Hydrocephalus is an independent factor affecting morbidity and mortality of ICH patients: Systematic review and meta-analysis

Hydrocephalus in primary brainstem hemorrhage risk predictors and management

This study explored the risk factors associated with hydrocephalus incidence and evaluated the effectiveness of surgical treatments in managing this condition. Patients with PBSH were retrospectively evaluated, identifying clinical and radiological characteristics. A multivariate logistic regression model was used for analyses. Of the 169 patients studied, 77 developed hydrocephalus. Midbrain hemorrhage, tegmental pons hemorrhage, disappearance of annular cisterna, combined cerebellar and intraventricular hematoma increased the risk of hydrocephalus (p < 0.05). A linear relationship was found between hematoma volume and hydrocephalus, with a volume > 6.1 mL associated with a higher risk. Patients with ≥ 2 the following factors: multiple hematoma sites, intraventricular hematoma, or hematoma volume > 6.1 mL, had a significantly increased risk of hydrocephalus. Forty-seven patients received surgical treatments including stereotactic puncture drainage of hematoma (SPDH) or external ventricular drainage (EVD). Both SPDH and EVD were effective in treating hydrocephalus (p < 0.001). The combination of SPDH and EVD showed the greatest benefit (p < 0.001); 30-day mortality and de-ventilator rates in the surgical group were significantly different from the non-surgical group. This finding provides valuable insights for early surgical intervention in patients with PBSH.

Incidence of Communicating Hydrocephalus Following Intraventricular Hemorrhage Among Adult Patients Treated at a Hospital in Jeddah, Saudi Arabia: A Retrospective Study

Introduction Intraventricular hemorrhage is a severe condition caused by bleeding within the brain ventricles. It is often due to trauma, tumors, vascular malformation, aneurysm, oxygen deprivation, or idiopathic. A common complication associated with intraventricular hemorrhage is hydrocephalus, which is the accumulation of cerebrospinal fluid in the ventricles. Hydrocephalus can be classified as communicating or non-communicating. This study aimed to evaluate the incidence of communicating hydrocephalus after intraventricular hemorrhage. Methods This retrospective study was conducted at King Abdulaziz University Hospital in Jeddah, Saudi Arabia, and included 52 adult patients treated between 2012-2022 who met the eligibility criteria. We examined the relationships among age, sex, length of hospitalization, presenting symptoms, co-morbidities, Evans index, Graeb score, Glasgow Coma Score, survival, and ventriculoperitoneal shunt complications through univariate and bivariate analyses. The Shapiro-Wilk test was used to evaluate data distribution. Differences between groups were analyzed using the chi-square test for categorical variables and the Mann-Whitney U test for non-parametric variables. Results The median age of the participants was 54 years, with a male predominance (57.7%). Motor dysfunction was the most frequently reported symptom at presentation (48.1%). Among the 30 patients who developed hydrocephalus after intraventricular hemorrhage, 70% had communicating hydrocephalus. There was a substantial correlation between mortality and hydrocephalus type (P =0.020). Conclusion Intraventricular bleeding is associated with an increased risk of communicating hydrocephalus, with an incidence rate of 3% per person-year.

Development and validation of a nomogram for predicting early acute hydrocephalus after spontaneous intracerebral hemorrhage: a single-center retrospective study

Acute hydrocephalus is a severe complication that may occur early after an intracerebral hemorrhage (ICH). However, clinical factors predicting the occurrence of acute hydrocephalus have rarely been studied. This study aimed to establish a nomogram model to predict early acute hydrocephalus after ICH. We retrospectively analyzed the data of 930 patients with ICH who were treated at our hospital between January 2017 and May 2024. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen for risk factors for acute hydrocephalus, and stepwise logistic regression analysis was used to construct the prediction model, which was visualized using a nomogram. Data were randomly divided into training (n = 652) and test (n = 278) sets at a 7:3 ratio. A total of 930 patients were included, of whom 123 (13.2%) developed acute hydrocephalus within 6 h of being diagnosed with ICH. Univariate analysis revealed that 11 indicators were associated with acute hydrocephalus. In the training set, LASSO and stepwise logistic regression analyses identified four independent risk factors that were used to establish a prediction model. These were the modified Graeb score, age, infratentorial hemorrhage > 15 mL, and thalamic hemorrhage > 15 mL. A graphical nomogram was then developed. The area under the receiver operating characteristic (ROC) curve was 0.974 (95% confidence interval 0.961-0.987). In the Hosmer-Lemeshow test, the p-value was 0.887. The mean absolute error of the calibration plot was 0.012. The decision curve analysis (DCA) validated the fitness and clinical application value of this nomogram. Internal validation showed the test set was in good accordance with the training set. The nomogram prediction model showed good accuracy and could be used to predict the risk of early acute hydrocephalus after ICH, thereby aiding neurologist in making rapid clinical decisions.

