Cardiooncology. 2026 Jun 6. doi: 10.1186/s40959-026-00518-7. Online ahead of print.
ABSTRACT
Heart failure (HF) remains a leading cause of morbidity and mortality worldwide and frequently coexists with Philadelphia-negative myeloproliferative neoplasms (MPNs), which share thrombo-inflammatory pathways that increase cardiovascular risk. HF is often underestimated in this population, and current risk models rarely include variables specific to this complex cardio-hematologic setting. From a cardio-oncology perspective, MPNs represent a prototypical at-risk cancer population in whom chronic inflammation, clonal hematopoiesis, and cancer therapy converge to accelerate cardiovascular damage.This narrative review summarizes current evidence on diagnostic and prognostic biomarkers, imaging parameters, and validated HF and MPNs risk scores, and proposes a pragmatic hybrid framework for integrated HF-MPNs cardiovascular risk assessment. PubMed, Embase, and Web of Science were searched up to December 2025 using combinations of terms related to HF, MPNs, biomarkers, imaging, and risk models. In HF, natriuretic peptides and cardiac troponins remain central for diagnosis and short-term prognosis, whereas emerging biomarkers such as galectin-3, soluble ST2, and GDF-15 refine long-term risk prediction. Advanced imaging tools, including global longitudinal strain, left atrial strain, and cardiac magnetic resonance tissue characterization, provide prognostic value beyond the left ventricular ejection fraction. Multidimensional HF models (e.g., MAGGIC, SHFM, BCN Bio-HF, H2FPEF, 3A3B, GWTG-HF, ADHERE, and EHMRG) combine different variables to estimate mortality and hospitalization risk. In MPNs, blood counts, driver mutations (JAK2, CALR, MPL), inflammatory markers, and scores such as IPSET-thrombosis, DIPSS/DIPSS-plus, and MIPSS stratify thrombotic risk, survival, and leukemic transformation but seldom incorporate HF-specific variables.No single biomarker or score fully captures the bidirectional cardiovascular risk in patients with HF and coexisting MPNs. We propose a simple 0-8-point composite algorithm that combines key HF and MPNs variables (natriuretic peptides or troponins, HF risk scores, driver mutations, leukocytosis/thrombocytosis, LDH) to identify high-risk HF-MPNs phenotypes across ACC/AHA HF Stages A-C, suitable for structured cardio-oncology follow-up. Prospective validation of this hybrid algorithm in dedicated cardio-oncology cohorts and the development of MPNs-adapted HF guidelines are key future priorities.
PMID:42251458 | DOI:10.1186/s40959-026-00518-7