The effectiveness of early systemic inflammatory indices in predicting advanced intraventricular hemorrhage in preterm infants.

Some systemic inflammatory indices have been reported to be associated with intracerebral hemorrhage in adults. However, the relationship between systemic inflammatory indices and intraventricular hemorrhage (IVH) in premature neonates is still not completely understood. OBJECTIVE: To evaluate the relationship between systemic inflammatory indices obtained on the first day of life in premature infants and the development of severe IVH. PATIENTS AND METHOD: Premature newborns < 32 weeks of gestational age were included. Eligible patients were divided into 2 groups: Group 1: without IVH or grade I and II hemorrhage, and Group 2: grade III and IV HIV. Demographic characteristics, clinical outcomes, monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII), pan-immune inflammation value (PIV), and Systemic inflammation response index (SIRI) were compared between groups. RESULTS: A total of 1176 newborns were included in the study, 1074 in Group 1 and 102 premature babies in Group 2. There was no difference between the groups in terms of the count of leukocytes, neutrophils, monocytes, lymphocytes and platelets (p > 0.05). The values of NLR, MLR, PLR, PIV, SII and SIRI were similar in both groups (p > 0.05). CONCLUSION: While the relationship between inflammation, hemodynamics and IVH is still under discussion, our results show that systemic inflammatory indices have no predictive value for IVH.

In situ implantable DNA hydrogel for diagnosis and therapy of postoperative rehemorrhage following intracerebral hemorrhage surgery.

Postoperative rehemorrhage following intracerebral hemorrhage surgery is intricately associated with a high mortality rate, yet there is now no effective clinical treatment. In this study, we developed a hemoglobin (Hb)-responsive in situ implantable DNA hydrogel comprising Hb aptamers cross-linked with two complementary chains and encapsulating deferoxamine mesylate (DFO). Functionally, the hydrogel generates signals upon postoperative rehemorrhage by capturing Hb, demonstrating a distinctive "self-diagnosis" capability. In addition, the ongoing capture of Hb mediates the gradual disintegration of the hydrogel, enabling the on-demand release of DFO without compromising physiological iron-dependent functions. This process achieves self-treatment by inhibiting the ferroptosis of neurocytes. In a collagenase and autologous blood injection model-induced mimic postoperative rehemorrhage model, the hydrogel exhibited a 5.58-fold increase in iron absorption efficiency, reducing hematoma size significantly (from 8.674 to 4.768 cubic millimeters). This innovative Hb-responsive DNA hydrogel not only offers a therapeutic intervention for postoperative rehemorrhage but also provides self-diagnosis feedback, holding notable promise for enhancing clinical outcomes.

Clinical features and treatment of apoplectic intratumoral hemorrhage of glioma.

OBJECTIVE: The primary objective of this study was to explore the clinical characteristics of apoplectic intratumoral hemorrhage in gliomas and offer insights for improving the diagnosis and treatment of this disease. METHODS: We analyzed the clinical data of 35 patients with glioma and hemorrhage. There were eight cases of multiple cerebral lobe involvement, and 22 cases involved a single lobe. Twenty-one patients had a preoperative Glasgow Coma Scale (GCS) score of ≥ 9 and had a craniotomy with tumor resection and hematoma evacuation after undergoing preoperative preparation. A total of 14 patients with GCS < 9, including one with thalamic hemorrhage breaking into the ventricles and acute obstructive hydrocephalus, underwent craniotomy for tumor resection after external ventricular drainage (EVD). One patient had combined thrombocytopenia, which was surgically treated after platelet levels were normalized through transfusion. The remaining 12 patients received immediate intervention in the form of craniotomy hematoma evacuation and tumor resection. RESULTS: We performed subtotal resection on three tumors of thalamic origin and two tumors of corpus callosum origin, but we were able to successfully resect all the tumors in other locations that were gross total resection Pathology results showed that 71.43% of cases accounted for WHO-grade 4 tumors. Among the 21 patients with a GCS score of ≥ 9, two died perioperatively. Fourteen patients had a GCS score < 9, of which eight patients died perioperatively. CONCLUSIONS: Patients with a preoperative GCS score ≥ 9 who underwent subemergency surgery and received aggressive treatment showed a reasonable prognosis. We found their long-term outcomes to be correlated with the pathology findings. On the other hand, patients with a preoperative GCS score < 9 required emergency treatment and had a high perioperative mortality rate.