Serial Diffusion Tensor Imaging and Rate of Ventricular Blood Clearance in Patients with Intraventricular Hemorrhage

BACKGROUND

We developed a noninvasive biomarker to quantify the rate of ventricular blood clearance in patients with intracerebral hemorrhage and extension to the ventricles-intraventricular hemorrhage.

METHODS

We performed magnetic resonance imaging in 26 patients at 1, 14, 28, and 42 days of onset and measured their hematoma volume (HV), ventricular blood volume (VBV), and two diffusion metrics: fractional anisotropy (FA), and mean diffusivity (MD). The ipasilesional ventricular cerebral spinal fluid's FA and MD were associated with VBV and stroke severity scores (National Institute of Health Stroke Scale [NIHSS]). A subcohort of 14 patients were treated with external ventricular drain (EVD). A generalized linear mixed model was applied for statistical analysis.

RESULTS

At day 1, the average HVs and NIHSS scores were 14.6 ± 16.7 cm3 and 16 ± 8, respectively. A daily rate of 2.1% and 1.3% blood clearance/resolution were recorded in HV and VBV, respectively. Ipsilesional ventricular FA (vFA) and ventricular MD (vMD) were simultaneously decreased (vFA = 1.3% per day, posterior probability [PP] > 99%) and increased (vMD = 1.5% per day, PP > 99%), respectively. Patients with EVD exhibited a faster decline in vFA (1.5% vs. 1.1% per day) and an increase in vMD (1.8% vs. 1.5% per day) as compared with patients without EVD. Temporal change in vMD was associated with VBV; a 1.00-cm3 increase in VBV resulted in a 5.2% decrease in vMD (PP < 99%). VBV was strongly associated with NIHSS score (PP = 97-99%). A larger cerebral spinal fluid drained volume was associated with a greater decrease (PP = 83.4%) in vFA, whereas a smaller volume exhibited a greater increase (PP = 94.8%) in vMD.

CONCLUSIONS

In conclusion, vFA and vMD may serve as biomarkers for VBV status.

Predicting ICU readmission risks in intracerebral hemorrhage patients: Insights from machine learning models using MIMIC databases

BACKGROUND

Intracerebral hemorrhage (ICH) is a stroke subtype characterized by high mortality and complex post-event complications. Research has extensively covered the acute phase of ICH; however, ICU readmission determinants remain less explored. Utilizing the MIMIC-III and MIMIC-IV databases, this investigation develops machine learning (ML) models to anticipate ICU readmissions in ICH patients.

METHODS

Retrospective data from 2242 ICH patients were evaluated using ICD-9 codes. Recursive feature elimination with cross-validation (RFECV) discerned significant predictors of ICU readmissions. Four ML models-AdaBoost, RandomForest, LightGBM, and XGBoost-underwent development and rigorous validation. SHapley Additive exPlanations (SHAP) elucidated the effect of distinct features on model outcomes.

RESULTS

ICU readmission rates were 9.6% for MIMIC-III and 10.6% for MIMIC-IV. The LightGBM model, with an AUC of 0.736 (95% CI: 0.668-0.801), surpassed other models in validation datasets. SHAP analysis revealed hydrocephalus, sex, neutrophils, Glasgow Coma Scale (GCS), specific oxygen saturation (SpO2) levels, and creatinine as significant predictors of readmission.

CONCLUSION

The LightGBM model demonstrates considerable potential in predicting ICU readmissions for ICH patients, highlighting the importance of certain clinical predictors. This research contributes to optimizing patient care and ICU resource management. Further prospective studies are warranted to corroborate and enhance these predictive insights for clinical utilization.