Early prophylactic anticoagulation after subarachnoid hemorrhage decreases systemic ischemia and improves outcome

Construction and validation of a perioperative concomitant lower extremity deep vein thrombosis line graph model in patients with aneurysmal subarachnoid hemorrhage

Background

To develop and validate a nomogram for predicting the probability of deep venous thrombosis (DVT) in patients with aneurysmal subarachnoid hemorrhage (aSAH) during the perioperative period, using clinical features and readily available biochemical parameters.

Methods

The least absolute shrinkage and selection operator (LASSO) regression technique was employed for data dimensionality reduction and selection of predictive factors. A multivariable logistic regression analysis was conducted to establish a predictive model and nomogram for post-aSAH DVT. The discriminative ability of the model was determined by calculating the area under the curve (AUC).

Results

A total of 358 aSAH patients were included in the study, with an overall incidence of DVT of 20.9%. LASSO regression identified four variables, including age, modified Fisher grade, total length of hospital stay, and anticoagulation therapy, as highly predictive factors for post-aSAH DVT. The patients were randomly divided into a modeling group and a validation group in a 6:4 ratio to construct the nomogram. The AUCs of the modeling and validation groups were 0.8511 (95% CI, 0.7922-0.9099) and 0.8633 (95% CI, 0.7968-0.9298), respectively.

Conclusions

The developed nomogram exhibits good accuracy, discriminative ability, and clinical utility in predicting DVT, aiding clinicians in identifying high-risk individuals and implementing appropriate preventive and treatment measures.

Venous thromboembolism and intracranial hemorrhage in patients undergoing glioblastoma surgery

In the perioperative management of patients with glioblastoma (GBM), physicians face the question of whether and when to administer prophylactic or therapeutic anticoagulation (AC). In this study, we investigate the effects of the timing of postoperative heparinization on thromboembolic events (TE) and postoperative hemorrhage (bleeding, PH) as well as the interactions between the two in the context of an underlying intracerebral malignancy. For this retrospective data analysis, 222 patients who underwent surgery for grade IV glioblastoma, IDH-wildtype (2016 CNS WHO) between 01/01/2014 and 31/12/2019 were included. We followed up for 12 months. We assessed various biographical and clinical data for risk factors and focused on the connection between timepoint of AC and adverse events. Subgroup analyses were performed for pulmonary artery embolism (PE), deep vein thrombosis, and postoperative intracranial hemorrhage (PH) that either required surgical intervention or was controlled radiologically only. Statistical analysis was performed using Mann-Whitney U-Test, Chi-square test, Fisher's exact test and univariate binomial logistic regression. p values below 0.05 were considered statistically significant. There was no significant association between prophylactic AC within 24 h and more frequent major bleeding (p = 0.350). AC in patients who developed major bleeding was regularly postponed by the physician/surgeon upon detection of the re-bleeding; therefore, patients with PH were anticoagulated significantly later (p = 0.034). The timing of anticoagulant administration did not differ significantly between patients who experienced a thromboembolic event and those who did not (p = 0.634). There was considerable overlap between the groups. Three of the six patients (50%) with PE had to be lysed or therapeutically anticoagulated and thereafter developed major bleeding (p < 0.001). Patients who experienced TE were more likely to die during hospitalization than those with major bleeding (p = 0.022 vs. p = 1.00). Prophylactic AC within 24 h after surgery does not result in more frequent bleeding. Our data suggests that postoperative intracranial hemorrhage is not caused by prophylactic AC but rather is a surgical complication or the result of antithrombotic therapy. However, thromboembolic events worsen patient outcomes far more than postoperative bleeding. The fact that bleeding may occur as a complication of life-saving lysis therapy in the setting of a thromboembolic event should be included in this cost-benefit consideration.

Effects of low-dose intravenous heparin therapy in aneurysmal subarachnoid hemorrhage: a randomized controlled clinical trial protocol

BACKGROUND

Heparin anticoagulation therapy is a widely used method to prevent cerebral vasospasm (CV) and venous thrombosis in patients with subarachnoid hemorrhage caused by ruptured cerebral aneurysms. Subcutaneous heparin injection is considered safe and effective, whereas continuous intravenous heparin infusion is still being debated due to the risk of bleeding. Although most retrospective studies have confirmed the safety and effectiveness of unfractionated heparin (UFH) after aneurysm embolization therapy and its ability to reduce CV, there is still no randomized clinical trial comparing UFH and subcutaneous low-molecular-weight heparin (LMWH) injection in this population. Therefore, this study aims to compare the clinical outcomes associated with these two treatment approaches.