Precranial artery calcification burden: a potential indicator of the clinical outcome of reperfusion in patients with acute large artery occlusion.

OBJECTIVE: With mechanical thrombectomy (MT), we investigated the prognostic importance of aortic arch calcification (AoAC) and carotid sinus calcification (CaSC) for symptomatic intracerebral hemorrhage (sICH) and poor outcome in acute large artery occlusion (LAO). METHODS: In this retrospective observational study, we calculated pre-cranial artery calcification burden (PACB) scores (burden score of AoAC and CaSC) using the AoAC grading scale score plus Woodcock visual score. The outcome measure was sICH per the European Cooperative Acute Stroke Study III definition. A 3-month modified Rankin scale score 3-6 was designated as poor outcome. RESULTS: Compared with patients who had PACB <3, those with PACB ≥3 showed substantially higher risks of sICH (odds ratio [OR] = 2.567, 95% confidence interval [CI] = 1.187-5.550) and poor outcome (OR = 4.777, 95% CI = 1.659-13.756). According to receiver operating characteristic (ROC) curves, adding PACB to the regression model enhanced the predictive value for poor outcome (area under the ROC curve [AUC]: 0.718 vs. 0.519, Z = 2.340) and in patients receiving MT (AUC: 0.714 vs. 0.584, Z = 2.021), independently. CONCLUSIONS: Factors related to PACB were consistent with common risk factors of systemic atherosclerosis. Low PACB scores indicated better prognosis. In patients with LAO following MT, PACB was useful in predicting sICH and poor clinical outcome.

Anemia From Inflammation After Intracerebral Hemorrhage and Relationships With Outcome.

BACKGROUND: Baseline anemia is associated with poor intracerebral hemorrhage (ICH) outcomes. However, underlying drivers for anemia and whether anemia development after ICH impacts clinical outcomes are unknown. We hypothesized that inflammation drives anemia development after ICH and assessed their relationship to outcomes. METHODS AND RESULTS: Patients with serial hemoglobin and iron biomarker concentrations from the HIDEF (High-Dose Deferoxamine in Intracerebral Hemorrhage) trial were analyzed. Adjusted linear mixed models assessed laboratory changes over time. Of 42 patients, significant decrements in hemoglobin occurred with anemia increasing from 19% to 45% by day 5. Anemia of inflammation iron biomarker criteria was met in 88%. A separate cohort of 521 patients with ICH with more granular serial hemoglobin and long-term neurological outcome data was also investigated. Separate regression models assessed whether (1) systemic inflammatory response syndrome (SIRS) scores related to hemoglobin changes over time and (2) hemoglobin changes related to poor 90-day outcome. In this cohort, anemia prevalence increased from 30% to 71% within 2 days of admission yet persisted beyond this time. Elevated systemic inflammatory response syndrome was associated with greater hemoglobin decrements over time (adjusted parameter estimate: -0.27 [95% CI, -0.37 to -0.17]) and greater hemoglobin decrements were associated with poor outcomes (adjusted odds ratio per 1 g/dL increase, 0.76 [95% CI, 0.62-0.93]) independent to inflammation and ICH severity. CONCLUSIONS: We identified novel findings that acute anemia development after ICH is common, rapid, and related to inflammation. Because anemia development is associated with poor outcomes, further work is required to clarify if anemia, or its underlying drivers, are modifiable treatment targets that can improve ICH outcomes. REGISTRATION: https://www.clinicaltrials.gov Unique identifier: NCT01662895.

Advancements in prognostic markers and predictive models for intracerebral hemorrhage: from serum biomarkers to artificial intelligence models.

