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

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          Abstract

          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.

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          Most cited references43

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          Cancer treatment and survivorship statistics, 2016

          The number of cancer survivors continues to increase because of both advances in early detection and treatment and the aging and growth of the population. For the public health community to better serve these survivors, the American Cancer Society and the National Cancer Institute collaborate to estimate the number of current and future cancer survivors using data from the Surveillance, Epidemiology, and End Results cancer registries. In addition, current treatment patterns for the most prevalent cancer types are presented based on information in the National Cancer Data Base and treatment-related side effects are briefly described. More than 15.5 million Americans with a history of cancer were alive on January 1, 2016, and this number is projected to reach more than 20 million by January 1, 2026. The 3 most prevalent cancers are prostate (3,306,760), colon and rectum (724,690), and melanoma (614,460) among males and breast (3,560,570), uterine corpus (757,190), and colon and rectum (727,350) among females. More than one-half (56%) of survivors were diagnosed within the past 10 years, and almost one-half (47%) are aged 70 years or older. People with a history of cancer have unique medical and psychosocial needs that require proactive assessment and management by primary care providers. Although there are a growing number of tools that can assist patients, caregivers, and clinicians in navigating the various phases of cancer survivorship, further evidence-based resources are needed to optimize care. CA Cancer J Clin 2016;66:271-289. © 2016 American Cancer Society.
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            Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.

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              Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group

              In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their observed covariates, and these differences can lead to biased estimates of treatment effects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as the conditional probability of being treated given the covariates, can be used to balance the covariates in the two groups, and therefore reduce this bias. In order to estimate the propensity score, one must model the distribution of the treatment indicator variable given the observed covariates. Once estimated the propensity score can be used to reduce bias through matching, stratification (subclassification), regression adjustment, or some combination of all three. In this tutorial we discuss the uses of propensity score methods for bias reduction, give references to the literature and illustrate the uses through applied examples.
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                Author and article information

                Contributors
                Journal
                Front Cardiovasc Med
                Front Cardiovasc Med
                Front. Cardiovasc. Med.
                Frontiers in Cardiovascular Medicine
                Frontiers Media S.A.
                2297-055X
                06 April 2021
                2021
                : 8
                : 620857
                Affiliations
                [1] 1Department of Cardiology, The University of Texas M.D. Anderson Cancer Center , Houston, TX, United States
                [2] 2Division of Cardiovascular Medicine, The University of Texas Health Sciences Center at Houston , Houston, TX, United States
                [3] 3Premier Heart and Vascular Center , Zephyrhills, FL, United States
                [4] 4Keele Cardiovascular Research Group, Department of Cardiology, Royal Stroke Hospital Stoke on Trent , Stoke-on-Trent, United Kingdom
                Author notes

                Edited by: Carlo Gabriele Tocchetti, University of Naples Federico II, Italy

                Reviewed by: Umberto Campia, Brigham and Women's Hospital and Harvard Medical School, United States; Abdelrahman Ibrahim Abushouk, Harvard Medical School, United States

                This article was submitted to Cardio-Oncology, a section of the journal Frontiers in Cardiovascular Medicine

                †These authors have contributed equally to this work

                Article
                10.3389/fcvm.2021.620857
                8055825
                33889598
                3ecab1ec-1402-47d3-9fa5-90d7d7a83d66
                Copyright © 2021 Monlezun, Lawless, Palaskas, Peerbhai, Charitakis, Marmagkiolis, Lopez-Mattei, Mamas and Iliescu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 October 2020
                : 15 February 2021
                Page count
                Figures: 2, Tables: 2, Equations: 0, References: 47, Pages: 11, Words: 7110
                Categories
                Cardiovascular Medicine
                Original Research

                pci - percutaneous coronary intervention,cancer,cardio-oncology,onco-cardiology,disparites,machine laerning

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