Quantitative CT densitometry for predicting intracerebral hemorrhage growth

CT-based radiomics models predict spontaneous intracerebral hemorrhage expansion and are comparable with CT angiography spot sign

Background and purpose

This study aimed to investigate the efficacy of radiomics, based on non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) images, in predicting early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (SICH). Additionally, the predictive performance of these models was compared with that of the established CTA spot sign.

Materials and methods

A retrospective analysis was conducted using CT images from 182 patients with SICH. Data from the patients were divided into a training set (145 cases) and a testing set (37 cases) using random stratified sampling. Two radiomics models were constructed by combining quantitative features extracted from NCCT images (the NCCT model) and CTA images (the CTA model) using a logistic regression (LR) classifier. Additionally, a univariate LR model based on the CTA spot sign (the spot sign model) was established. The predictive performance of the two radiomics models and the spot sign model was compared according to the area under the receiver operating characteristic (ROC) curve (AUC).

Results

For the training set, the AUCs of the NCCT, CTA, and spot sign models were 0.938, 0.904, and 0.726, respectively. Both the NCCT and CTA models demonstrated superior predictive performance compared to the spot sign model (all P < 0.001), with the performance of the two radiomics models being comparable (P = 0.068). For the testing set, the AUCs of the NCCT, CTA, and spot sign models were 0.925, 0.873, and 0.720, respectively, with only the NCCT model exhibiting significantly greater predictive value than the spot sign model (P = 0.041).

Conclusion

Radiomics models based on NCCT and CTA images effectively predicted HE in patients with SICH. The predictive performances of the NCCT and CTA models were similar, with the NCCT model outperforming the spot sign model. These findings suggest that this approach has the potential to reduce the need for CTA examinations, thereby reducing radiation exposure and the use of contrast agents in future practice for the purpose of predicting hematoma expansion.

Predicting the 90-day prognosis of stereotactic brain hemorrhage patients by multiple machine learning using radiomic features combined with clinical features

Hypertensive Intracerebral Hemorrhage (HICH) is one of the most common types of cerebral hemorrhage with a high mortality and disability rate. Currently, preoperative non-contrast computed tomography (NCCT) scanning-guided stereotactic hematoma removal has achieved good results in treating HICH, but some patients still have poor prognoses. This study collected relevant clinical and radiomic data by retrospectively collecting and analyzing 432 patients who underwent stereotactic hematoma removal for HICH from January 2017 to December 2020 at the Liuzhou Workers Hospital. The prognosis of patients after 90 days was judged by the modified Rankin Scale (mRS) scale and divided into the good prognosis group (mRS ≤ 3) and the poor prognosis group (mRS > 3). The 268 patients were randomly divided into training and test sets in the ratio of 8:2, with 214 patients in the training set and 54 patients in the test set. The least absolute shrinkage and selection operator (Lasso) was used to screen radiomics features. They were combining clinical features and radiomic features to build a joint prediction model of the nomogram. The AUCs of the clinical model for predicting different prognoses of patients undergoing stereotactic HICH were 0.957 and 0.922 in the training and test sets, respectively, while the AUCs of the radiomics model were 0.932 and 0.770, respectively, and the AUCs of the combined prediction model for building a nomogram were 0.987 and 0.932, respectively. Compared with a single clinical or radiological model, the nomogram constructed by fusing clinical variables and radiomic features could better identify the prognosis of HICH patients undergoing stereotactic hematoma removal after 90 days.

The Added Value of Intraventricular Hemorrhage on the Radiomics Analysis for the Prediction of Hematoma Expansion of Spontaneous Intracerebral Hemorrhage

Background: Among patients undergoing head computed tomography (CT) scans within 3 h of spontaneous intracerebral hemorrhage (sICH), 28% to 38% have hematoma expansion (HE) on follow-up CT. This study aimed to predict HE using radiomics analysis and investigate the impact of intraventricular hemorrhage (IVH) compared with the conventional approach based on intraparenchymal hemorrhage (IPH) alone. Methods: This retrospective study enrolled 127 patients with baseline and follow-up non-contrast CT (NCCT) within 4~72 h of sICH. IPH and IVH were outlined separately for performing radiomics analysis. HE was defined as an absolute hematoma growth > 6 mL or percentage growth > 33% of either IPH (HEP) or a combination of IPH and IVH (HEP+V) at follow-up. Radiomic features were extracted using PyRadiomics, and then the support vector machine (SVM) was used to build the classification model. For each case, a radiomics score was generated to indicate the probability of HE. Results: There were 57 (44.9%) HEP and 70 (55.1%) non-HEP based on IPH alone, and 58 (45.7%) HEP+V and 69 (54.3%) non-HEP+V based on IPH + IVH. The majority (>94%) of HE patients had poor early outcomes (death or modified Rankin Scale > 3 at discharge). The radiomics model built using baseline IPH to predict HEP (RMP) showed 76.4% accuracy and 0.73 area under the ROC curve (AUC). The other model using IPH + IVH to predict HEP+V (RMP+V) had higher accuracy (81.9%) with AUC = 0.80, and this model could predict poor outcomes. The sensitivity/specificity of RMP and RMP+V for HE prediction were 71.9%/80.0% and 79.3%/84.1%, respectively. Conclusion: The proposed radiomics approach with additional IVH information can improve the accuracy in prediction of HE, which is associated with poor clinical outcomes. A reliable radiomics model may provide a robust tool to help manage ICH patients and to enroll high-risk ICH cases into anti-expansion or neuroprotection drug trials.

Radiomics Features on Computed Tomography Combined With Clinical-Radiological Factors Predicting Progressive Hemorrhage of Cerebral Contusion

Background

Traumatic brain injury (TBI) is the main cause of death and severe disability in young adults worldwide. Progressive hemorrhage (PH) worsens the disease and can cause a poor neurological prognosis. Radiomics analysis has been used for hematoma expansion of hypertensive intracerebral hemorrhage. This study attempts to develop an optimal radiomics model based on non-contrast CT to predict PH by machine learning (ML) methods and compare its prediction performance with clinical-radiological models.

Methods

We retrospectively analyzed 165 TBI patients, including 89 patients with PH and 76 patients without PH, whose data were randomized into a training set and a testing set at a ratio of 7:3. A total of 10 different machine learning methods were used to predict PH. Univariate and multivariable logistic regression analyses were implemented to screen clinical-radiological factors and to establish a clinical-radiological model. Then, a combined model combining clinical-radiological factors with the radiomics score was constructed. The area under the receiver operating characteristic curve (AUC), accuracy and F1 score, sensitivity, and specificity were used to evaluate the models.

