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Stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type
AIMS: Previous studies have shown that patients with stress (Takotsubo) cardiomyopathy (SC) and cancer have higher in‐hospital mortality than patients with SC alone. No studies have examined outcomes in patients with active cancer and SC compared to patients with active cancer without SC. We aimed t...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8712856/ https://www.ncbi.nlm.nih.gov/pubmed/34612022 http://dx.doi.org/10.1002/ehf2.13647 |
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author | Javaid, Awad I. Monlezun, Dominique J. Iliescu, Gloria Tran, Phi Filipescu, Alexandru Palaskas, Nicolas Lopez‐Mattei, Juan Hassan, Saamir Kim, Peter Madjid, Mohammad Cilingiroglu, Mehmet Charitakis, Konstantinos Marmagkiolis, Konstantinos Iliescu, Cezar Koutroumpakis, Efstratios |
author_facet | Javaid, Awad I. Monlezun, Dominique J. Iliescu, Gloria Tran, Phi Filipescu, Alexandru Palaskas, Nicolas Lopez‐Mattei, Juan Hassan, Saamir Kim, Peter Madjid, Mohammad Cilingiroglu, Mehmet Charitakis, Konstantinos Marmagkiolis, Konstantinos Iliescu, Cezar Koutroumpakis, Efstratios |
author_sort | Javaid, Awad I. |
collection | PubMed |
description | AIMS: Previous studies have shown that patients with stress (Takotsubo) cardiomyopathy (SC) and cancer have higher in‐hospital mortality than patients with SC alone. No studies have examined outcomes in patients with active cancer and SC compared to patients with active cancer without SC. We aimed to assess the potential association between primary malignancy type and SC and their shared interaction with inpatient mortality. METHODS AND RESULTS: We analysed SC by primary malignancy type with propensity score adjusted multivariable regression and machine learning analysis using the 2016 United States National Inpatient Sample. Of 30 195 722 adult hospitalized patients, 4 719 591 had active cancer, of whom 568 239 had SC. The mean age of patients with cancer and SC was 69.1, of which 74.7% were women. Among patients with cancer, those with SC were more likely to be female and have white race, Medicare insurance, hypertension, heart failure with reduced ejection fraction, obesity, cerebrovascular disease, anaemia, and chronic obstructive pulmonary disease (P < 0.003 for all). In machine learning‐augmented, propensity score multivariable regression adjusted for age, race, and income, only lung cancer [OR 1.25; 95% CI: 1.08–1.46; P = 0.003] and breast cancer [OR 1.81; 95% CI: 1.62–2.02; P < 0.001] were associated with a significantly increased likelihood of SC. Neither SC alone nor having both SC and cancer was significantly associated with in‐hospital mortality. The presence of concomitant SC and breast cancer was significantly associated with reduced mortality (OR 0.48; 95% CI: 0.25–0.94; P = 0.032). CONCLUSIONS: This analysis demonstrates that primary malignancy type influences the likelihood of developing SC. Further studies will be necessary to delineate characteristics in patients with lung cancer and breast cancer which contribute to development of SC. Additional investigation should confirm lower mortality in patients with SC and breast cancer and determine possible explanations and protective factors. |
format | Online Article Text |
id | pubmed-8712856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87128562022-01-04 Stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type Javaid, Awad I. Monlezun, Dominique J. Iliescu, Gloria Tran, Phi Filipescu, Alexandru Palaskas, Nicolas Lopez‐Mattei, Juan Hassan, Saamir Kim, Peter Madjid, Mohammad Cilingiroglu, Mehmet Charitakis, Konstantinos Marmagkiolis, Konstantinos Iliescu, Cezar Koutroumpakis, Efstratios ESC Heart Fail Original Articles AIMS: Previous studies have shown that patients with stress (Takotsubo) cardiomyopathy (SC) and cancer have higher in‐hospital mortality than patients with SC alone. No studies have examined outcomes in patients with active cancer and SC compared to patients with active cancer without SC. We aimed to assess the potential association between primary malignancy type and SC and their shared interaction with inpatient mortality. METHODS AND RESULTS: We analysed SC by primary malignancy type with propensity score adjusted multivariable regression and machine learning analysis using the 2016 United States National Inpatient Sample. Of 30 195 722 adult hospitalized patients, 4 719 591 had active cancer, of whom 568 239 had SC. The mean age of patients with cancer and SC was 69.1, of which 74.7% were women. Among patients with cancer, those with SC were more likely to be female and have white race, Medicare insurance, hypertension, heart failure with reduced ejection fraction, obesity, cerebrovascular disease, anaemia, and chronic obstructive pulmonary disease (P < 0.003 for all). In machine learning‐augmented, propensity score multivariable regression adjusted for age, race, and income, only lung cancer [OR 1.25; 95% CI: 1.08–1.46; P = 0.003] and breast cancer [OR 1.81; 95% CI: 1.62–2.02; P < 0.001] were associated with a significantly increased likelihood of SC. Neither SC alone nor having both SC and cancer was significantly associated with in‐hospital mortality. The presence of concomitant SC and breast cancer was significantly associated with reduced mortality (OR 0.48; 95% CI: 0.25–0.94; P = 0.032). CONCLUSIONS: This analysis demonstrates that primary malignancy type influences the likelihood of developing SC. Further studies will be necessary to delineate characteristics in patients with lung cancer and breast cancer which contribute to development of SC. Additional investigation should confirm lower mortality in patients with SC and breast cancer and determine possible explanations and protective factors. John Wiley and Sons Inc. 2021-10-05 /pmc/articles/PMC8712856/ /pubmed/34612022 http://dx.doi.org/10.1002/ehf2.13647 Text en © 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles Javaid, Awad I. Monlezun, Dominique J. Iliescu, Gloria Tran, Phi Filipescu, Alexandru Palaskas, Nicolas Lopez‐Mattei, Juan Hassan, Saamir Kim, Peter Madjid, Mohammad Cilingiroglu, Mehmet Charitakis, Konstantinos Marmagkiolis, Konstantinos Iliescu, Cezar Koutroumpakis, Efstratios Stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type |
title | Stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type |
title_full | Stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type |
title_fullStr | Stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type |
title_full_unstemmed | Stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type |
title_short | Stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type |
title_sort | stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8712856/ https://www.ncbi.nlm.nih.gov/pubmed/34612022 http://dx.doi.org/10.1002/ehf2.13647 |
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