Optimizing Implanted Cardiac Device Follow-Up Care

Cardiovascular implantable electronic devices (CIEDs) are life-saving devices programmed to detect cardiac arrhythmias and intervene with pacing or shocks to avoid cardiac death. Currently, three to four million Americans rely on CIEDs and this number is growing rapidly with approximately 400,000 new device implantations each year. Worldwide, around one million new device implantations are performed annually. CIEDs consist of battery-powered pulse generators connected to the heart by one or more electrode wires, called "leads," embedded within a patient's vein. To achieve the maximum possible clinical benefit, modern CIEDs can automatically transmit data to the clinician's office through various media, such as email and text messaging, to allow for remote monitoring. This dissertation concentrates on improving the quality of care of patients with CIEDs, i.e., maximizing the expected lifetime of these patients, by focusing on three major challenges inherent to these devices: (i) cardiac leads fail stochastically and it is not clear whether to abandon them or to extract them, either immediately or at a later time; (ii) the average life span of CIED batteries is not as long as the average patient's expected lifetime and it is not clear when to replace the battery-powered pulse generators; (iii) the remote monitoring of CIEDs can adversely affect the battery's remaining lifetime and it is not clear how frequently the remote transmissions should be performed. We use methodologies including Markov decision processes as well as applied probability and statistics to formulate and analyze decision models that enable clinicians to provide patients with better quality of care. Using clinical data and expert opinion, we carefully calibrate the models concerning challenges (i) and (ii); for (iii), we provide insightful numerical examples for a stylized model. Our results suggest that behaving optimally can significantly extend patients' lives while simultaneously decreasing the burden on the healthcare system by reducing the number of surgeries, in-office visits, and so on, without compromising the patients' well-being