Results

Among the 10 various ML algorithms, the support vector machine (SVM) had the best prediction performance based on 12 radiomics features, including the AUC (training set: 0.918; testing set: 0.879) and accuracy (training set: 0.872; test set: 0.834). Among the clinical and radiological factors, the onset-to-baseline CT time, the scalp hematoma, and fibrinogen were associated with PH. The radiomics model's prediction performance was better than the clinical-radiological model, while the predictive nomogram combining the radiomics features with clinical-radiological characteristics performed best.

Conclusions

The radiomics model outperformed the traditional clinical-radiological model in predicting PH. The nomogram model of the combined radiomics features and clinical-radiological factors is a helpful tool for PH.

The Attenuation Value Within the Non-hypodense Region on Non-contrast Computed Tomography of Spontaneous Cerebral Hemorrhage: A Long-Neglected Predictor of Hematoma Expansion

Background and Purpose

The ability of attenuation value of the non-hypodense region of hematoma in non-contrast computed tomography (NCCT) for predicting hematoma expansion (HE) remains unclear. Our purpose is to explore this relationship.

Methods

Two cohorts of patients were collected for analysis. The region where we measured hematoma attenuation values was limited to the non-hypodense region that was not adjacent to the normal brain tissue on NCCT. The critical attenuation value was derived via receiver operating characteristic (ROC) curve analysis in the derivation cohort and its predictive ability was validated in the validation cohort. Independent relationships between predictors, such as critical attenuation value of the non-hypodense region and HE were analyzed using the least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic analysis.

Results

The results showed that the attenuation value <64 Hounsfield units (HU) was independently associated with HE [odds ratio (OR), 4.118; 95% confidential interval (CI), 1.897–9.129, p < 0.001] and the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the curve (AUC) for predicting HE were 36.11%, 81.71%, 1.97, 0.78, 44.8%, 75.7%, and 0.589, respectively.

Conclusions

Our research explored and validated the relationship between the attenuation value of the non-hypodense region of hematoma and HE. The attenuation value < 64 HU was an appropriate indicator of early HE.

Monitoring and Predicting Treatment Response of Extraocular Muscles in Grave's Orbitopathy by 99mTc-DTPA SPECT/CT

Objective: To investigate single-photon emission computed tomography/computed tomography (SPECT/CT) for assessing inflammation in the extraocular muscles (EOMs) and predicting the therapeutic efficacy of periocular glucocorticoid therapy (PGT) for Grave's ophthalmopathy (GO).

Materials and Methods: A total of 412 eyes from 206 patients with GO referred for ^99mTc-DTPA orbital SPECT/CT were enrolled. Fourteen age- and gender-matched healthy controls (28 eyes) were included. The thickness and uptake ratio (UR) of four EOMs were derived from SPECT/CT. Eighty-six eyes from patients with GO patients received PGT. Changes in SPECT/CT parameters were evaluated between the pre- and post-treatment.

Results: 195 eyes and 217 eyes were classified as active and inactive stages by clinical activity score (CAS). Values of the thickness and UR of each EOM, T_max, and U_max were all significantly higher in the active GO than in the inactive GO and controls (p < 0.01). Among the 86 eyes (48 GO patients) included in the efficacy analysis, 56 eyes and 30 eyes were classified as responders and non-responders. Values of thicknesses and UR of each EOM, the maximum thickness (T_max), and the maximum UR (U_max) all dropped following PGT in the responders (p < 0.01). Logistic regression analysis identified the U_max as an independent predictor for the responders (p < 0.01). Moreover, the U_max demonstrated incremental predictive value over clinical characters and CAS, as evidenced by the improved area under the curve (0.85 vs. 0.78) and global chi-square (34.12 vs. 18.1).

Conclusion:^99mTc-DTPA SPECT/CT has the potential to assess inflammatory activity by detecting the involvement of EOMs in GO. Pre-treatment UR provides independent and incremental values for the prediction of PGT treatment response.

Establishment and validation of a prediction model for intraparenchymal hematoma expansion in patients with cerebral contusion: A reliable Nomogram

BACKGROUND AND OBJECTIVE

Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). Traumatic intraparenchymal hematoma (TICH) expansion severely affects the patient's prognosis. In this study, the baseline data, imaging features, and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a nomogram predictive model assessing the risk factors for TICH expansion.

METHODS

Totally 258 patients were included and retrospectively analyzed herein, who met the CC inclusion criteria, from July 2018 to July 2021. TICH expansion was defined as increased hematoma volume ≥ 30% relative to primary volume or an absolute hematoma increase ≥ 5 ml at CT review.

RESULTS

Univariate and binary logistic regression analyses were performed to screen out the independent predictors significantly correlated with TICH expansion: Age, subdural hematoma (SDH), contusion site, multihematoma fuzzy sign (MFS), contusion volume, and traumatic coagulation abnormalities (TCA). Based on these, the nomogram model was established. The differences between the contusion volume and glasgow outcome scale (GOS) were analyzed by the nonparametric tests. Larger contusion volume was associated with poor prognosis.

CONCLUSION

This study established a Nomogram model to predict TICH expansion in patients with CC. Meanwhile, the study found that the risk of bleeding tended to decrease when the hematoma volume was > 15 ml, but the larger initial hematoma volume would indicate worse prognosis. We advocate the use of predictive models for TICH expansion risk assessment in hospitalized CC patients, which is low-cost and easy-to-apply, especially in acute settings.

Association of Satellite Sign with Postoperative Rebleeding in Patients Undergoing Stereotactic Minimally Invasive Surgery for Hypertensive Intracerebral Haemorrhage

There are few studies regarding imaging markers for predicting postoperative rebleeding after stereotactic minimally invasive surgery (MIS) for hypertensive intracerebral haemorrhage (ICH), and little is known about the relationship between satellite sign on computed tomography (CT) scans and postoperative rebleeding after MIS. This study aimed to determine the value of the CT satellite sign in predicting postoperative rebleeding in patients with hypertensive ICH who undergo stereotactic MIS. We retrospectively examined and analysed 105 patients with hypertensive ICH who underwent standard stereotactic MIS for hematoma evacuation within 72 h following admission. Postoperative rebleeding occurred in 14 of 65 (21.5%) patients with the satellite sign on baseline CT, and in 5 of the 40 (12.5%) patients without the satellite sign. This difference was statistically significant. Positive and negative values of the satellite sign for predicting postoperative rebleeding were 21.5% and 87.5%, respectively. Multivariate logistic regression analysis verified that baseline ICH volume and intraventricular rupture were independent predictors of postoperative rebleeding. In conclusion, the satellite sign on baseline CT scans may not predict postoperative rebleeding following stereotactic MIS for hypertensive ICH.

Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion

PURPOSE

The objective of this study was to predict hematoma expansion (HE) by radiomic models based on different machine learning methods and determine the best radiomic model through the comparison.

METHOD

A total of 108 patients with intracerebral hemorrhage were retrospectively evaluated. Images of baseline non-contrast computed tomography (NCCT) and follow-up NCCT scan within 24 h were retrospectively reviewed. An HE was defined as a volume increase of more than 33% or an increase greater than 12.5 mL from the volume of the baseline NCCT. Texture parameters of the baseline NCCT images were selected by the least absolute shrinkage and selection operator (LASSO) regression. We used support vector machine (SVM), decision tree (DT), conditional inference trees (CIT), random forest (RF), k‑nearest neighbors (KNN), back-propagation neural network (BPNet) and Bayes to build models. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) was performed and compared among models.

RESULTS

Every model had a relatively high AUC (all > 0.75), SVM and KNN had the highest AUC of 0.91. There were significant differences between SVM and CIT (Z > 2.266, p = 0.02345), KNN and CIT (Z = 2.4834, p = 0.01301), RF and CIT (Z = 2.6956, p = 0.007027), KNN and BPNet (Z = 2.0122, p = 0.0442), RF and BPNet (Z = 1.9793, p = 0.04778). There was no significant difference among SVM, DT, RF, KNN and Bayes (p > 0.05). The SVM obtained the largest net benefit when the threshold probability was less than 0.33, while KNN obtained the largest net benefit when the threshold probability was greater than 0.33. Combined with ROC and DCA, SVM and KNN performed better in all the models for predicting HE.

CONCLUSION

Radiomic models based on different machine learning methods can be used to predict HE and the models generated by SVM and KNN performed best.

Are computed-tomography-based hematoma radiomics features reproducible and predictive of intracerebral hemorrhage expansion? an in vitro experiment and clinical study

OBJECTIVES

To identify reproducible hematoma radiomics features (RFs) for use in predicting hematoma expansion (HE) in patients with acute intracerebral hemorrhage (ICH).

METHODS

For test-retest analysis, three syringes with different volumes of blood collected at the same time (to mimic homogeneous hematoma) and a phantom (FT/HK 2000; Huake, Szechwan, China) containing three cylindrical inserts were scanned seven times within 6 h on the same CT scanner. Three additional syringes with mixed blood collected at different time points (to mimic heterogeneous hematoma) were tied together with the first three syringes as well as the phantom were scanned using modified CT acquisition parameters for intra CT analysis. A coefficient of variation below 10% served as the cutoff value for reproducibility. Finally, reproducible and potentially useful RFs were used to predict HE in 144 acute ICH patients, with the area under the receiver operating characteristic curves (AUC) used to evaluate their diagnostic performance.

RESULTS

A total of 630 RFs including 18 first-order, 24 gray-level co-occurrence matrix (GLCM), 16 gray-level run length matrix (GLRLM), five neighborhood gray-tone difference matrix (NGTDM), 63 Laplacian of Gaussian (LoG), and 504 Wavelet features were evaluated. In the test-retest analysis, the percentages of reproducible RFs ranged from 42.54% (268/630) to 45.4% (286/630) for the three homogeneous hematoma samples and 79.05% (498/630) to 81.43% (513/630) for the phantom. In the intra-CT analysis, the percentages varied from 31.43% (198/630) to 42.38% (267/630) for the six hematoma samples and 48.89% (308/630) to 53.97% (340/630) for the phantom. In the in vitro experiment, 148 RFs were reproducible for all hematoma samples in both the test-retest and intra-CT analyses; however, only 80 were statistically different between homogeneous and heterogeneous hematoma samples. Finally, HE occurred in 25% (growth >6 ml, 36/144) to 31.94% (growth >3 ml or 33%, 46/144) of the patients. The AUCs in predicting HE ranged from 0.625 to 0.703.

CONCLUSIONS

Only a few CT-based RFs from the in vitro hematoma were reproducible and can distinguish between homogeneous and heterogeneous hematomas. The use of RFs alone to predict HE in acute ICH showed only a moderate performance.

ADVANCES IN KNOWLEDGE

Using an in vitro experiment and clinical validation, this study demonstrated for the first time that CT-based hematoma RFs can be used to predict HE in acute ICH; nonetheless, only a few RFs are reproducible and can be used for prediction.

A nomogram to predict early hematoma expansion of hypertensive cerebral hemorrhage

Early hematoma expansion of hypertensive cerebral hemorrhage is affected by various factors. This study aimed to clarify the risk factors and develop a nomogram to predict early hematoma expansion.A retrospective analysis was carried out in patients with hypertensive cerebral hemorrhage admitted to our institution between January 1, 2012 and December 31, 2018; the patients were divided into 2 groups according to the presence of early hematoma expansion. Univariate and multivariate analyses were performed to analyze the risk factors of hematoma expansion. The nomogram was developed based on a multivariate logistic regression model, and the discriminative ability of the model was analyzed.A total of 477 patients with hypertensive cerebral hemorrhage and with a baseline hematoma volume <30 ml were included in our retrospective analysis. The hematoma expansion rate was 34.2% (163/477). After multivariate logistic regression, 9 variables (alcohol history, Glasgow coma scale score, total serum calcium, blood glucose, international normalized ratio, hematoma shape, hematoma density, volume of hematoma on initial computed tomography scan, and presence of intraventricular hemorrhage) identified as independent predictors of hematoma expansion were used to generate the nomogram. The area under the receiver operating characteristic curve of the nomogram was 0.883 (95% confidence interval 0.851-0.914), and the cutoff score was -0.19 with sensitivity of 75.5% and specificity of 87.3%.The nomogram can accurately predict the risk of early hematoma expansion.

Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage

BACKGROUND

Rapid diagnosis and proper management of intracerebral hemorrhage (ICH) play a crucial role in the outcome. Prediction of the outcome with a high degree of accuracy based on admission data including imaging information can potentially influence clinical decision-making practice.