Intracerebral hemorrhage (ICH) is a severe form of stroke with high morbidity and mortality, accounting for 10-15% of all strokes globally. Recent advancements in prognostic biomarkers and predictive models have shown promise in enhancing the prediction and management of ICH outcomes. Serum sestrin2, a stress-responsive protein, has been identified as a significant prognostic marker, correlating with severity indicators such as NIHSS scores and hematoma volume. Its levels predict early neurological deterioration and poor prognosis, offering predictive capabilities comparable to traditional measures. Furthermore, a deep learning-based AI model demonstrated superior performance in predicting early hematoma enlargement, with higher sensitivity and specificity than conventional methods. Additionally, long-term outcome prediction models using CT radiomics and machine learning have achieved high accuracy, particularly with the Random Forest algorithm. These advancements underscore the potential of integrating novel biomarkers and advanced computational techniques to improve prognostication and management of ICH, aiming to enhance patient care and survival rates. The incorporation of serum sestrin2, AI, and machine learning in predictive models represents a significant step forward in the clinical management of ICH, offering new avenues for research and clinical application.

Serum secretoneurin as a promising biomarker for predicting poor prognosis in intracerebral hemorrhage: A prospective cohort study.

OBJECTIVE: Secretoneurin may play a brain-protective role. We aim to discover the relationship between serum secretoneurin levels and severity plus neurological outcome after intracerebral hemorrhage (ICH). METHODS: In this prospective cohort study, serum secretoneurin levels were measured in 110 ICH patients and 110 healthy controls. Glasgow Coma Scale (GCS) and hematoma volume were used to assess stroke severity. Poor prognosis was defined as Glasgow Outcome Scale (GOS) scores of 1-3 at 90 days after ICH. A multivariate logistic regression model was constructed to determine independent correlation of serum secretoneurin levels with severity and poor prognosis. Under receiver operating characteristic (ROC) curve, prognostic ability of serum secretoneurin levels was assessed. Restricted cubic spline (RCS) model and subgroups analysis were used for discovering association of serum secretoneurin levels with risk of poor prognosis. Calibration curve and decision curve were evaluated to confirm performance of nomogram. RESULTS: Serum secretoneurin levels of patients were significantly higher than those of healthy controls. Serum secretoneurin levels of patients were independently correlated with GCS scores and hematoma volume. There were 42 patients with poor prognosis at 90 days following ICH. Serum secretoneurin levels were significantly higher in patients with poor outcome than in those with good outcome. Under the ROC curve, serum secretoneurin levels significantly differentiated poor outcome. Serum secretoneurin levels ≥ 22.8 ng/mL distinguished patients at risk of poor prognosis at 90 days with a sensitivity of 66.2% and a specificity of 81.0%. Besides, serum secretoneurin levels independently predicted a 90-day poor prognosis. Subgroup analysis showed that serum secretoneurin levels had non-significant interactions with other variables. The nomogram, including independent prognostic predictors, showed reliable prognosis capability using calibration curve and decision curve. Area under the curve of the predictive model was significantly higher than those of GCS scores and hematoma volume. CONCLUSION: Serum secretoneurin levels are strongly related to ICH severity and poor prognosis at 90 days after ICH. Thus, serum secretoneurin may be a promising prognostic biomarker in ICH.

Higher blood glucose is associated with the severity of hemorrhagic transformation after endovascular therapy for stroke.

