What is the clinical evidence from this study?

Study design: Cohort. Population: Hypertension (n=139). Intervention: Heart Rate Variability (HRV) analysis vs. Echographic parameters (LVMi and IMT). Primary outcome: Prediction of cardiovascular and cerebrovascular events (Accuracy 85.7%, 95% CI 78.7-88.1).

March 20, 2015PLoS ONE186 citationsOpen Access

Automatic Prediction of Cardiovascular and Cerebrovascular Events Using Heart Rate Variability Analysis

PMPaolo MelilloUniversity of Campania "Luigi Vanvitelli"RIRaffaele IzzoGeneral / Preventive / Lipids AOAda OrricoRegione Campania

Key Result

A predictive model based on random forest using heart rate variability measures identified high-risk hypertensive patients for future vascular events with 71.4% sensitivity and 87.8% specificity, outperforming conventional echographic parameters.

Study Design

Type

Cohort (n=139)

Multicenter

Structured PICO

Does Heart Rate Variability analysis using data-mining algorithms improve the prediction of vascular events in hypertensive patients compared to conventional echographic parameters?

Population

139 hypertensive patients aged 55 and older, followed for 12 months to predict major cardiovascular and cerebrovascular events using heart rate variability analysis.

Exposure

Predictive models based on data-mining algorithms (e.g., random forest, support vector machine, artificial neural network) analyzing Heart Rate Variability from Holter recordings.

Comparator

Conventional echographic parameters.

Outcome

Prediction of vascular events (composite of myocardial infarction, stroke, syncopal event) assessed by receiver-operator characteristics curve.composite

Data-mining algorithms analyzing heart rate variability from Holter recordings can effectively stratify the risk of future vascular events in hypertensive patients, outperforming conventional echocardiographic parameters.

Main Result

Effect estimate: Accuracy 85.7% (95% CI 78.7-88.1)

Absolute Event Rate: 85.7% vs 69.5%

Limitations

Only linear and nonlinear HRV features were used, excluding strong risk markers such as Heart Rate Turbulence or T wave alterations.
The dataset was relatively small and unbalanced.
Further investigations are needed to assess whether the proposed models can perform well using other datasets.

Abstract

BACKGROUND: There is consensus that Heart Rate Variability is associated with the risk of vascular events. However, Heart Rate Variability predictive value for vascular events is not completely clear. The aim of this study is to develop novel predictive models based on data-mining algorithms to provide an automatic risk stratification tool for hypertensive patients. METHODS: A database of 139 Holter recordings with clinical data of hypertensive patients followed up for at least 12 months were collected ad hoc. Subjects who experienced a vascular event (i.e., myocardial infarction, stroke, syncopal event) were considered as high-risk subjects. Several data-mining algorithms (such as support vector machine, tree-based classifier, artificial neural network) were used to develop automatic classifiers and their accuracy was tested by assessing the receiver-operator characteristics curve. Moreover, we tested the echographic parameters, which have been showed as powerful predictors of future vascular events. RESULTS: The best predictive model was based on random forest and enabled to identify high-risk hypertensive patients with sensitivity and specificity rates of 71.4% and 87.8%, respectively. The Heart Rate Variability based classifier showed higher predictive values than the conventional echographic parameters, which are considered as significant cardiovascular risk factors. CONCLUSIONS: Combination of Heart Rate Variability measures, analyzed with data-mining algorithm, could be a reliable tool for identifying hypertensive patients at high risk to develop future vascular events.

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Cite This Study

Melillo et al. (Fri,) conducted a cohort in Hypertension (n=139). Heart Rate Variability (HRV) analysis vs. Echographic parameters (LVMi and IMT) was evaluated on Prediction of cardiovascular and cerebrovascular events (Accuracy 85.7%, 95% CI 78.7-88.1). A predictive model based on random forest using heart rate variability measures identified high-risk hypertensive patients for future vascular events with 71.4% sensitivity and 87.8% specificity, outperforming conventional echographic parameters.

synapsesocial.com/papers/6a46fdb78dd8ee62990509e3 https://doi.org/https://doi.org/10.1371/journal.pone.0118504

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