Cargando…
Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients
BACKGROUND: Despite 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. METHODS: Backward propagation neural n...
Autores principales: | , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8882615/ https://www.ncbi.nlm.nih.gov/pubmed/35237670 http://dx.doi.org/10.3389/fcvm.2021.793877 |
_version_ | 1784659733487026176 |
---|---|
author | Thomason, Nicole Monlezun, Dominique J. Javaid, Awad Filipescu, Alexandru Koutroumpakis, Efstratios Shobayo, Fisayomi Kim, Peter Lopez-Mattei, Juan Cilingiroglu, Mehmet Iliescu, Gloria Marmagkiolis, Kostas Ramirez, Pedro T. Iliescu, Cezar |
author_facet | Thomason, Nicole Monlezun, Dominique J. Javaid, Awad Filipescu, Alexandru Koutroumpakis, Efstratios Shobayo, Fisayomi Kim, Peter Lopez-Mattei, Juan Cilingiroglu, Mehmet Iliescu, Gloria Marmagkiolis, Kostas Ramirez, Pedro T. Iliescu, Cezar |
author_sort | Thomason, Nicole |
collection | PubMed |
description | BACKGROUND: Despite 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. METHODS: Backward 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. RESULTS: Of 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. CONCLUSION: This large propensity score analysis suggests that PCI may cost inefficiently reduce mortality for gynecological cancer patients, amid income and geographic disparities in outcomes. |
format | Online Article Text |
id | pubmed-8882615 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88826152022-03-01 Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients Thomason, Nicole Monlezun, Dominique J. Javaid, Awad Filipescu, Alexandru Koutroumpakis, Efstratios Shobayo, Fisayomi Kim, Peter Lopez-Mattei, Juan Cilingiroglu, Mehmet Iliescu, Gloria Marmagkiolis, Kostas Ramirez, Pedro T. Iliescu, Cezar Front Cardiovasc Med Cardiovascular Medicine BACKGROUND: Despite 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. METHODS: Backward 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. RESULTS: Of 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. CONCLUSION: This large propensity score analysis suggests that PCI may cost inefficiently reduce mortality for gynecological cancer patients, amid income and geographic disparities in outcomes. Frontiers Media S.A. 2022-02-14 /pmc/articles/PMC8882615/ /pubmed/35237670 http://dx.doi.org/10.3389/fcvm.2021.793877 Text en Copyright © 2022 Thomason, Monlezun, Javaid, Filipescu, Koutroumpakis, Shobayo, Kim, Lopez-Mattei, Cilingiroglu, Iliescu, Marmagkiolis, Ramirez and Iliescu. https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Cardiovascular Medicine Thomason, Nicole Monlezun, Dominique J. Javaid, Awad Filipescu, Alexandru Koutroumpakis, Efstratios Shobayo, Fisayomi Kim, Peter Lopez-Mattei, Juan Cilingiroglu, Mehmet Iliescu, Gloria Marmagkiolis, Kostas Ramirez, Pedro T. Iliescu, Cezar Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients |
title | Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients |
title_full | Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients |
title_fullStr | Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients |
title_full_unstemmed | Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients |
title_short | Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients |
title_sort | percutaneous coronary intervention in patients with gynecological cancer: machine learning-augmented propensity score mortality and cost analysis for 383,760 patients |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8882615/ https://www.ncbi.nlm.nih.gov/pubmed/35237670 http://dx.doi.org/10.3389/fcvm.2021.793877 |
work_keys_str_mv | AT thomasonnicole percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT monlezundominiquej percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT javaidawad percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT filipescualexandru percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT koutroumpakisefstratios percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT shobayofisayomi percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT kimpeter percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT lopezmatteijuan percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT cilingiroglumehmet percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT iliescugloria percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT marmagkioliskostas percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT ramirezpedrot percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients AT iliescucezar percutaneouscoronaryinterventioninpatientswithgynecologicalcancermachinelearningaugmentedpropensityscoremortalityandcostanalysisfor383760patients |