OBJECTIVES: Hyperglycemia is associated with poor outcome in large vessel occlusion (LVO) stroke, with mechanism for this effect unknown. MATERIALS AND METHODS: We used our prospective, multicenter, observational study, Blood Pressure After Endovascular Stroke Therapy (BEST), of anterior circulation LVO stroke undergoing endovascular therapy (EVT) from 11/2017-7/2018 to determine association between increasing blood glucose (BG) and intracerebral hemorrhage (ICH). Our primary outcome was degree of ICH, classified as none, asymptomatic ICH, or symptomatic ICH (≥4-point increase in National Institutes of Health Stroke Scale [NIHSS] at 24 h with any hemorrhage on imaging). Secondary outcomes included 24 h NIHSS, early neurologic recovery (ENR, NIHSS 0-1 or NIHSS reduction by ≥8 within 24 h), and 90-day modified Rankin Scale (mRS) using univariate and multivariable regression. RESULTS: Of 485 enrolled patients, increasing BG was associated with increasing severity of ICH (adjusted OR, aOR 1.06, 95 % CI 1.02-1.1, p < 0.001), higher 24 h NIHSS (aOR 1.22, 95 % CI 1.11-1.34, p < 0.001), ENR (aOR 0.90, 95 % CI 0.82-1.00, p < 0.002), and 90-day mRS (aOR 1.06, 95 % CI 1.03-1.09, p < 0.001) when adjusted for age, presenting NIHSS, ASPECTS, 24-hour peak systolic blood pressure, time from last known well, and successful recanalization. CONCLUSIONS: In the BEST study, increasing BG was associated with greater odds of increasing ICH severity. Further study is warranted to determine whether treatment of will decrease ICH severity following EVT.

Advances and Future Trends in the Diagnosis and Management of Intracerebral Hemorrhage.

Spontaneous intracerebral hemorrhage accounts for approximately 10% to 15% of all strokes in the United States and remains one of the deadliest. Of concern is the increasing prevalence, especially in younger populations. This article reviews the following: epidemiology, risk factors, outcomes, imaging findings, medical management, and updates to surgical management.

Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms.

Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers' role in prognosticating outcomes in ICH patients. We assessed 22 protein biomarkers using targeted proteomics in serum samples obtained from the ICH patient dataset (N = 150). We defined poor outcomes as modified Rankin scale score of 3-6. We incorporated clinical variables and protein biomarkers in regression models and random forest-based machine learning algorithms to predict poor outcomes and mortality. We report Odds Ratio (OR) or Hazard Ratio (HR) with 95% Confidence Interval (CI). We used five-fold cross-validation and bootstrapping for internal validation of prediction models. We included 149 patients for 90-day and 144 patients with ICH for 180-day outcome analyses. In multivariable logistic regression, UCH-L1 (adjusted OR 9.23; 95%CI 2.41-35.33), alpha-2-macroglobulin (aOR 5.57; 95%CI 1.26-24.59), and Serpin-A11 (aOR 9.33; 95%CI 1.09-79.94) were independent predictors of 90-day poor outcome; MMP-2 (aOR 6.32; 95%CI 1.82-21.90) was independent predictor of 180-day poor outcome. In multivariable Cox regression models, IGFBP-3 (aHR 2.08; 95%CI 1.24-3.48) predicted 90-day and MMP-9 (aOR 1.98; 95%CI 1.19-3.32) predicted 180-day mortality. Machine learning identified additional predictors, including haptoglobin for poor outcomes and UCH-L1, APO-C1, and MMP-2 for mortality prediction. Overall, random forest models outperformed regression models for predicting 180-day poor outcomes (AUC 0.89), and 90-day (AUC 0.81) and 180-day mortality (AUC 0.81). Serum biomarkers independently predicted short-term poor outcomes and mortality after ICH. Further research utilizing a multi-omics platform and temporal profiling is needed to explore additional biomarkers and refine predictive models for ICH prognosis.

Can baseline serum calcium levels predict outcomes of intracerebral hemorrhage? A systematic review and meta-analysis.

