readily available in most stroke hospitals. NCCT markers can be divided into two types based on shape and density [13,24], and NCCT markers representing hematoma density are more common than those representing shapes. NCCT markers based on hematoma density, such as the swirl sign, black hole sign, blend sign, blood-fluid level, and density category of hematoma, were described as “heterogeneous”, representing local or global hematoma heterogeneity. Notably, the definition of NCCT markers is relatively complex; there is no consensus, and there is inter-reviewer variability [41]. Recent studies have shown that quantitative NCCT markers can be used to predict hematoma expansion [42,43]; however, the need for specialist software may limit the widespread use of this technique. In addition, the artificial intelligence model offers improved specificity and sensitivity in predicting HE [44]. In conclusion, predicting HE using NCCT images is a promising prospect. We recommend that all NCCT markers be prospectively validated and evaluated for inter- and intra-rater reliability.
Fisher et al. proposed an "avalanche" model to explain the mechanism of hematoma expansion [45]. In this model, the hematoma stretches the surrounding brain tissue, causing numerous small blood vessels to be cut and ruptured. As the hematoma increases further, the pressure on the surrounding brain tissue increases, and the hemorrhage stops when the hematoma and surrounding tissue pressure are balanced [16,46]. In this model, the earlier hematoma continuously adds new liquid blood until the bleeding stops. The clot shrinks after bleeding to obtain hemostasis, the serum is extruded, and the CT attenuation of the hematoma increases [47]. Fluid blood from an active hemorrhage hypoattenuates on a CT scan relative to the surrounding brain or high attenuation thrombus [46]. Thus, hematoma heterogeneity may represent the coexistence of relatively early high-attenuation hematomas with relatively new bleeding low-attenuation hematomas in active bleeding. We speculate that, if the hematoma is slowly and continuously expanding, the further active bleeding may be of multiple origins, which may be more consistent with a heterogeneous density score. This type of hematoma expansion is probably the most common. However, if there is active bleeding on top of a relatively stable hematoma, i.e., if there are two peaks of bleeding, this may be more consistent with blend sign.
This study has several limitations. First, the sample size was relatively small. Second, the study was retrospective and had all the typical constraints of a retrospective analysis. Third, all of the definitions utilized herein commonly used growth volume (>6 mL or 33%), which may limit the applicability of our results. Fourth, patients with more than 12 h between onset and the initial CT scan were excluded from this study; therefore, our results may not apply to this population. Fifth, this was an NCCT-based study that did not compare NCCT markers to the CTA spot sign. Finally, this study only showed the association of NCCT with HE and offered no further investigation of the association of NCCT with poor outcomes, limiting the clinical value of the results.
In summary, our study showed that the time to baseline CT, blend sign, and heterogeneous density was independently associated with HE in certain subtypes. We found that different temporal HE definitions affected the relationship between predictors and HE, but heterogeneous hematoma independently predicted HE in all definition types. Heterogeneous hematoma as a stable independent predictor of HE may be a substitute when CTA scans cannot be performed, and individuals with heterogeneous hematoma may be candidates for therapy that limits early hematoma expansion. In the subsequent research, our results will be validated using prospective, multicenter, large-sample-size RIS-MIS-ICH project data.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/brainsci13040608/s1, Supplementary Figure S1. Several instances of NCCT markers. Axial images of four different ICH patients. (A) Density cat-egory III (white arrow) is classified as “heterogeneous” while (B) density category I (white arrow) is characterized as “homogeneous”. Both (A) and (B) are the largest levels of the axial scan of the hematoma. (B) is defined as the black hole sign. The low-attenuation region (white arrow) is encased within the high-attenuation hematoma. The density difference between the two is approximately 36 Hounsfield units (HU). (C) means for blend sign. There is a visible demarcation (black dashed line) between the low attenuation area and the neighboring high attenuation area, and the density dif-ference between the two is around 18 HU. (D) is defined as swirl sign. The relatively low-attenuation region (white arrow) is wrapped within the high-attenuation hematoma. The border between the two is indistinct, with a density difference of 26 HU. Supplementary Figure S2. Flow chart of patients enrolled in the study. CT, computed tomography; NCCT, non-contrast computed tomography; BCT, time to baseline CT; FCT, time to follow-up CT.

Author Contributions

Conceptualization, L.Y.; methodology, Q.H.; formal analysis, M.Z. and J.Z.; investigation, Y.Z. and K.M.; resources, D.K.; data curation, Y.Z. and K.M.; writing—original draft preparation, M.Z.; writing—review and editing, L.Y. and Y.L.; supervision, F.L.; project administration, F.L. and Y.L.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.

Funding

Project on research and application of effective intervention techniques for a high-risk population of stroke from the National Health and Family Planning Commission in China (GN-2018R002); Social Development Guided Project of Fujian Provincial Department of Science and Technology (2019Y0017); National Cerebrovascular and Nervous System Difficult Diseases Diagnosis and Treatment Capacity Improvement Project; Fujian Province High-level Neuromedical Center Construction Fund (HLNCC-FJFY-003); Fujian Science and Technology Innovation Joint Fund Project (2019Y9118).

