Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients

Abstract

BackgroundDespite the growing number of patients with both coronary artery disease and gynecological cancer, there are no nationally representative studies of mortality and cost effectiveness for percutaneous coronary interventions (PCI) and this cancer type.MethodsBackward propagation neural network machine learning supported and propensity score adjusted multivariable regression was conducted for the above outcomes in this case-control study of the 2016 National Inpatient Sample (NIS), the United States' largest all-payer hospitalized dataset. Regression models were fully adjusted for age, race, income, geographic region, cancer metastases, mortality risk, and the likelihood of undergoing PCI (and also with length of stay [LOS] for cost). Analyses were also adjusted for the complex survey design to produce nationally representative estimates. Centers for Disease Control and Prevention (CDC)-based cost effectiveness ratio (CER) analysis was performed.ResultsOf the 30,195,722 hospitalized patients meeting criteria, 1.27% had gynecological cancer of whom 0.02% underwent PCI including 0.04% with metastases. In propensity score adjusted regression among all patients, the interaction of PCI and gynecological cancer (vs. not having PCI) significantly reduced mortality (OR 0.53, 95%CI 0.36–0.77; p = 0.001) while increasing LOS (Beta 1.16 days, 95%CI 0.57–1.75; p < 0.001) and total cost (Beta $31,035.46, 95%CI 26758.86–35312.06; p < 0.001). Among gynecological cancer patients, mortality was significantly reduced by PCI (OR 0.58, 95%CI 0.39–0.85; p = 0.006) and being in East North Central, West North Central, South Atlantic, and Mountain regions (all p < 0.03) compared to New England. PCI reduced mortality but not significantly for metastatic patients (OR 0.74, 95%CI 0.32–1.71; p = 0.481). Eighteen extra gynecological cancer patients' lives were saved with PCI for a net national cost of $3.18 billion and a CER of $176.50 million per averted death.ConclusionThis large propensity score analysis suggests that PCI may cost inefficiently reduce mortality for gynecological cancer patients, amid income and geographic disparities in outcomes.

Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline

“…However, the level of evidence in evidence-based medicine is still insufficient. As the accuracy and operational advantages of ML in large-scale medical data analysis are increasingly recognized, ML can be used to improve propensity scores, so that the two can be deeply combined to improve the selection accuracy of covariates in propensity scores [ 136 ].…”