Performance of a diagnostic model for the presence of unruptured intracranial aneurysms in the general population

Introduction: The prevalence of unruptured intracranial aneurysms (UIAs) in the general population is 3%. Aneurysmal subarachnoid hemorrhage can be prevented by screening for UIAs followed by monitoring and, if needed, preventive neurosurgical or endovascular treatment of identified UIAs. Therefore, we developed a diagnostic model for the presence of UIAs in the general population to help identify persons at high risk of having UIAs. Methods: Between 2005 and 2015, participants from the populationbased Rotterdam Study underwent brain magnetic resonance imaging at 1.5 T, on which the presence of incidental UIAs was evaluated. We developed a multivariable logistic regression model using candidate diagnostic markers that were selected based on the literature, including sex, age, hypertension, smoking, hypercholesterolemia, diabetes, alcohol, and their interactions. We corrected for overfitting using bootstrapping. Model performance was assessed with discrimination, calibration, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: 5,835 persons were included (55.0% women, mean age 64.9 ± 10.9 years) with a 2.2% UIA prevalence. Sex, age, hypertension, smoking, diabetes, and interactions of sex with age, hypertension, and smoking were independent diagnostic markers. The resulting model had a c-statistic of 0.65 (95% confidence interval [CI]: 0.60 0.68) and 56% sensitivity, 52% specificity, 98% PPV, and 3% NPV for UIA presence at a cutoff value of 4%. Because of interactions with sex, additional models for men and women separately were developed. The model for men had a c-statistic of 0.70 (95% CI: 0.62 0.78) with age, hypertension, and smoking as diagnostic markers and comparable additional performance values as for the full model. The model for women had a c-statistic of 0.58 (95% CI: 0.52 0.63) with smoking as the only diagnostic marker. Conclusion: Our diagnostic model had insufficient performance to help identify persons at high risk of having UIAs in the general population. Rather, it provides insight in risk factors contributing to UIA risk and shows that these may be in part sex-specific.