METHODS

We conducted a retrospective multicenter study of consecutive ICH patients admitted between 2012-2017. Medical history, admission data, and initial head computed tomography (CT) scan were collected. CT scans were semiautomatically segmented for hematoma volume, hematoma density histograms, and sphericity index (SI). Discharge unfavorable outcomes were defined as death or severe disability (modified Rankin Scores 4-6). We compared (1) hematoma volume alone; (2) multiparameter imaging data including hematoma volume, location, density heterogeneity, SI, and midline shift; and (3) multiparameter imaging data with clinical information available on admission for ICH outcome prediction. Multivariate analysis and predictive modeling were used to determine the significance of hematoma characteristics on the outcome.

RESULTS

We included 430 subjects in this analysis. Models using automated hematoma segmentation showed incremental predictive accuracies for in-hospital mortality using hematoma volume only: area under the curve (AUC): 0.85 [0.76-0.93], multiparameter imaging data (hematoma volume, location, CT density, SI, and midline shift): AUC: 0.91 [0.86-0.97], and multiparameter imaging data plus clinical information on admission (Glasgow Coma Scale (GCS) score and age): AUC: 0.94 [0.89-0.99]. Similarly, severe disability predictive accuracy varied from AUC: 0.84 [0.76-0.93] for volume-only model to AUC: 0.88 [0.80-0.95] for imaging data models and AUC: 0.92 [0.86-0.98] for imaging plus clinical predictors.

CONCLUSIONS

Multiparameter models combining imaging and admission clinical data show high accuracy for predicting discharge unfavorable outcome after ICH.

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional outcome at mRS 2, 3, and 4. Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach. Receiver-operating-characteristic area under the curve (ROC AUC) of the machine learning classifier using image features only was 0.80 (95% CI [0.77; 0.82]) for predicting mRS ≤ 2, 0.80 (95% CI [0.78; 0.81]) for mRS ≤ 3, and 0.79 (95% CI [0.77; 0.80]) for mRS ≤ 4. Trained on survival prediction (mRS ≤ 5), the classifier reached an AUC of 0.80 (95% CI [0.78; 0.82]) which was equivalent to results of the ICH Score. If combined, the integrated model showed a significantly higher AUC of 0.84 (95% CI [0.83; 0.86], P value <0.05). Accordingly, sensitivities were significantly higher at Youden Index maximum cut-offs (77% vs. 74% sensitivity at 76% specificity, P value <0.05). Machine learning–based evaluation of quantitative high-end image features provided the same discriminatory power in predicting functional outcome as multidimensional clinical scoring systems. The integration of conventional scores and image features had synergistic effects with a statistically significant increase in AUC.

Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges

Stroke is a leading cause of disability and mortality worldwide, resulting in substantial economic costs for post-stroke care each year. Neuroimaging, such as cranial computed tomography or magnetic resonance imaging, is the backbone of stroke management strategies, which can guide treatment decision-making (thrombolysis or hemostasis) at an early stage. With advances in computational technologies, particularly in machine learning, visual image information can now be converted into numerous quantitative features in an objective, repeatable, and high-throughput manner, in a process known as radiomics. Radiomics is mainly used in the field of oncology, which remains an area of active research. Over the past few years, investigators have attempted to apply radiomics to stroke in the hope of gaining benefits similar to those obtained in cancer management, i.e., in promoting the development of personalized precision medicine. Currently, radiomic analysis has shown promise for a variety of applications in stroke, including the diagnosis of stroke lesions, early prediction of outcomes, and evaluation for long-term prognosis. In this article, we elaborate the contributions of radiomics to stroke, as well as the subprocesses and techniques involved in radiomics studies. We also discuss the potential challenges facing its widespread implementation in routine practice and the directions for future research.

Is the CT Blend Sign Composed of Two Parts of Blood with Different Age?

BACKGROUND

Blend sign on initial computed tomography (CT) is associated with poor outcome in patients with intracerebral hemorrhage (ICH). However, the mechanisms underlying the blend sign formation are poorly understood. The present study aimed to explore the possible mechanism of the CT blend sign in patients with ICH.

METHODS

Seventy healthy rabbits were selected to prepare an ICH model. The animals were assigned to a whole blood group + whole blood group (ww group, 50 rabbits), a whole blood + plasma group (wp group, 10 rabbits) or a whole blood + serum group (ws group, 10 rabbits). The animals of the ww group were allocated to five subgroups based on the interval between the first infusion of blood and the second one. The subgroups included ww 1 h group (with an interval of 1 h), ww 2 h group, ww 3 h group, ww 4 h group and ww 5 h group. The rabbits from each group received first infusion of 0.3 mL of whole blood into the basal ganglia area to form a hematoma. Then, they received a second infusion of the same amount of whole blood, plasma or serum into the brain to form another hematoma adjacent to the first one.

RESULTS

A hematoma with two densities on brain CT could be formed in each group after a second infusion of blood into the brain. A significant difference in CT attenuation values was observed between the hyperattenuation and the hypoattenuation in all the groups. However, only the morphological features of the hematoma in the ww group was in accordance with the CT blend sign observed in humans. The CT attenuation values in the hypodensity area of the ww 4 h group or the ww 5 h group were decreased compared with the ww 1 h group to the ww 3 h group.

CONCLUSIONS

The CT blend sign observed in humans might be composed of two parts of blood with different ages. The hypodense area might be blood with older age and the hyperdense area might be new bleeding.

