Neurosurgical Review最新文献

Multiple prognostic scores have been developed to predict morbidity and mortality in patients with spontaneous intracerebral hemorrhage(sICH). Since the advent of machine learning(ML), different ML models have also been developed for sICH prognostication. There is however a need to verify the validity of these ML models in diverse patient populations. We aim to create machine learning models for prognostication purposes in the Qatari population. By incorporating inpatient variables into model development, we aim to leverage more information. 1501 consecutive patients with acute sICH admitted to Hamad General Hospital(HGH) between 2013 and 2023 were included. We trained, evaluated, and compared several ML models to predict 90-day mortality and functional outcomes. For our dataset, we randomly selected 80% patients for model training and 20% for validation and used k-fold cross validation to train our models. The ML workflow included imbalanced class correction and dimensionality reduction in order to evaluate the effect of each. Evaluation metrics such as sensitivity, specificity, F-1 score were calculated for each prognostic model. Mean age was 50.8(SD 13.1) years and 1257(83.7%) were male. Median ICH volume was 7.5 ml(IQR 12.6). 222(14.8%) died while 897(59.7%) achieved good functional outcome at 90 days. For 90-day mortality, random forest(RF) achieved highest AUC(0.906) whereas for 90-day functional outcomes, logistic regression(LR) achieved highest AUC(0.888). Ensembling provided similar results to the best performing models, namely RF and LR, obtaining an AUC of 0.904 for mortality and 0.883 for functional outcomes. Random Forest achieved the highest AUC for 90-day mortality, and LR achieved the highest AUC for 90-day functional outcomes. Comparing ML models, there is minimal difference between their performance. By creating an ensemble of our best performing individual models we maintained maximum accuracy and decreased variance of functional outcome and mortality prediction when compared with individual models.

The treatment of brain tumors is significantly hindered by the Blood-Brain Barrier (BBB), a selective barrier that restricts the passage of therapeutic agents to the brain. Recent advancements in BBB-targeting therapies offer promising strategies to overcome this challenge, providing new avenues for the effective treatment of brain cancer. This article reviews innovative approaches, including Convection-Enhanced Delivery (CED) and RNA-based therapeutics, which enhance drug delivery directly to tumor sites, bypassing the BBB and reducing systemic toxicity. Additionally, the use of theranostic nanoparticles and CRISPR-Cas9 gene editing presents novel opportunities for real-time monitoring and precision-targeted therapy, respectively. Techniques such as magnetic nanoparticles, intranasal drug administration, and focused ultrasound with microbubbles are also being refined to improve drug penetration across the BBB. Furthermore, peptide-based delivery systems and small molecules designed to mimic endogenous transport pathways are accelerating the discovery of more effective therapies. The exploration of combination therapies that synergize BBB-penetrant drugs with conventional chemotherapeutic agents or immunotherapies holds the potential to enhance treatment efficacy and patient outcomes. Continued research and interdisciplinary collaboration are essential to develop predictive models, personalized treatment strategies, and alternative delivery methods that ensure the long-term safety and effectiveness of these novel therapies. Advancements in BBB-targeting therapeutics are poised to transform the landscape of brain cancer treatment, offering renewed hope for improved survival rates and quality of life for patients.

Pneumonia is a common postoperative complication in patients with aneurysmal subarachnoid hemorrhage (aSAH), which is associated with poor prognosis and increased mortality. The aim of this study was to develop a predictive model for postoperative pneumonia (POP) in patients with aSAH. A retrospective analysis was conducted on 308 patients with aSAH who underwent surgery at the Neurosurgery Department of the First Affiliated Hospital of Soochow University. Univariate and multivariate logistic regression and lasso regression analysis were used to analyze the risk factors for POP. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the constructed model. Finally, the effectiveness of modeling these six variables in different machine learning methods was investigated. In our patient cohort, 23.4% (n = 72/308) of patients experienced POP. Univariate, multivariate logistic regression analysis and lasso regression analysis revealed age, Hunt-Hess grade, mechanical ventilation, leukocyte count, lymphocyte count, and platelet count as independent risk factors for POP. Subsequently, these six factors were used to build the final model. We found that age, Hunt-Hess grade, mechanical ventilation, leukocyte count, lymphocyte count, and platelet count were independent risk factors for POP in patients with aSAH. Through validation and comparison with other studies and machine learning models, our novel predictive model has demonstrated high efficacy in effectively predicting the likelihood of pneumonia during the hospitalization of aSAH patients.

This letter recognizes the authors' commendable work in examining the role of social media in promoting neurosurgical research. It also underscores the necessity for more robust methodologies in future studies to clearly assess the impact of social media promotion and to ensure that findings are generalizable, addressing the limitations of the current study.

Idiopathic normal pressure hydrocephalus (iNPH) affects approximately 1.5% of the population, with a higher prevalence in men than women. Ventriculoperitoneal shunting (VPS) is the standard treatment for iNPH, but it poses a notable risk of infection, occurring in 8-10% of cases. Recent advancements in non-invasive diagnostic techniques, such as superb microvascular ultrasound (SMI), have demonstrated potential in evaluating cerebrospinal fluid (CSF) flow within VPS systems. A single-center feasibility study involving 19 asymptomatic patients with VPS systems showed that SMI reliably detected CSF flow in the proximal catheter in all patients and in the distal catheter in 89.5%, while reductions in optic nerve sheath diameter (ONSD) indicated lowered intracranial pressure after shunt activation. These findings suggest that SMI could serve as a safer alternative to invasive methods for assessing shunt function. Additionally, artificial intelligence (AI)-based approaches are being explored to reduce infection risk and enhance shunt efficacy. An artificial neural network (ANN) model achieved an 83.1% accuracy in predicting infection risk, surpassing traditional logistic regression models. However, the study's limitations, including its retrospective design, small sample size, and single-center nature, underscore the need for larger multi-center studies to confirm the generalizability of these findings. Further research is essential to validate the effectiveness of these innovations and their potential to improve patient outcomes in hydrocephalus management.

This study by Aboukais et al. (2024) evaluates postoperative outcomes in patients with unruptured giant middle cerebral artery (MCA) aneurysms associated with intracranial hypertension and midline brain shift. Analyzing data from 2012 to 2022, the authors compare surgical approaches, emphasizing the potential benefits of systematic decompressive hemicraniotomy in improving patient outcomes. While the study's findings are valuable, the small sample size and absence of a control group limit its generalizability. The retrospective nature of the study introduces potential biases, and long-term cognitive outcomes are not fully explored. Future research should involve larger, prospective cohorts with control groups, incorporating advanced imaging and monitoring techniques to enhance surgical precision and long-term recovery assessments. This study provides important insights but underscores the need for further investigation to optimize treatment strategies for this complex condition.