Current electrocardiographic (ECG) criteria are of low diagnostic value compared with echocardiography (ECHO) for LVH, establishing a more effective ECG predictive model is required. The research purpose was to establish and validate a model to improve the diagnostic capability of ECG for LVH in pediatric primary hypertension. A retrospective study of 502 hypertensive children were recruited in the study between January 2019 and December 2024. The cohort were randomly divided into training (n = 402) and test sets (n = 100) with a proportion of 8:2. LVH was diagnosed using ECHO criteria. A total of 22 ECG parameters were evaluated. A predictive nomogram was developed using least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. LVH was identified in 117 (29.1%) of the training set and 29 (29.0%) of the test set. Body mass index (BMI), RI + SV4, and SD + SV4 were identified as independent predictors of LVH. The nomogram model showed good performance, with an area under the curve (AUC) of 0.822 in the training set and 0.803 in the test set. Calibration curves and Hosmer–Lemeshow test indicated good agreement between predicted and actual probabilities. DCA demonstrated clinical usefulness. The model outperformed previous models, as confirmed by NRI and IDI.Conclusions: The nomogram model incorporating BMI, RI + SV4, and SD + SV4 significantly improves the ECG diagnosis of LVH in pediatric primary hypertension, serving as a reliable tool for early LVH detection in hypertensive children. <Table Float="No" ID="Taba"> <tgroup cols="1"> <colspec align="left" colname="c1" colnum="1" /> <tbody> <row> <entry align="left" colname="c1"> What is Known: • We established a pediatric-specific nomogram integrating BMI and two composite ECG indices (RI + SV4, SD + SV4); this model outperforms conventional adult ECG criteria and exhibits reliable discrimination, calibration and clinical practicability for diagnosing LVH in children with primary hypertensiondiagnosing LVH in children with primary hypertension. • This work innovatively uses BMI as a correction term to compensate for ECG voltage attenuation induced by chest wall fat in obese children, substantially lifting the overall ECG diagnostic accuracy of pediatric hypertensive LVH. </entry> </row> <row> <entry align="left" colname="c1"> What is New: • Our current study developed a nomogram model combining BMI, RI + SV4, and SD + SV4 that significantly improves ECG diagnostic accuracy for LVH in children with primary hypertension. • The research innovatively transforms BMI into a corrective factor addressing voltage attenuation caused by chest wall fat in obese children. • The model identifies pediatric-specific composite ECG indices superior to adult-derived criteria, demonstrating good discrimination, calibration, and clinical utility. </entry> </row> </tbody> </tgroup> </Table>

A multivariate electrocardiographic predictive model for left ventricular hypertrophy in children with primary hypertension

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发表日期：

2026年7月10日

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Yang Liu,
Yaxi Cui,
Yao Lin,
Tong Zheng,
Min Bao,
Yanyan Liu,
He Jiang,
Lin Shi

摘要

Current electrocardiographic (ECG) criteria are of low diagnostic value compared with echocardiography (ECHO) for LVH, establishing a more effective ECG predictive model is required. The research purpose was to establish and validate a model to improve the diagnostic capability of ECG for LVH in pediatric primary hypertension. A retrospective study of 502 hypertensive children were recruited in the study between January 2019 and December 2024. The cohort were randomly divided into training (n = 402) and test sets (n = 100) with a proportion of 8:2. LVH was diagnosed using ECHO criteria. A total of 22 ECG parameters were evaluated. A predictive nomogram was developed using least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. LVH was identified in 117 (29.1%) of the training set and 29 (29.0%) of the test set. Body mass index (BMI), R_I + S_V4, and S_D + S_V4 were identified as independent predictors of LVH. The nomogram model showed good performance, with an area under the curve (AUC) of 0.822 in the training set and 0.803 in the test set. Calibration curves and Hosmer–Lemeshow test indicated good agreement between predicted and actual probabilities. DCA demonstrated clinical usefulness. The model outperformed previous models, as confirmed by NRI and IDI.

Conclusions: The nomogram model incorporating BMI, R_I + S_V4, and S_D + S_V4 significantly improves the ECG diagnosis of LVH in pediatric primary hypertension, serving as a reliable tool for early LVH detection in hypertensive children.

What is Known:

• We established a pediatric-specific nomogram integrating BMI and two composite ECG indices (R_I + S_V4, S_D + S_V4); this model outperforms conventional adult ECG criteria and exhibits reliable discrimination, calibration and clinical practicability for diagnosing LVH in children with primary hypertensiondiagnosing LVH in children with primary hypertension.

• This work innovatively uses BMI as a correction term to compensate for ECG voltage attenuation induced by chest wall fat in obese children, substantially lifting the overall ECG diagnostic accuracy of pediatric hypertensive LVH.

What is New:

• Our current study developed a nomogram model combining BMI, RI + SV4, and SD + SV4 that significantly improves ECG diagnostic accuracy for LVH in children with primary hypertension.

• The research innovatively transforms BMI into a corrective factor addressing voltage attenuation caused by chest wall fat in obese children.

• The model identifies pediatric-specific composite ECG indices superior to adult-derived criteria, demonstrating good discrimination, calibration, and clinical utility.