Intracranial Densitometry-Augmented Machine Learning Enhances the Prognostic Value of Brain CT in Pediatric Patients With Traumatic Brain Injury: A Retrospective Pilot Study

Background: The inter- and intrarater variability of conventional computed tomography (CT) classification systems for evaluating the extent of ischemic-edematous insult following traumatic brain injury (TBI) may hinder the robustness of TBI prognostic models. Objective: This study aimed to employ fully automated quantitative densitometric CT parameters and a cutting-edge machine learning algorithm to construct a robust prognostic model for pediatric TBI. Methods: Fifty-eight pediatric patients with TBI who underwent brain CT were retrospectively analyzed. Intracranial densitometric information was derived from the supratentorial region as a distribution representing the proportion of Hounsfield units. Furthermore, a machine learning-based prognostic model based on gradient boosting (i.e., CatBoost) was constructed with leave-one-out cross-validation. At discharge, the outcome was assessed dichotomously with the Glasgow Outcome Scale (favorability: 1-3 vs. 4-5). In-hospital mortality, length of stay (>1 week), and need for surgery were further evaluated as alternative TBI outcome measures. Results: Densitometric parameters indicating reduced brain density due to subtle global ischemic changes were significantly different among the TBI outcome groups, except for need for surgery. The skewed intracranial densitometry of the unfavorable outcome became more distinguishable in the follow-up CT within 48 h. The prognostic model augmented by intracranial densitometric information achieved adequate AUCs for various outcome measures [favorability = 0.83 (95% CI: 0.72-0.94), in-hospital mortality = 0.91 (95% CI: 0.82-1.00), length of stay = 0.83 (95% CI: 0.72-0.94), and need for surgery = 0.71 (95% CI: 0.56-0.86)], and this model showed enhanced performance compared to the conventional CRASH-CT model. Conclusion: Densitometric parameters indicative of global ischemic changes during the acute phase of TBI are predictive of a worse outcome in pediatric patients. The robustness and predictive capacity of conventional TBI prognostic models might be significantly enhanced by incorporating densitometric parameters and machine learning techniques.

An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 1: Hemorrhagic Stroke Imaging and Triage

Hemorrhagic stroke is a medical emergency. Artificial intelligence techniques and algorithms may be used to automatically detect and quantitate intracranial hemorrhage in a semiautomated fashion. This article reviews the use of deep learning convolutional neural networks for managing hemorrhagic stroke. Such a capability may be used to alert appropriate care teams, make decisions about patient transport from a primary care center to a comprehensive stroke center, and assist in treatment selection. This article reviews artificial intelligence algorithms for intracranial hemorrhage detection, quantification, and prognostication. Multiple algorithms currently being explored are described and illustrated with the help of examples.

Abilities of a Densitometric Analysis of Computed Tomography Images and Hemorrhagic Parameters to Predict Outcome Favorability in Patients With Intracerebral Hemorrhage

BACKGROUND

Intracerebral hemorrhage (ICH) is one of the most devastating subtypes of stroke. A rapid assessment of ICH severity involves the use of computed tomography (CT) and derivation of the hemorrhage volume, which is often estimated using the ABC/2 method. However, these estimates are highly inaccurate and may not be feasible for anticipating outcome favorability.

OBJECTIVE

To predict patient outcomes via a quantitative, densitometric analysis of CT images, and to compare the predictive power of these densitometric parameters with the conventional ABC/2 volumetric parameter and segmented hemorrhage volumes.

METHODS

Noncontrast CT images of 87 adult patients with ICH (favorable outcomes = 69, unfavorable outcomes = 12, and deceased = 6) were analyzed. In-house software was used to calculate the segmented hemorrhage volumes, ABC/2 and densitometric parameters, including the skewness and kurtosis of the density distribution, interquartile ranges, and proportions of specific pixels in sets of CT images. Nonparametric statistical analyses were conducted.

RESULTS

The densitometric parameter interquartile range exhibited greatest accuracy (82.7%) in predicting favorable outcomes. The combination of skewness and the interquartile range effectively predicted mortality (accuracy = 83.3%). The actual volume of the ICH exhibited good coherence with ABC/2 (R = 0.79). Both parameters predicted mortality with moderate accuracy (<78%) but were less effective in predicting unfavorable outcomes.

CONCLUSION

Hemorrhage volume was rapidly estimated and effectively predicted mortality in patients with ICH; however, this value may not be useful for predicting favorable outcomes. The densitometric analysis exhibited significantly higher power in predicting mortality and favorable outcomes in patients with ICH.

Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model

OBJECTIVES

To develop a radiomics model for predicting hematoma expansion in patients with intracerebral hemorrhage (ICH) and to compare its predictive performance with a conventional radiological feature-based model.

METHODS

We retrospectively analyzed 251 consecutive patients with acute ICH. Two radiologists independently assessed baseline noncontrast computed tomography (NCCT) images. For each radiologist, a radiological model was constructed from radiological variables; a radiomics score model was constructed from high-dimensional quantitative features extracted from NCCT images; and a combined model was constructed using both radiological variables and radiomics score. Development of models was constructed in a primary cohort (n = 177). We then validated the results in an independent validation cohort (n = 74). The primary outcome was hematoma expansion. We compared the three models for predicting hematoma expansion. Predictive performance was assessed with the receiver operating characteristic (ROC) curve analysis.

RESULTS

In the primary cohort, combined model and radiomics model showed greater AUCs than radiological model for both readers (all p < .05). In the validation cohort, combined model and radiomics model showed greater AUCs, sensitivities, and accuracies than radiological model for reader 2 (all p < .05). Combined model showed greater AUC than radiomics model for reader 1 only in the primary cohort (p = .03). Performance of three models was comparable between reader 1 and reader 2 in both cohorts (all p > .05).

CONCLUSIONS

NCCT-based radiomics model showed high predictive performance and outperformed radiological model in the prediction of early hematoma expansion in ICH patients.

KEY POINTS

• Radiomics model showed better performance for prediction of hematoma expansion in patients with intracerebral hemorrhage than radiological feature-based model. • Hematomas which expanded in follow-up NCCT tended to be larger in baseline volume, more irregular in shape, more heterogeneous in composition, and coarser in texture. • A radiomics model provides a convenient and objective tool for prediction of hematoma expansion that helps to define subsets of patients who would benefit from anti-expansion therapy.

Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine

Background

Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansion in spontaneous ICH from routinely available variables by using support vector machine (SVM) method.

Methods

We retrospectively reviewed 1157 patients with spontaneous ICH who underwent initial computed tomography (CT) scan within 6 h and follow-up CT scan within 72 h from symptom onset in our hospital between September 2013 and August 2018. Hematoma region was manually segmented at each slice to guarantee the measurement accuracy of hematoma volume. Hematoma expansion was defined as a proportional increase of hematoma volume > 33% or an absolute growth of hematoma volume > 6 mL from initial CT scan to follow-up CT scan. Univariate and multivariate analyses were performed to assess the association between clinical variables and hematoma expansion. SVM machine learning model was developed to predict hematoma expansion.

