American College of Cardiology Extended Learning
ACCEL interviews and topical summaries of cardiology’s most interesting research areas
Digestible Microchips Take a Bite Out of Adherence Problems Pills tell you if they’ve been taken
About 50% of patients do not take their medicines as prescribed. Specifically, in a review of about 17,000 patients from a total of 95 studies, a steady drop-off in adherence occurs across the first year of therapy due to discontinuation of treatment (nonpersistence).1 For those who are dosed correctly at baseline (about 80%), the rate at 1 year falls to just above 50%.
However, as George Savage, MD, cofounder and chief medical officer of Proteus Digital Health, noted at ACC.15, there are issues relating to capacity, resources, and practical factors that can lead to “unintentional nonadherence”; however, that’s not the most common issue—most nonadherence is intentional. This includes motivational beliefs (“I’m doing fine”), preferences, and perceptual factors. In general, as he puts it, the problem is “misplaced concerns with a background of logic.”
Dr. Savage and colleagues noted there are several approaches currently used to assess medication adherence.2 The most reliable method is direct observation, which consists of a clinician observing and documenting the date and time of the patient’s swallowing each dose of medication. Not exactly realistic although, in some cases, this is done by video recording of the patient taking the medication.
Other indirect methods for monitoring adherence include patient questioning, patient pill diaries, pill counts, daily weighing of pill containers, and prescription refill rates. Electronically documenting the date and time that the cover of a pill container has been opened is another option.
Each of these methods, however, is limited in scope and provides only an estimated measure of actual drug intake, as none of them reliably record whether the patient has actually ingested the medication.
Have You Taken Your Microchip Today?
The company Dr. Savage cofounded has developed digestible microchips embedded in drugs that can tell doctors whether a patient is taking their medications as prescribed. These are the first FDA-approved ingestible devices.
The sand-particle sized sensor consists of a minute silicon chip containing trace amounts of magnesium and copper. When swallowed, it generates a slight voltage in response to digestive juices, which conveys a signal to the surface of a person’s skin where a patch then relays the information to a mobile phone belonging to a health care provider.
“The point is not for doctors to castigate people, but to understand how people are responding to their treatments,” said Dr. Savage. “This way, doctors can prescribe a different dose or a different medicine if they learn that it’s not being taken appropriately.”
The ingestible sensor secured FDA clearance in July 2012, but in July 2015 the company received an FDA update; the agency cleared the device specifically for measuring medication adherence. The first approval came as a de novo device with its “intended use” as “a miniaturized, wearable data-logger for ambulatory recording of physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity, and time-stamped, patient-logged events, including events signaled by swallowing the Ingestible Sensor accessory.”
In a Proteus press release issued at the time of the initial approval, Eric Topol, MD, FACC, director of the Scripps Translational Science Institute in La Jolla, CA, and author of “The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Healthcare,” called the FDA clearance “a major milestone in digital medicine.” According to Dr. Topol, “Directly digitizing pills, for the first time, in conjunction with our wireless infrastructure, may prove to be the new standard for influencing medication adherence and significantly aid chronic disease management.”
First Digital Drug under Review
The approach covers a number of important components to the advancement of clinical goals:
- Combines Therapy and Analytics: promotes adherence to proven medicines while providing insights physicians want to know.
- Closes the Outcome Loop: measures ingestions, full panel of physiologic metrics (including: physical activity, rest, and heart rate), and patient progress.
- Value Proposition Grounded in Evidence: promotes adherence when used with medicines having substantive clinical studies and a solid experiential evidence base.
Payment Based on Value Exchange: provides quicker reimbursement. As health care moves (swiftly) to value-based reimbursement, “payback” may be seen within weeks, according to Dr. Savage, with long-term return-on-investment data supporting an investment in digital leadership.
That last bullet point is more theoretical at this point because Proteus is currently engaged in post-market studies of the economic impact of digital interventions. They have assessed the technology for safety and efficacy in 66 studies, so far, enrolling more than 800 subjects.
Finally, on September 10, 2015, the FDA accepted the first digital medicine new drug application. The product embeds Otsuka’s aripiprazole (ABILIFY®) for serious mental illness with the ingestible sensor in a single tablet to digitally record ingestion and, with patient consent, share information with their health care professionals and caregivers.
