ArticleHypertension (Dallas, Tex. : 1979)2022
Machine Learning-Based Prediction of Masked Hypertension Among Children With Chronic Kidney Disease.
Article in Hypertension (Dallas, Tex. : 1979), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00327860 (Chronic Kidney Disease in Children Prospective Cohort Study), which is not on this map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Chronic Kidney Disease in Children Prospective Cohort Study (CKiD)
Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Predicting anemia treatment outcomes in maintenance hemodialysis patients using multiple machine learning models.BMC medical informatics and decision making · 2026Article
- Current perspectives and challenges of digital hypertension: artificial intelligence in the management of hypertension.Clinical hypertension · 2026Review
- A Machine Learning-Based Model to Estimate the Risk of Pulmonary Hypertension in Chronic Kidney Disease Patients.Journal of clinical hypertension (Greenwich, Conn.) · 2025Article
- Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024.International urology and nephrology · 2025Review
- Measurement of Blood Pressure in Children and Adolescents Outside the Office for the Diagnosis of Hypertension.Current cardiology reports · 2025Review
- Predicting Job Burnout Among Female Nurses in China With Machine Learning and Shapley Additive Explanations.Journal of nursing management · 2025Article
- Machine learning model for detecting masked hypertension in young adults.Frontiers in physiology · 2025Article
- Hypertension and Cardiovascular Risk Among Children with Chronic Kidney Disease.Current hypertension reports · 2024Review
- Masked Hypertension in Healthy Children and Adolescents: Who Should Be Screened?Current hypertension reports · 2023Review
- 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.The journal of pain · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundAmbulatory 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.
methodsTo 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.
resultsMasked 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
conclusionsABPM 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.