Background and Purpose: Perihematomal edema (PHE) is associated with poor functional outcomes after intracerebral hemorrhage (ICH). Early identification of risk factors associated with PHE growth may allow for targeted therapeutic interventions.Methods: We used data contained in the risk stratification and minimally invasive surgery in acute intracerebral hemorrhage (Risa-MIS-ICH) patients: a prospective multicenter cohort study. Patients' clinical, laboratory, and radiological data within 24 h of admission were obtained from their medical records. The absolute increase in PHE volume from baseline to day 3 was defined as iPHE volume. Poor outcome was defined as modified Rankin Scale (mRS) of 4 to 6 at 90 days. Binary logistic regression was used to assess the relationship between iPHE volume and poor outcome. The receiver operating characteristic curve was used to find the best cutoff. Linear regression was used to identify variables associated with iPHE volume (ClinicalTrials.gov Identifier: NCT03862729).Results: One hundred ninety-seven patients were included in this study. iPHE volume was significantly associated with poor outcome [P = 0.003, odds ratio (OR) 1.049, 95% confidence interval (CI) 1.016–1.082] after adjustment for hematoma volume. The best cutoff point of iPHE volume was 7.98 mL with a specificity of 71.4% and a sensitivity of 47.5%. Diabetes mellitus (P = 0.043, β = 7.66 95% CI 0.26–15.07), black hole sign (P = 0.002, β = 18.93 95% CI 6.84–31.02), and initial ICH volume (P = 0.018, β = 0.20 95% CI 0.03–0.37) were significantly associated with iPHE volume. After adjusting for hematoma expansion, the black hole sign could still independently predict the increase of PHE (P < 0.001, β = 21.62 95% CI 10.10–33.15).Conclusions: An increase of PHE volume >7.98 mL from baseline to day 3 may lead to poor outcome. Patients with diabetes mellitus, black hole sign, and large initial hematoma volume result in more PHE growth, which should garner attention in the treatment.

BackgroundEarly hematoma growth is associated with poor functional outcomes in patients with intracerebral hemorrhage (ICH). We aimed to explore whether quantitative hematoma heterogeneity in non-contrast computed tomography (NCCT) can predict early hematoma growth.MethodsWe used data from the Risk Stratification and Minimally Invasive Surgery in Acute Intracerebral Hemorrhage (Risa-MIS-ICH) trial. Our study included patients with ICH with a time to baseline NCCT <12 h and a follow-up CT duration <72 h. To get a Hounsfield unit histogram and the coefficient of variation (CV) of Hounsfield units (HUs), the hematoma was segmented by software using the auto-segmentation function. Quantitative hematoma heterogeneity is represented by the CV of hematoma HUs. Multivariate logistic regression was utilized to determine hematoma growth parameters. The discriminant score predictive value was assessed using the area under the ROC curve (AUC). The best cutoff was determined using ROC curves. Hematoma growth was defined as a follow-up CT hematoma volume increase of >6 mL or a hematoma volume increase of 33% compared with the baseline NCCT.ResultsA total of 158 patients were enrolled in the study, of which 31 (19.6%) had hematoma growth. The multivariate logistic regression analysis revealed that time to initial baseline CT (P = 0.040, odds ratio [OR]: 0.824, 95 % confidence interval [CI]: 0.686–0.991), “heterogeneous” in the density category (P = 0.027, odds ratio [OR]: 5.950, 95 % confidence interval [CI]: 1.228–28.828), and CV of hematoma HUs (P = 0.018, OR: 1.301, 95 % CI: 1.047–1.617) were independent predictors of hematoma growth. By evaluating the receiver operating characteristic curve, the CV of hematoma HUs (AUC = 0.750) has a superior predictive value for hematoma growth than for heterogeneous density (AUC = 0.638). The CV of hematoma HUs had an 18% cutoff, with a specificity of 81.9 % and a sensitivity of 58.1 %.ConclusionThe CV of hematoma HUs can serve as a quantitative hematoma heterogeneity index that predicts hematoma growth in patients with early ICH independently.

BACKGROUND:The optimal timing of cranioplasty (CP) and predictors of overall postoperative complications are still controversial. OBJECTIVE: To determine the optimal timing of CP. METHODS: Patients were divided into collapsed group and noncollapsed group based on brain collapse or not, respectively. Brain collapse volume was calculated in a 3-dimensional way. The primary outcomes were overall complications and outcomes at the 12-month follow-up after CP. RESULTS: Of the 102 patients in this retrospective observation cohort study, 56 were in the collapsed group, and 46 were in the noncollapsed group. Complications were noted in 30.4% (n = 31), 24 (42.9%) patients in the collapsed group and 7 (15.2%) patients in the noncollapsed group, with a significant difference (P = .003). Thirty-three (58.9%) patients had good outcomes (modified Rankin Scale 0-3) in the collapsed group, and 34 (73.9%) patients had good outcomes in the noncollapsed group without a statistically significant difference (P = .113). Brain collapse (P = .005) and Karnofsky Performance Status score at the time of CP (P = .025) were significantly associated with overall postoperative complications. The cut-off value for brain collapse volume was determined as 11.26 cm 3 in the receiver operating characteristic curve. The DC-CP interval was not related to brain collapse volume or postoperative complications. CONCLUSION: Brain collapse and lower Karnofsky Performance Status score at the time of CP were independent predictors of overall complications after CP. The optimal timing of CP may be determined by tissue window based on brain collapse volume instead of time window based on the decompressive craniectomy-CP interval.