Institutional Review Board Statement

The study protocol adhered to the principles of the Helsinki Declaration and was authorized by the Ethics Committee of Fujian Medical University’s First Affiliated Hospital (GN: MRCTA, ECFAH of FUM (2018)082).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The authors will make the raw data supporting the results of this article available.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cordonnier, C.; Demchuk, A.; Ziai, W.; Anderson, C.S. Intracerebral haemorrhage: Current approaches to acute management. Lancet 2018, 392, 1257–1268. [Google Scholar] [CrossRef] [PubMed]
  2. Davis, S.M.; Broderick, J.; Hennerici, M.; Brun, N.C.; Diringer, M.N.; Mayer, S.A.; Begtrup, K.; Steiner, T. Hematoma growth is a determinant of mortality and poor outcome after intracerebral hemorrhage. Neurology 2006, 66, 1175–1181. [Google Scholar] [CrossRef] [PubMed]
  3. Rodriguez-Luna, D.; Rubiera, M.; Ribo, M.; Coscojuela, P.; Piñeiro, S.; Pagola, J.; Hernandez-Guillamon, M.; Ibarra, B.; Romero, F.; Alvarez-Sabin, J.; et al. Ultraearly hematoma growth predicts poor outcome after acute intracerebral hemorrhage. Neurology 2011, 77, 1599–1604. [Google Scholar] [CrossRef]
  4. Wang, W.J.; Lu, J.J.; Liu, L.P.; Jia, J.K.; Zhao, X.Q. Ultraearly Hematoma Growth in Acute Spontaneous Intracerebral Hemorrhage Predicts Early and Long-Term Poor Clinical Outcomes: A Prospective, Observational Cohort Study. Front. Neurol. 2021, 12, 747551. [Google Scholar] [CrossRef] [PubMed]
  5. Demchuk, A.M.; Dowlatshahi, D.; Rodriguez-Luna, D.; Molina, C.A.; Blas, Y.S.; Dzialowski, I.; Kobayashi, A.; Boulanger, J.M.; Lum, C.; Gubitz, G.; et al. Prediction of haematoma growth and outcome in patients with intracerebral haemorrhage using the CT-angiography spot sign (PREDICT): A prospective observational study. Lancet Neurol. 2012, 11, 307–314. [Google Scholar] [CrossRef]
  6. Wada, R.; Aviv, R.I.; Fox, A.J.; Sahlas, D.J.; Gladstone, D.J.; Tomlinson, G.; Symons, S.P. CT angiography “spot sign” predicts hematoma expansion in acute intracerebral hemorrhage. Stroke 2007, 38, 1257–1262. [Google Scholar] [CrossRef] [Green Version]
  7. Delgado Almandoz, J.E.; Yoo, A.J.; Stone, M.J.; Schaefer, P.W.; Goldstein, J.N.; Rosand, J.; Oleinik, A.; Lev, M.H.; Gonzalez, R.G.; Romero, J.M. Systematic characterization of the computed tomography angiography spot sign in primary intracerebral hemorrhage identifies patients at highest risk for hematoma expansion: The spot sign score. Stroke 2009, 40, 2994–3000. [Google Scholar] [CrossRef] [Green Version]
  8. Dowlatshahi, D.; Brouwers, H.B.; Demchuk, A.M.; Hill, M.D.; Aviv, R.I.; Ufholz, L.A.; Reaume, M.; Wintermark, M.; Hemphill, J.C., 3rd; Murai, Y.; et al. Predicting Intracerebral Hemorrhage Growth with the Spot Sign: The Effect of Onset-to-Scan Time. Stroke 2016, 47, 695–700. [Google Scholar] [CrossRef] [Green Version]
  9. Phan, T.G.; Krishnadas, N.; Lai, V.W.Y.; Batt, M.; Slater, L.A.; Chandra, R.V.; Srikanth, V.; Ma, H. Meta-Analysis of Accuracy of the Spot Sign for Predicting Hematoma Growth and Clinical Outcomes. Stroke 2019, 50, 2030–2036. [Google Scholar] [CrossRef]
  10. Orito, K.; Hirohata, M.; Nakamura, Y.; Takeshige, N.; Aoki, T.; Hattori, G.; Sakata, K.; Abe, T.; Uchiyama, Y.; Sakamoto, T.; et al. Leakage Sign for Primary Intracerebral Hemorrhage: A Novel Predictor of Hematoma Growth. Stroke 2016, 47, 958–963. [Google Scholar] [CrossRef]
  11. Fu, F.; Sun, S.; Liu, L.; Gu, H.; Su, Y.; Li, Y. Iodine Sign as a Novel Predictor of Hematoma Expansion and Poor Outcomes in Primary Intracerebral Hemorrhage Patients. Stroke 2018, 49, 2074–2080. [Google Scholar] [CrossRef]
  12. Boulouis, G.; Morotti, A.; Charidimou, A.; Dowlatshahi, D.; Goldstein, J.N. Noncontrast Computed Tomography Markers of Intracerebral Hemorrhage Expansion. Stroke 2017, 48, 1120–1125. [Google Scholar] [CrossRef] [Green Version]
  13. Morotti, A.; Boulouis, G.; Dowlatshahi, D.; Li, Q.; Barras, C.D.; Delcourt, C.; Yu, Z.; Zheng, J.; Zhou, Z.; Aviv, R.I.; et al. Standards for Detecting, Interpreting, and Reporting Noncontrast Computed Tomographic Markers of Intracerebral Hemorrhage Expansion. Ann. Neurol. 2019, 86, 480–492. [Google Scholar] [CrossRef]
  14. Selariu, E.; Zia, E.; Brizzi, M.; Abul-Kasim, K. Swirl sign in intracerebral haemorrhage: Definition, prevalence, reliability and prognostic value. BMC Neurol. 2012, 12, 109. [Google Scholar] [CrossRef] [Green Version]
  15. Pfleger, M.J.; Hardee, E.P.; Contant, C.F., Jr.; Hayman, L.A. Sensitivity and specificity of fluid-blood levels for coagulopathy in acute intracerebral hematomas. AJNR Am. J. Neuroradiol. 1994, 15, 217–223. [Google Scholar]
