Fractional Flow Reserve Cardio-Oncology Effects on Inpatient Mortality, Length of Stay, and Cost Based on Malignancy Type: Machine Learning Supported Nationally Representative Case-Control Study of 30 Million Hospitalizations
Siddharth Chauhan,
Dominique J. Monlezun,
Jin wan Kim,
Harsh Goel,
Alex Hanna,
Kenneth Hoang,
Nicolas Palaskas,
Juan Lopez-Mattei,
Saamir Hassan,
Peter Kim,
Mehmet Cilingiroglu,
Konstantinos Marmagkiolis,
Cezar A. Iliescu
Affiliations
Siddharth Chauhan
Department of Cardiology, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Dominique J. Monlezun
Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Jin wan Kim
Department of Cardiology, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Harsh Goel
Department of Cardiology, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Alex Hanna
Department of Cardiology, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Kenneth Hoang
Department of Cardiology, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Nicolas Palaskas
Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Juan Lopez-Mattei
Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Saamir Hassan
Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Peter Kim
Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Mehmet Cilingiroglu
Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Konstantinos Marmagkiolis
Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Cezar A. Iliescu
Department of Cardiology, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Background and Objectives: There are no nationally representative studies of mortality and cost effectiveness for fractional flow reserve (FFR) guided percutaneous coronary interventions (PCI) in patients with cancer. Our study aims to show how this patient population may benefit from FFR-guided PCI. Materials and Methods: Propensity score matched analysis and backward propagation neural network machine learning supported multivariable regression was performed for inpatient mortality in this case-control study of the 2016 National Inpatient Sample (NIS). Regression results were adjusted for age, race, income, geographic region, metastases, mortality risk, and the likelihood of undergoing FFR versus non-FFR PCI. All analyses were adjusted for the complex survey design to produce nationally representative estimates. Results: Of the 30,195,722 hospitalized patients meeting criteria, 3.37% of the PCIs performed included FFR. In propensity score adjusted multivariable regression, FFR versus non-FFR PCI significantly reduced inpatient mortality (OR 0.47, 95%CI 0.35–0.63; p p = 0.001) while increasing cost (in USD; beta $5708.63, 95%CI, 3042.70–8374.57; p p p = 0.009). Conclusions: FFR-guided PCI may be safely utilized in patients with cancer as it does not significantly increase inpatient mortality, complications, and LOS. These findings support the need for an increased utilization of FFR-guided PCI and further studies to evaluate its long-term impact.