Abstract
The objective of this work was to adapt and evaluate the performance of a Bayesian hybrid model to characterize objective temporal medication ingestion parameters from two clinical studies in patients with serious mental illness (SMI) receiving treatment with a digital medicine system. This system provides a signal from an ingested sensor contained in the dosage form to a patient-worn patch and transmits this signal via the patient’s mobile device. A previously developed hybrid Markov-von Mises model was used to obtain maximum-likelihood estimates for medication ingestion behavior parameters for individual patients. The individual parameter estimates were modeled to obtain distribution parameters of priors implemented in a Markov chain-Monte Carlo framework. Clinical and demographic covariates associated with model ingestion parameters were also assessed. We obtained individual estimates of overall observed ingestion percent (median:75.9%, range:18.2–98.3%, IQR:32.9%), rate of excess dosing events (median:0%, range:0–14.3%, IQR:3.0%) and observed ingestion duration. The modeling also provided estimates of the Markov-dependence probabilities of dosing success following a dosing success or failure. The ingestion-timing deviations were modeled with the von Mises distribution. A subset of 17 patients (22.1%) were identified as prompt correctors based on Markov-dependence probability of a dosing failure followed by a dosing success of unity. The prompt corrector sub-group had a better overall digital medicine ingestion parameter profile compared to those who were not prompt correctors. Our results demonstrate the potential utility of a Bayesian Hybrid Markov-von Mises model for characterizing digital medicine ingestion patterns in patients with SMI.
Highlights
Lack of adherence to medication is an important factor that contributes to increased healthcare utilization[1,2]: Among patients with serious mental illness (SMI)—which includes schizophrenia, bipolar disorder, and major depression—this is of particular concern, with some reports estimating rates of nonadherence as high as 60%
Description of the digital medicine system The digital medicine system leveraged in this work is composed of a wearable sensor, a mobile application, and an ingestible sensor embedded in an active pharmaceutical, which has been developed to capture medication ingestions in patients with SMI14 (Fig. 1)
Digital medicine systems are providing near real-time access to objective medication ingestion information,[14] enabling physicians and care teams to make more informed treatment decisions: Is medication being taken as prescribed? If there is a lack of clinical improvement, is medication adherence a factor? Objective digital medicine ingestion data will reduce the reliance on patient and caregiver reports that care teams consider when addressing such treatment-related issues
Summary
Lack of adherence to medication is an important factor that contributes to increased healthcare utilization[1,2]: Among patients with serious mental illness (SMI)—which includes schizophrenia, bipolar disorder, and major depression—this is of particular concern, with some reports estimating rates of nonadherence as high as 60%.1,3 Within the SMI population, effective pharmacotherapy is critical for managing the risk of serious potential adverse events such as relapse of psychosis, recurrence of symptoms, poor social functioning, hospitalizations, and suicide attempts.[4,5]. Lack of adherence to medication is an important factor that contributes to increased healthcare utilization[1,2]: Among patients with serious mental illness (SMI)—which includes schizophrenia, bipolar disorder, and major depression—this is of particular concern, with some reports estimating rates of nonadherence as high as 60%.1,3. Within the SMI population, effective pharmacotherapy is critical for managing the risk of serious potential adverse events such as relapse of psychosis, recurrence of symptoms, poor social functioning, hospitalizations, and suicide attempts.[4,5]. J. et al Effectiveness of digital medicines to improve clinical outcomes in patients with uncontrolled hypertension and type 2 diabetes: prospective, openlabel, cluster-randomized pilot clinical trial. Bayesian population modeling of drug dosing adherence.
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