  16. Barras, C.D.; Tress, B.M.; Christensen, S.; MacGregor, L.; Collins, M.; Desmond, P.M.; Skolnick, B.E.; Mayer, S.A.; Broderick, J.P.; Diringer, M.N.; et al. Density and shape as CT predictors of intracerebral hemorrhage growth. Stroke 2009, 40, 1325–1331. [Google Scholar] [CrossRef] [Green Version]
  17. Li, Q.; Zhang, G.; Huang, Y.J.; Dong, M.X.; Lv, F.J.; Wei, X.; Chen, J.J.; Zhang, L.J.; Qin, X.Y.; Xie, P. Blend Sign on Computed Tomography: Novel and Reliable Predictor for Early Hematoma Growth in Patients with Intracerebral Hemorrhage. Stroke 2015, 46, 2119–2123. [Google Scholar] [CrossRef] [Green Version]
  18. Li, Q.; Zhang, G.; Xiong, X.; Wang, X.C.; Yang, W.S.; Li, K.W.; Wei, X.; Xie, P. Black Hole Sign: Novel Imaging Marker That Predicts Hematoma Growth in Patients with Intracerebral Hemorrhage. Stroke 2016, 47, 1777–1781. [Google Scholar] [CrossRef] [Green Version]
  19. Dowlatshahi, D.; Demchuk, A.M.; Flaherty, M.L.; Ali, M.; Lyden, P.L.; Smith, E.E. Defining hematoma expansion in intracerebral hemorrhage: Relationship with patient outcomes. Neurology 2011, 76, 1238–1244. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Chen, Q.; Liu, J.; Xu, H.; He, W.; Li, Y.; Jiao, L.; Xiang, Y.; Zhan, C.; Chen, J.; Yang, X.; et al. Association Between Eosinophilic Leukocyte Count and Hematoma Expansion in Acute Spontaneous Intracerebral Hemorrhage. Front. Neurol. 2019, 10, 1164. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Qin, J.; Wei, H.; Liu, Y.; Du, L.; Xia, J. Association between leukocyte subpopulations and hematoma expansion after spontaneous intracerebral hemorrhage: A retrospective cohort study. Front. Neurol. 2022, 13, 992851. [Google Scholar] [CrossRef] [PubMed]
  22. Hemphill, J.C., 3rd; Greenberg, S.M.; Anderson, C.S.; Becker, K.; Bendok, B.R.; Cushman, M.; Fung, G.L.; Goldstein, J.N.; Macdonald, R.L.; Mitchell, P.H.; 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, 2032–2060. [Google Scholar] [CrossRef] [Green Version]
  23. Morotti, A.; Phuah, C.L.; Anderson, C.D.; Jessel, M.J.; Schwab, K.; Ayres, A.M.; Pezzini, A.; Padovani, A.; Gurol, M.E.; Viswanathan, A.; et al. Leukocyte Count and Intracerebral Hemorrhage Expansion. Stroke 2016, 47, 1473–1478. [Google Scholar] [CrossRef] [Green Version]
  24. Blacquiere, D.; Demchuk, A.M.; Al-Hazzaa, M.; Deshpande, A.; Petrcich, W.; Aviv, R.I.; Rodriguez-Luna, D.; Molina, C.A.; Silva Blas, Y.; Dzialowski, I.; et al. Intracerebral Hematoma Morphologic Appearance on Noncontrast Computed Tomography Predicts Significant Hematoma Expansion. Stroke 2015, 46, 3111–3116. [Google Scholar] [CrossRef] [Green Version]
  25. Boulouis, G.; Morotti, A.; Brouwers, H.B.; Charidimou, A.; Jessel, M.J.; Auriel, E.; Pontes-Neto, O.; Ayres, A.; Vashkevich, A.; Schwab, K.M.; et al. Association Between Hypodensities Detected by Computed Tomography and Hematoma Expansion in Patients with Intracerebral Hemorrhage. JAMA Neurol. 2016, 73, 961–968. [Google Scholar] [CrossRef]
  26. Fujii, Y.; Tanaka, R.; Takeuchi, S.; Koike, T.; Minakawa, T.; Sasaki, O. Hematoma enlargement in spontaneous intracerebral hemorrhage. J. Neurosurg. 1994, 80, 51–57. [Google Scholar] [CrossRef]
  27. Brott, T.; Broderick, J.; Kothari, R.; Barsan, W.; Tomsick, T.; Sauerbeck, L.; Spilker, J.; Duldner, J.; Khoury, J. Early hemorrhage growth in patients with intracerebral hemorrhage. Stroke 1997, 28, 1–5. [Google Scholar] [CrossRef]
  28. Li, Z.; You, M.; Long, C.; Bi, R.; Xu, H.; He, Q.; Hu, B. Hematoma Expansion in Intracerebral Hemorrhage: An Update on Prediction and Treatment. Front. Neurol. 2020, 11, 702. [Google Scholar] [CrossRef]
  29. Roh, D.; Boehme, A.; Young, C.; Roth, W.; Gutierrez, J.; Flaherty, M.; Rosand, J.; Testai, F.; Woo, D.; Elkind, M.S.V. Hematoma expansion is more frequent in deep than lobar intracerebral hemorrhage. Neurology 2020, 95, e3386–e3393. [Google Scholar] [CrossRef]
  30. Wang, W.; Jin, W.; Feng, H.; Wu, G.; Wang, W.; Jia, J.; Ji, R.; Wang, A.; Zhao, X. Higher Cerebral Blood Flow Predicts Early Hematoma Expansion in Patients with Intracerebral Hemorrhage: A Clinical Study. Front. Neurol. 2021, 12, 735771. [Google Scholar] [CrossRef]
  31. Brouwers, H.B.; Chang, Y.; Falcone, G.J.; Cai, X.; Ayres, A.M.; Battey, T.W.; Vashkevich, A.; McNamara, K.A.; Valant, V.; Schwab, K.; et al. Predicting hematoma expansion after primary intracerebral hemorrhage. JAMA Neurol. 2014, 71, 158–164. [Google Scholar] [CrossRef] [Green Version]
  32. Roh, D.J.; Albers, D.J.; Magid-Bernstein, J.; Doyle, K.; Hod, E.; Eisenberger, A.; Murthy, S.; Witsch, J.; Park, S.; Agarwal, S.; et al. Low hemoglobin and hematoma expansion after intracerebral hemorrhage. Neurology 2019, 93, e372–e380. [Google Scholar] [CrossRef] [PubMed]