OBJECTIVE: The prognostic role of baseline calcium levels in patients with intracerebral hemorrhage (ICH) is conflicting. We aimed to conduct the first meta-analysis in the literature to examine if baseline calcium levels can predict outcomes after ICH. METHODS: English-language studies listed on the databases of Embase, PubMed, ScienceDirect, and Web of Science were searched up to 20th November 2023. Meta-analysis was conducted for baseline hematoma volume, hematoma expansion, unfavorable functional outcome, and mortality. RESULTS: Ten studies were included. Meta-analysis showed that patients with hypocalcemia have significantly higher baseline hematoma volume (MD: 8.6 95 % CI: 3.30, 13.90 I2 = 88 %) but did not have a higher risk of hematoma expansion (OR: 1.82 95 % CI: 0.89, 3.73 I2 = 82 %). Meta-analysis of crude (OR: 1.86 95 % CI: 1.25, 2.78 I2 = 63 %) and adjusted data (OR: 2.05 95 % CI: 1.27, 3.28 I2 = 64 %) showed those with hypocalcemia had a significantly higher risk of unfavorable functional outcomes. Meta-analysis of both crude (OR: 2.09 95 % CI: 1.51, 2.88 I2 = 80 %) and adjusted data (OR: 1.38 95 % CI: 1.14, 1.69 I2 = 70 %) also demonstrated a significantly higher risk of mortality in patients with hypocalcemia. CONCLUSION: Baseline serum calcium may have a prognostic role in ICH. Hypocalcemia at baseline may lead to large hematoma volume and poor functional and survival outcomes. However, there seems to be no relation between hypocalcemia and the risk of hematoma expansion. Further studies examining the role of calcium on ICH prognosis are needed.

Challenges in diagnosis and management of cerebral venous thrombosis as underlying cause of lobar intracerebral hemorrhage.

OBJECTIVES: Cerebral venous thrombosis is an uncommon, yet life-threatening condition, affecting mainly young and middle-aged individuals. Moreover, it represents an underrecognised etiology of lobar intracerebral hemorrhage (ICH). The clinical course of CVT is variable in the first days after diagnosis and medical complications including pulmonary embolism (PE) may result in early neurological deterioration and death if left untreated. MATERIALS AND METHODS: Case report. RESULTS: We describe a 46-year-old man with acute left hemiparesis and dysarthria in the context of lobar ICH due to underlying CVT of Trolard vein. Diagnosis was delayed because of misinterpretation of the initial neuroimaging study. Subsequently, the patient rapidly deteriorated and developed submassive PE and left iliofemoral venous thrombosis in the setting of previously undiagnosed hereditary thrombophilia (heterozygous prothrombin gene mutation G2021A). Emergent aspiration thrombectomy was performed resulting in the successful management of PE. A follow-up MRI study confirmed the thrombosed Trolard vein, thus establishing the CVT diagnosis. Anticoagulation treatment was immediately escalated to enoxaparine therapeutic dose resulting in clinical improvement of neurological deficits. CONCLUSIONS: Delayed diagnosis of cerebral venous thrombosis with underlying causes of lobar ICH may result in dire complications. Swift initiation of anticoagulants is paramount even in patients with lobar intracerebral hemorrhage as the initial manifestation of cerebral venous thrombosis.

Establishing reliable selection criteria for performing fibrinolytic therapy in patients with intracerebral haemorrhage based on prognostic tools.

OBJECTIVES: Minimally invasive surgery combined with fibrinolytic therapy is a promising treatment option for patients with intracerebral haemorrhage (ICH), but a meticulous patient selection is required, because not every patient benefits from it. The ICH score facilitates a reliable patient selection for fibrinolytic therapy except for ICH-4. This study evaluated whether an additional use of other prognostic tools can overcome this limitation. MATERIALS AND METHODS: A consecutive ICH patient cohort treated with fibrinolytic therapy between 2010 and 2020 was retrospectively analysed. The following prognostic tools were calculated: APACHE II, ICH-GS, ICH-FUNC, and ICH score. The discrimination power of every score was determined by ROC-analysis. Primary outcome parameters regarding the benefit of fibrinolytic therapy were the in-hospital mortality and a poor outcome defined as modified Rankin scale (mRS) > 4. RESULTS: A total of 280 patients with a median age of 72 years were included. The mortality rates according to the ICH score were ICH-0 = 0 % (0/0), ICH-1 = 0 % (0/22), ICH-2 = 7.1 % (5/70), ICH-3 = 17.3 % (19/110), ICH-4 = 67.2 % (45/67), ICH-5 = 100 % (11/11). The APACHE II showed the best discrimination power for in-hospital mortality (AUC = 0.87, p < 0.0001) and for poor outcome (AUC = 0.79, p < 0.0001). In the subgroup with ICH-4, APACHE II with a cut-off of 24.5 showed a good discriminating power for in-hospital mortality (AUC = 0.83, p < 0.001) and for poor outcome (AUC = 0.87, p < 0.001). CONCLUSIONS: An additional application of APACHE II score increases the discriminating power of ICH score 4 enabling a more precise appraisal of in-hospital mortality and of functional outcome, which could support the patient selection for fibrinolytic therapy.