Stratification of spontaneous intracerebral hemorrhage (sICH) patients without cerebral herniation at admission, to determine the subgroups may be suffered from poor outcomes or benefit from surgery, is important for following treatment decision. The aim of this study was to establish and verify a de novo nomogram predictive model for long-term survival in sICH patients without cerebral herniation at admission. This study recruited sICH patients from our prospectively maintained ICH patient database (RIS-MIS-ICH, ClinicalTrials.gov Identifier: NCT03862729) between January 2015 and October 2019. All eligible patients were randomly classified into a training cohort and a validation cohort according to the ratio of 7:3. The baseline variables and long-term survival outcomes were collected. And the long-term survival information of all the enrolled sICH patients, including the occurrence of death and overall survival. Follow-up time was defined as the time from the onset to death of the patient or the last clinical visit. The nomogram predictive model was established based on the independent risk factors at admission for long-term survival after hemorrhage. The concordance index (C-index) and ROC curve were used to evaluate the accuracy of the predictive model. Discrimination and calibration were used to validate the nomogram in both the training cohort and the validation cohort. A total of 692 eligible sICH patients were enrolled. During the average follow-up time of 41.77 ± 0.85 months, a total of 178 (25.7%) patients died. The Cox Proportional Hazard Models showed that age (HR 1.055, 95% CI 1.038–1.071, P < 0.001), Glasgow Coma Scale (GCS) at admission (HR 2.496, 95% CI 2.014–3.093, P < 0.001) and hydrocephalus caused by intraventricular hemorrhage (IVH) (HR 1.955, 95% CI 1.362–2.806, P < 0.001) were independent risk factors. The C index of the admission model was 0.76 and 0.78 in the training cohort and validation cohort, respectively. In the ROC analysis, the AUC was 0.80 (95% CI 0.75–0.85) in the training cohort and was 0.80 (95% CI 0.72–0.88) in the validation cohort. SICH patients with admission nomogram scores greater than 87.75 were at high risk of short survival time. For sICH patients without cerebral herniation at admission, our de novo nomogram model based on age, GCS and hydrocephalus on CT may be useful to stratify the long-term survival outcomes and provide suggestions for treatment decision-making.

Background and Purpose: Spontaneous intracerebral hemorrhage (ICH) is the deadliest type of stroke, and surgery is still one of the main treatment options for ICH. The aim of this study was to establish a prognostic model for surgically treated ICH patients. Methods: Data for this study were drawn from a national multicenter observational cohort study (ClinicalTrials identifier NCT03862729). Poor outcome was defined as modified Rankin Scale ≥ 4 at discharge. Overall survival (OS) was defined as the time from surgery to death or last follow-up. Multivariate logistic regression analysis was performed to identify significant variables associated with poor outcome. Associations of the variables with OS were assessed by Cox proportional hazard regression models. Prognostic scores were developed based on the regression coefficients. Receiver operating characteristic (ROC) curve analyses and the area under the ROC curve (AUC) were used to assess the predictive value of the model. Subgroup analyses were performed by splitting centers to assess the applicability of the two models. Results: Out of 1164 patients in the database, a total of 182 patients who met the inclusion criteria were included in this study. Preoperative Glasgow Coma Scale (GCS), deep ICH and presence of intraventricular hemorrhage were independent predictors of poor outcome. Age, preoperative GCS, presence of hydrocephalus and postoperative re-hemorrhage were independently associated with OS. Based on the results, two risk score models were established. The AUC of poor outcome risk (POR) score was 0.850 (95% CI 0.782 - 0.918) and the cut-off value was -0.982. 93.7% of patients identified as high-risk group had poor outcomes. The C-index of overall survival risk (OSR) score was 0.802 (95% CI 0.748-0.856). The Kaplan-Meier survival curves showed significantly (P < 0.001) lower survival probability in the high-risk group. Subgroup analyses showed no significant change in C-index and AUC values between groups. Conclusions: Our study proposed two new prognostic models for surgically treated ICH patients.

Background: Stroke-associated pneumonia (SAP) contributes to high mortality rates in spontaneous intracerebral hemorrhage (sICH) populations. The accurate prediction and early intervention of SAP are associated with prognosis. Although various predictive scoring systems have been previously developed, none are widely accepted. We aimed to derive and validate novel supervised machine learning (ML) models to predict SAP events in supratentorial sICH populations.Methods: In this work, the data of eligible supratentorial sICH individuals were extracted from the database of the Risa-MIS-ICH study, and the participants were split into training, internal validation, and external validation datasets. The primary outcome was SAP during hospitalization. Univariate and multivariate analyses were used for variable filtrations, and logistic regression (LR), Gaussian naïve Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and ensemble soft voting model (ESVM) were adopted for ML model derivations. The metrics of accuracy, sensitivity, specificity, and area under the curve (AUC) were adopted to evaluate the predictive value of each model with internal/cross-/external validations.Results: After screening 909 individuals with sICH, a total of 468 were included in this work. Six independent variables [nasogastric feeding, airway support, unconscious onset, surgery of external ventricular drainage (EVD), sICH volume, and intensive care unit (ICU) stay] for SAP were identified and selected for seven ML prediction model derivations and validations. The internal and cross-validations revealed the superior and robust performance of the GNB model with the highest AUC value (0.861, 95% CI: 0.793-0.930), while the LR model had the highest AUC value (0.867, 95% CI: 0.812-0.923) in external validation. The ESVM method combining the other six methods had moderate but robust abilities in both cross- and external validations and achieved an AUC of 0.843 (95% CI: 0.784, 0.902) in the external validation.Conclusion: The ML models could effectively predict SAP events in sICH populations, and our novel ensemble models demonstrated reliable robust performance outcomes despite the populational and algorithmic differences.Registration: URL: https://www.clinicaltrials.gov. Unique Identifier: NCT03862729