A Multivariate prediction model for amlodipine therapeutic efficacy in pediatric primary hypertension
BackgroundThere are currently no biomarker-based prediction models for amlodipine therapeutic efficacy in pediatric hypertension. This study aimed to identify potential biomarkers and establish a biomarker-based model for predicting amlodipine therapeutic efficacy in pediatric primary hypertension (...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
|
| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1542276/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859401114517504 |
|---|---|
| author | Yao Lin Hui Wang Yaqi Li Yang Liu Yanyan Liu Hongwei Zhang Yanjun Deng Lin Shi |
| author_facet | Yao Lin Hui Wang Yaqi Li Yang Liu Yanyan Liu Hongwei Zhang Yanjun Deng Lin Shi |
| author_sort | Yao Lin |
| collection | DOAJ |
| description | BackgroundThere are currently no biomarker-based prediction models for amlodipine therapeutic efficacy in pediatric hypertension. This study aimed to identify potential biomarkers and establish a biomarker-based model for predicting amlodipine therapeutic efficacy in pediatric primary hypertension (PH).MethodsFrom January 2022 to December 2023, 165 children and adolescents with PH prescribed amlodipine were recruited at our department for a prospective observational study. Patients were grouped into Responders and Non-responders after one month treatment. The baseline data in the two groups were analyzed to identify variables associated with amlodipine treatment responsiveness; furthermore, a nomogram prediction model was established based on those potential predictors derived from multivariate regression analysis. This model’s discrimination and calibration were evaluated by a series of statistical methods and internal validation was done using the bootstrap sampling method (1000 resamples).ResultsEighty-nine patients responded to amlodipine while 76 did not. After statistical adjustment, 4 variables were found to be independently associated with therapeutic efficacy, including hyperinsulinemia (OR = 3.000, 95% CI: 1.409-6.386, p = 0.004), insulin resistance (OR = 2.354, 95% CI: 1.032-5.370, p = 0.042), the baseline plasma Endothelin-1 level (OR = 0.627, 95% CI: 0.532-0.740, p < 0.001) and amlodipine dosages (OR = 1.743, 95% CI: 1.400-2.169, p <0.001). Compared to the baseline model, the full model with the four variables had a good calibration with an area under the curve (AUC) of 0.967 (95% CI: 0.945-0.990), yielding a sensitivity and a specificity of 91.0% and 92.1%, respectively; the clinical decision curve showed a positive net benefit. Additionally, a nomogram model was established based on the four variables and evaluated by bootstrap internal validation with the c-statistic of 0.865 and the calibration curve being close to the ideal line (p > 0.05).ConclusionA nomogram model with high predictive value for amlodipine therapeutic efficacy in pediatric PH was established. This model may be potentially applied to guide the selection of amlodipine for the treatment of pediatric PH. |
| format | Article |
| id | doaj-art-6f01f20842c8402fa43d5264a11ae755 |
| institution | Kabale University |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-6f01f20842c8402fa43d5264a11ae7552025-02-11T05:10:17ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-02-011610.3389/fendo.2025.15422761542276A Multivariate prediction model for amlodipine therapeutic efficacy in pediatric primary hypertensionYao LinHui WangYaqi LiYang LiuYanyan LiuHongwei ZhangYanjun DengLin ShiBackgroundThere are currently no biomarker-based prediction models for amlodipine therapeutic efficacy in pediatric hypertension. This study aimed to identify potential biomarkers and establish a biomarker-based model for predicting amlodipine therapeutic efficacy in pediatric primary hypertension (PH).MethodsFrom January 2022 to December 2023, 165 children and adolescents with PH prescribed amlodipine were recruited at our department for a prospective observational study. Patients were grouped into Responders and Non-responders after one month treatment. The baseline data in the two groups were analyzed to identify variables associated with amlodipine treatment responsiveness; furthermore, a nomogram prediction model was established based on those potential predictors derived from multivariate regression analysis. This model’s discrimination and calibration were evaluated by a series of statistical methods and internal validation was done using the bootstrap sampling method (1000 resamples).ResultsEighty-nine patients responded to amlodipine while 76 did not. After statistical adjustment, 4 variables were found to be independently associated with therapeutic efficacy, including hyperinsulinemia (OR = 3.000, 95% CI: 1.409-6.386, p = 0.004), insulin resistance (OR = 2.354, 95% CI: 1.032-5.370, p = 0.042), the baseline plasma Endothelin-1 level (OR = 0.627, 95% CI: 0.532-0.740, p < 0.001) and amlodipine dosages (OR = 1.743, 95% CI: 1.400-2.169, p <0.001). Compared to the baseline model, the full model with the four variables had a good calibration with an area under the curve (AUC) of 0.967 (95% CI: 0.945-0.990), yielding a sensitivity and a specificity of 91.0% and 92.1%, respectively; the clinical decision curve showed a positive net benefit. Additionally, a nomogram model was established based on the four variables and evaluated by bootstrap internal validation with the c-statistic of 0.865 and the calibration curve being close to the ideal line (p > 0.05).ConclusionA nomogram model with high predictive value for amlodipine therapeutic efficacy in pediatric PH was established. This model may be potentially applied to guide the selection of amlodipine for the treatment of pediatric PH.https://www.frontiersin.org/articles/10.3389/fendo.2025.1542276/fullpediatric primary hypertensionamlodipinepredictorsantihypertensive therapytherapeutic efficacy |
| spellingShingle | Yao Lin Hui Wang Yaqi Li Yang Liu Yanyan Liu Hongwei Zhang Yanjun Deng Lin Shi A Multivariate prediction model for amlodipine therapeutic efficacy in pediatric primary hypertension Frontiers in Endocrinology pediatric primary hypertension amlodipine predictors antihypertensive therapy therapeutic efficacy |
| title | A Multivariate prediction model for amlodipine therapeutic efficacy in pediatric primary hypertension |
| title_full | A Multivariate prediction model for amlodipine therapeutic efficacy in pediatric primary hypertension |
| title_fullStr | A Multivariate prediction model for amlodipine therapeutic efficacy in pediatric primary hypertension |
| title_full_unstemmed | A Multivariate prediction model for amlodipine therapeutic efficacy in pediatric primary hypertension |
| title_short | A Multivariate prediction model for amlodipine therapeutic efficacy in pediatric primary hypertension |
| title_sort | multivariate prediction model for amlodipine therapeutic efficacy in pediatric primary hypertension |
| topic | pediatric primary hypertension amlodipine predictors antihypertensive therapy therapeutic efficacy |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1542276/full |
| work_keys_str_mv | AT yaolin amultivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT huiwang amultivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT yaqili amultivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT yangliu amultivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT yanyanliu amultivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT hongweizhang amultivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT yanjundeng amultivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT linshi amultivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT yaolin multivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT huiwang multivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT yaqili multivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT yangliu multivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT yanyanliu multivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT hongweizhang multivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT yanjundeng multivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension AT linshi multivariatepredictionmodelforamlodipinetherapeuticefficacyinpediatricprimaryhypertension |