Literature DB >> 35862083

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

Sunjae Bae1, Joshua A Samuels2, Joseph T Flynn3,4, Mark M Mitsnefes5, Susan L Furth6,7, Bradley A Warady8, Derek K Ng9.   

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 C 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.

Entities:  

Keywords:  ambulatory blood pressure monitoring; chronic kidney disease; masked hypertension prediction; risk factors

Mesh:

Year:  2022        PMID: 35862083      PMCID: PMC9378451          DOI: 10.1161/HYPERTENSIONAHA.121.18794

Source DB:  PubMed          Journal:  Hypertension        ISSN: 0194-911X            Impact factor:   9.897


  28 in total

1.  Masked hypertension.

Authors:  Thomas G Pickering; Karina Davidson; William Gerin; Joseph E Schwartz
Journal:  Hypertension       Date:  2002-12       Impact factor: 10.190

2.  Clinical correlates of ambulatory BP monitoring among patients with CKD.

Authors:  Satoshi Iimuro; Enyu Imai; Tsuyoshi Watanabe; Kosaku Nitta; Tadao Akizawa; Seiichi Matsuo; Hirofumi Makino; Yasuo Ohashi; Akira Hishida
Journal:  Clin J Am Soc Nephrol       Date:  2013-02-14       Impact factor: 8.237

3.  Predictors of Rapid Progression of Glomerular and Nonglomerular Kidney Disease in Children and Adolescents: The Chronic Kidney Disease in Children (CKiD) Cohort.

Authors:  Bradley A Warady; Alison G Abraham; George J Schwartz; Craig S Wong; Alvaro Muñoz; Aisha Betoko; Mark Mitsnefes; Frederick Kaskel; Larry A Greenbaum; Robert H Mak; Joseph Flynn; Marva M Moxey-Mims; Susan Furth
Journal:  Am J Kidney Dis       Date:  2015-03-19       Impact factor: 8.860

4.  The effect of ambient temperature and barometric pressure on ambulatory blood pressure variability.

Authors:  Megan Jehn; Lawrence J Appel; Frank M Sacks; Edgar R Miller
Journal:  Am J Hypertens       Date:  2002-11       Impact factor: 2.689

5.  Design and methods of the Chronic Kidney Disease in Children (CKiD) prospective cohort study.

Authors:  Susan L Furth; Stephen R Cole; Marva Moxey-Mims; Frederick Kaskel; Robert Mak; George Schwartz; Craig Wong; Alvaro Muñoz; Bradley A Warady
Journal:  Clin J Am Soc Nephrol       Date:  2006-07-19       Impact factor: 8.237

6.  Masked hypertension associates with left ventricular hypertrophy in children with CKD.

Authors:  Mark Mitsnefes; Joseph Flynn; Silvia Cohn; Joshua Samuels; Tom Blydt-Hansen; Jeffrey Saland; Thomas Kimball; Susan Furth; Bradley Warady
Journal:  J Am Soc Nephrol       Date:  2009-11-16       Impact factor: 10.121

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

Authors:  Sunjae Bae; Allan B Massie; Brian S Caffo; Kyle R Jackson; Dorry L Segev
Journal:  Transpl Int       Date:  2020-07-28       Impact factor: 3.782

8.  Predictive Model for Ambulatory Hypertension Based on Office Blood Pressure in Obese Children.

Authors:  Girish C Bhatt; Abhijit P Pakhare; Priya Gogia; Shikha Jain; Nayan Gupta; Sudhir K Goel; Rajesh Malik
Journal:  Front Pediatr       Date:  2020-05-19       Impact factor: 3.418

9.  Prediction of Ambulatory Hypertension Based on Clinic Blood Pressure Percentile in Adolescents.

Authors:  Gilad Hamdani; Joseph T Flynn; Richard C Becker; Stephen R Daniels; Bonita Falkner; Coral D Hanevold; Julie R Ingelfinger; Marc B Lande; Lisa J Martin; Kevin E Meyers; Mark Mitsnefes; Bernard Rosner; Joshua A Samuels; Elaine M Urbina
Journal:  Hypertension       Date:  2018-10       Impact factor: 10.190

Review 10.  Seasonal variation of blood pressure in children.

Authors:  Niels Ziegelasch; Mandy Vogel; Werner Siekmeyer; Heiko Billing; Ingo Dähnert; Wieland Kiess
Journal:  Pediatr Nephrol       Date:  2020-11-19       Impact factor: 3.714

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