Noncontrast Computed Tomography Hypodensities Predict Poor Outcome in Intracerebral Hemorrhage Patients.
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ABSTRACT:
SUBMITTER: Boulouis G
PROVIDER: S-EPMC5039101 | biostudies-literature | 2016 Oct
REPOSITORIES: biostudies-literature
Publications
Noncontrast Computed Tomography Hypodensities Predict Poor Outcome in Intracerebral Hemorrhage Patients.
Boulouis Gregoire G Morotti Andrea A Brouwers H Bart HB Charidimou Andreas A Jessel Michael J MJ Auriel Eitan E Pontes-Neto Octavio O Ayres Alison A Vashkevich Anastasia A Schwab Kristin M KM Rosand Jonathan J Viswanathan Anand A Gurol Mahmut E ME Greenberg Steven M SM Goldstein Joshua N JN
Stroke 20160906 10
<h4>Background and purpose</h4>Noncontrast computed tomographic (CT) hypodensities have been shown to be associated with hematoma expansion in intracerebral hemorrhage (ICH), but their impact on functional outcome is yet to be determined. We evaluated whether baseline noncontrast CT hypodensities are associated with poor clinical outcome.<h4>Methods</h4>We performed a retrospective review of a prospectively collected cohort of consecutive patients with primary ICH presenting to a single academic ...[more]
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Project description:Hematoma expansion (HE) is related to clinical deterioration after intracerebral hemorrhage (ICH) and noncontrast computed tomography (NCCT) signs are indicated as predictors for HE but with inconsistent conclusions. We aim to clarify the correlations of NCCT heterogeneity signs with HE by meta-analysis of related studies.PubMed, Embase, and Cochrane library were searched for eligible studies exploring the relationships between NCCT heterogeneity signs (hypodensity, mixed density, swirl sign, blend sign, and black hole sign) and HE. Poor outcome and mortality were considered as secondary outcomes. Odds ratio (OR) and its 95% confidence intervals (CIs) were selected as the effect size and combined using random effects model.Fourteen studies were included, involving 3240 participants and 435 HEs. The summary results suggested statistically significant correlations of heterogeneity signs with HE (OR, 5.17; 95% CI, 3.72-7.19, P < 0.001), poor outcome (OR, 3.60; 95% CI, 1.98-6.54, P < 0.001), and mortality (OR, 4.64; 95%, 2.96-7.27, P < 0.001).Our findings suggested that hematoma heterogeneity signs on NCCT were positively associated with the increased risk of HE, poor outcome, and mortality rate in ICH.
| S-EPMC5818889 | biostudies-literature
Project description:BACKGROUND AND PURPOSE:Although the computed tomographic angiography spot sign performs well as a biomarker for hematoma expansion (HE), computed tomographic angiography is not routinely performed in the emergency setting. We developed and validated a score to predict HE-based on noncontrast computed tomography (NCCT) findings in spontaneous acute intracerebral hemorrhage. METHODS:After developing the score in a single-center cohort of patients with intracerebral hemorrhage (n=344), we validated it in a large clinical trial population (n=954) and in a multicenter intracerebral hemorrhage cohort (n=241). The following NCCT markers of HE were analyzed: hypodensities, blend sign, hematoma shape and density, and fluid level. HE was defined as hematoma growth >6 mL or >33%. The score was created using the estimates from multivariable logistic regression after final predictors were selected from bootstrap samples. RESULTS:Presence of blend sign (odds ratio, 3.09; 95% confidence interval [CI],1.49-6.40; P=0.002), any intrahematoma hypodensity (odds ratio, 4.54; 95% CI, 2.44-8.43; P<0.0001), and time from onset to NCCT <2.5 hours (odds ratio, 3.73; 95% CI, 1.86-7.51; P=0.0002) were predictors of HE. A 5-point score was created (BAT score: 1 point for blend sign, 2 points for any hypodensity, and 2 points for timing of NCCT <2.5 hours). The c statistic was 0.77 (95% CI, 0.70-0.83) in the development population, 0.65 (95% CI 0.61-0.68) and 0.70 (95% CI, 0.64-0.77) in the 2 validation cohorts. A dichotomized score (BAT score ?3) predicted HE with 0.50 sensitivity and 0.89 specificity. CONCLUSIONS:An easy to use 5-point prediction score can identify subjects at high risk of HE with good specificity and accuracy. This tool requires just a baseline NCCT scan and may help select patients with intracerebral hemorrhage for antiexpansion clinical trials.