Development of a Nomogram Model Based on Lactate-To-Albumin Ratio for Prognostic Prediction in Hospitalized Patients with Intracerebral Hemorrhage.

OBJECTIVE: This study aims to develop a nomogram model incorporating lactate-to-albumin ratio (LAR) to predict the prognosis of hospitalized patients with intracerebral hemorrhage (ICH) and demonstrate its excellent predictive performance. METHODS: A total of 226 patients with ICH from the Medical information mart for intensive care III (MIMIC Ⅲ) database were randomly split into 8:2 ratio training and experimental groups, and 38 patients from the eICU-CRD for external validation. Univariate and multivariate Cox proportional hazards regression analysis was performed to identify independent factors associated with ICH, and multivariate Cox regression was used to construct nomograms for 7-day and 14-day overall survival (OS). The performance of nomogram was verified by the calibration curves, decision curves, and receiver operating characteristic (ROC) curves. RESULTS: Our study identified LAR, glucose, mean blood pressure, sodium, and ethnicity as independent factors influencing in-hospital prognosis. The predictive performance of our nomogram model for predicting 7-day and 14 -day OS (AUCs: 0.845 and 0.830 respectively) are both superior to Oxford Acute Severity of Illness Score, Simplified acute physiology score II, and SIRS (AUCs: 0.617, 0.620 and 0.591 and AUCs: 0.709, 0.715 and 0.640, respectively) in internal validation, and also demonstrate favorable predictive performance in external validation (AUCs: 0.778 and 0.778 respectively). CONCLUSIONS: LAR as a novel biomarker is closely associated with an increased risk of in-hospital mortality of patients with ICH. The nomogram model incorporating LAR along with glucose, mean blood pressure, sodium, and ethnicity demonstrate excellent predictive performance for predicting the prognosis of 7- and 14-day OS of hospitalized patients with ICH.

Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data.

INTRODUCTION: Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a high risk of post-stroke epilepsy. However, there are no reliable methods to accurately predict those at higher risk for developing seizures despite their importance in planning treatments, allocating resources, and advancing post-stroke seizure research. Existing risk models have limitations and have not taken advantage of readily available real-world data and artificial intelligence. This study aims to evaluate the performance of Machine-learning-based models to predict post-stroke seizures at 1 year and 5 years after an intracerebral hemorrhage in unselected patients across multiple healthcare organizations. DESIGN/METHODS: We identified patients with intracerebral hemorrhage (ICH) without a prior diagnosis of seizures from 2015 until inception (11/01/22) in the TriNetX Diamond Network, using the International Classification of Diseases, Tenth Revision (ICD-10) I61 (I61.0, I61.1, I61.2, I61.3, I61.4, I61.5, I61.6, I61.8, and I61.9). The outcome of interest was any ICD-10 diagnosis of seizures (G40/G41) at 1 year and 5 years following the first occurrence of the diagnosis of intracerebral hemorrhage. We applied a conventional logistic regression and a Light Gradient Boosted Machine (LGBM) algorithm, and the performance of the model was assessed using the area under the receiver operating characteristics (AUROC), the area under the precision-recall curve (AUPRC), the F1 statistic, model accuracy, balanced-accuracy, precision, and recall, with and without seizure medication use in the models. RESULTS: A total of 85,679 patients had an ICD-10 code of intracerebral hemorrhage and no prior diagnosis of seizures, constituting our study cohort. Seizures were present in 4.57 % and 6.27 % of patients within 1 and 5 years after ICH, respectively. At 1-year, the AUROC, AUPRC, F1 statistic, accuracy, balanced-accuracy, precision, and recall were respectively 0.7051 (standard error: 0.0132), 0.1143 (0.0068), 0.1479 (0.0055), 0.6708 (0.0076), 0.6491 (0.0114), 0.0839 (0.0032), and 0.6253 (0.0216). Corresponding metrics at 5 years were 0.694 (0.009), 0.1431 (0.0039), 0.1859 (0.0064), 0.6603 (0.0059), 0.6408 (0.0119), 0.1094 (0.0037) and 0.6186 (0.0264). These numerical values indicate that the statistical models fit the data very well. CONCLUSION: Machine learning models applied to electronic health records can improve the prediction of post-hemorrhagic stroke epilepsy, presenting a real opportunity to incorporate risk assessments into clinical decision-making in post-stroke care clinical care and improve patients' selection for post-stroke epilepsy research.

