Why the study?
Cancer treatment prognosis depends partly on chemotherapy cardiotoxicity, prompting the need for a feasible algorithm to detect antitumor therapy cardiotoxicity early in breast cancer patients.
Does a conventional biomarker panel combined with echocardiography predict chemotherapy-linked cardiotoxicity in breast cancer patients?
Does a conventional biomarker panel combined with echocardiography predict chemotherapy-linked cardiotoxicity in breast cancer patients?
This study protocol outlines a prospective cohort study to evaluate a biomarker panel and echocardiography for the early detection of chemotherapy-induced cardiotoxicity in breast cancer patients.
The prognosis of cancer treatment depends on, among other aspects, the cardiotoxicity of chemotherapy. This research aims to create a feasible algorithm for the early diagnosis of antitumor therapy cardiotoxicity in breast cancer patients. The paper represents a protocol for a prospective cohort study with N 120 eligible participants admitted for treatment with anthracyclines and/or trastuzumab. These patients will be allocated into four risk groups regarding potential cardiotoxic complications. Patients will be examined five times every three months for six biomarkers,: cardiac troponin I (cTnI), brain natriuretic peptide (BNP), C-reactive protein (CRP), myeloperoxidase (MPO), galectin-3 (Gal-3), and D-dimer, simultaneously with echocardiographic methods, including speckle tracking. The adjusted relative risk (aOR) of interrupting an entire course of chemotherapy due to cardiotoxic events will be assessed using multiple analyses of proportional Cox risks. The Cox model will also assess associations between baseline biomarker values and time to cardiotoxic events. Moreover, partly conditional survival models will be applied to determine associations between repeated assessments of changes in biomarkers from baseline and time to cancer therapy-related cardiac dysfunction. All models will be adjusted for cancer therapy regimen, baseline LVEF, groups at risk, baseline biomarker values, and age. The decision-tree and principal component analysis (PCA) methods will also be applied. Thus, feasible patterns will be detected.
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Balmagambetova et al. (2022) studied this question.
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