Identification of cognitively impaired patients at risk for development of Alzheimer's disease dementia: an analysis of US Medicare claims data.

BACKGROUND: Identifying factors associated with transitioning from mild cognitive impairment (MCI) to dementia due to Alzheimer's disease (AD dementia) or dementia due to any cause (all-cause dementia) may inform economic assessments of disease and early care planning. RESEARCH DESIGN AND METHODS: A multivariate logistic regression approach identified potential predictors of progression to AD dementia or all-cause dementia in individuals with MCI or cognitive impairment (CI). Eligible patients and variables of interest were identified using claims data from the Medicare Advantage Patient Database, by Optum. RESULTS: Predictors of an AD dementia diagnosis included age (odds ratio [OR], 1.71) and use of antipsychotics (OR, 2.50) and hypertension medication (OR, 1.25). Medication use for comorbid conditions was a better indicator of risk than comorbidity coding. Diagnosis of CI by a neurologist increased the odds of an AD dementia diagnosis. Possible protective factors for progression included the use of anxiolytics (OR, 0.76), inpatient status at time of diagnosis (OR, 0.49), and a history of stroke (OR, 0.87). None of these factors differentiated AD dementia from all-cause dementia. CONCLUSIONS: Identifying patients at risk for AD dementia allows for improved system-level planning to guide policy and optimize economic and clinical outcomes for patients, caregivers, and society.

Decision makers need an approach to determine digital therapeutic product quality, access, and appropriate use.

DISCLOSURES: No funding was received for the writing of this article. Parcher is employed by Xcenda and has nothing to disclose. Coder is employed by Digital Therapeutics Alliance, which is a member of the USP Convention. Coder reports fees from PsychU/Otsuka.

Evaluation of the cost-utility of a prescription digital therapeutic for the treatment of opioid use disorder.

Background: The opioid epidemic continues to generate a significant mental and physical health burden on patients, and claims the life of almost 150 Americans daily. Making matters worse, an increase in relapses and/or opioid-related deaths has been reported in more than 40 U.S. states since the start of the COVID-19 pandemic. Opioid use disorder (OUD) is one of the single most expensive disorders in the United States, generating average medical costs of $60B from just 2 million Americans diagnosed with the disorder. In commercial use since 2019, reSET-O is a non-drug, prescription digital therapeutic (PDT) that delivers evidence-based neurobehavioral treatment for OUD and helps overcome the barriers associated with access to care, stigma, and social distancing. Although shown to be cost effective and efficacious in clinical trials and real-world evidence studies, respectively, information on its value for money from a health utilities and cost per quality-adjusted life-year is needed to inform policy discussions.Objectives: To evaluate the impact of reSET-O on health utilities and assess its overall cost per quality-adjusted life year (QALY) gained vs. treatment-as-usual (TAU).Methods: Decision analytic model comparing reSET-O plus TAU to TAU alone (i.e. buprenorphine, face-to-face counseling, and contingency management) over 12 weeks. Clinical effectiveness data (abstinence and health utility) were obtained from a clinical trial, and resource utilization and cost data were adapted from a recent claims data analysis to reflect less frequent face-to-face counseling with the therapeutic.Results: The addition of reSET-O to TAU decreases total health care costs by -$131 and resulted in post-treatment utility values within population norms, with a corresponding gain of 0.003 QALYs. reSET-O when used adjunctively to TAU was economically dominant (less costly, more effective) vs. TAU alone.Conclusion: reSET-O is an economically-dominant adjunctive treatment for OUD and is associated with an overall reduction in total incremental cost vs TAU.

Single-injection HPLC method for rapid analysis of a combination drug delivery system.

Developing combination drug delivery systems (CDDS) is a challenging but necessary task to meet the needs of complex therapy regimes for patients. As the number of multi-drug regimens being administered increases, so does the difficulty of characterizing the CDDS as a whole. We present a single-step method for quantifying three model therapeutics released from a model hydrogel scaffold using high-performance liquid chromatography (HPLC). Poly(ethylene glycol) dimethacrylate (PEGDMA) hydrogel tablets were fabricated via photoinitiated crosslinking and subsequently loaded with model active pharmaceutical ingredients (APIs), namely, porcine insulin (PI), fluorescein isothiocyanate-labeled bovine serum albumin (FBSA), prednisone (PSE), or a combination of all three. The hydrogel tablets were placed into release chambers and sampled over 21 days, and APIs were quantified using the method described herein. Six compounds were isolated and quantified in total. Release kinetics based on chemical properties of the APIs did not give systematic relationships; however, PSE was found to have improved device loading versus PI and FBSA. Rapid analysis of three model APIs released from a PEGDMA CDDS was achieved with a direct, single-injection HPLC method. Development of CDDS platforms is posited to benefit from such analytical approaches, potentially affording innovative solutions to complex disease states.