ArticleJournal of clinical hypertension (Greenwich, Conn.)2024
Nomogram-based risk assessment model for left ventricular hypertrophy in patients with essential hypertension: Incorporating clinical characteristics and biomarkers.
Article in Journal of clinical hypertension (Greenwich, Conn.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Applications and challenges of biomarker-based predictive models in proactive health management.Frontiers in public health · 2025Pooled it
- Investigating the role of age, admission systolic blood pressure, and neutrophil-to-lymphocyte ratio in predicting left ventricular hypertrophy among patients with primary hypertension.The Journal of international medical research · 2025Article
- Soluble ST2 Is a Biomarker Associated With Left Ventricular Hypertrophy and Concentric Hypertrophy in Patients With Essential Hypertension.American journal of hypertension · 2024Article
- Nomogram-based risk assessment model for left ventricular hypertrophy in patients with essential hypertension: Incorporating clinical characteristics and biomarkers.Journal of clinical hypertension (Greenwich, Conn.) · 2024Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Left ventricular hypertrophy (LVH) is a hypertensive heart disease that significantly escalates the risk of clinical cardiovascular events. Its etiology potentially incorporates various clinical attributes such as gender, age, and renal function. From mechanistic perspective, the remodeling process of LVH can trigger increment in certain biomarkers, notably sST2 and NT-proBNP. This multicenter, retrospective study aimed to construct an LVH risk assessment model and identify the risk factors. A total of 417 patients with essential hypertension (EH), including 214 males and 203 females aged 31-80 years, were enrolled in this study; of these, 161 (38.6%) were diagnosed with LVH. Based on variables demonstrating significant disparities between the LVH and Non-LVH groups, three multivariate stepwise logistic regression models were constructed for risk assessment: the "Clinical characteristics" model, the "Biomarkers" model (each based on their respective variables), and the "Clinical characteristics + Biomarkers" model, which amalgamated both sets of variables. The results revealed that the "Clinical characteristics + Biomarkers" model surpassed the baseline models in performance (AUC values of the "Clinical characteristics + Biomarkers" model, the "Biomarkers" model, and the "Clinical characteristics" model were .83, .75, and .74, respectively; P < .0001 for both comparisons). The optimized model suggested that being female (OR: 4.26, P <.001), being overweight (OR: 1.88, p = .02) or obese (OR: 2.36, p = .02), duration of hypertension (OR: 1.04, P = .04), grade III hypertension (OR: 2.12, P < .001), and sST2 (log-transformed, OR: 1.14, P < .001) were risk factors, while eGFR acted as a protective factor (OR: .98, P = .01). These findings suggest that the integration of clinical characteristics and biomarkers can enhance the performance of LVH risk assessment.
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