  33. Morotti, A.; Boulouis, G.; Charidimou, A.; Li, Q.; Poli, L.; Costa, P.; De Giuli, V.; Leuci, E.; Mazzacane, F.; Busto, G.; et al. Hematoma Expansion in Intracerebral Hemorrhage with Unclear Onset. Neurology 2021, 96, e2363–e2371. [Google Scholar] [CrossRef] [PubMed]
  34. Rodriguez-Luna, D.; Coscojuela, P.; Rubiera, M.; Hill, M.D.; Dowlatshahi, D.; Aviv, R.I.; Silva, Y.; Dzialowski, I.; Lum, C.; Czlonkowska, A.; et al. Ultraearly hematoma growth in active intracerebral hemorrhage. Neurology 2016, 87, 357–364. [Google Scholar] [CrossRef] [Green Version]
  35. Liu, J.; Xu, H.; Chen, Q.; Zhang, T.; Sheng, W.; Huang, Q.; Song, J.; Huang, D.; Lan, L.; Li, Y.; et al. Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine. EBioMedicine 2019, 43, 454–459. [Google Scholar] [CrossRef] [Green Version]
  36. Di Napoli, M.; Parry-Jones, A.R.; Smith, C.J.; Hopkins, S.J.; Slevin, M.; Masotti, L.; Campi, V.; Singh, P.; Papa, F.; Popa-Wagner, A.; et al. C-reactive protein predicts hematoma growth in intracerebral hemorrhage. Stroke 2014, 45, 59–65. [Google Scholar] [CrossRef] [Green Version]
  37. Camps-Renom, P.; Alejaldre-Monforte, A.; Delgado-Mederos, R.; Martínez-Domeño, A.; Prats-Sánchez, L.; Pascual-Goñi, E.; Martí-Fàbregas, J. Does prior antiplatelet therapy influence hematoma volume and hematoma growth following intracerebral hemorrhage? Results from a prospective study and a meta-analysis. Eur. J. Neurol. 2017, 24, 302–308. [Google Scholar] [CrossRef]
  38. Kazui, S.; Naritomi, H.; Yamamoto, H.; Sawada, T.; Yamaguchi, T. Enlargement of spontaneous intracerebral hemorrhage. Incidence and time course. Stroke 1996, 27, 1783–1787. [Google Scholar] [CrossRef]
  39. Yao, X.; Wu, B.; Xu, Y.; Siwila-Sackman, E.; Selim, M. Day-night variability of hematoma expansion in patients with spontaneous intracerebral hemorrhage. J. Biol. Rhythm. 2015, 30, 242–250. [Google Scholar] [CrossRef]
  40. Chen, Q.; Zhu, D.; Liu, J.; Zhang, M.; Xu, H.; Xiang, Y.; Zhan, C.; Zhang, Y.; Huang, S.; Yang, Y. Clinical-radiomics Nomogram for Risk Estimation of Early Hematoma Expansion after Acute Intracerebral Hemorrhage. Acad. Radiol. 2021, 28, 307–317. [Google Scholar] [CrossRef]
  41. Nehme, A.; Ducroux, C.; Panzini, M.A.; Bard, C.; Bereznyakova, O.; Boisseau, W.; Deschaintre, Y.; Diestro, J.D.B.; Guilbert, F.; Jacquin, G.; et al. Non-contrast CT markers of intracerebral hematoma expansion: A reliability study. Eur. Radiol. 2022, 32, 6126–6135. [Google Scholar] [CrossRef] [PubMed]
  42. Chu, H.; Huang, C.; Dong, J.; Yang, X.; Xiang, J.; Mao, Y.; Dong, Q.; Tang, Y. Minimal Computed Tomography Attenuation Value Within the Hematoma is Associated with Hematoma Expansion and Poor Outcome in Intracerebral Hemorrhage Patients. Neurocritical Care 2019, 31, 455–465. [Google Scholar] [CrossRef]
  43. Zhao, M.; Huang, W.; Huang, S.; Lin, F.; He, Q.; Zheng, Y.; Gao, Z.; Cai, L.; Ye, G.; Chen, R.; et al. Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage. Front. Neurol. 2022, 13, 999223. [Google Scholar] [CrossRef]
  44. Teng, L.; Ren, Q.; Zhang, P.; Wu, Z.; Guo, W.; Ren, T. Artificial Intelligence Can Effectively Predict Early Hematoma Expansion of Intracerebral Hemorrhage Analyzing Noncontrast Computed Tomography Image. Front. Aging Neurosci. 2021, 13, 632138. [Google Scholar] [CrossRef]
  45. Fisher, C.M. Pathological observations in hypertensive cerebral hemorrhage. J. Neuropathol. Exp. Neurol. 1971, 30, 536–550. [Google Scholar] [CrossRef]
  46. New, P.F.; Aronow, S. Attenuation measurements of whole blood and blood fractions in computed tomography. Radiology 1976, 121 Pt 1, 635–640. [Google Scholar] [CrossRef]
  47. Parizel, P.M.; Makkat, S.; Van Miert, E.; Van Goethem, J.W.; van den Hauwe, L.; De Schepper, A.M. Intracranial hemorrhage: Principles of CT and MRI interpretation. Eur. Radiol. 2001, 11, 1770–1783. [Google Scholar] [CrossRef]
Brainsci 13 00608 g001 550
Figure 1. Receiver-operating characteristic curve analysis of significant predictors and different hematoma expansion definitions. (A) The AUC of “heterogeneous” was 0.674 (p = 0.004), and the time to baseline CT was 0.715 (p = 0.001) in the definition type of BCT within 6 h and FCT within 24 h. (B) The AUC of “heterogeneous” was 0.638 (p = 0.005) and the blend sign was 0.618 (p = 0.012) in the definition type of BCT within 6 h and FCT within 72 h. (C) The AUC of “heterogeneous” was 0.660 (p = 0.003) in the definition type of BCT within 12 h and FCT within 24 h. (D) The AUC of “heterogeneous” was 0.638 (p = 0.002) and the blend sign was 0.613 (p = 0.005) in the definition type of BCT within 12 h and FCT within 72 h. AUC, the area under the curve; BCT, time to baseline CT; FCT, time to follow-up CT.
