Literature DB >> 28582382

Predictive model for the development of hypertensive cardiopathy: A prospective cohort study.

Alexis Álvarez Aliaga1, Julio César González-Aguilera2, Liliana Del Rosario Maceo-Gómez3, Alexis Suárez-Quesada3.   

Abstract

INTRODUCTION: Predictive models of cardiovascular conditions are useful tools for risk stratification. The high morbidity and mortality resulting from hypertensive cardiopathy creates a need for the use of tools to predict the risk of cardiovascular disease.
OBJECTIVE: To evaluate the capacity of a model based on risk factors to predict the development of hypertensive cardiopathy after ten years in patients with a diagnosis of essential arterial hypertension.
METHODS: A prospective cohort study was carried out in hypertensive patients cared for at the specialized arterial hypertension physician’s office of the Specialty Policlinic attached to “Carlos Manuel de Céspedes” Hospital, Bayamo Municipality, Granma Province, Cuba, from January 1, 2000 to December 31, 2009. A predictive model was constructed and validated through a process that included the random split of the whole sample in two parts: one for development (parameters estimation) and the other for validation.
RESULTS: The binary regression model adjusted by the “step-by-step backward method,” showed that in step six, 13 variables sufficed to estimate the risk of developing hypertensive cardiopathy. In the estimation sample, the area under the receiver operating characteristic curve obtained for the prediction of hypertensive heart disease was 0.985 (confidence interval: 0.980-0.990; p = <0.0005). In the validation sample the area under the receiver operating characteristic curve was 0.963 (confidence interval: 0.953-0, 0.973, p<0.0005). The calibration of the model was also adequate (p = 0.863).
CONCLUSIONS: The model obtained proved is a clinical and epidemiological surveillance instrument, useful to identify subjects with greater likelihood to acquire hypertensive heart disease, and to stratify their risk in the following ten-year period.

Entities:  

Keywords:  hypertensive cardiopathy; predictive models; arterial hypertension

Mesh:

Year:  2017        PMID: 28582382     DOI: 10.5867/medwave.2017.04.6954

Source DB:  PubMed          Journal:  Medwave        ISSN: 0717-6384


  1 in total

1.  Predicting hypertension using machine learning: Findings from Qatar Biobank Study.

Authors:  Latifa A AlKaabi; Lina S Ahmed; Maryam F Al Attiyah; Manar E Abdel-Rahman
Journal:  PLoS One       Date:  2020-10-16       Impact factor: 3.240

  1 in total

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