Findings

246 of 1157 (21.3%) patients experienced hematoma expansion. Multivariate analyses revealed the following 6 independent factors associated with hematoma expansion: male patient (odds ratio [OR] = 1.82), time to initial CT scan (OR = 0.73), Glasgow Coma Scale (OR = 0.86), fibrinogen level (OR = 0.72), black hole sign (OR = 2.52), and blend sign (OR = 4.03). The SVM model achieved a mean sensitivity of 81.3%, specificity of 84.8%, overall accuracy of 83.3%, and area under receiver operating characteristic curve (AUC) of 0.89 in prediction of hematoma expansion.

Interpretation

The designed SVM model presented good performance in predicting hematoma expansion from routinely available variables.

Fund

This work was supported by Health Foundation for Creative Talents in Zhejiang Province, China, Natural Science Foundation of Zhejiang Province, China (LQ15H180002), the Science and Technology Planning Projects of Wenzhou, China (Y20180112), Scientific Research Staring Foundation for the Returned Overseas Chinese Scholars of Ministry of Education of China, and Project Foundation for the College Young and Middle-aged Academic Leader of Zhejiang Province, China. The funders had no role in study design, data collection, data analysis, interpretation, writing of the report.

Computed Tomographic Black Hole Sign Predicts Postoperative Rehemorrhage in Patients with Spontaneous Intracranial Hemorrhage Following Stereotactic Minimally Invasive Surgery

OBJECTIVES

Determining the value of the computed tomographic black hole sign in predicting postoperative rehemorrhage in patients with intracranial hemorrhage (ICH) underwent minimally invasive surgery (MIS).

METHODS

Two hundred ninety-five patients with spontaneous ICH underwent stereotactic MIS within 24 hours after admission. Ninety-eight patients (33%) demonstrated a black hole sign on initial computed tomography (CT). Postoperative rehemorrhage occurred in 68 patients (named the rehemorrhage group, including patients with and without black hole sign) and the other 227 patients (non-rehemorrhage group) did not show rehemorrhage. Multivariable logistic regression analyses were performed to assess the values of the black hole sign.

RESULTS

Postoperative rehemorrhage occurred in 57 of the 98 (58.2%) patients with the black hole sign, and in 11 of the 197 (5.58%) patients without the black hole sign. In the rehemorrhage group, 39 patients (57.4%) were found to have the black hole sign. However, only 59 patients (25.99%) from the non-rehemorrhage group showed the black hole sign. The sensitivity, specificity, and positive and negative predictive values of the black hole sign for predicting postoperative rehemorrhage were 57.4%, 74%, 39.8%, and 85.3%, respectively. The odd ratio for the black hole sign, the hematoma irregularity, and the CT value for predicting the postoperative rehemorrhage were 10.501, 9.631, and 4.750, respectively.

CONCLUSIONS

The black hole sign on initial CT could predict the postoperative rehemorrhage following the minimally invasive procedures.

Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement

OBJECTIVE

To objectively quantify intracranial hematoma (ICH) enlargement by analysing the image texture of head CT scans and to provide objective and quantitative imaging parameters for predicting early hematoma enlargement.

METHODS

We retrospectively studied 108 ICH patients with baseline non-contrast computed tomography (NCCT) and 24-h follow-up CT available. Image data were assessed by a chief radiologist and a resident radiologist. Consistency analysis between observers was tested. The patients were divided into training set (75%) and validation set (25%) by stratified sampling. Patients in the training set were dichotomized according to 24-h hematoma expansion ≥ 33%. Using the Laplacian of Gaussian bandpass filter, we chose different anatomical spatial domains ranging from fine texture to coarse texture to obtain a series of derived parameters (mean grayscale intensity, variance, uniformity) in order to quantify and evaluate all data. The parameters were externally validated on validation set.

RESULTS

Significant differences were found between the two groups of patients within variance at V_1.0 and in uniformity at U_1.0, U_1.8 and U_2.5. The intraclass correlation coefficients for the texture parameters were between 0.67 and 0.99. The area under the ROC curve between the two groups of ICH cases was between 0.77 and 0.92. The accuracy of validation set by CTTA was 0.59-0.85.

CONCLUSION

NCCT texture analysis can objectively quantify the heterogeneity of ICH and independently predict early hematoma enlargement.

KEY POINTS

• Heterogeneity is helpful in predicting ICH enlargement. • CTTA could play an important role in predicting early ICH enlargement. • After filtering, fine texture had the best diagnostic performance. • The histogram-based uniformity parameters can independently predict ICH enlargement. • CTTA is more objective, more comprehensive, more independently operable, than previous methods.

Clinical and Radiographic Predictors of Intracerebral Hemorrhage Outcome

BACKGROUND

Intracerebral hemorrhage (ICH) represents 10-15% of all stroke cases in the US annually. Fewer than 40% of these patients ever reach long-term functional independence, and mortality rate is roughly 40% at 1 month. Due to the high morbidity and mortality rates after ICH, early detection of high-risk patients would be beneficial in directing the management course and goals of care. This review aims to discuss relevant clinical and radiographic characteristics that can serve as predictors of poor prognosis and examine their efficacy in predicting patient outcomes after ICH.

SUMMARY

A literature review was conducted on various clinical and radiographic factors. They were examined for their predictive value in relation to ICH outcome. Studies that focused on each of these factors were included, and their results analyzed for trends with regard to incidence, patient outcome, and mortality rate.

KEY MESSAGE

In this review, we examined clinical and radiographic characteristics that have been found to be significantly associated to a varying degree with poor outcome. Clinical and radiographic predictors of poor patient outcome are invaluable when it comes to identifying high-risk patients and triaging accordingly as well as guiding decision-making.

Association Between Hypodensities Detected by Computed Tomography and Hematoma Expansion in Patients With Intracerebral Hemorrhage

IMPORTANCE

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.

OBJECTIVE

To determine whether hypodense regions, irrespective of their specific patterns, are associated with hematoma expansion in patients with ICH.

DESIGN, SETTING, AND PARTICIPANTS

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.

MAIN OUTCOMES AND MEASURES

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.

RESULTS

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 (κ = 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).

CONCLUSION AND RELEVANCE

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.

