Public Health Interventions and Overdose-Related Outcomes Among Persons With Opioid Use Disorder.

Importance: Given the high number of opioid overdose deaths in the US and the complex epidemiology of opioid use disorder (OUD), systems models can serve as a tool to identify opportunities for public health interventions. Objective: To estimate the projected 3-year association between public health interventions and opioid overdose-related outcomes among persons with OUD. Design, Setting, and Participants: This decision analytical model used a simulation model of the estimated US population aged 12 years and older with OUD that was developed and analyzed between January 2019 and December 2023. The model was parameterized and calibrated using 2019 to 2020 data and used to estimate the relative change in outcomes associated with simulated public health interventions implemented between 2021 and 2023. Main Outcomes and Measures: Projected OUD and medications for OUD (MOUD) prevalence in 2023 and number of nonfatal and fatal opioid-involved overdoses among persons with OUD between 2021 and 2023. Results: In a baseline scenario assuming parameters calibrated using 2019 to 2020 data remained constant, the model projected more than 16 million persons with OUD not receiving MOUD treatment and nearly 1.7 million persons receiving MOUD treatment in 2023. Additionally, the model projected over 5 million nonfatal and over 145 000 fatal opioid-involved overdoses among persons with OUD between 2021 and 2023. When simulating combinations of interventions that involved reducing overdose rates by 50%, the model projected decreases of up to 35.2% in nonfatal and 36.6% in fatal opioid-involved overdoses among persons with OUD. Interventions specific to persons with OUD not currently receiving MOUD treatment demonstrated the greatest reduction in numbers of nonfatal and fatal overdoses. Combinations of interventions that increased MOUD initiation and decreased OUD recurrence were projected to reduce OUD prevalence by up to 23.4%, increase MOUD prevalence by up to 137.1%, and reduce nonfatal and fatal opioid-involved overdoses among persons with OUD by 6.7% and 3.5%, respectively. Conclusions and Relevance: In this decision analytical model study of persons with OUD, findings suggested that expansion of evidence-based interventions that directly reduce the risk of overdose fatality among persons with OUD, such as through harm reduction efforts, could engender the highest reductions in fatal overdoses in the short-term. Interventions aimed at increasing MOUD initiation and retention of persons in treatment projected considerable improvement in MOUD and OUD prevalence but could require a longer time horizon for substantial reductions in opioid-involved overdoses.

Longitudinal dose patterns among patients newly initiated on long-term opioid therapy in the United States, 2018 to 2019: an observational cohort study and time-series cluster analysis.

ABSTRACT: Opioid prescribing varies widely, and prescribed opioid dosages for an individual can fluctuate over time. Patterns in daily opioid dosage among patients prescribed long-term opioid therapy have not been previously examined. This study uses a novel application of time-series cluster analysis to characterize and visualize daily opioid dosage trajectories and associated demographic characteristics of patients newly initiated on long-term opioid therapy. We used 2018 to 2019 data from the IQVIA Longitudinal Prescription (LRx) all-payer pharmacy database, which covers 92% of retail pharmacy prescriptions dispensed in the United States. We identified a cohort of 277,967 patients newly initiated on long-term opioid therapy during 2018. Patients were stratified into 4 categories based on their mean daily dosage during a 90-day baseline period (<50, 50-89, 90-149, and ≥150 morphine milligram equivalent [MME]) and followed for a 270-day follow-up period. Time-series cluster analysis identified 2 clusters for each of the 3 baseline dosage categories <150 MME and 3 clusters for the baseline dosage category ≥150 MME. One cluster in each baseline dosage category comprised opioid dosage trajectories with decreases in dosage at the end of the follow-up period (80.7%, 98.7%, 98.7%, and 99.0%, respectively), discontinuation (58.5%, 80.0%, 79.3%, and 81.7%, respectively), and rapid tapering (50.8%, 85.8%, 87.5%, and 92.9%, respectively). These findings indicate multiple clusters of patients newly initiated on long-term opioid therapy who experience discontinuation and rapid tapering and highlight potential areas for clinician training to advance evidence-based guideline-concordant opioid prescribing, including strategies to minimize sudden dosage changes, discontinuation, or rapid tapering, and the importance of shared decision-making.

Chronic Pain Among Adults - United States, 2019-2021.

