Evidence map›Paper›PMID 42783026›Full record

ArticleJournal of cardiovascular development and disease2026

A Nomogram Integrating Inflammatory Biomarkers and Echocardiographic Parameters for Predicting Left Ventricular Outflow Tract Obstruction Risk in Hypertrophic Cardiomyopathy.

Jinlei Li, Fen Ai, Yu Li, Bingxin Cheng, Zhen Chen

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Article in Journal of cardiovascular development and disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jinlei LiDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China.ORCID 0009-0002-1549-1378
Fen AiDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China.
Yu LiDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China.
Bingxin ChengDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China.
Zhen ChenDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China.

Funding

Beijing Dadi Medical Charity Foundation DDYL-A-KT-20251031-0183Science and Technology Department of Hubei Province 2022CFC002
6 · The paper itself

Abstract

objectiveAlthough echocardiography is the gold standard for diagnosing left ventricularoutflow tract obstruction (LVOTO) in hypertrophic cardiomyopathy (HCM), relying solely on imaging is insufficient for precise risk stratification, particularly in borderline or atypical patients, and fails to capture systemic pathophysiological alterations. In recent years, novel inflammatory biomarkers derived from routine blood tests have shown sensitivity in capturing micro-inflammatory states; however, their specific roles in the obstructive phenotype of HCM remain unclear. This study aims to screen hematological and cardiac parameters associated with HCM obstruction and to construct an individualized predictive model. Therefore, this study aims to screen hematological and cardiac parameters associated with HCM obstruction and to develop and validate a nomogram for individualized prediction of current LVOTO risk in HCM patients.

methodsA total of 230 HCM patients hospitalized at The Central Hospital of Wuhan from January 2019 to December 2025 were retrospectively enrolled. Based on the left ventricular outflow tract pressure gradient (LVOTPG), they were divided into a non-obstruction group (

resultsMultivariate logistic regression analysis revealed that a history of alcohol consumption (OR = 3.68, 95% CI: 1.49-9.09), peak LVOTPG (OR = 3.96, 95% CI: 1.42-11.05), maximum ventricular wall thickness (OR = 1.02, 95% CI: 1.00-1.04), peak mitral valve E-wave velocity (OR = 1.02, 95% CI: 1.00-1.04), and neutrophil count (OR = 2.42, 95% CI: 1.13-5.21) were independent predictors of LVOTO in HCM patients. The nomogram model constructed based on these factors achieved area under the curve (AUC) values of 0.810 and 0.850 in the training and validation sets, respectively. The calibration curve demonstrated good agreement between predicted and actual probabilities, and DCA indicated that the model provided clinical net benefit within a threshold probability range of 10% to 30%.

conclusionsA nomogram integrating alcohol consumption history, peak LVOTPG, maximum ventricular wall thickness, peak mitral E-wave velocity, and neutrophil count accurately predicts LVOTO risk in HCM patients (AUC: 0.810-0.850), providing a low-cost tool for early risk stratification. External prospective validation is warranted.

Indexed as

hypertrophic cardiomyopathyleft ventricular outflow tract obstructionneutrophilnomogrampredictive model

Identifiers

PMID42783026
PMCPMC13607843

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.