Management of intracerebral hemorrhage

Intracerebral hemorrhage (ICH) is a potentially devastating neurologic injury representing 10-15% of stroke cases in the USA each year. Numerous risk factors, including age, hypertension, male gender, coagulopathy, genetic susceptibility, and ethnic descent, have been identified. Timely identification, workup, and management of this condition remain a challenge for clinicians as numerous factors can present obstacles to achieving good functional outcomes. Several large clinical trials have been conducted over the prior decade regarding medical and surgical interventions. However, no specific treatment has shown a major impact on clinical outcome. Current management guidelines do exist based on medical evidence and consensus and these provide a framework for care. While management of hypertension and coagulopathy are generally considered basic tenets of ICH management, a variety of measures for surgical hematoma evacuation, intracranial pressure control, and intraventricular hemorrhage can be further pursued in the emergent setting for selected patients. The complexity of management in parenchymal cerebral hemorrhage remains challenging and offers many areas for further investigation. A systematic approach to the background, pathology, and early management of spontaneous parenchymal hemorrhage is provided.

Noncontrast Computed Tomography Hypodensities Predict Poor Outcome in Intracerebral Hemorrhage Patients

BACKGROUND AND PURPOSE

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.

METHODS

We performed a retrospective review of a prospectively collected cohort of consecutive patients with primary ICH presenting to a single academic medical center between 1994 and 2016. The presence of CT hypodensities was assessed by 2 independent raters on the baseline CT. Unfavorable outcome was defined as a modified Rankin score >3 at 90 days. The associations between CT hypodensities and unfavorable outcome were investigated using uni- and multivariable logistic regression models.

RESULTS

During the study period, 1342 patients presented with ICH and 800 met restrictive inclusion criteria (baseline CT available for review, and 90-day outcome available). Three hundred and four (38%) patients showed hypodensities on CT, and 520 (65%) patients experienced unfavorable outcome. In univariate analysis, patients with unfavorable outcome were more likely to demonstrate hypodensities (48% versus 20%; P<0.0001). After adjustment for age, admission Glasgow coma scale, warfarin use, intraventricular hemorrhage, baseline ICH volume, and location, CT hypodensities were found to be independently associated with an increase in the odds of unfavorable outcome (odds ratio 1.70, 95% confidence interval [1.10-2.65]; P=0.018).

CONCLUSIONS

The presence of noncontract CT hypodensities at baseline independently predicts poor outcome and comes as a useful and widely available addition to our ability to predict ICH patients' clinical evolution.

Continuing versus Stopping Prestroke Antihypertensive Therapy in Acute Intracerebral Hemorrhage: A Subgroup Analysis of the Efficacy of Nitric Oxide in Stroke Trial

BACKGROUND AND PURPOSE

More than 50% of patients with acute intracerebral hemorrhage (ICH) are taking antihypertensive drugs before ictus. Although antihypertensive therapy should be given long term for secondary prevention, whether to continue or stop such treatment during the acute phase of ICH remains unclear, a question that was addressed in the Efficacy of Nitric Oxide in Stroke (ENOS) trial.

METHODS

ENOS was an international multicenter, prospective, randomized, blinded endpoint trial. Among 629 patients with ICH and systolic blood pressure between 140 and 220 mmHg, 246 patients who were taking antihypertensive drugs were assigned to continue (n = 119) or to stop (n = 127) taking drugs temporarily for 7 days. The primary outcome was the modified Rankin Score at 90 days. Secondary outcomes included death, length of stay in hospital, discharge destination, activities of daily living, mood, cognition, and quality of life.

RESULTS

Blood pressure level (baseline 171/92 mmHg) fell in both groups but was significantly lower at 7 days in those patients assigned to continue antihypertensive drugs (difference 9.4/3.5 mmHg, P < .01). At 90 days, the primary outcome did not differ between the groups; the adjusted common odds ratio (OR) for worse outcome with continue versus stop drugs was .92 (95% confidence interval, .45-1.89; P = .83). There was no difference between the treatment groups for any secondary outcome measure, or rates of death or serious adverse events.

CONCLUSIONS

Among patients with acute ICH, immediate continuation of antihypertensive drugs during the first week did not reduce death or major disability in comparison to stopping treatment temporarily.

Performance characteristics of methods for quantifying spontaneous intracerebral haemorrhage: data from the Efficacy of Nitric Oxide in Stroke (ENOS) trial

BACKGROUND

Poor prognosis after intracerebral haemorrhage (ICH) is related to haemorrhage characteristics. Along with developing therapeutic interventions, we sought to understand the performance of haemorrhage descriptors in large clinical trials.

METHODS

Clinical and neuroimaging data were obtained for 548 participants with ICH from the Efficacy of Nitric Oxide in Stroke (ENOS) trial. Independent observers performed visual categorisation of the largest diameter, measured volume using ABC/2, modified ABC/2, semiautomated segmentation (SAS), fully automatic measurement methods; shape, density and intraventricular haemorrhage were also assessed. Intraobserver and interobserver reliability were determined for these measures.

RESULTS

ICH volume was significantly different among standard ABC/2, modified ABC/2 and SAS: (mean) 12.8 (SD 16.3), 8.9 (9.2), 12.8 (13.1) cm(3), respectively (p<0.0001). There was excellent agreement for haemorrhage volume (n=193): ABC/2 intraobserver intraclass correlation coefficient (ICC) 0.96-0.97, interobserver ICC 0.88; modified ABC/2 intraobserver ICC 0.95-0.97, interobserver ICC 0.91; SAS intraobserver ICC 0.95-0.99, interobserver ICC 0.93; largest diameter: (visual) interadjudicator ICC 0.82, (visual vs measured) adjudicator vs observer ICC 0.71; shape intraobserver ICC 0.88 interobserver ICC 0.75; density intraobserver ICC 0.86, interobserver ICC 0.73. Graeb score (mean 3.53) and modified Graeb (5.22) scores were highly correlated. Using modified ABC/2, ICH volume was underestimated in regular (by 2.2-2.5 cm(3), p<0.0001) and irregular-shaped haemorrhages (by 4.8-4.9 cm(3), p<0.0001). Fully automated measurement of haemorrhage volume was possible in only 5% of cases.

CONCLUSIONS

Formal measurement of haemorrhage characteristics and visual estimates are reproducible. The standard ABC/2 method is superior to the modified ABC/2 method for quantifying ICH volume.

CLINICAL TRIAL REGISTRATION

ISRCTN9941422.

Prediction of Intracerebral Haemorrhage Expansion with Clinical, Laboratory, Pharmacologic, and Noncontrast Radiographic Variables

Background

Hematoma expansion confers excess mortality in intracerebral haemorrhage, and is potentially preventable if at-risk patients can be identified. Contrast extravasation on initial computed tomographic angiography strongly predicts hematoma expansion but is not very sensitive, and most centers have not yet integrated computed tomographic angiography into acute intracerebral haemorrhage management. We therefore asked whether other presentation variables can predict hematoma expansion.

