Influencing Factors and Nomogram for the Development of Epilepsy in Advanced Lung Cancer Patients With Brain Metastases

Abstract

Background

Epilepsy is a prevalent comorbidity in patients with brain metastases (BM) and could result in sudden and accidental damage, as well as increased disease burden due to its rapid onset. Foreseeing the potential for the development of epilepsy may permit timely and efficient measures. This study aimed to analyze the influencing factors of epilepsy in advanced lung cancer (ALC) patients with BM and construct a nomogram model to predict the likelihood of developing epilepsy.

Methods

Socio-demographic and clinical data of ALC patients with BM were retrospectively collected from the First Affiliated Hospital of Zhejiang University School of Medicine between September 2019 and June 2021. Univariate and multivariate logistic regression analyses were applied to determine the influencing factors for epilepsy in ALC patients with BM. Based on the results of the logistic regression analysis, a nomogram was built to represent the contribution of each influencing factor in predicting the probability of epilepsy development in ALC patients with BM. The Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve were utilized to evaluate the goodness of fit and prediction performance of the model.

Results

The incidence of epilepsy among 138 ALC patients with BM was 29.7%. On the multivariate analysis, having a higher number of supratentorial lesions (odds ratio [OR] = 1.727; p = 0.022), hemorrhagic foci (OR = 4.922; p = .021), and a high-grade of peritumoral edema (OR = 2.524; p < .001) were independent risk factors for developing epilepsy, while undergoing gamma knife radiosurgery (OR = .327; p = .019) was an independent protective factor. The p-value of the Hosmer-Lemeshow test was .535 and the area under the ROC curve (AUC) was .852 (95% CI: .807–.897), suggesting the model had a good fit and exhibited strong predictive accuracy.

Conclusion

The nomogram was constructed that can predict the probability of epilepsy development for ALC patients with BM, which is helpful for healthcare professionals to identify high-risk groups early and allows for individualized interventions.