Figure 1. Receiver-operating characteristic curve analysis of significant predictors and different hematoma expansion definitions. (A) The AUC of “heterogeneous” was 0.674 (p = 0.004), and the time to baseline CT was 0.715 (p = 0.001) in the definition type of BCT within 6 h and FCT within 24 h. (B) The AUC of “heterogeneous” was 0.638 (p = 0.005) and the blend sign was 0.618 (p = 0.012) in the definition type of BCT within 6 h and FCT within 72 h. (C) The AUC of “heterogeneous” was 0.660 (p = 0.003) in the definition type of BCT within 12 h and FCT within 24 h. (D) The AUC of “heterogeneous” was 0.638 (p = 0.002) and the blend sign was 0.613 (p = 0.005) in the definition type of BCT within 12 h and FCT within 72 h. AUC, the area under the curve; BCT, time to baseline CT; FCT, time to follow-up CT.
Brainsci 13 00608 g001
Table
Table 1. Clinical and imaging characteristics of patients with intracerebral hemorrhage who were included in the study.
Table 1. Clinical and imaging characteristics of patients with intracerebral hemorrhage who were included in the study.
VariableTotal (n = 158)
Age, y, mean (SD) 61.0 (12.5)
Male sex, n (%)128 (81.0)
Hypertension, n (%)110 (69.6)
Diabetes mellitus, n (%)29 (18.3)
Oral anticoagulants, n (%)3 (1.9%)
Oral antiplatelet drugs, n (%)4 (2.5%)
Admission SBP, mmHg, mean (SD)163.6 (27.7)
Admission DBP, mmHg, mean (SD)95.4 (16.4)
Baseline GCS, median (IQR)13 (10–15)
Deep ICH, n (%)130 (82.3)
IVH, n (%)47 (29.7)
Hydrocephalus, n (%)19 (12.0)
Density category
Homogeneous, n (%)142 (89.9)
Heterogeneous, n (%)16 (10.1)
Swirl sign, n (%)38 (24.1)
Black hole sign, n (%)15 (9.5)
Blend sign, n (%)12 (7.6)
Blood-fluid level, n (%)4 (2.5)
Time to baseline CT, h, median (IQR)3.5 (2.0–6.3)
ICH volume, mL, median (IQR)24.3 (14.5–36.5)
UHG, mL/h, median (IQR)5.5 (3.3–13.6)
WBC, 109/L, mean (SD)9.5 (3.3)
HGB, g/L, mean (SD)142.3 (18.0)
PLT, 109/L, mean (SD)212.6 (63.7)
GLU, mmol/L, mean (SD)7.4 (3.4)
SD, standard deviation; SBP, systolic blood pressure; DBP, diastolic blood pressure; GCS, Glasgow Coma Scale; IQR, interquartile range; ICH, intracerebral hemorrhage; IVH, intraventricular hemorrhage; CT, computed tomography; UHG, ultraearly hematoma growth; WBC, white blood cell; HGB, hemoglobin; PLT, platelet; GLU, blood glucose.
Table
Table 2. Comparison of baseline clinical and radiological characteristics of individuals with and without hematoma expansion for the time to baseline CT within 6 h.
Table 2. Comparison of baseline clinical and radiological characteristics of individuals with and without hematoma expansion for the time to baseline CT within 6 h.