| S-EPMC6034631 | biostudies-literature
Project description:Background Noncontrast computed tomography (NCCT) markers are the emerging predictors of hematoma expansion in intracerebral hemorrhage. However, the relationship between NCCT markers and the dynamic change of hematoma in parenchymal tissues and the ventricular system remains unclear. Methods and Results We included 314 consecutive patients with intracerebral hemorrhage admitted to our hospital from July 2011 to May 2017. The intracerebral hemorrhage volumes and intraventricular hemorrhage (IVH) volumes were measured using a semiautomated, computer-assisted technique. Revised hematoma expansion (RHE) was defined by incorporating the original definition of hematoma expansion into IVH growth. Receiver operating characteristic curve analysis was used to compare the performance of the NCCT markers in predicting the IVH growth and RHE. Of 314 patients in our study, 61 (19.4%) had IVH growth and 93 (23.9%) had RHE. After adjustment for potential confounding variables, blend sign, black hole sign, island sign, and expansion-prone hematoma could independently predict IVH growth and RHE in the multivariate logistic regression analysis. Expansion-prone hematoma had a higher predictive performance of RHE than any single marker. The diagnostic accuracy of RHE in predicting poor prognosis was significantly higher than that of hematoma expansion. Conclusions The NCCT markers are independently associated with IVH growth and RHE. Furthermore, the expansion-prone hematoma has a higher predictive accuracy for prediction of RHE and poor outcome than any single NCCT marker. These findings may assist in risk stratification of NCCT signs for predicting active bleeding.
| S-EPMC7955436 | biostudies-literature
Project description:ObjectiveTo determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH).Materials and methodsWe retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power.ResultsThe AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively. The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively.ConclusionNCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.
| S-EPMC7909850 | biostudies-literature
Project description:Although intracerebral hemorrhage (ICH) volume and location are important predictors of outcome in adults, few data exist in children.A consecutive cohort of children, including full-term newborns to those younger than 18 years of age with nontraumatic, acute ICH and head CT available for analysis were studied. Clinical information was abstracted via chart review. Hemorrhage volume was expressed as percentage of total brain volume (TBV) with large hemorrhage defined as >or=4% of TBV. Hemorrhages were manually traced on each head CT slice and volumes were calculated by multiplying by slice thickness. Location was classified as supratentorial or infratentorial. Logistic regression was used to identify predictors of poor neurological outcome, defined as a Glasgow outcome scale <or=2 (death or persistent vegetative state).Thirty children were included, median age 6 years. Median ICH volume was 20.4 cm(3) and median ICH size as a percentage of TBV was 1.9%. Only 4 of 22 children with ICH <4% of TBV had poor outcomes, vs 5 of 8 children with ICH >or=4% of TBV (P=0.03). In multivariate analysis, hemorrhage >or=4% of TBV (OR, 22.5; 95% CI, 1.4-354; P=0.03) independently predicted poor outcome 30 days after ICH. In this small sample, infratentorial hemorrhage location and the presence of intraventricular hemorrhage did not predict poor outcome.ICH volume predicts neurological outcome at 30 days in children, with worst outcome when hemorrhage is >or=4% of TBV. Location and ICH etiology may also be important. These findings identify children with ICH who are candidates for aggressive management and may influence counseling regarding prognosis.
