A nomogram predictive model for long-term survival in spontaneous intracerebral hemorrhage patients without cerebral herniation at admission.

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.

Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage.

Background: Early 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. Methods: We 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. Results: A 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 %. Conclusion: The CV of hematoma HUs can serve as a quantitative hematoma heterogeneity index that predicts hematoma growth in patients with early ICH independently.

Novel machine learning models to predict pneumonia events in supratentorial intracerebral hemorrhage populations: An analysis of the Risa-MIS-ICH study.

Background: Stroke-associated pneumonia (SAP) contributes to high mortality rates in spontaneous intracerebral hemorrhage (sICH) populations. Accurate prediction and early intervention of SAP are associated with prognosis. None of the previously developed predictive scoring systems are widely accepted. We aimed to derive and validate novel supervised machine learning (ML) models to predict SAP events in supratentorial sICH populations. Methods: The data of eligible supratentorial sICH individuals were extracted from the Risa-MIS-ICH database and split into training, internal validation, and external validation datasets. The primary outcome was SAP during hospitalization. Univariate and multivariate analyses were used for variable filtering, 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 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: A total of 468 individuals with sICH were included in this work. Six independent variables [nasogastric feeding, airway support, unconscious onset, surgery for external ventricular drainage (EVD), larger sICH volume, and intensive care unit (ICU) stay] for SAP were identified and selected for 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-validation and external validation and achieved an AUC of 0.843 (95% CI: 0.784-0.902) in external validation. Conclusion: The ML models could effectively predict SAP in sICH populations, and our novel ensemble model demonstrated reliable robust performance outcomes despite the populational and algorithmic differences. This attempt indicated that ML application may benefit in the early identification of SAP.

Early Predictors of the Increase in Perihematomal Edema Volume After Intracerebral Hemorrhage: A Retrospective Analysis From the Risa-MIS-ICH Study.

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, ß = 7.66 95% CI 0.26-15.07), black hole sign (P = 0.002, ß = 18.93 95% CI 6.84-31.02), and initial ICH volume (P = 0.018, ß = 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, ß = 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.

Early Deterioration and Long-Term Prognosis of Patients With Intracerebral Hemorrhage Along With Hematoma Volume More Than 20 ml: Who Needs Surgery?

Background and Purpose: The treatment of patients with intracerebral hemorrhage along with moderate hematoma and without cerebral hernia is controversial. This study aimed to explore risk factors and establish prediction models for early deterioration and poor prognosis. Methods: We screened patients from the prospective intracerebral hemorrhage (ICH) registration database (RIS-MIS-ICH, ClinicalTrials.gov Identifier: NCT03862729). The enrolled patients had no brain hernia at admission, with a hematoma volume of more than 20 ml. All patients were initially treated by conservative methods and followed up ≥ 1 year. A decline of Glasgow Coma Scale (GCS) more than 2 or conversion to surgery within 72 h after admission was defined as early deterioration. Modified Rankin Scale (mRS) ≥ 4 at 1 year after stroke was defined as poor prognosis. The independent risk factors of early deterioration and poor prognosis were determined by univariate and multivariate regression analysis. The prediction models were established based on the weight of the independent risk factors. The accuracy and value of models were tested by the receiver operating characteristic (ROC) curve. Results: After screening 632 patients with ICH, a total of 123 legal patients were included. According to statistical analysis, admission GCS (OR, 1.43; 95% CI, 1.18-1.74; P < 0.001) and hematoma volume (OR, 0.9; 95% CI, 0.84-0.97; P = 0.003) were the independent risk factors for early deterioration. Hematoma location (OR, 0.027; 95% CI, 0.004-0.17; P < 0.001) and hematoma volume (OR, 1.09; 95% CI, 1.03-1.15; P < 0.001) were the independent risk factors for poor prognosis, and island sign had a trend toward significance (OR, 0.5; 95% CI, 0.16-1.57; P = 0.051). The admission GCS and hematoma volume score were combined for an early deterioration prediction model with a score from 2 to 5. ROC curve showed an area under the curve (AUC) was 0.778 and cut-off point was 3.5. Combining the score of hematoma volume, island sign, and hematoma location, a long-term prognosis prediction model was established with a score from 2 to 6. ROC curve showed AUC was 0.792 and cutoff point was 4.5. Conclusions: The novel early deterioration and long-term prognosis prediction models are simple, objective, and accurate for patients with ICH along with a hematoma volume of more than 20 ml.

Treatments and outcomes of untreated cerebral cavernous malformations in China: study protocol of a nationwide multicentre prospective cohort study.

INTRODUCTION: The treatment decision and long-term outcomes of previously untreated cerebral cavernous malformation (U-CCM) are still controversial. Therefore, we are conducting a nationwide multicentre prospective registry study in China to determine the natural history and effect of surgical treatment on long-term outcomes in Chinese people with U-CCM. METHODS AND ANALYSIS: This study was started on 1 January 2018 and is currently ongoing. It is a cohort follow-up study across a 5-year period. Patients will be followed up for at least 3 years after inception. Patients with U-CCM will be enrolled from 24 Grade III, level A hospitals distributed all over China. The cohort size is estimated to be 1200 patients. Patients are registered in surgically treated group and conservatively treated group. Clinical characteristics, radiology information and laboratory data are prospectively collected using an electronic case report form through an electronic data capture system. The primary outcome of this study is poor clinical outcome at the last follow-up (modified Rankin Scale score >2 lasting at least 1 year). The secondary outcome includes symptomatic haemorrhage, drug refractory epilepsy, focal neurological deficits, morbidity and all-cause mortality during follow-up. Univariate and multivariate regression analysis will be performed to determine the risk factors for poor outcomes in all patients, and to estimate the effect of surgery. Life tables, Kaplan-Meier estimates, log-rank test and proportional hazards Cox regression will be used to analyse the follow-up data of conservatively treated patients to determine the natural history of U-CCM. Initial presentation and location of U-CCM are prespecified subgroup factors. ETHICS AND DISSEMINATION: The study protocol and informed consent form have been reviewed and approved by the Research Ethical Committee of First Affiliated Hospital of Fujian Medical University (FAHFMU-2018-003).Written informed consent will be obtained from each adult participant or from the guardian of each paediatric participant. The final results will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT03467295.