Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction

Abstract

BackgroundPrevious studies indicate that decreased heart-rate variability (HRV) is related to the risk of death in patients after acute myocardial infarction (AMI). However, the conventional indices of HRV have poor predictive value for mortality. Our aim was to develop novel predictive models based on support vector machine (SVM) to study the integrated features of HRV for improving risk stratification after AMI.MethodsA series of heart-rate dynamic parameters from 208 patients were analyzed after a mean follow-up time of 28 months. Patient electrocardiographic data were classified as either survivals or cardiac deaths. SVM models were established based on different combinations of heart-rate dynamic variables and compared to left ventricular ejection fraction (LVEF), standard deviation of normal-to-normal intervals (SDNN) and deceleration capacity (DC) of heart rate. We tested the accuracy of predictors by assessing the area under the receiver-operator characteristics curve (AUC).ResultsWe evaluated a SVM algorithm that integrated various electrocardiographic features based on three models: (A) HRV complex; (B) 6 dimension vector; and (C) 8 dimension vector. Mean AUC of HRV complex was 0.8902, 0.8880 for 6 dimension vector and 0.8579 for 8 dimension vector, compared with 0.7424 for LVEF, 0.7932 for SDNN and 0.7399 for DC.ConclusionsHRV complex yielded the largest AUC and is the best classifier for predicting cardiac death after AMI.

Cardiovascular and metabolic risk markers are related to parasympathetic indices in pre-pubertal adolescents

“…With this in mind, heart rate variability has been used in different studies in order to analyse autonomic modulation patterns, 2 principally because it is a simple and non-invasive marker for identifying disorders in autonomic patterns through oscillations between consecutive heartbeat intervals. 2 In recent years, studies have linked heart rate variability in adults with increased risk of hospitalisation and early mortality; 3,4 however, most of these studies involved adults with some previous cardiovascular complicationfor example, acute myocardial infarction and acute coronary syndrome - 3,4 and it is not clear whether these results can be applied to healthy populations.…”

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

“…Singh and Guttag proposed classification tree-based risk stratification models to predict 90 day mortality in patients who suffered from a non-ST elevation acute coronary syndrome[ 20 ]. Recently, Song et al developed Support Vector Machine (SVM) models to quantify the risk of cardiac death in patients after acute myocardial infarction[ 21 ], while Ebrahimzadeh et al proposed a novel approach to distinguish between patients prone to Sudden Cardiac Death and normal people[ 22 ].…”

Can Illness Perceptions Predict Lower Heart Rate Variability following Acute Myocardial Infarction?

“…In one of the first studies investigating the relationship between indices of HRV and mortality, Kleiger et al (1987) showed that decreased HRV was a significant and independent predictor of mortality in post-MI patients. Further studies have since documented adverse prognostic implications of reduced HRV in MI patients (Cripps et al, 1991; Tsuji et al, 1996; Boskovic et al, 2014; Song et al, 2014). Psychosocial factors, such as stressful life events (Pieper et al, 2010; van Ockenburg et al, 2015), general stress (Kang et al, 2004; Brosschot et al, 2006; Chandola et al, 2010; Chida and Steptoe, 2010), hostility (Virtanen et al, 2003; Chida and Steptoe, 2009), depression (Stein et al, 2000; Gehi et al, 2005; Ha et al, 2015; Sgoifo et al, 2015) and anxiety (Friedman and Thayer, 1998; Alvares et al, 2013) have all been found to be associated with lowered HRV.…”