| S-EPMC2830068 | biostudies-other
Project description:Rationale: Given the rapid spread of COVID-19, an updated risk-stratify prognostic tool could help clinicians identify the high-risk patients with worse prognoses. We aimed to develop a non-invasive and easy-to-use prognostic signature by chest CT to individually predict poor outcome (death, need for mechanical ventilation, or intensive care unit admission) in patients with COVID-19.Methods: From November 29, 2019 to February 19, 2020, a total of 492 patients with COVID-19 from four centers were retrospectively collected. Since different durations from symptom onsets to the first CT scanning might affect the prognostic model, we designated the 492 patients into two groups: 1) the early-phase group: CT scans were performed within one week after symptom onset (0-6 days, n = 317); and 2) the late-phase group: CT scans were performed one week later after symptom onset (?7 days, n = 175). In each group, we divided patients into the primary cohort (n = 212 in the early-phase group, n = 139 in the late-phase group) and the external independent validation cohort (n = 105 in the early-phase group, n = 36 in the late-phase group) according to the centers. We built two separate radiomics models in the two patient groups. Firstly, we proposed an automatic segmentation method to extract lung volume for radiomics feature extraction. Secondly, we applied several image preprocessing procedures to increase the reproducibility of the radiomics features: 1) applied a low-pass Gaussian filter before voxel resampling to prevent aliasing; 2) conducted ComBat to harmonize radiomics features per scanner; 3) tested the stability of the features in the radiomics signature by several image transformations, such as rotating, translating, and growing/shrinking. Thirdly, we used least absolute shrinkage and selection operator (LASSO) to build the radiomics signature (RadScore). Afterward, we conducted a Fine-Gray competing risk regression to build the clinical model and the clinic-radiomics signature (CrrScore). Finally, performances of the three prognostic signatures (clinical model, RadScore, and CrrScore) were estimated from the two aspects: 1) cumulative poor outcome probability prediction; 2) 28-day poor outcome prediction. We also did stratified analyses to explore the potential association between the CrrScore and the poor outcomes regarding different age, type, and comorbidity subgroups.Results: In the early-phase group, the CrrScore showed the best performance in estimating poor outcome (C-index = 0.850), and predicting the probability of 28-day poor outcome (AUC = 0.862). In the late-phase group, the RadScore alone achieved similar performance to the CrrScore in predicting poor outcome (C-index = 0.885), and 28-day poor outcome probability (AUC = 0.976). Moreover, the RadScore in both groups successfully stratified patients with COVID-19 into low- or high-RadScore groups with significantly different survival time in the training and validation cohorts (all P < 0.05). The CrrScore in both groups can also significantly stratify patients with different prognoses regarding different age, type, and comorbidities subgroups in the combined cohorts (all P < 0.05).Conclusions: This research proposed a non-invasive and quantitative prognostic tool for predicting poor outcome in patients with COVID-19 based on CT imaging. Taking the insufficient medical recourse into account, our study might suggest that the chest CT radiomics signature of COVID-19 is more effective and ideal to predict poor outcome in the late-phase COVID-19 patients. For the early-phase patients, integrating radiomics signature with clinical risk factors can achieve a more accurate prediction of individual poor prognostic outcome, which enables appropriate management and surveillance of COVID-19.
| S-EPMC7330838 | biostudies-literature
Project description:Hematoma expansion is a potentially modifiable predictor of poor outcome following an acute intracerebral hemorrhage (ICH). The ability to identify patients with ICH who are likeliest to experience hematoma expansion and therefore likeliest to benefit from expansion-targeted treatments remains an unmet need. Hypodensities within an ICH detected by noncontrast computed tomography (NCCT) have been suggested as a predictor of hematoma expansion.To determine whether hypodense regions, irrespective of their specific patterns, are associated with hematoma expansion in patients with ICH.We analyzed a large cohort of 784 patients with ICH (the development cohort; 55.6% female), examined NCCT findings for any hypodensity, and replicated our findings on a different cohort of patients (the replication cohort; 52.7% female). Baseline and follow-up NCCT data from consecutive patients with ICH presenting to a tertiary care hospital between 1994 and 2015 were retrospectively analyzed. Data analyses were performed between December 2015 and January 2016.Hypodensities were analyzed by 2 independent blinded raters. The association between hypodensities and hematoma expansion (>6 cm3 or 33% of baseline volume) was determined by multivariable logistic regression after controlling for other variables associated with hematoma expansion in univariate analyses with P???.10.A total of 1029 patients were included in the analysis. In the development and replication cohorts, 222 of 784 patients (28.3%) and 99 of 245 patients (40.4%; 321 of 1029 patients [31.2%]), respectively, had NCCT scans that demonstrated hypodensities at baseline (??=?0.87 for interrater reliability). In univariate analyses, hypodensities were associated with hematoma expansion (86 of 163 patients with hematoma expansion had hypodensities [52.8%], whereas 136 of 621 patients without hematoma expansion had hypodensities [21.9%]; P?<?.001). The association between hypodensities and hematoma expansion remained significant (odds ratio, 3.42 [95% CI, 2.21-5.31]; P?<?.001) in a multivariable model; other independent predictors of hematoma expansion were a CT angiography spot sign, a shorter time to CT, warfarin use, and older age. The independent predictive value of hypodensities was again demonstrated in the replication cohort (odds ratio, 4.37 [95% CI, 2.05-9.62]; P?<?.001).Hypodensities within an acute ICH detected on an NCCT scan may predict hematoma expansion, independent of other clinical and imaging predictors. This novel marker may help clarify the mechanism of hematoma expansion and serve as a useful addition to clinical algorithms for determining the risk of and treatment stratification for hematoma expansion.
