Machine Learning-Augmented Propensity Score Analysis of Percutaneous Coronary Intervention in Over 30 Million Cancer and Non-cancer Patients

Background: It is unknown to what extent the clinical benefits of PCI outweigh the risks and costs in patients with vs. without cancer and within each cancer type. We performed the first known nationally representative propensity score analysis of PCI mortality and cost among all eligible adult inpa...

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Main Authors: Dominique J. Monlezun, Sean Lawless, Nicolas Palaskas, Shareez Peerbhai, Konstantinos Charitakis, Konstantinos Marmagkiolis, Juan Lopez-Mattei, Mamas Mamas, Cezar Iliescu
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-04-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2021.620857/full
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spelling doaj-e49c6e603f5d45f3b3b3dea2b11b60bf2021-04-06T04:48:08ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2021-04-01810.3389/fcvm.2021.620857620857Machine Learning-Augmented Propensity Score Analysis of Percutaneous Coronary Intervention in Over 30 Million Cancer and Non-cancer PatientsDominique J. Monlezun0Sean Lawless1Nicolas Palaskas2Shareez Peerbhai3Konstantinos Charitakis4Konstantinos Marmagkiolis5Juan Lopez-Mattei6Mamas Mamas7Cezar Iliescu8Department of Cardiology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United StatesDivision of Cardiovascular Medicine, The University of Texas Health Sciences Center at Houston, Houston, TX, United StatesDepartment of Cardiology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United StatesDivision of Cardiovascular Medicine, The University of Texas Health Sciences Center at Houston, Houston, TX, United StatesDivision of Cardiovascular Medicine, The University of Texas Health Sciences Center at Houston, Houston, TX, United StatesPremier Heart and Vascular Center, Zephyrhills, FL, United StatesDepartment of Cardiology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United StatesKeele Cardiovascular Research Group, Department of Cardiology, Royal Stroke Hospital Stoke on Trent, Stoke-on-Trent, United KingdomDepartment of Cardiology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United StatesBackground: It is unknown to what extent the clinical benefits of PCI outweigh the risks and costs in patients with vs. without cancer and within each cancer type. We performed the first known nationally representative propensity score analysis of PCI mortality and cost among all eligible adult inpatients by cancer and its types.Methods: This multicenter case-control study used machine learning–augmented propensity score–adjusted multivariable regression to assess the above outcomes and disparities using the 2016 nationally representative National Inpatient Sample.Results: Of the 30,195,722 hospitalized patients, 15.43% had a malignancy, 3.84% underwent an inpatient PCI (of whom 11.07% had cancer and 0.07% had metastases), and 2.19% died inpatient. In fully adjusted analyses, PCI vs. medical management significantly reduced mortality for patients overall (among all adult inpatients regardless of cancer status) and specifically for cancer patients (OR 0.82, 95% CI 0.75–0.89; p < 0.001), mainly driven by active vs. prior malignancy, head and neck and hematological malignancies. PCI also significantly reduced cancer patients' total hospitalization costs (beta USD$ −8,668.94, 95% CI −9,553.59 to −7,784.28; p < 0.001) independent of length of stay. There were no significant income or disparities among PCI subjects.Conclusions: Our study suggests among all eligible adult inpatients, PCI does not increase mortality or cost for cancer patients, while there may be particular benefit by cancer type. The presence or history of cancer should not preclude these patients from indicated cardiovascular care.https://www.frontiersin.org/articles/10.3389/fcvm.2021.620857/fullPCI - percutaneous coronary interventioncancercardio-oncologyonco-cardiologydisparitesmachine laerning
collection DOAJ
language English
format Article
sources DOAJ
author Dominique J. Monlezun
Sean Lawless
Nicolas Palaskas
Shareez Peerbhai
Konstantinos Charitakis
Konstantinos Marmagkiolis
Juan Lopez-Mattei
Mamas Mamas
Cezar Iliescu
spellingShingle Dominique J. Monlezun
Sean Lawless
Nicolas Palaskas
Shareez Peerbhai
Konstantinos Charitakis
Konstantinos Marmagkiolis
Juan Lopez-Mattei
Mamas Mamas
Cezar Iliescu
Machine Learning-Augmented Propensity Score Analysis of Percutaneous Coronary Intervention in Over 30 Million Cancer and Non-cancer Patients
Frontiers in Cardiovascular Medicine
PCI - percutaneous coronary intervention
cancer
cardio-oncology
onco-cardiology
disparites
machine laerning
author_facet Dominique J. Monlezun
Sean Lawless
Nicolas Palaskas
Shareez Peerbhai
Konstantinos Charitakis
Konstantinos Marmagkiolis
Juan Lopez-Mattei
Mamas Mamas
Cezar Iliescu
author_sort Dominique J. Monlezun
title Machine Learning-Augmented Propensity Score Analysis of Percutaneous Coronary Intervention in Over 30 Million Cancer and Non-cancer Patients
title_short Machine Learning-Augmented Propensity Score Analysis of Percutaneous Coronary Intervention in Over 30 Million Cancer and Non-cancer Patients
title_full Machine Learning-Augmented Propensity Score Analysis of Percutaneous Coronary Intervention in Over 30 Million Cancer and Non-cancer Patients
title_fullStr Machine Learning-Augmented Propensity Score Analysis of Percutaneous Coronary Intervention in Over 30 Million Cancer and Non-cancer Patients
title_full_unstemmed Machine Learning-Augmented Propensity Score Analysis of Percutaneous Coronary Intervention in Over 30 Million Cancer and Non-cancer Patients
title_sort machine learning-augmented propensity score analysis of percutaneous coronary intervention in over 30 million cancer and non-cancer patients
publisher Frontiers Media S.A.
series Frontiers in Cardiovascular Medicine
issn 2297-055X
publishDate 2021-04-01
description Background: It is unknown to what extent the clinical benefits of PCI outweigh the risks and costs in patients with vs. without cancer and within each cancer type. We performed the first known nationally representative propensity score analysis of PCI mortality and cost among all eligible adult inpatients by cancer and its types.Methods: This multicenter case-control study used machine learning–augmented propensity score–adjusted multivariable regression to assess the above outcomes and disparities using the 2016 nationally representative National Inpatient Sample.Results: Of the 30,195,722 hospitalized patients, 15.43% had a malignancy, 3.84% underwent an inpatient PCI (of whom 11.07% had cancer and 0.07% had metastases), and 2.19% died inpatient. In fully adjusted analyses, PCI vs. medical management significantly reduced mortality for patients overall (among all adult inpatients regardless of cancer status) and specifically for cancer patients (OR 0.82, 95% CI 0.75–0.89; p < 0.001), mainly driven by active vs. prior malignancy, head and neck and hematological malignancies. PCI also significantly reduced cancer patients' total hospitalization costs (beta USD$ −8,668.94, 95% CI −9,553.59 to −7,784.28; p < 0.001) independent of length of stay. There were no significant income or disparities among PCI subjects.Conclusions: Our study suggests among all eligible adult inpatients, PCI does not increase mortality or cost for cancer patients, while there may be particular benefit by cancer type. The presence or history of cancer should not preclude these patients from indicated cardiovascular care.
topic PCI - percutaneous coronary intervention
cancer
cardio-oncology
onco-cardiology
disparites
machine laerning
url https://www.frontiersin.org/articles/10.3389/fcvm.2021.620857/full
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