title: Assessing the interaction of respiration and heart rate in heart failure and controls using ambulatory Holter recordings.
abstract: Breathing is a critical component of cardiopulmonary function, but few tools exist to evaluate respiration in ambulatory patients. Holter monitoring allows accurate diagnosis of a host of cardiac issues, and several investigators have demonstrated the ability to detect respiratory effort on the electrocardiogram. In this study we introduce a myogram signal derived from 12-lead, high frequency Holter as a means of detecting respiratory effort. Using the combined myogram and ECG signal, four novel variables were created: total number of Cheyne-Stokes episodes; the BWRatio, the ratio of power (above baseline) measured one second after peak-to-peak respiratory power, an assessment of the "shape" of the respiratory effort; DRR, the change in RR interval centering around peak inspiration; and minutes of synchronized breathing, a fixed ratio of heart beats to respiratory cycles. These variables were assessed in 24-hour recordings from three cohorts: healthy volunteers (n=33), heart failure subjects from the GISSI HF trial (n=383), and subjects receiving implantable defibrillators with severely depressed left ventricular function enrolled in the M2Risk trial (n=470). We observed a statistically significant 6-fold increase in the number of Cheyne-Stokes episodes (p=0.01 by ANOVA), decreases in BWRatio (p<0.001), as well as decrease in DRR in heart failure subjects; only minutes of synchronized breathing was not significantly decreased in heart failure. This study provides "proof of concept" that novel variables incorporating Holter-derived respiration can distinguish healthy subjects from heart failure. The utility of these variables for predicting heart failure, arrhythmia, and death risk in prospective studies needs to be assessed.
authors.affiliation: Uniformed Services University of the Health Sciences, Bethesda, MD, USA. Electronic address: mark.haigney@usuhs.edu.
authors.forename: Mark
authors.initials: M
authors.lastname: Haigney
authors.affiliation: University of Rochester, Rochester, NY, USA.
authors.forename: Wojceich
authors.initials: W
authors.lastname: Zareba
authors.affiliation: S. Maugeri Foundation, IRCCS, Scientific Institute of Montescano, Montescano, Pavia, Italy.
authors.forename: Maria Teresa
authors.initials: MT
authors.lastname: La Rovere
authors.affiliation: Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
authors.forename: Ian
authors.initials: I
authors.lastname: Grasso
authors.affiliation: University of California San Francisco, San Francisco, CA, USA.
authors.forename: David
authors.initials: D
authors.lastname: Mortara
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D015716:Electrocardiography, Ambulatory / Q000379:methods*; D006333:Heart Failure / Q000175:diagnosis* / Q000503:physiopathology*; D006339:Heart Rate; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D056152:Respiratory Rate; D012680:Sensitivity and Specificity
keywords: Cheyne-Stokes; Heart failure; Holter; Respiration; Sleep apnea