VariableHematoma Expansion for BCT within 6 h and FCT within 24 h (n = 92)Hematoma Expansion for BCT within 6 h and FCT within 72 h (n = 118)
Yes (n = 18)No (n = 74)p-ValueYes (n = 25)No (n = 93)p-Value
Age, y, mean (SD)59.8 (13.3)61.0 (12.3)0.73357.4 (12.6)60.9 (12.0)0.206
Male Sex, n (%)16 (88.9)57 (77.0)0.42922 (88.0)74 (79.6)0.502
Hypertension, n (%)12 (66.7)56 (75.7)0.63015 (60.0)68 (73.1)0.202
Diabetes mellitus, n (%)3 (16.7)6 (8.1)0.5135 (20.0)7 (7.5)0.145
Admission SBP, mmHg, mean (SD)168.9 (30.7)167.7 (26.2)0.856163.8 (29.8)163.8 (26.2)0.998
Admission DBP, mmHg, mean (SD)97.1 (17.8)98.6 (15.7)0.72591.2 (16.7)98.1 (16.1)0.063
Baseline GCS, median (IQR)13 (9–15)13 (10–15)0.96014 (9.5–15)13 (10–15)0.699
Deep ICH, n (%)16 (88.9)62 (83.8)0.86123 (92.0)79 (84.9)0.558
IVH, n (%)3 (16.7)23 (31.1)0.2235 (20.0)24 (25.8)0.549
Hydrocephalus, n (%)2 (11.1)9 (12.2)0.9021 (4.0)12 (12.9)0.367
Heterogeneity, n (%)7 (38.9)3 (4.1)<0.0018 (32.0)4 (4.3)<0.001
Swirl sign, n (%)6 (33.3)16 (21.6)0.4617 (28.0)24 (25.8)0.825
Black hole sign, n (%)3 (16.7)4 (5.4)0.2623 (12.0)8 (8.6)0.896
Blend sign, n (%)3 (16.7)4 (5.4)0.2627 (28.0)4 (4.3)0.001
Blood-fluid level, n (%)1 (5.6)2 (2.7)0.4841 (4.0)3 (3.2)0.849
Time to baseline CT, h, median (IQR)1.8 (1.3–2.9)2.8 (1.8–4.1)0.0052.0 (1.6–3.4)2.8 (1.9–4.1)0.023
ICH volume, mL, median (IQR)24.9 (16.9–36.0)21.0 (12.9–31.3)0.34024.3 (15.9–33.6)21.6(13.5–31.5)0.453
UHG, mL/h, median (IQR)14.2 (6.8–30.3)7.2 (3.8–14.9)0.02210.8 (5.1–26.3)7.2 (4.0–15.5)0.047
WBC, 109/L, mean (SD)9.6 (4.8)9.5 (2.9)0.8698.8 (4.6)9.6 (3.0)0.320
HGB, g/L, mean (SD)142.6 (18.8)140.4 (19.0)0.665143.9 (17.4)141.2 (18.2)0.497
PLT, 109/L, mean (SD)208.6 (77.3)223.4 (57.8)0.367199.4 (74.3)216.5 (61.4)0.242
GLU, mmol/L, mean (SD)8.5 (4.5)7.6 (3.4)0.3948.3 (4.5)7.4 (3.2)0.244
BCT, time to baseline CT; FCT, time to follow-up CT; SD, standard deviation; SBP, systolic blood pressure; DBP, diastolic blood pressure; GCS, Glasgow Coma Scale; IQR, interquartile range; ICH, intracerebral hemorrhage; IVH, intraventricular hemorrhage; CT, computed tomography; UHG, ultraearly hematoma growth; WBC, white blood cell; HGB, hemoglobin; PLT, platelet; GLU, blood glucose.
Table
Table 3. Univariate analysis of the predictors of hematoma expansion for the time to baseline CT within 6 h.
Table 3. Univariate analysis of the predictors of hematoma expansion for the time to baseline CT within 6 h.
VariableBCT within 6 h and FCT within 24 h (n = 92)BCT within 6 h and FCT within 72 h (n = 118)
OR (95% CI)p-ValueOR (95% CI)p-Value
Age0.993 (0.952–1.035)0.7300.975 (0.939–1.014)0.206
Sex0.419 (0.087–2.008)0.2770.531 (0.144–1.963)0.343
Hypertension0.643 (0.211–1.960)0.4370.551 (0.219–1.387)0.206
Diabetes mellitus2.267 (0.509–10.102)0.2833.071 (0.883–10.683)0.078
Admission SBP1.002 (0.983–1.021)0.8541.000 (0.984–1.017)0.998
Admission DBP0.994 (0.962–1.027)0.7220.973 (0.945–1.002)0.067
Baseline GCS0.961 (0.812–1.136)0.6410.997 (0.860–1.157)0.973
Deep ICH 1.548 (0.314–7.628)0.5912.038 (0.431–9.627)0.369
IVH0.443 (0.117–1.683)0.2320.719 (0.243–2.126)0.551
Hydrocephalus0.903 (0.177–4.593)0.9020.281 (0.035–2.274)0.234
Heterogeneity15.061 (3.380–67.106)<0.00110.471 (2.832–38.711)<0.001
Swirl sign1.812 (0.588–5.586)0.3001.118 (0.416–3.006)0.825
Black hole sign3.500 (0.708–17.291)0.1241.449 (0.355–5.918)0.606
Blend sign3.500 (0.708–17.291)0.1248.653 (2.291–32.677)0.001
Blood-fluid level2.118 (0.181–24.736)0.5501.250 (0.124–12.562)0.850
Time to baseline CT0.504 (0.309–0.821)0.0060.665 (0.465–0.949)0.025
ICH volume1.015 (0.985–1.045)0.3371.011 (0.983–1.040)0.442
UHG1.048 (1.008–1.090)0.0171.044 (1.008–1.081)0.016
WBC1.013 (0.870–1.180)0.8670.930 (0.805–1.073)0.318
HGB1.006 (0.978–1.035)0.6611.009 (0.983–1.035)0.493
PLT0.996 (0.987–1.005)0.3630.996 (0.989–1.003)0.241
GLU1.058 (0.930–1.203)0.3941.069 (0.954–1.198)0.248
BCT, time to baseline CT; FCT, time to follow-up CT; OR, odds ratio; 95% CI, 95% confidence interval; SBP, systolic blood pressure; DBP, diastolic blood pressure; GCS, Glasgow Coma Scale; ICH, intracerebral hemorrhage; IVH, intraventricular hemorrhage; CT, computed tomography; UHG, ultraearly hematoma growth; WBC, white blood cell; HGB, hemoglobin; PLT, platelet; GLU, blood glucose.
Table
Table 4. Multivariate analysis of the predictors for hematoma expansion in different types.
Table 4. Multivariate analysis of the predictors for hematoma expansion in different types.