- Blaschke TF, Osterberg L, Vrijens B, Urquhart J. Annu Rev Pharmacol Toxicol. 2012;52:275-301.
- DiCarlo L, Moon G, Intondi A, et al. IEEE Pulse. 2012;3:23-6.
Home-based Intervention Works for Complex Heart Disease
But for low-complexity patients, do not try this at home
Despite a wealth of evidence, the role of disease management across the full spectrum of heart disease remains unclear. An integrated program of trials of nurse-led, home-based intervention (HBI) was recently completed. Professor Simon Stewart, PhD, Australian Catholic University and colleagues prospectively tested whether HBI is superior to high levels of standard care in preventing recurrent hospitalization and premature mortality overall.
Compared with high-level standard care, HBI might be increasingly more effective as the clinical complexity of CHD increases (and the potential to prevent poor health outcomes might increase, too).
The study they conducted was based on an analysis of three recently reported clinical trials addressing HBI—the Which Intervention Is most cost-effective and Consumer friendly in reducing heart failure Hospital stay (WHICH?),1 Standard vs. Atrial Fibrillation-specific Management Strategy (SAFETY),2 and Nurse-led Intervention for Less Chronic Heart Failure (NIL-CHF)3 trials. In order, the trials enrolled patients with HF (either reduced or preserved ejection fraction), patients with chronic AF but without HF, and ACS patients without HF.
This program of three studies was led by Dr. Stewart. The primary endpoint of this composite analysis was the proportion of actual versus maximal days alive and out of hospital, while the secondary endpoints were all-cause mortality and rate of hospitalization/hospital stay.
The combined study cohort included 1,226 individuals—809 men and 417 women—who were all randomized to home-based intervention (n = 612) or to standard post-discharge care (n = 614). Participants were older (about 67 years for men and 72 years for women) with multiple comorbidities and high clinical complexity, but appropriate levels of treatment. Baseline characteristics were well-matched for the HBI and standard management groups. Follow-up was a minimum of 3 years for both WHICH? and NIL-CHF and a minimum of 2 years for the SAFETY trial.
The most common reasons for hospitalization were AF (20%), ACS (19%), and acute HF (17%). Roughly one third of cases were complicated by concurrent diabetes mellitus, depression, anemia, and/or renal dysfunction.
Patients in the home-based intervention group experienced 1,210 ± 463 days alive and free from unplanned hospitalization compared with those in the standard-care group who experienced 1,184 ± 494 days alive and out of hospital (TABLE). Overall, those randomized to HBI were alive and out of hospital for 90.1% of the time versus 87.2% for standard management (p = 0.02).
All-cause mortality was superior with HBI versus standard management (HR: 0.56; p = 0.001), and this prolonged survival was independent of other covariates like age, comorbidity score, renal function, or presence of AF and/or hypertension.Over long-term follow-up, HBI was associated with:
- significantly less time spent in the hospital (~200 fewer days per 100 patients),
- significantly better survival (~5 fewer deaths per 100 patients), and
- significantly prolonged period of time alive and out-of-hospital (~1,100 more days per 100 patients).
Low-Complexity Patients: Risk of Harm
The authors did note that across the three studies, HBI seemed to be associated with a trend toward increased events/mortality among low-complexity patients. There was a similar pattern noted in relation to all-cause mortality/survival; with low-complexity patients doing better with standard management versus high-complexity patients where HBI was superior. Thus, Dr. Simon and colleagues noted that HBI should be preserved for clinically complex cases.
In his presentation, Dr. Stewart said “Where (HBI) worked the most—producing highly significant differences—was when clinical complexity increased. I think this is a very important message in terms of disease management.”
Why would HBI increase events or mortality in low-complexity cases? Again, in his presentation, Dr. Stewart noted, “We know very well the clinical cascade, where looking more closely where you don’t need to, can provoke deadly health care and anxiety where it is not required.”
Overall, this represents a unique analysis of the same disease management program evaluated in the same way but across the spectrum of heart disease.
- Stewart S, Carrington MJ, Horowitz JD, et al. Int J Cardiol. 2014;174:600-10.
- Stewart S, Ball J, Horowitz JD, et al. Lancet. 2015;385:775-84.
- Stewart S, Chan YK, Wong C, et al. Eur J Heart Fail. 2015;17:620-30.
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