A Comprehensive Nomogram Integrating Phonocardiogram and Echocardiogram Features for the Diagnosis of Heart Failure With Preserved Ejection Fraction

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-10-28 DOI:10.1002/clc.70022

Linchun Cao, Xingming Guo, Kangla Liao, Jian Qin, Yineng Zheng

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Abstract

Background

Heart failure with preserved ejection fraction (HFpEF) is associated with high hospitalization and mortality rates, representing a significant healthcare burden. This study aims to utilize various information including echocardiogram and phonocardiogram to construct and validate a nomogram, assisting in clinical decision-making.

Methods

This study analyzed 204 patients (68 HFpEF and 136 non-HFpEF) from the First Affiliated Hospital of Chongqing Medical University. A total of 49 features were integrated and used, including phonocardiogram, echocardiogram features, and clinical parameters. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal matching factors, and a stepwise logistic regression was employed to determine independent risk factors and develop a nomogram. Model performance was evaluated by the area under receiver operating characteristic (ROC) curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).

Results

The nomogram was constructed using five significant indicators, including NT-proBNP (OR = 4.689, p = 0.015), E/e′ (OR = 1.219, p = 0.032), LAVI (OR = 1.088, p < 0.01), D/S (OR = 0.014, p < 0.01), and QM1 (OR = 1.058, p < 0.01), and showed a better AUC of 0.945 (95% CI = 0.908–0.982) in the training set and 0.933 (95% CI = 0.873–0.992) in the testing set compared to conventional nomogram without phonocardiogram features. The calibration curve and Hosmer–Lemeshow test demonstrated no statistical significance in the training and testing sets (p = 0.814 and p = 0.736), indicating the nomogram was well-calibrated. The DCA and CIC results confirmed favorable clinical usefulness.

Conclusion

The nomogram, integrating phonocardiogram and echocardiogram features, enhances HFpEF diagnostic efficiency, offering a valuable tool for clinical decision-making.

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整合声心动图和超声心动图特征的综合提名图，用于诊断射血分数保留型心力衰竭。

背景：射血分数保留型心力衰竭（HFpEF）的住院率和死亡率都很高，给医疗保健带来沉重负担。本研究旨在利用包括超声心动图和声心动图在内的各种信息构建并验证一个提名图，以协助临床决策：方法：本研究分析了重庆医科大学附属第一医院的 204 名患者（68 名 HFpEF 和 136 名非 HFpEF）。共整合并使用了 49 个特征，包括声心动图、超声心动图特征和临床参数。采用最小绝对收缩和选择算子（LASSO）回归法来选择最佳匹配因素，并采用逐步逻辑回归法来确定独立的风险因素并建立提名图。模型性能通过接收者操作特征曲线（ROC）下面积（AUC）、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）进行评估：利用五个重要指标构建了提名图，包括 NT-proBNP（OR=4.689，p=0.015）、E/e'（OR=1.219，p=0.032）、LAVI（OR=1.088，p 结论：该提名图是一个综合了心电图和超声心动图的提名图：该提名图综合了声心动图和超声心动图的特征，提高了高频心动过速（HFpEF）的诊断效率，为临床决策提供了有价值的工具。

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