Risk prediction with office and ambulatory blood pressure using artificial intelligence

Abstract Background To develop and validate a novel, machine learning-derived model for prediction of cardiovascular (CV) mortality risk using office (OBP) and ambulatory blood pressure (ABP), to compare its performance with existing risk scores, and to assess the possibility of predicting ABP phenotypes (i.e. white-coat, ambulatory and masked hypertension) utilizing clinical variables.Methods Using data from 63,910 patients enrolled in the Spanish ABP monitoring registry, machine-learning approaches (logistic regression, support vector machine, gradient boosted decision trees, and deep neural networks) and stepwise forward feature selection were used for the classification of the data.Results Over a median follow-up of 4.7 years, 3,808 deaths occurred from which 1,295 were from CV causes. The performance for all tested classifiers increased while adding up to 10 features and converged thereafter. For the prediction of CV mortality, deep neural networks yielded the highest clinical performance. The novel mortality prediction models using OBP (CV-MortalityOBP) and ABP (CV-MortalityABP) outperformed all other risk scores. The area under the curve (AUC) achieved by the novel approach, using OBP variables only, was already significantly higher when compared with the AUC of Framingham score (0.685 vs 0.659, p = 1.97×10−22), the SCORE (0.679 vs 0.613, p = 6.21×10−22), and ASCVD (0.722 vs 0.639, p = 8.03×10−30) risk score. However, prediction of CV mortality with ABP instead of OBP data led to a significant increase in AUC (0.781 vs 0.752, p = 1.73×10−42), accuracy, balanced accuracy and sensitivity. The sensitivity and specificity for detection of ambulatory, masked, and white-coat hypertension ranged between 0.653-0.661 and 0.573-0.651, respectively.Conclusion We developed a novel risk calculator for CV death using artificial intelligence based on a large cohort of patients included in the Spanish ABP monitoring registry. The receiver operating characteristic curves for CV-MortalityOBP and CV-MortalityABP with deep neural networks models outperformed all other risk metrics. Prediction of CV mortality using ABP data led to a significant increase in performance metrics. The prediction of ambulatory phenotypes using clinical characteristics, including OBP, was limited..

Medienart:	Preprint

Erscheinungsjahr:	2020
Erschienen:	2020

Enthalten in:	bioRxiv.org - (2020) vom: 03. Nov. Zur Gesamtaufnahme - year:2020

Sprache:	Englisch

Beteiligte Personen:	Guimarães, Pedro [VerfasserIn] Keller, Andreas [VerfasserIn] Böhm, Michael [VerfasserIn] Lauder, Lucas [VerfasserIn] Ayala, José L. [VerfasserIn] Banegas, José R. [VerfasserIn] de la Sierra, Alejandro [VerfasserIn] Vinyoles, Ernest [VerfasserIn] Gorostidi, Manuel [VerfasserIn] Segura, Julián [VerfasserIn] Ruiz-Hurtado, Gema [VerfasserIn] Ruilope, Luis M. [VerfasserIn] Mahfoud, Felix [VerfasserIn]

Links:	Volltext [kostenfrei]

doi:	10.1101/2020.01.17.20017798

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PPN (Katalog-ID):	XBI000705470