UnadjustedAdjusted *
OR (95% CI)p-ValueOR (95% CI)p-Value
BCT within 6 h
and FCT within 24 h
Heterogeneity40.536 (5.366–306.223)<0.00188.445(5.387–1452.191)0.002
Time to baseline CT0.278 (0.106–0.729)0.0090.234 (0.077–0.712)0.011
UHG0.971 (0.910–1.036)0.3680.973 (0.909–1.042)0.437
BCT within 6 h
and FCT within 72 h
Heterogeneity8.833 (2.165–36.037)0.00231.703 (3.036–331.026)0.004
Blend sign7.121 (1.651–30.709)0.0086.985 (1.364–35.776)0.020
Time to baseline CT0.722 (0.414–1.259)0.2510.660 (0.360–1.209)0.179
UHG1.011 (0.956–1.071)0.6951.024 (0.963–1.088) 0.447
BCT within 12 h
and FCT within 24 h
Heterogeneity11.259 (1.873–67.687)0.00810.478 (1.657–66.252)0.013
Black hole sign1.061 (0.136–8.293)0.9551.214 (0.161–9.173)0.851
UHG1.044 (1.003–1.087)0.0361.044 (0.990–1.100)0.110
BCT within 12 h
and FCT within 72 h
Diabetes mellitus3.255 (0.838–12.645)0.0883.259 (0.836–12.702)0.089
IVH0.368 (0.108–1.261)0.1120.367 (0.105–1.281)0.116
Heterogeneity6.473 (1.579–26.541)0.0096.465 (1.569–26.637)0.010
Black hole sign1.292 (0.296–5.634)0.7331.292 (0.296–5.635)0.733
Blend sign6.197 (1.428–26.886)0.0156.203 (1.424–27.014)0.015
UHG1.044 (1.004–1.086)0.0291.045 (0.995–1.097)0.081
OR, odds ratio; 95% CI, 95% confidence interval; BCT, time to baseline CT; FCT, time to follow-up CT; CT, computed tomography; UHG, ultraearly hematoma growth; IVH, intraventricular hemorrhage. * Adjusting for potential predictors of p < 0.05 in other types.
Table
Table 5. Comparison of baseline clinical and radiological characteristics of individuals with and without hematoma expansion for time to baseline CT within 12 h.
Table 5. Comparison of baseline clinical and radiological characteristics of individuals with and without hematoma expansion for time to baseline CT within 12 h.
VariableHematoma Expansion for BCT within 12 h and FCT within 24 h (n = 115)Hematoma Expansion for BCT within 12 h and FCT within 72 h (n = 158)
Yes (n = 22)No (n = 93)p-ValueYes (n = 31)No (n = 127)p-Value
Age, y, mean (SD)59.6 (12.6)60.8 (12.2)0.66957.7 (12.5)61.8 (12.4)0.106
Male Sex, n (%)20 (90.9)71 (76.3)0.22228 (90.3)100 (78.7)0.140
Hypertension, n (%)16 (72.7)66 (71.0)0.87020 (64.5)90 (70.9)0.491
Diabetes mellitus, n (%)3 (13.6)7 (7.5)0.62120 (64.5)9 (7.1)<0.001
Admission SBP, mmHg, mean (SD)171.4 (31.8)165.2 (27.1)0.354165.1 (30.5)163.3 (27.1)0.747
Admission DBP, mmHg, mean (SD)97.2 (20.3)96.4 (15.6)0.84997.0 (19.0)95.0 (15.7)0.552
Baseline GCS, median (IQR)13 (9.75–15)13 (10–15)0.87814 (15–10)13 (15–10)0.542
Deep ICH, n (%)19 (86.4)75 (80.6)0.75128 (90.3%)102 (80.3%)0.191
IVH, n (%)4 (18.2)33 (35.5)0.1184 (12.9)43 (33.9)0.022
Hydrocephalus, n (%)3 (13.6)13 (14.0)0.9673 (9.7)16 (12.6)0.888
Heterogeneity, n (%)8 (36.4)4 (4.3)<0.00110 (32.3)6 (4.7)<0.001
Swirl sign, n (%)7 (31.8)20 (21.5)0.3058 (25.8)30 (23.6)0.799
Black hole sign, n (%)5 (22.7)5 (5.4)0.0306 (19.3)9 (7.1)0.024
Blend sign, n (%)3 (13.6)4 (4.3)0.2508 (25.8)4 (3.1)<0.001
Blood–fluid level, n (%)1 (4.5)2 (2.2)0.4742 (6.5)2 (1.6)0.173
Time to baseline CT, h, median (IQR)2.1 (1.6–3.7)3.5 (2.2–5.5)0.0302.1 (4.5–1.6)3.7 (6.5–2.3)0.018
ICH volume, mL, median (IQR)27.4 (19.1–44.4)23.3 (13.7–36.8)0.14626.2 (40.0–17.8)23.8 (36.4–14.1)0.278
UHG, mL/h, median (IQR)11.2 (4.8–25.5)5.6 (3.3–12.8)0.0108.8 (4.2–24.8)5.2 (3.3–11.7)0.022
WBC, 109/L, mean (SD)9.1 (4.7)9.5 (2.9)0.6198.7 (4.4)9.7 (3.0)0.142
HGB, g/L, mean (SD)138.7 (21.3)141.6 (18.0)0.507141.1 (19.4)142.6 (17.7)0.676
PLT, 109/L, mean (SD)201.6 (80.4)219.7 (56.5)0.219200.1 (78.4)215.6 (59.5)0.223
GLU, mmol/L, mean (SD)8.6 (4.9)7.5 (3.2)0.1668.3 (4.8)7.2 (2.9)0.248
BCT, time to baseline CT; FCT, time to follow-up CT; SD, standard deviation; SBP, systolic blood pressure; DBP, diastolic blood pressure; GCS, Glasgow Coma Scale; IQR, interquartile range; ICH, intracerebral hemorrhage; IVH, intraventricular hemorrhage; CT, computed tomography; UHG, ultraearly hematoma growth; WBC, white blood cell; HGB, hemoglobin; PLT, platelet; GLU, blood glucose.