| S-EPMC5584601 | biostudies-literature
Project description:PurposeComputed tomography (CT)-derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT-Perfusion (CT-P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion.MethodsCT-Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT-P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT-P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT-ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter τ . Spatial Spearman correlation between single photon emission CT perfusion (SPECT-P) and the proposed CT-P method was assessed in two patient cohorts via a parameter sweep of τ . The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT-P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT-P and 4DCT images acquired prior to radiotherapy. For each test case, CT-P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT-P and the resulting CT-P images were computed.ResultsThe median correlations between CT-P and SPECT-P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT-P and SPECT-P correlations across all 30 test cases ranged between 0.02 and 0.82. A one-sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two-sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One-sample sign test was statistically significant with 96.5 % confidence interval: 0.20-0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one-sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45-0.71, P < 0.00001.ConclusionCT-Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT-P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT-P imaging.
| S-EPMC8252085 | biostudies-literature
Project description:ObjectivesNon-contrast computed tomography (NCCT) markers are robust predictors of parenchymal hematoma expansion in intracerebral hemorrhage (ICH). We investigated whether NCCT features can also identify ICH patients at risk of intraventricular hemorrhage (IVH) growth.MethodsPatients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. NCCT markers were rated by two investigators for heterogeneous density, hypodensity, black hole sign, swirl sign, blend sign, fluid level, island sign, satellite sign, and irregular shape. ICH and IVH volumes were semi-manually segmented. IVH growth was defined as IVH expansion > 1 mL (eIVH) or any delayed IVH (dIVH) on follow-up imaging. Predictors of eIVH and dIVH were explored with multivariable logistic regression. Hypothesized moderators and mediators were independently assessed in PROCESS macro models.ResultsA total of 731 patients were included, of whom 185 (25.31%) suffered from IVH growth, 130 (17.78%) had eIVH, and 55 (7.52%) had dIVH. Irregular shape was significantly associated with IVH growth (OR 1.68; 95%CI [1.16-2.44]; p = 0.006). In the subgroup analysis stratified by the IVH growth type, hypodensities were significantly associated with eIVH (OR 2.06; 95%CI [1.48-2.64]; p = 0.015), whereas irregular shape (OR 2.72; 95%CI [1.91-3.53]; p = 0.016) in dIVH. The association between NCCT markers and IVH growth was not mediated by parenchymal hematoma expansion.ConclusionsNCCT features identified ICH patients at a high risk of IVH growth. Our findings suggest the possibility to stratify the risk of IVH growth with baseline NCCT and might inform ongoing and future studies.Clinical relevance statementNon-contrast CT features identified ICH patients at a high risk of intraventricular hemorrhage growth with subtype-specific differences. Our findings may assist in the risk stratification of intraventricular hemorrhage growth with baseline CT and might inform ongoing and future clinical studies.Key points• NCCT features identified ICH patients at a high risk of IVH growth with subtype-specific differences. • The effect of NCCT features was not moderated by time and location or indirectly mediated by hematoma expansion. • Our findings may assist in the risk stratification of IVH growth with baseline NCCT and might inform ongoing and future studies.
| S-EPMC10598100 | biostudies-literature
Project description:Intracerebral hemorrhage carries a high mortality and survivors are frequently left with significant disability. Immunological mechanisms may play an important role in hemorrhage-induced brain injury, however, research linking these mechanisms with clinical outcome remains limited. We aim to identify serum inflammatory mediators that are associated with outcome after intracerebral hemorrhage in order to translate data from experimental models to a patient cohort and identify potential targets worthy of reverse translation.A prospective cohort study at two comprehensive stroke centers enrolled patients with spontaneous intracerebral hemorrhage. Peripheral blood was collected at 6, 24, and 72 h from onset. Functional outcome was assessed at 90 days using the modified Rankin Scale (mRS). Serum inflammatory mediators were measured using multiplex ELISA. Multivariable modeling identified serum biomarkers independently associated with functional outcome at 90 days.115 patients completed the study. At 6 h after onset, patients with elevated CCL2 had worse mRS score at day 90 (OR 4.07, 95% CI 1.27-13.10, P = 0.02) after adjusting for age, gender, ICH volume, IVH, infratentorial location and NIHSS score. At 24 and 72 h after onset, elevation in CXCL10 was independently associated with worse 90 days mRS score (24 h: OR 8.08, 95% CI 2.69-24.30, P < 0.001; 72 h: OR 3.89, 95% CI 1.12-13.49, P = 0.03).Acute and subacute elevations in specific immune factors are associated with poor outcome, highlighting potential pathways that may contribute to ongoing brain injury in patients with intracerebral hemorrhage.
| S-EPMC6093844 | biostudies-other