Heart Rate Multiscale Entropy at Three Hours Predicts Hospital Mortality in 3,154 Trauma Patients

Abstract

Complexity is a measure of variation and randomness potentially indicating improvement or deterioration in critically ill patients. Previously, we have shown integer heart rate (HR) multiscale entropy (MSE), an indicator of complexity, predicts death based on long duration (12 h) and dense (>or=0.4 Hz) windows of HR data. However, such restrictions reduce the use of MSE in the clinical setting. We hypothesized MSE predicts death using HR data of shorter duration and lower density. During the initial 24 h of intensive care unit stay, 3,154 patients had at least 3 h of continuous integer HR sampled. The first continuous window of 3, 6, 9, and 12 h was selected for each patient regardless of density, and an open-source MSE algorithm was applied (M. Costa, www.physionet.org; m = 2; r = 0.15). Risk of death based on MSE, alone and with covariates (age, sex, injury severity score), was assessed using randomly selected logistic regression in half of the cases. Area under the receiver operator curve (AUC) was computed in the other half in subgroups having various durations and densities of HR data. At days 2.3 (median) and 4.9 (mean), 441 patients (14%) died. Multiscale entropy stratified patients by mortality and was an independent predictor of death using 3 h or more of data. Multiscale entropy alone (AUC = 0.66 - 0.71) predicted death comparably to covariates alone (AUC = 0.72). We conclude: (1) Heart rate MSE within hours of admission predicts death occurring days later. (2) Multiscale entropy is robust to variation in bedside data duration and density occurring in a working intensive care unit. (3) Complexity may be a new clinical biomarker of outcome.

Validation of pulse rate variability as a surrogate for heart rate variability in chronically instrumented rabbits

“…To assess the fidelity of the tachograms of different pulse detection methods to nonlinear aspects of the R-R tachogram, the entropy difference (|HRV Ϫ PRV|) for each scale factor was computed for each baseline recording. For the remainder of the study, we used the sum of the entropy over all the computed scales to represent multiscale entropy because of its prognostic value in large patient studies (21,22,26). All other nonlinear indexes were computed in Kubios HRV, including Shannon, approximate, and sample entropy.…”

“…Nonlinear measures encompass a heterogeneous group of statistics, several of which quantify the entropy or complexity of the signal. Multiscale sample entropy is one such statistic that has been used to distinguish patients with cardiovascular pathologies from healthy subjects (10,29,30) and is predictive of mortality in trauma and heart failure patients (17,21,22,26).…”

Methods derived from nonlinear dynamics for analysing heart rate variability

“…A very promising way to quantify complexity over multiple scales was recently introduced by Costa et al (2002Costa et al ( , 2005. The apparent loss of multiscale complexity in life-threatening conditions (Norris et al 2008) suggests a clinical importance of this multiscale complexity measure.…”

Clinical impact of evaluation of cardiovascular control by novel methods of heart rate dynamics

“…Methods of HRV analysis based on nonlinear system theory and beat-to-beat dynamics have gained recent interest as they may reveal delicate changes of heart rate time series. Therefore, novel methods of HRV analysis are constantly being developed (Saul et al 1987;Goldberger 1990b;Yamamoto & Hughson 1991;Skinner et al 1993;Pincus & Goldberger 1994;Peng et al 1995;Goldberger 1996;Voss et al 1998;Schmidt et al 1999;Bauer et al 2006;Tuzcu et al 2006;Norris et al 2008a). Several types of different fractal scaling measures, power-law analyses, complexity measures, measures of symbolic dynamics, turbulence and deceleration capacity of heart rate have been studied in various patient populations.…”