Chronic pain (i.e., pain lasting ≥3 months) is a debilitating condition that affects daily work and life activities for many adults in the United States and has been linked with depression (1), Alzheimer disease and related dementias (2), higher suicide risk (3), and substance use and misuse (4). During 2016, an estimated 50 million adults in the United States experienced chronic pain, resulting in substantial health care costs and lost productivity (5,6). Addressing chronic pain and improving the lives of persons living with pain is a public health imperative. Population research objectives in the National Pain Strategy, which was released in 2016 by the Interagency Pain Research Coordinating Committee, call for more precise estimates of the prevalence of chronic pain and high-impact chronic pain (i.e., chronic pain that results in substantial restriction to daily activities) in the general population and within various population groups to guide efforts to reduce the impact of chronic pain (3). Further, a 2022 review of U.S. chronic pain surveillance systems identified the National Health Interview Survey (NHIS) as the best source for pain surveillance data (7). CDC analyzed data from the 2019-2021 NHIS to provide updated estimates of the prevalence of chronic pain and high-impact chronic pain among adults in the United States and within population groups defined by demographic, geographic, socioeconomic, and health status characteristics. During 2021, an estimated 20.9% of U.S. adults (51.6 million persons) experienced chronic pain, and 6.9% (17.1 million persons) experienced high-impact chronic pain. New findings from this analysis include that non-Hispanic American Indian or Alaska Native (AI/AN) adults, adults identifying as bisexual, and adults who are divorced or separated are among the populations experiencing a higher prevalence of chronic pain and high-impact chronic pain. Clinicians, practices, health systems, and payers should vigilantly attend to health inequities and ensure access to appropriate, affordable, diversified, coordinated, and effective pain management care for all persons (8).

Identifying individual social risk factors using unstructured data in electronic health records and their relationship with adverse clinical outcomes.

Objective: To determine the prevalence of individual-level social risk factors documented in unstructured data from electronic health records (EHRs) and the relationship between social risk factors and adverse clinical outcomes. Study setting: Inpatient encounters for adults (≥18 years) at the University of Virginia Medical Center during a 12-month study period between July 2018 and June 2019. Inpatient encounters for labor and delivery patients were excluded, as well as encounters where the patient was discharged to hospice, left against medical advice, or expired in the hospital. The study population included 21,402 inpatient admissions, representing 15,116 unique patients who had at least one inpatient admission during the study period. Study design: We identified measures related to individual social risk factors in EHRs through existing workflows, flowsheets, and clinical notes. Multivariate binomial logistic regression was performed to determine the association of individual social risk factors with unplanned inpatient readmissions, post-discharge emergency department (ED) visits, and extended length of stay (LOS). Other predictors included were age, sex, severity of illness, location of residence, and discharge destination. Results: Predictors of 30-day unplanned readmissions included severity of illness (OR = 3.96), location of residence (OR = 1.31), social and community context (OR = 1.26), and economic stability (OR = 1.37). For 30-day post-discharge ED visits, significant predictors included location of residence (OR = 2.56), age (OR = 0.60), economic stability (OR = 1.39), education (OR = 1.38), social and community context (OR = 1.39), and neighborhood and built environment (OR = 1.61). For extended LOS, significant predictors were age (OR = 0.51), sex (OR = 1.18), severity of illness (OR = 2.14), discharge destination (OR = 2.42), location of residence (OR = 0.82), economic stability (OR = 1.14), neighborhood and built environment (OR = 1.31), and education (OR = 0.79). Conclusions: Individual-level social risk factors are associated with increased risk for unplanned hospital readmissions, post-discharge ED visits, and extended LOS. While individual-level social risk factors are currently documented on an ad-hoc basis in EHRs, standardized SDoH screening tools using validated metrics could help eliminate bias in the collection of SDoH data and facilitate social risk screening.

Mathematical Model Predicts that Acceleration of Diabetic Wound Healing is Dependent on Spatial Distribution of VEGF-A mRNA (AZD8601).

INTRODUCTION: Pharmacologic approaches for promoting angiogenesis have been utilized to accelerate healing of chronic wounds in diabetic patients with varying degrees of success. We hypothesize that the distribution of proangiogenic drugs in the wound area critically impacts the rate of closure of diabetic wounds. To evaluate this hypothesis, we developed a mathematical model that predicts how spatial distribution of VEGF-A produced by delivery of a modified mRNA (AZD8601) accelerates diabetic wound healing. METHODS: We modified a previously published model of cutaneous wound healing based on coupled partial differential equations that describe the density of sprouting capillary tips, chemoattractant concentration, and density of blood vessels in a circular wound. Key model parameters identified by a sensitivity analysis were fit to data obtained from an in vivo wound healing study performed in the dorsum of diabetic mice, and a pharmacokinetic model was used to simulate mRNA and VEGF-A distribution following injections with AZD8601. Due to the limited availability of data regarding the spatial distribution of AZD8601 in the wound bed, we performed simulations with perturbations to the location of injections and diffusion coefficient of mRNA to understand the impact of these spatial parameters on wound healing. RESULTS: When simulating injections delivered at the wound border, the model predicted that injections delivered on day 0 were more effective in accelerating wound healing than injections delivered at later time points. When the location of the injection was varied throughout the wound space, the model predicted that healing could be accelerated by delivering injections a distance of 1-2 mm inside the wound bed when compared to injections delivered on the same day at the wound border. Perturbations to the diffusivity of mRNA predicted that restricting diffusion of mRNA delayed wound healing by creating an accumulation of VEGF-A at the wound border. Alternatively, a high mRNA diffusivity had no effect on wound healing compared to a simulation with vehicle injection due to the rapid loss of mRNA at the wound border to surrounding tissue. CONCLUSIONS: These findings highlight the critical need to consider the location of drug delivery and diffusivity of the drug, parameters not typically explored in pre-clinical experiments, when designing and testing drugs for treating diabetic wounds. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12195-021-00678-9.

