Comparing Cheyne-Stokes Respirations in Healthy and Critically-ill Cohorts

University Of California, San Francisco, San Francisco CA

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Abstract

? DESCRIPTION (provided by applicant): Cheyne-Stokes respiration and periodic breathing have been associated with increased mortality in heart failure patients (1-5). New software has been developed to measure these two respiratory parameters with continuous electrocardiographic (ECG) monitoring. The proposed research will determine whether ECG-derived Cheyne-Stokes respiration and periodic breathing measurements will be valuable to predict risk for [adverse outcomes (cardiopulmonary arrest, acute ventilatory support, acute myocardial infarct, acute stroke and 30 day mortality)] in critically-ill patients treated in an Intensive Care Unit (ICU). It is reported that signs of patient deterioration are likely present hours before a hospital cardiopulmonary arrest (6). The study aims are to measure 24-hour ECG-derived Cheyne-Stokes respiration and periodic breathing to: (a) compare an ICU patient sample with a healthy sample; (b) determine whether these measurements correlate with in-hospital [adverse event] in ICU patients; and (c) describe patterns of hourly rate change in the 24 hours preceding in-ICU [adverse outcome]. The proposed study will involve a secondary analysis of ECG data collected for two prospective studies: 1.) 100 healthy participants who were recruited from January to March of 2013 and who wore a Holter ECG monitor continuously for 24 hrs; and, 2.) 462 adult ICU patients who were continuously monitored with physiologic monitors at Moffitt Hospital, UCSF Medical Center during the month of March 2013. Novel Super ECG software technology (Mortara Instrument, Milwaukee, WI) uses subtle changes in QRS morphology with breathing to calculate Cheyne- Stokes and periodic breathing episodes from Holter ECG monitor data and from hospital physiologic monitor ECG data. Understanding Cheyne-Stokes respiration and periodic breathing patterns in critically-ill patients may identify those who are at greater risk of [adverse outcome] and help clinicians guide their plan of care.