Outcome Trajectories after Intracerebral Hemorrhage.

Spontaneous intracerebral hemorrhage (ICH) is the most morbid of all stroke types with a high early mortality and significant early disability burden. Traditionally, outcome assessments after ICH have mirrored those of acute ischemic stroke, with 3 months post-ICH being considered a standard time point in most clinical trials, observational studies, and clinical practice. At this time point, the majority of ICH survivors remain with moderate to severe functional disability. However, emerging data suggest that recovery after ICH occurs over a more protracted course and requires longer periods of follow-up, with more than 40% of ICH survivors with initial severe disability improving to partial or complete functional independence over 1 year. Multiple other domains of recovery impact ICH survivors including cognition, mood, and health-related quality of life, all of which remain under studied in ICH. To further complicate the picture, the most important driver of mortality after ICH is early withdrawal of life-sustaining therapies, before initiation of treatment and evaluating effects of prolonged supportive care, influenced by early pessimistic prognostication based on baseline severity factors and prognostication biases. Thus, our understanding of the true natural history of ICH recovery remains limited. This review summarizes the existing literature on outcome trajectories in functional and nonfunctional domains, describes limitations in current prognostication practices, and highlights areas of uncertainty that warrant further research.

The Presence of Blood in a Strain Gauge Pressure Transducer Has a Clinical Effect on the Accuracy of Intracranial Pressure Readings.

IMPORTANCE: Patients admitted with cerebral hemorrhage or cerebral edema often undergo external ventricular drain (EVD) placement to monitor and manage intracranial pressure (ICP). A strain gauge transducer accompanies the EVD to convert a pressure signal to an electrical waveform and assign a numeric value to the ICP. OBJECTIVES: This study explored ICP accuracy in the presence of blood and other viscous fluid contaminates in the transducer. DESIGN: Preclinical comparative design study. SETTING: Laboratory setting using two Natus EVDs, two strain gauge transducers, and a sealed pressure chamber. PARTICIPANTS: No human subjects or animal models were used. INTERVENTIONS: A control transducer primed with saline was compared with an investigational transducer primed with blood or with saline/glycerol mixtures in mass:mass ratios of 25%, 50%, 75%, and 100% glycerol. Volume in a sealed chamber was manipulated to reflect changes in ICP to explore the impact of contaminates on pressure measurement. MEASUREMENTS AND MAIN RESULTS: From 90 paired observations, ICP readings were statistically significantly different between the control (saline) and experimental (glycerol or blood) transducers. The time to a stable pressure reading was significantly different for saline vs. 25% glycerol (< 0.0005), 50% glycerol (< 0.005), 75% glycerol (< 0.0001), 100% glycerol (< 0.0005), and blood (< 0.0005). A difference in resting stable pressure was observed for saline vs. blood primed transducers (0.041). CONCLUSIONS AND RELEVANCE: There are statistically significant and clinically relevant differences in time to a stable pressure reading when contaminates are introduced into a closed drainage system. Changing a transducer based on the presence of blood contaminate should be considered to improve accuracy but must be weighed against the risk of introducing infection.

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning.