METHODS

The study is an open-label, single-center, randomized controlled trial and aims to recruit 456 patients, with 228 patients in each group. The primary outcome was CV; the second outcomes measures are occurrence of bleeding events, ischemic events, heparin-induced thrombocytopenia, deep vein thrombosis, cerebral venous circulation time, brain edema score, and hydrocephalus incidence.

ETHICS AND DISSEMINATION

This study protocol obtained ethical approval from the Ethics Committee of Baoan People's Hospital, Shenzhen, Guangdong (approval number: BYL20220805). This work will be published in peer-reviewed international medical journals and presented at medical conferences.

TRIAL REGISTRATION

ClinicalTrials ID: NCT05696639. Registered on March 30, 2023.

Characterizing Risk of In-Hospital Mortality Following Subarachnoid Hemorrhage Using Machine Learning: A Retrospective Study

Background

Subarachnoid hemorrhage has a high rate of disability and mortality, and the ability to use existing disease severity scores to estimate the risk of adverse outcomes is limited. Collect relevant information of patients during hospitalization to develop more accurate risk prediction models, using logistic regression (LR) and machine learning (ML) technologies, combined with biochemical information.

Methods

Patient-level data were extracted from MIMIC-IV data. The primary outcome was in-hospital mortality. The models were trained and tested on a data set (ratio 70:30) including age and key past medical history. The recursive feature elimination (RFE) algorithm was used to screen the characteristic variables; then, the ML algorithm was used to analyze and establish the prediction model, and the validation set was used to further verify the effectiveness of the model.

Result

Of the 1,787 patients included in the mimic database, a total of 379 died during hospitalization. Recursive feature abstraction (RFE) selected 20 variables. After simplification, we determined 10 features, including the Glasgow coma score (GCS), glucose, sodium, chloride, SPO₂, bicarbonate, temperature, white blood cell (WBC), heparin use, and sepsis-related organ failure assessment (SOFA) score. The validation set and Delong test showed that the simplified RF model has a high AUC of 0.949, which is not significantly different from the best model. Furthermore, in the DCA curve, the simplified GBM model has relatively higher net benefits. In the subgroup analysis of non-traumatic subarachnoid hemorrhage, the simplified GBM model has a high AUC of 0.955 and relatively higher net benefits.

Conclusions

ML approaches significantly enhance predictive discrimination for mortality following subarachnoid hemorrhage compared to existing illness severity scores and LR. The discriminative ability of these ML models requires validation in external cohorts to establish generalizability.

Impact of obesity and diabetes on postoperative outcomes following surgical treatment of nontraumatic subarachnoid hemorrhage: Analysis of the ACS-NSQIP Database

BACKGROUND

Nontraumatic subarachnoid hemorrhage (SAH) refers to high pressure extravasation of blood into the subarachnoid space that typically occurs spontaneously from rupture of cerebral aneurysm. The purpose of this study was to identify postoperative complications requiring increased surveillance in obese, diabetic, and hypertensive patients.

METHODS

Patients who underwent surgical treatment for nontraumatic SAH were queried in the American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database from the years 2012-2018. Cases were identified using ICD codes and then classified independently by three dichotomous diagnoses: obesity, diabetes, and hypertension.

RESULTS

Among 1,002 patients meeting inclusion criteria, 311 (31%) were obese (BMI >30), 86 (9%) had diabetes treated with insulin or non-insulin agents and 409 (41%) required medication for hypertension. There was a statistically significant association between diabetes and post-operative pneumonia (OR = 1.694; 95% CI 0.995 - 2.883; p = 0.050), prolonged ventilator dependence (OR = 1.700; 95% CI 1.087 - 2.661; p = 0.019), and death (OR = 1.846; 95% CI 1.071 - 3.179; p = 0.025). Medicationdependent hypertension was statistically associated with incidence of stroke/CVA (OR = 1.763; 95% CI 1.056 - 2.943; p = 0.023). Obesity was not associated with adverse outcomes in this population.

CONCLUSIONS

In patients undergoing surgical management of SAH, hypertensive and diabetic patients had poorer outcomes, including prolonged ventilator dependence, pneumonia, stroke/CVA, and death. Surprisingly, preexisting obesity was not associated with poor outcomes. In fact, overweight BMI, Class I, and Class II obesity had decreased need for transfusion in the 30-day postoperative period.