Model-Based Analysis Reveals a Sustained and Dose-Dependent Acceleration of Wound Healing by VEGF-A mRNA (AZD8601).

Intradermal delivery of AZD8601, an mRNA designed to produce vascular endothelial growth factor A (VEGF-A), has previously been shown to accelerate cutaneous wound healing in a murine diabetic model. Here, we develop population pharmacokinetic and pharmacodynamic models aiming to quantify the effect of AZD8601 injections on the dynamics of wound healing. A dataset of 584 open wound area measurements from 131 mice was integrated from 3 independent studies encompassing different doses, dosing timepoints, and number of doses. Evaluation of several candidate models showed that wound healing acceleration is not likely driven directly by time-dependent VEGF-A concentration. Instead, we found that administration of AZD8601 induced a sustained acceleration of wound healing depending on the accumulated dose, with a dose producing 50% of the maximal effect of 92 µg. Simulations with this model showed that a single dose of 200 µg AZD8601 can reduce the time to reach 50% wound healing by up to 5 days.

Multiscale Coupling of an Agent-Based Model of Tissue Fibrosis and a Logic-Based Model of Intracellular Signaling.

Wound healing and fibrosis following myocardial infarction (MI) is a dynamic process involving many cell types, extracellular matrix (ECM), and inflammatory cues. As both incidence and survival rates for MI increase, management of post-MI recovery and associated complications are an increasingly important focus. Complexity of the wound healing process and the need for improved therapeutics necessitate a better understanding of the biochemical cues that drive fibrosis. To study the progression of cardiac fibrosis across spatial and temporal scales, we developed a novel hybrid multiscale model that couples a logic-based differential equation (LDE) model of the fibroblast intracellular signaling network with an agent-based model (ABM) of multi-cellular tissue remodeling. The ABM computes information about cytokine and growth factor levels in the environment including TGFß, TNFα, IL-1ß, and IL-6, which are passed as inputs to the LDE model. The LDE model then computes the network signaling state of individual cardiac fibroblasts within the ABM. Based on the current network state, fibroblasts make decisions regarding cytokine secretion and deposition and degradation of collagen. Simulated fibroblasts respond dynamically to rapidly changing extracellular environments and contribute to spatial heterogeneity in model predicted fibrosis, which is governed by many parameters including cell density, cell migration speeds, and cytokine levels. Verification tests confirmed that predictions of the coupled model and network model alone were consistent in response to constant cytokine inputs and furthermore, a subset of coupled model predictions were validated with in vitro experiments with human cardiac fibroblasts. This multiscale framework for cardiac fibrosis will allow for systematic screening of the effects of molecular perturbations in fibroblast signaling on tissue-scale extracellular matrix composition and organization.

Safety and Efficacy of Allogeneic Lung Spheroid Cells in a Mismatched Rat Model of Pulmonary Fibrosis.

Idiopathic pulmonary fibrosis is a devastating interstitial lung disease characterized by the relentless deposition of extracellular matrix causing lung distortions and dysfunctions. The prognosis after detection is merely 3-5 years and the only two Food and Drug Administration-approved drugs treat the symptoms, not the disease, and have numerous side effects. Stem cell therapy is a promising treatment strategy for pulmonary fibrosis. Current animal and clinical studies focus on the use of adipose or bone marrow-derived mesenchymal stem cells. We, instead, have established adult lung spheroid cells (LSCs) as an intrinsic source of therapeutic lung stem cells. In the present study, we compared the efficacy and safety of syngeneic and allogeneic LSCs in immuno-competent rats with bleomycin-induced pulmonary inflammation in an effort to mitigate fibrosis development. We found that infusion of allogeneic LSCs reduces the progression of inflammation and fibrotic manifestation and preserves epithelial and endothelial health without eliciting significant immune rejection. Our study sheds light on potential future developments of LSCs as an allogeneic cell therapy for humans with pulmonary fibrosis. Stem Cells Translational Medicine 2017;9:1905-1916.

A dual wavelength-activatable gold nanorod complex for synergistic cancer treatment.

A multifunctional gold nanorod (AuNR) complex is described with potential utility for theranostic anticancer treatment. The AuNR was functionalized with cyclodextrin for encapsulation of doxorubicin, with folic acid for targeting, and with a photo-responsive dextran-azo compound for intracellular controlled drug release. The interaction of a AuNR complex with HeLa cells was facilitated via a folic acid targeting ligand as displayed in the dark-field images of cells. Enhanced anticancer efficacy was demonstrated through the synergistic combination of promoted drug release upon ultraviolet (UV) light irradiation and photothermal therapy upon infrared (IR) irradiation. This multifunctional AuNR-based system represents a novel theranostic strategy for spatiotemporal delivery of anticancer therapeutics.