Table
Table 6. Univariate analysis of the predictors of hematoma expansion for a time to baseline CT within 12 h.
Table 6. Univariate analysis of the predictors of hematoma expansion for a time to baseline CT within 12 h.
VariableBCT within 12 h and FCT within 24 h (n = 115)BCT within 12 h and FCT within 72 h (n = 158)
OR (95% CI)p-ValueOR (95% CI)p-Value
Age0.992 (0.954–1.031)0.6660.973 (0.941–1.006)0.108
Sex0.323 (0.070–1.491)0.1470.397 (0.112–1.405)0.152
Hypertension1.091 (0.386–3.085)0.8700.747 (0.326–1.713)0.492
Diabetes mellitus1.940 (0.459–8.195)0.3673.147 (1.027–9.639)0.045
Admission SBP1.008 (0.991–1.025)0.3521.002 (0.988–1.017)0.745
Admission DBP1.003 (0.975–1.031)0.8471.007 (0.984–1.032)0.549
Baseline GCS0.971 (0.827–1.141)0.7211.011 (0.879–1.164)0.876
Deep ICH 1.520 (0.405–5.701)0.5352.288 (0.643–8.133)0.201
IVH0.404 (0.126–1.294)0.1270.289 (0.095–0.880)0.029
Hydrocephalus0.972 (0.252–3.753)0.9670.743 (0.202–2.730)0.655
Heterogeneity12.714 (3.376–47.878)<0.0019.603 (3.155–29.231)<0.001
Swirl sign1.703 (0.611–4.745)0.3081.125 (0.456–2.774)0.799
Black hole sign5.176 (1.350–19.847)0.0163.147 (1.027–9.639)0.045
Blend sign3.513 (0.726–17.001)0.11810.696 (2.973–38.474)<0.001
Blood-fluid level2.167 (0.188–25.030)0.5364.310 (0.583–31.886)0.152
Time to baseline CT0.855 (0.692–1.056)0.1460.878 (0.751–1.026)0.101
ICH volume1.022 (0.996–1.049)0.1021.016 (0.992–1.041)0.199
UHG1.047 (1.010–1.086)0.0121.045 (1.012–1.079)0.007
WBC0.963 (0.829–1.117)0.6160.906 (0.795–1.033)0.142
HGB0.992 (0.968–1.016)0.5040.995 (0.974–1.017)0.674
PLT0.995 (0.987–1.003)0.2180.996 (0.990–1.002)0.223
GLU1.081 (0.966–1.210)0.1741.082 (0.977–1.199)0.129
BCT, time to baseline CT; FCT, Time to follow-up CT; OR, odds ratio; 95% CI, 95% confidence interval; SBP, systolic blood pressure; DBP, diastolic blood pressure; GCS, Glasgow Coma Scale; ICH, intracerebral hemorrhage; IVH, intraventricular hemorrhage; CT, computed tomography; UHG, ultraearly hematoma growth; WBC, white blood cell; HGB, hemoglobin; PLT, platelet; GLU, blood glucose.
Table
Table 7. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of hematoma heterogeneity to predict hematoma expansion in different types.
Table 7. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of hematoma heterogeneity to predict hematoma expansion in different types.
VariableSensitivity (%)Specificity (%)PPV (%)NPV (%)AUC
BCT within 6 h
and FCT within 24 h		38.996.070.086.60.674
BCT within 6 h
and FCT within 72 h		32.095.766.784.00.638
BCT within 12 h
and FCT within 24 h		36.495.766.786.40.660
BCT within 12 h
and FCT within 72 h		32.395.362.585.2 0.638
BCT, time to baseline CT; FCT, time to follow-up CT; PPV, positive predictive value; NPV, negative predictive value; AUC, area under the curve; CT, computed tomography.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yu, L.; Zhao, M.; Lin, Y.; Zeng, J.; He, Q.; Zheng, Y.; Ma, K.; Lin, F.; Kang, D. Noncontrast Computed Tomography Markers Associated with Hematoma Expansion: Analysis of a Multicenter Retrospective Study. Brain Sci. 2023, 13, 608. https://doi.org/10.3390/brainsci13040608

AMA Style

Yu L, Zhao M, Lin Y, Zeng J, He Q, Zheng Y, Ma K, Lin F, Kang D. Noncontrast Computed Tomography Markers Associated with Hematoma Expansion: Analysis of a Multicenter Retrospective Study. Brain Sciences. 2023; 13(4):608. https://doi.org/10.3390/brainsci13040608

Chicago/Turabian Style

Yu, Lianghong, Mingpei Zhao, Yuanxiang Lin, Jiateng Zeng, Qiu He, Yan Zheng, Ke Ma, Fuxin Lin, and Dezhi Kang. 2023. "Noncontrast Computed Tomography Markers Associated with Hematoma Expansion: Analysis of a Multicenter Retrospective Study" Brain Sciences 13, no. 4: 608. https://doi.org/10.3390/brainsci13040608

APA Style

Yu, L., Zhao, M., Lin, Y., Zeng, J., He, Q., Zheng, Y., Ma, K., Lin, F., & Kang, D. (2023). Noncontrast Computed Tomography Markers Associated with Hematoma Expansion: Analysis of a Multicenter Retrospective Study. Brain Sciences, 13(4), 608. https://doi.org/10.3390/brainsci13040608

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop