A Cancer Paradox: Machine-Learning Backed Propensity-Score Analysis of Coronary Angiography Findings in Cardio-Oncology.

OBJECTIVES: Cancer has been proposed as a cardiovascular risk factor. We aimed to assess the cardiovascular risk profile and coronary angiography (CA) findings of cancer patients and compare them to those of patients without cancer.

METHODS: A retrospective case-control analysis was conducted on randomly enrolled cancer and non-cancer patients from a high-volume cardio-oncology center and a tertiary cardiology center, respectively, who underwent CA from April 2008 to June 2018. Baseline demographics, laboratory findings, cancer status and treatment, and current and prior CA findings were collected by chart review. Coronary artery disease (CAD) burden was assessed with machine-learning (neural-network) guided propensity-score adjusted multivariable regression, controlling for known CAD confounders.

RESULTS: Of the 480 enrolled patients, a total of 240 (50%) had cancer. Fewer cancer vs non-cancer patients had clinically significant lesions on the left anterior descending artery (25.00% vs 39.17%, respectively; P<.01) and left circumflex artery (15.83% vs 30.00%, respectively; P<.001). Left main and right coronary artery disease prevalence was similar. Subjects with cancer were less likely to have multivessel CAD (odds ratio, 0.53; 95% confidence interval, 0.29-0.98; P=.04) and significant left circumflex artery lesions (odds ratio, 0.47; 95% confidence interval, 0.26-0.85; P=.01), independent of known CAD confounders.

CONCLUSIONS: Patients with cancer have a lower burden of angiographically detected coronary atherosclerosis. Cancer patients are more likely than non-cancer patients to undergo CA for reasons other than suspicion of CAD. Further studies should prospectively analyze the impact of cancer on the development of CAD.