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基于机器学习的慢性肾脏病儿童隐匿性高血压预测。

Machine Learning-Based Prediction of Masked Hypertension Among Children With Chronic Kidney Disease.

机构信息

Department of Surgery, Johns Hopkins University, Baltimore, MD (S.B.).

University of Texas Health Sciences Center, Houston (J.A.S.).

出版信息

Hypertension. 2022 Sep;79(9):2105-2113. doi: 10.1161/HYPERTENSIONAHA.121.18794. Epub 2022 Jul 7.


DOI:10.1161/HYPERTENSIONAHA.121.18794
PMID:35862083
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9378451/
Abstract

BACKGROUND: Ambulatory blood pressure monitoring (ABPM) is routinely performed in children with chronic kidney disease to identify masked hypertension, a risk factor for accelerated chronic kidney disease progression. However, ABPM is burdensome, and developing an accurate prediction of masked hypertension may allow using ABPM selectively rather than routinely. METHODS: To create a prediction model for masked hypertension using clinic blood pressure (BP) and other clinical characteristics, we analyzed 809 ABPM studies with nonhypertensive clinic BP among the participants of the Chronic Kidney Disease in Children study. RESULTS: Masked hypertension was identified in 170 (21.0%) observations. We created prediction models for masked hypertension via gradient boosting, random forests, and logistic regression using 109 candidate predictors and evaluated its performance using bootstrap validation. The models showed statistics from 0.660 (95% CI, 0.595-0.707) to 0.732 (95% CI, 0.695-0.786) and Brier scores from 0.148 (95% CI, 0.141-0.154) to 0.167 (95% CI, 0.152-0.183). Using the possible thresholds identified from this model, we stratified the dataset by clinic systolic/diastolic BP percentiles. The prevalence of masked hypertension was the lowest (4.8%) when clinic systolic/diastolic BP were both <20th percentile, and relatively low (9.0%) with clinic systolic BP<20th and diastolic BP<80th percentiles. Above these thresholds, the prevalence was higher with no discernable pattern. CONCLUSIONS: ABPM could be used selectively in those with low clinic BP, for example, systolic BP<20th and diastolic BP<80th percentiles, although careful assessment is warranted as masked hypertension was not completely absent even in this subgroup. Above these clinic BP levels, routine ABPM remains recommended.

摘要

背景:为了识别隐匿性高血压(一种加速慢性肾脏病进展的危险因素),常规对慢性肾脏病患儿进行动态血压监测(ABPM)。然而,ABPM 较为繁琐,且开发隐匿性高血压的准确预测模型可使 ABPM 得以有选择性地而非常规使用。

方法:为了使用诊所血压(BP)和其他临床特征创建隐匿性高血压的预测模型,我们对 Chronic Kidney Disease in Children 研究中的 809 项非高血压诊所 BP 的 ABPM 研究进行了分析。

结果:在 170 项(21.0%)观察中发现了隐匿性高血压。我们使用 109 个候选预测因子,通过梯度提升、随机森林和逻辑回归创建了隐匿性高血压预测模型,并使用自举验证评估了其性能。这些模型的 AUC 值为 0.660(95%CI,0.595-0.707)至 0.732(95%CI,0.695-0.786),Brier 得分从 0.148(95%CI,0.141-0.154)至 0.167(95%CI,0.152-0.183)。根据该模型确定的可能阈值,我们根据诊所收缩压/舒张压的百分位数对数据集进行分层。当诊所收缩压/舒张压均<第 20 百分位时,隐匿性高血压的患病率最低(4.8%),而当诊所收缩压<第 20 百分位且舒张压<第 80 百分位时,患病率相对较低(9.0%)。在这些阈值之上,患病率较高,且无明显模式。

结论:在诊所 BP 较低的情况下(例如,收缩压<第 20 百分位且舒张压<第 80 百分位),ABPM 可选择性使用,尽管在该亚组中,即使隐匿性高血压并非完全不存在,仍需仔细评估。在这些诊所 BP 水平之上,仍建议常规进行 ABPM。

相似文献

[1]
Machine Learning-Based Prediction of Masked Hypertension Among Children With Chronic Kidney Disease.