Methods

We searched the electronic medical records of a large integrated healthcare delivery system to identify patients with a hospitalization discharge diagnosis of intracerebral haemorrhage between the years 2008 and 2010. Hematoma expansion was defined as radiographic increase by 1/3 or by 12·5 ml within 48 h of presentation. Pre-specified patient demographic and clinical presentation variables were extracted. Stepwise multivariable logistic regression was performed to model hematoma expansion. Because some patients may have died from hematoma expansion without a second head computed tomography, we constructed a separate model including patients that died without a second head computed tomography in 48 h, hematoma expansion or death.

Results

Ninety-one of 257 patients (35%) had hematoma expansion. Antithrombotic use (odds ratio = 1·9, P = 0·04) and initial mNIHSS (modified National Institutes of Health Stroke Scale; odds ratio = 1·06, P = 0·001) were significant predictors in the hematoma expansion model (area under the Receiver–Operator Characteristics curve, AUROC = 0·6712, pseudo- r2 = 0·0641). 163 of 343 patients (48%) had hematoma expansion or death. Age (odds ratio = 1·02, P = 0·02), initial mNIHSS (odds ratio = 1·07, P < 0·001), and initial hematoma volume (odds ratio = 1·01, P = 0·03) were significant predictors of hematoma expansion or death (AUROC = 0·7579, pseudo- r2 = 0·1722).

Conclusion

Clinical and noncontrast radiographic variables only weakly predict hematoma expansion. Examination of other indicators, such as computed tomographic angiography contrast extravasation (the ‘spot sign’), may prove more valuable in acute intracerebral haemorrhage care.

Predictors of mortality and poor functional outcome in severe spontaneous intracerebral hemorrhage: a prospective observational study

OBJECTIVE

To analyze mortality and functional outcome in patients with severe spontaneous intracerebral hemorrhage (ICH), and identify the clinical characteristics, radiological findings and therapeutic procedures predictive of mortality in the Intensive Care Unit (ICU) and during hospitalization, as well as of poor functional results at 6 months.

DESIGN

A prospective, observational study was carried out.

SETTING

Neurocritical Care Unit of a university hospital.

PATIENTS

Patients diagnosed with ICH were included over a period of 23 months.

VARIABLES OF INTEREST

Demographic characteristics, cardiovascular risk factors, regular medication, laboratory test parameters, cranial CT findings, therapeutic procedures and outcome data.

INTERVENTION

None.

RESULTS

A total of 186 patients with ICH met the inclusion criteria. Surgery to evacuate ICH was performed in 25.8% of the patients. The mortality rate was 46.7%. The modified Rankin score at 6 months was 5 (RI: 4.6). Multivariate Cox regression analysis showed the presence of diabetes, prior anticoagulation, as well as APACHE II severity and the type of bleeding on the cranial CT scan to be predictors of mortality and poor functional outcomes. On the other hand, neurosurgical procedures and intracranial pressure (ICP) monitoring were associated with better outcomes.

CONCLUSION

The presence of comorbidities such as diabetes, or previous anticoagulation, as well as the CT findings were associated to poorer outcomes. In contrast, ICP monitoring and early neurosurgery were predictive of longer survival and better functional outcomes.

Quantitative analysis of computed tomography images and early detection of cerebral edema for pediatric traumatic brain injury patients: retrospective study

BACKGROUND

The purpose of this study was to identify whether the distribution of Hounsfield Unit (HU) values across the intracranial area in computed tomography (CT) images can be used as an effective diagnostic tool for determining the severity of cerebral edema in pediatric traumatic brain injury (TBI) patients.

METHODS

CT images, medical records and radiology reports on 70 pediatric patients were collected. Based on radiology reports and the Marshall classification, the patients were grouped as mild edema patients (n=37) or severe edema patients (n=33). Automated quantitative analysis using unenhanced CT images was applied to eliminate artifacts and identify the difference in HU value distribution across the intracranial area between these groups.

RESULTS

The proportion of pixels with HU=17 to 24 was highly correlated with the existence of severe cerebral edema (P<0.01). This proportion was also able to differentiate patients who developed delayed cerebral edema from mild TBI patients. A significant difference between deceased patients and surviving patients in terms of the HU distribution came from the proportion of pixels with HU=19 to HU=23 (P<0.01).

CONCLUSIONS

The proportion of pixels with an HU value of 17 to 24 in the entire cerebral area of a non-enhanced CT image can be an effective basis for evaluating the severity of cerebral edema. Based on this result, we propose a novel approach for the early detection of severe cerebral edema.

Critical care management of intracerebral hemorrhage

Primary, spontaneous intracerebral hemorrhage (ICH) confers significant early mortality and long-term morbidity worldwide. Advances in acute care including investigative, diagnostic, and management strategies are important to improving outcomes for patients with ICH. Physicians caring for patients with ICH should anticipate the need for emergent blood pressure reduction, coagulopathy reversal, cerebral edema management, and surgical interventions including ventriculostomy and hematoma evacuation. This article reviews the pathogenesis and diagnosis of ICH, and details the acute management of spontaneous ICH in the critical care setting according to existing evidence and published guidelines.

Progressing haemorrhagic stroke: categories, causes, mechanisms and managements

Haemorrhagic stroke is a severe stroke subtype with high rates of morbidity and mortality. Although this condition has been recognised for a long time, the progressing haemorrhagic stroke has not received adequate attention, and it accounts for an even worse clinical outcome than the nonprogressing types of haemorrhagic stroke. In this review article, we categorised the progressing haemorrhagic stroke into acute progressing haemorrhagic stroke, subacute haemorrhagic stroke, and chronic progressing haemorrhagic stroke. Haematoma expansion, intraventricular haemorrhage, perihaematomal oedema, and inflammation, can all cause an acute progression of haemorrhagic stroke. Specific 'second peak' of perihaematomal oedema after intracerebral haemorrhage and 'tension haematoma' are the primary causes of subacute progression. For the chronic progressing haemorrhagic stroke, the occult vascular malformations, trauma, or radiologic brain surgeries can all cause a slowly expanding encapsulated haematoma. The mechanisms to each type of progressing haemorrhagic stroke is different, and the management of these three subtypes differs according to their causes and mechanisms. Conservative treatments are primarily considered in the acute progressing haemorrhagic stroke, whereas surgery is considered in the remaining two types.