Key result
A machine learning model using clinic blood pressure identified children with chronic kidney disease at low risk for masked hypertension.
Why the study?
ABPM is burdensome in children with chronic kidney disease, and accurate prediction of masked hypertension may allow selective rather than routine ABPM use.
Can a machine learning-based prediction model using clinic blood pressure accurately identify masked hypertension in children with chronic kidney disease?
Observational (n=809)
Can a machine learning-based prediction model using clinic blood pressure accurately identify masked hypertension in children with chronic kidney disease?
Answers are grounded in this paper and cite their sources.
A machine learning model identified that children with CKD and clinic systolic BP <20th percentile and diastolic BP <80th percentile have a low prevalence of masked hypertension, suggesting ABPM could be used selectively in this group.
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: 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.
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Bae et al. (2022) conducted an observational in Chronic kidney disease with nonhypertensive clinic blood pressure (n=809). Machine learning-based prediction model using clinic blood pressure was evaluated on Masked hypertension. A machine learning model using clinic blood pressure identified children with chronic kidney disease at low risk for masked hypertension.
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