Literature DB >> 30611123

A Cancer Paradox: Machine-Learning Backed Propensity-Score Analysis of Coronary Angiography Findings in Cardio-Oncology.

Dinu Valentin Balanescu, Dominique J Monlezun, Teodora Donisan, David Boone, Frances Cervoni-Curet, Nicolas Palaskas, Juan Lopez-Mattei, Peter Kim, Cezar Iliescu1, Serban Mihai Balanescu.   

Abstract

OBJECTIVES: Cancer has been proposed as a cardiovascular risk factor. We aimed to assess the cardiovascular risk profile and coronary angiography (CA) findings of cancer patients and compare them to those of patients without cancer.
METHODS: A retrospective case-control analysis was conducted on randomly enrolled cancer and non-cancer patients from a high-volume cardio-oncology center and a tertiary cardiology center, respectively, who underwent CA from April 2008 to June 2018. Baseline demographics, laboratory findings, cancer status and treatment, and current and prior CA findings were collected by chart review. Coronary artery disease (CAD) burden was assessed with machine-learning (neural-network) guided propensity-score adjusted multivariable regression, controlling for known CAD confounders.
RESULTS: Of the 480 enrolled patients, a total of 240 (50%) had cancer. Fewer cancer vs non-cancer patients had clinically significant lesions on the left anterior descending artery (25.00% vs 39.17%, respectively; P<.01) and left circumflex artery (15.83% vs 30.00%, respectively; P<.001). Left main and right coronary artery disease prevalence was similar. Subjects with cancer were less likely to have multivessel CAD (odds ratio, 0.53; 95% confidence interval, 0.29-0.98; P=.04) and significant left circumflex artery lesions (odds ratio, 0.47; 95% confidence interval, 0.26-0.85; P=.01), independent of known CAD confounders.
CONCLUSIONS: Patients with cancer have a lower burden of angiographically detected coronary atherosclerosis. Cancer patients are more likely than non-cancer patients to undergo CA for reasons other than suspicion of CAD. Further studies should prospectively analyze the impact of cancer on the development of CAD.

Entities:  

Keywords:  cardio-oncology; coronary angiography; coronary artery disease; machine learning; risk factors

Year:  2019        PMID: 30611123

Source DB:  PubMed          Journal:  J Invasive Cardiol        ISSN: 1042-3931            Impact factor:   2.022


  5 in total

Review 1.  Machine Learning in Causal Inference: Application in Pharmacovigilance.

Authors:  Yiqing Zhao; Yue Yu; Hanyin Wang; Yikuan Li; Yu Deng; Guoqian Jiang; Yuan Luo
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

2.  Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients.

Authors:  Nicole Thomason; Dominique J Monlezun; Awad Javaid; Alexandru Filipescu; Efstratios Koutroumpakis; Fisayomi Shobayo; Peter Kim; Juan Lopez-Mattei; Mehmet Cilingiroglu; Gloria Iliescu; Kostas Marmagkiolis; Pedro T Ramirez; Cezar Iliescu
Journal:  Front Cardiovasc Med       Date:  2022-02-14

Review 3.  Artificial Intelligence, Machine Learning, and Cardiovascular Disease.

Authors:  Pankaj Mathur; Shweta Srivastava; Xiaowei Xu; Jawahar L Mehta
Journal:  Clin Med Insights Cardiol       Date:  2020-09-09

4.  TAVR and cancer: machine learning-augmented propensity score mortality and cost analysis in over 30 million patients.

Authors:  Dominique J Monlezun; Logan Hostetter; Prakash Balan; Nicolas Palaskas; Juan Lopez-Mattei; Mehmet Cilingiroglu; Zaza Iakobishvili; Michael Ewer; Konstantinos Marmagkiolis; Cezar Iliescu
Journal:  Cardiooncology       Date:  2021-06-28

Review 5.  Meta-analysis and machine learning-augmented mixed effects cohort analysis of improved diets among 5847 medical trainees, providers and patients.

Authors:  Dominique J Monlezun; Christopher Carr; Tianhua Niu; Francesco Nordio; Nicole DeValle; Leah Sarris; Timothy Harlan
Journal:  Public Health Nutr       Date:  2021-06-28       Impact factor: 4.022

  5 in total

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