Background: Numerous randomized controlled trials (RCTs) relevant to the cerebrovascular field have been performed. The fragility index was recently developed to complement the P value and measure the robustness and reproducibility of clinical findings of RCTs.

Objective: In this study, we evaluate the fragility index for key surgical and endovascular cerebrovascular RCTs and propose a novel RCT classification system based on the fragility index.

Methods: Cerebrovascular RCTs reported between 2000 and 2018 were reviewed. Six key areas were specifically targeted in relation to stroke, carotid stenosis, cerebral aneurysms, and subarachnoid hemorrhage. The correlation between fragility index, number of patients lost to follow-up, and fragility quotient were evaluated to propose a classification system for the robustness of the studies.

Results: A total of 20 RCTs that reported significant differences between both study groups in terms of the primary outcome were included. The median fragility index for the trials was 5.5. An additional 30 randomly selected RCTs were added to propose a classification system with high reliability. The difference between the number of patients lost to follow-up and fragility index inversely correlated with the fragility quotient and was used to divide the robustness of the RCTs into 3 classes reflecting the reproducibility of the trial.

Conclusions: Neurosurgeons and neurointerventionalists should exercise caution with interpreting the results of cerebrovascular RCTs, especially when the sample size and events numbers are small and there is a high number of patients who were lost to follow-up, as quantitatively identified using the proposed classification system.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.wneu.2020.04.106DOI Listing

Publication Analysis

Top Keywords

classification system
16
cerebrovascular rcts
12
number patients
12
patients lost
12
lost follow-up
12
randomized controlled
8
controlled trials
8
fragility
8
rcts propose
8
rcts reported
8

Similar Publications

Prognostic evaluation of nutrition risk screening tools in hospitalized adults with normal weight range, overweight, or obesity: A comparative analysis.

JPEN J Parenter Enteral Nutr

January 2025

Department of Nutrition, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil.

Background: Many nutrition risk screening tools include low body mass index (BMI). It remains uncertain whether it affects the validity of these tools in patients with overweight or obesity. We aimed to determine the frequency of malnutrition risk and evaluate its association with hospital length of stay in hospitalized adults according to BMI classification.

View Article and Find Full Text PDF

Background: There is limited data showing the predictive accuracy of traditional cardiovascular risk scores (CVRS) to predict asymptomatic coronary artery disease (CAD) determined by coronary computed tomography angiography (CCTA).

Methods: Asymptomatic individuals without known CAD undergoing a screening CCTA and sufficient data to calculate their CVRS, were extracted retrospectively. Atherosclerosis was extracted using natural language processing of the CCTA report, including the coronary artery calcium score (CACS) and the extent and severity of CAD.

View Article and Find Full Text PDF

Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) has become standard treatment for peritoneal cancers and metastases, significantly enhancing survival rates. This study evaluated the relationship between tumor burden, hemodynamic management, and postoperative outcomes after CRS-HIPEC. This study included 203 patients undergoing CRS-HIPEC.

View Article and Find Full Text PDF

Effective data representation in machine learning and deep learning is paramount. For an algorithm or neural network to capture patterns in data and be able to make reliable predictions, the data must appropriately describe the problem domain. Although there exists much literature on data preprocessing for machine learning and data science applications, novel data representation methods for enhancing machine learning model performance remain highly absent within the literature.

View Article and Find Full Text PDF

In right-sided colon cancer surgery, lymph node dissection around the superior mesenteric artery is necessary but technically challenging. Here we introduce the concept of "outermost layer-oriented robotic surgery" to improve the safety, efficacy, and reproducibility of superior mesenteric artery nodal dissection. In this procedure, the thin, loose connective tissue layer between the autonomic nerve sheath of the superior mesenteric artery and adipose tissue bearing lymph nodes, termed "the outermost layer of the autonomic nerve," is dissected.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!