The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This study aims to develop machine learning models to detect potential (re)hemorrhage in sporadic CCM patients. This study was based on a dataset of 731 sporadic CCM patients in open data platform Dryad. Sporadic CCM patients were followed up 5 years from January 2003 to December 2018. Support vector machine (SVM), stacked generalization, and extreme gradient boosting (XGBoost) were used to construct models. The performance of models was evaluated by area under receiver operating characteristic curves (AUROC), area under the precision-recall curve (PR-AUC) and other metrics. A total of 517 patients with sporadic CCM were included (330 female [63.8%], mean [SD] age at diagnosis, 42.1 [15.5] years). 76 (re)hemorrhage (14.7%) occurred during follow-up. Among 3 machine learning models, XGBoost model yielded the highest mean (SD) AUROC (0.87 [0.06]) in cross-validation. The top 4 features of XGBoost model were ranked with SHAP (SHapley Additive exPlanations). All-Elements XGBoost model achieved an AUROCs of 0.84 and PR-AUC of 0.49 in testing set, with a sensitivity of 0.86 and a specificity of 0.76. Importantly, 4-Elements XGBoost model developed using top 4 features got a AUROCs of 0.83 and PR-AUC of 0.40, a sensitivity of 0.79, and a specificity of 0.72 in testing set. Two machine learning-based models achieved accurate performance in identifying potential (re)hemorrhages within 5 years in sporadic CCM patients. These models may provide insights for clinical decision-making.

Predicting severe intraventricular hemorrhage or early death using machine learning algorithms in VLBWI of the Korean Neonatal Network Database.

Severe intraventricular hemorrhage (IVH) in premature infants can lead to serious neurological complications. This retrospective cohort study used the Korean Neonatal Network (KNN) dataset to develop prediction models for severe IVH or early death in very-low-birth-weight infants (VLBWIs) using machine-learning algorithms. The study included VLBWIs registered in the KNN database. The outcome was the diagnosis of IVH Grades 3-4 or death within one week of birth. Predictors were categorized into three groups based on their observed stage during the perinatal period. The dataset was divided into derivation and validation sets at an 8:2 ratio. Models were built using Logistic Regression with Ridge Regulation (LR), Random Forest, and eXtreme Gradient Boosting (XGB). Stage 1 models, based on predictors observed before birth, exhibited similar performance. Stage 2 models, based on predictors observed up to one hour after birth, showed improved performance in all models compared to Stage 1 models. Stage 3 models, based on predictors observed up to one week after birth, showed the best performance, particularly in the XGB model. Its integration into treatment and management protocols can potentially reduce the incidence of permanent brain injury caused by IVH during the early stages of birth.

Red cell distribution width to lymphocyte ratio could serve as a new inflammatory biomarker for predicting hematoma expansion in patients with intracerebral hemorrhage.

BACKGROUND: Hematoma expansion is a critical factor associated with increased mortality and adverse outcomes in patients with intracerebral hemorrhage (ICH). Identifying and preventing hematoma expansion early on is crucial for effective therapeutic intervention. This study aimed to investigate the potential association between the Red cell distribution width to lymphocyte ratio (RDWLR) and hematoma expansion in ICH patients. METHODS: We conducted a retrospective analysis of clinical data from 303 ICH patients treated at our department between May 2018 and May 2023. Demographic, clinical, radiological, and laboratory data, including RDWLR upon admission, were assessed. Binary logistic regression analysis was employed to determine independent associations between various variables and hematoma expansion. RESULTS: The study included 303 ICH patients, comprising 167 (55.1%) males and 136 (44.9%) females, with a mean age of 65.25 ± 7.32 years at admission. Hematoma expansion occurred in 73 (24.1%) cases. Multivariate analysis revealed correlations between hematoma volume at baseline (OR, 2.73; 95% CI: 1.45 -4,78; P < 0.001), admission systolic blood pressure (OR, 2.98 ; 95% CI: 1.54-4.98; P < 0.001), Glasgow Coma Scale (GCS) (OR, 1.58; 95% CI: 1.25-2.46; P = 0.017), and RDWLR (OR, 1.58; 95% CI: 1.13-2.85; P = 0.022) and hematoma expansion in these patients. CONCLUSIONS: Our findings suggest that RDWLR could serve as a new inflammatory biomarker for hematoma expansion in ICH patients. This cost-effective and readily available biomarker has the potential for early prediction of hematoma expansion in these patients.