Hypertension. 2022-9

[2]
Can office blood pressure readings predict masked hypertension?

Pediatr Nephrol. 2016-1

[3]
Blood Pressure Control in Hypertensive Patients, Cardiovascular Risk Profile and the Prevalence of Masked Uncontrolled Hypertension (MUCH).

Med Arch. 2016-7-27

[4]
Association of Cardiovascular Outcomes With Masked Hypertension Defined by Home Blood Pressure Monitoring in a Japanese General Practice Population.

JAMA Cardiol. 2018-7-1

[5]
[2013 Ambulatory blood pressure monitoring recommendations for the diagnosis of adult hypertension, assessment of cardiovascular and other hypertension-associated risk, and attainment of therapeutic goals (summary). Joint recommendations from the International Society for Chronobiology (ISC), American Association of Medical Chronobiology and Chronotherapeutics (AAMCC), Spanish Society of Applied Chronobiology, Chronotherapy, and Vascular Risk (SECAC), Spanish Society of Atherosclerosis (SEA), and Romanian Society of Internal Medicine (RSIM)].

Clin Investig Arterioscler. 2013

[6]
White-coat and masked hypertension diagnoses in chronic kidney disease patients.

J Clin Hypertens (Greenwich). 2020-7

[7]
Office blood pressure versus ambulatory blood pressure measurement in childhood obesity.

BMC Pediatr. 2023-4-29

[8]
Prevalence of masked hypertension and its association with left ventricular hypertrophy in children and young adults with chronic kidney disease: a systematic review and meta-analysis.

J Hypertens. 2023-5-1

[9]
Do children with solitary or hypofunctioning kidney have the same prevalence for masked hypertension?

Pediatr Nephrol. 2021-7

[10]
Magnitude of the Difference Between Clinic and Ambulatory Blood Pressures and Risk of Adverse Outcomes in Patients With Chronic Kidney Disease.

J Am Heart Assoc. 2019-5-7

引用本文的文献

[1]
Measurement of Blood Pressure in Children and Adolescents Outside the Office for the Diagnosis of Hypertension.

Curr Cardiol Rep. 2025-1-18

[2]
Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024.

Int Urol Nephrol. 2025-3

[3]
Hypertension and Cardiovascular Risk Among Children with Chronic Kidney Disease.

Curr Hypertens Rep. 2024-10

[4]
Masked Hypertension in Healthy Children and Adolescents: Who Should Be Screened?

Curr Hypertens Rep. 2023-9

[5]
The Problem of Pain in the United States: A Population-Based Characterization of Biopsychosocial Correlates of High Impact Chronic Pain Using the National Health Interview Survey.

J Pain. 2023-6

本文引用的文献

[1]
Ambulatory Blood Pressure Monitoring in Children and Adolescents: 2022 Update: A Scientific Statement From the American Heart Association.

Hypertension. 2022-7

[2]
Seasonal variation of blood pressure in children.

Pediatr Nephrol. 2021-8

[3]
Machine learning to predict transplant outcomes: helpful or hype? A national cohort study.

Transpl Int. 2020-11

[4]
Predictive Model for Ambulatory Hypertension Based on Office Blood Pressure in Obese Children.

Front Pediatr. 2020-5-19

[5]
Calibration: the Achilles heel of predictive analytics.

BMC Med. 2019-12-16

[6]
Machine Learning in Medicine.

N Engl J Med. 2019-4-4

[7]
Prediction of Ambulatory Hypertension Based on Clinic Blood Pressure Percentile in Adolescents.

Hypertension. 2018-10

[8]
Discrimination and Calibration of Clinical Prediction Models: Users' Guides to the Medical Literature.

JAMA. 2017-10-10

[9]
Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents.

Pediatrics. 2017-8-21

[10]
Identifying subgroups of enhanced predictive accuracy from longitudinal biomarker data using tree-based approaches: applications to fetal growth.

J R Stat Soc Ser A Stat Soc. 2017-1

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