Abstract
The purpose of this study is to investigate the relationship between chronic pain and the mortality rate of cerebrovascular disease (CVD) in cancer patients. Thus, we performed a case-control investigation was conducted by utilizing data from the Surveillance, Epidemiology, and End Results (SEER) database between 1975 and 2019. Multiple demographics, pain rating and other clinical characteristics were extracted to assess predictors for the death from CVD in cancer patients. Different machine learning algorithms were applied to construct pain-related prediction model. The analysis involved 16,850 case patients and 710,729 controls. Among cancer patients, approximately 2.3% succumbed to subsequent CVD. Cancer pain (Pain rating II) was associated with a decreased risk of CVD. Univariate and multivariate COX analyses indicated that older age at cancer diagnosis, male gender, single marital status, Black or Other race, and lack of systemic therapy correlated with a higher risk of CVD-related death. Propensity score matching revealed a significantly lower proportion of Pain rating II in the case group. The logistic regression algorithm demonstrated superior predictive ability for 5-year and 10-year CVD risk in cancer patients. Notably, survival time, age, and pain rating emerged as the top three crucial variables. This study firstly investigated pain and various risk factors for CVD in cancer patients, highlighting pain as a novel and possible protective factor for CVD. The development of a risk model based on pain could aid in identifying individuals at high risk for CVD and may inspire innovative strategies for preventing CVD in cancer patients.