Percutaneous Coronary Intervention Mortality, Cost, Complications, and Disparities after Radiation Therapy: Artificial Intelligence-Augmented, Cost Effectiveness, and Computational Ethical Analysis
- PMID: 37998503
- PMCID: PMC10672341
- DOI: 10.3390/jcdd10110445
Percutaneous Coronary Intervention Mortality, Cost, Complications, and Disparities after Radiation Therapy: Artificial Intelligence-Augmented, Cost Effectiveness, and Computational Ethical Analysis
Abstract
The optimal cardio-oncology management of radiation therapy and its complications are unknown despite the high patient and societal costs. This study is the first known nationally representative, multi-year, artificial intelligence and propensity score-augmented causal clinical inference and computational ethical and policy analysis of percutaneous coronary intervention (PCI) mortality, cost, and disparities including by primary malignancy following radiation therapy. Bayesian Machine learning-augmented Propensity Score translational (BAM-PS) statistics were conducted in the 2016-2020 National Inpatient Sample. Of the 148,755,036 adult hospitalizations, 2,229,285 (1.50%) had a history of radiation therapy, of whom, 67,450 (3.00%) had an inpatient AMI, and of whom, 18,400 (28.69%) underwent PCI. Post-AMI mortality, costs, and complications were comparable with and without radiation across cancers in general and across the 30 primary malignancies tested, except for breast cancer, in which PCI significantly increased mortality (OR 3.70, 95%CI 1.10-12.43, p = 0.035). In addition to significant sex, race, and insurance disparities, significant regional disparities were associated with nearly 50 extra inpatient deaths and over USD 500 million lost. This large clinical, cost, and pluralistic ethical analysis suggests PCI when clinically indicated should be provided to patients regardless of sex, race, insurance, or region to generate significant improvements in population health, cost savings, and social equity.
Keywords: PCI; artificial intelligence; cardio-oncology; cost; equity; ethics; radiation.
Conflict of interest statement
The author declares no conflict of interest.
Figures



Similar articles
-
Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score.Medicina (Kaunas). 2022 Aug 3;58(8):1039. doi: 10.3390/medicina58081039. Medicina (Kaunas). 2022. PMID: 36013506 Free PMC article.
-
Machine Learning-Augmented Propensity Score Analysis of Percutaneous Coronary Intervention in Over 30 Million Cancer and Non-cancer Patients.Front Cardiovasc Med. 2021 Apr 6;8:620857. doi: 10.3389/fcvm.2021.620857. eCollection 2021. Front Cardiovasc Med. 2021. PMID: 33889598 Free PMC article.
-
Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients.Front Cardiovasc Med. 2022 Feb 14;8:793877. doi: 10.3389/fcvm.2021.793877. eCollection 2021. Front Cardiovasc Med. 2022. PMID: 35237670 Free PMC article.
-
Percutaneous Ventricular Assist Devices: A Health Technology Assessment.Ont Health Technol Assess Ser. 2017 Feb 7;17(2):1-97. eCollection 2017. Ont Health Technol Assess Ser. 2017. PMID: 28232854 Free PMC article. Review.
-
Comparative cost-effectiveness of surgery, angioplasty, or medical therapy in patients with multivessel coronary artery disease: MASS II trial.Cost Eff Resour Alloc. 2018 Nov 3;16:55. doi: 10.1186/s12962-018-0158-z. eCollection 2018. Cost Eff Resour Alloc. 2018. PMID: 30410425 Free PMC article. Review.
Cited by
-
Applications of artificial intelligence and the challenges in health technology assessment: a scoping review and framework with a focus on economic dimensions.Health Econ Rev. 2025 Jun 4;15(1):46. doi: 10.1186/s13561-025-00645-4. Health Econ Rev. 2025. PMID: 40461901 Free PMC article. Review.
-
A novel machine learning-based cancer-specific cardiovascular disease risk score among patients with breast, colorectal, or lung cancer.JNCI Cancer Spectr. 2025 Jan 3;9(1):pkaf016. doi: 10.1093/jncics/pkaf016. JNCI Cancer Spectr. 2025. PMID: 39883570 Free PMC article.
-
Bayesian inference in racial health inequity analyses for noncommunicable diseases: a systematic review.Syst Rev. 2025 Jul 10;14(1):145. doi: 10.1186/s13643-025-02898-w. Syst Rev. 2025. PMID: 40640883 Free PMC article.
References
-
- Thakker R., Suthar K., Bhakta P., Lee M., Abu Jazar D., Patel M., Elbadawi A., Albaeni A., Hasan S.M., Faluk M., et al. Percutaneous Coronary Intervention Outcomes in Patients with Prior Thoracic Radiation Therapy: A Systematic Review and Meta-Analysis. Cardiol. Res. 2022;13:333–338. doi: 10.14740/cr1426. - DOI - PMC - PubMed
-
- Reed G.W., Masri A., Griffin B.P., Kapadia S.R., Ellis S.G., Desai M.Y. Long-Term Mortality in Patients with Radiation-Associated Coronary Artery Disease Treated with Percutaneous Coronary Intervention. Circ. Cardiovasc. Interv. 2016;9:e003483. doi: 10.1161/CIRCINTERVENTIONS.115.003483. - DOI - PubMed
-
- Monlezun D.J., Lawless S., Palaskas N., Peerbhai S., Charitakis K., Marmagkiolis K., Lopez-Mattei J., Mamas M., Iliescu C. Machine learning-augmented propensity score analysis of percutaneous coronary intervention in over 30 million cancer and non-cancer patients. Front. Cardiovasc. Med. 2021;8:620857. doi: 10.3389/fcvm.2021.620857. - DOI - PMC - PubMed
LinkOut - more resources
Full Text Sources
Miscellaneous