# Comprehensive analysis of risk factors for intracranial aneurysm rupture: a retrospective cohort study

## TL;DR

This study identifies key risk factors for intracranial aneurysm rupture, including age, sex, inflammation markers, and coagulation parameters, to improve early detection and patient outcomes.

## Contribution

The study introduces a predictive model combining demographic, inflammatory, and coagulation factors to assess intracranial aneurysm rupture risk.

## Key findings

- Younger age, female sex, and elevated inflammation markers are significant risk factors for aneurysm rupture.
- The predictive model achieved an AUC of 0.815, showing good calibration and clinical utility.
- Electrolyte imbalances and coagulation parameters like fibrinogen and D-dimer are strongly associated with rupture.

## Abstract

Intracranial aneurysms (IAs) can lead to subarachnoid hemorrhage, a life-threatening event associated with high morbidity and mortality. Identifying individuals at elevated risk is crucial for guiding timely interventions and improving patient outcomes.

In this retrospective cohort study, 850 patients who received interventional or surgical treatment for IAs between January 2018 and January 2024 were included. Demographic data (e.g., age, sex), lifestyle factors, and comorbidities were recorded. Hematologic, biochemical, and coagulation parameters were measured to evaluate their potential association with IA rupture. A univariate logistic regression was first conducted, followed by a multivariate logistic regression with a backward stepwise approach to derive the final predictive model. The model’s performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis.

Younger age, female sex, higher neutrophil count, lower hematocrit, and elevated markers of inflammation and coagulation (including fibrinogen and D-dimer) emerged as key risk factors. Electrolyte imbalances, such as low potassium, and elevated lactate dehydrogenase were also significantly associated with rupture. The optimized model achieved an AUC of 0.815, with good calibration and clinical utility indicated by decision curve analysis.

These findings highlight the interplay of demographic, inflammatory, metabolic, and coagulation parameters in determining rupture risk in patients with IAs. Incorporating these risk factors into clinical practice may enhance early detection, guide targeted prevention strategies, and ultimately improve outcomes for high-risk individuals.