Events Prediction Ability in Patients with Hypertension using Artificial Neural Network Analysis of Ambulatory Blood Pressure Monitoring Compared to Clinical Risk Stratification
Year: 2025
Type: article
Abstract: Background: There is no available evidence comparing the predictive value of an artificial neural network (ANN)-based analysis method that integrates ambulatory blood pressure monitoring (ABPM) variables versus clinical risk stratification (CRS) for serious events in hypertensive patients at follow-up.Methods: We analyzed ABPM studies that included 27 measurements each one.The variables were daytime, nighttime and 24-hour mean, systolic and diastolic blood pressure, pulse pressure and heart rate; hypertensive load; standard deviations of pressures and heart rate; circadian rhythm.The dependent variable was the combined endpoint of death, stroke, acute myocardial infarction, heart failure and kidney disease.For clinical risk stratification, the Argentine Consensus on Hypertension was used as a model.We evaluated the discriminative ability to predict the endpoint using ANN-ABPM and CRS by logistic regression through the analysis of the area under the receiver operating characteristic curve (AUC-ROC).Both AUC-ROC were compared by De Long test.SPSS 23.0 Statistics was used for statistical analyses and ANN modelling.Results: Data from 491 ABPM studies were analyzed.Mean age was 69 ± 14 years; 53% of population was female; 11.6% had diabetes; 51% had dyslipidemia; mean body mass index was 26 ± 4 kg/m 2 ; 14.3% were smokers.Median follow-up was 6.6 years (interquartile range 4.5-8).The best predictive ANN model was the Multilayer Perceptron one with a hidden layer; neuronal architecture (27/7/2).Nocturnal systolic blood pressure (SBP) had 100% independent normalized importance for modelling.The AUC-ROC for the combined endpoint was 0.81 (95% CI 0.77-0.90)using neural network analysis with ABPM variables, and 0.67 (95% CI 0.56-0.77)using CRS; De Long's test p < 0.001.Conclusion: We observed a higher discriminative ability to predict events at follow-up using ANN analysis with ABPM variables compared to conventional CRS.This observation raises a research hypothesis to be validated prospectively to optimize risk stratification and treatment in hypertensive patients.
Authors: Federico Di Gennaro, María P. Catalano, Alejandro García Aguirre, María Lía Fernández, Romina Llanos, Santiago Pérez Lloret, Claudio Higa
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4409913905