Our research protocol was developed and approved by the institutional review board (IRB). The study was conducted in compliance with the ethical standards of the responsible institution on human subjects as well as with the Helsinki Declaration. We included patients with history of New York Heart Association (NYHA) class III systolic HF longer than 6 months with a pre-implanted intracardiac defibrillator (ICD) capable of monitoring TI (St. Jude Medical’s Corvue™ Birmingham, MN) and pre-implanted CardioMEMs™ remote HF monitoring device. Eligible patients were identified using the Merlin.net™ database of patients with CardioMEMs™ device previously implanted at our institution (Ascension Providence Hospital, Southfield) and cross-checking them with our pacemaker clinic to assess whether they have implanted ICDs which can measure TI (St. Jude Medical’s Corvue™). After initial identification, subjects were brought in for a routine clinic visit where consent for enrolment in the study was obtained. During this initial visit, each patient was assessed for volume status and current medications, baseline assessment of pulmonary artery diastolic pressure (PAdP) via CardioMems device along with their St. Jude ICD interrogation. Patients were subsequently prospectively followed over a period of 12 weeks. Medication changes, if needed, were left to the treating cardiologist’s discretion.
Remote hemodynamic data were acquired from the St. Jude portal (Merlin) for weekly CardioMems PAdP transmissions and biweekly transmissions from CorVue TI recordings. Weekly PAdP values (mm Hg) were averaged (defined to be valid for at least three valid transmissions per week) for an individual subject during the follow-up duration.
TI data were acquired from St. Jude’s CorVue remote device monitoring system. The weekly values (ohms) were acquired from reconstructed graphs/curves using the web-based software program, WebPlotDigitizer [20 ]. For comparison across a uniform variable for the two methods, weekly PAdP measurements were averaged. Weekly percentage change was then calculated as: Weekly percentage change = (week 2 - week1)/week 1 × 100.