Purpose of ReviewTo describe models of integrated and co-located care for opioid use disorder (OUD), hepatitis C (HCV), and HIV.Recent FindingsThe design and scale-up of multidisciplinary care models that engage, retain, and treat individuals with HIV, HCV, and OUD are critical to preventing continued spread of HIV and HCV. We identified 17 models within primary care (N = 3), HIV specialty care (N = 5), opioid treatment programs (N = 6), transitional clinics (N = 2), and community-based harm reduction programs (N = 1), as well as two emerging models.SummaryKey components of such models are the provision of (1) medication-assisted treatment for OUD, (2) HIV and HCV treatment, (3) HIV pre-exposure prophylaxis, and (4) behavioral health services. Research is needed to understand differences in effectiveness between co-located and fully integrated care, combat the deleterious racial and ethnic legacies of the “War on Drugs,” and inform the delivery of psychiatric care. Increased access to harm reduction services is crucial.Electronic supplementary materialThe online version of this article (10.1007/s11904-018-0396-x) contains supplementary material, which is available to authorized users.

Background-The opioid crisis requires rapid scale-up of evidence-based interventions to treat opioid use disorder (OUD), of which pharmacologic therapies with methadone, buprenorphine or long-acting naltrexone are most effective. With recently-developed formulations, there are unprecedented treatment options. Even when pharmacologic treatment is accessible, however, uptake remains low, suggesting individual-level barriers. Decision aids are an evidence-based strategy that may overcome these barriers. This study aims to inform such a tool by describing and rank-ordering patients' considerations when deciding whether to start medication and, if starting, choosing a medication. Methods-Adults with OUD (N=81) attending an addiction treatment center or syringe exchange program completed focus groups using nominal group technique, a consensus method that generates and ranks response. The qualitative component generates a broad array of responses, followed by rank-ordering to prioritize responses. Responses to questions about starting any medications and the pros and cons of five specific medications were ranked and coded. Results-The decision to initiate pharmacologic therapy and choose among medications was influenced by six key attributes in decreasing priority: (1) benefits, (2) side effects of treatment, (3) medication delivery strategies, (4) convenience, (5) how expectations for treatment are met, and (6) how medication (especially methadone) can represents trading one addiction for another. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Despite recognition that Acute Kidney Injury (AKI) leads to substantial increases in morbidity, mortality, and length of stay, accurate prognostication of these clinical events remains difficult. It remains unclear which approaches to variable selection and model building are most robust. We used data from a randomized trial of AKI alerting to develop time-updated prognostic models using stepwise regression compared to more advanced variable selection techniques. We randomly split data into training and validation cohorts. Outcomes of interest were death within 7 days, dialysis within 7 days, and length of stay. Data elements eligible for model-building included lab values, medications and dosages, procedures, and demographics. We assessed model discrimination using the area under the receiver operator characteristic curve and r-squared values. 2241 individuals were available for analysis. Both modeling techniques created viable models with very good discrimination ability, with AUCs exceeding 0.85 for dialysis and 0.8 for death prediction. Model performance was similar across model building strategies, though the strategy employing more advanced variable selection was more parsimonious. Very good to excellent prediction of outcome events is feasible in patients with AKI. More advanced techniques may lead to more parsimonious models, which may facilitate adoption in other settings.

BackgroundClinical decision support systems, including electronic alerts, ideally provide immediate and relevant patient-specific information to improve clinical decision-making. Despite the growing capabilities of such alerts in conjunction with an expanding electronic medical record, there is a paucity of information regarding their perceived usefulness. We surveyed healthcare providers' opinions concerning the practicality and efficacy of a specific text-based automated electronic alert for acute kidney injury (AKI) in a single hospital during a randomized trial of AKI alerts.MethodsProviders who had received at least one electronic AKI alert in the previous 6 months, as part of a separate randomized controlled trial (clinicaltrials.gov #01862419), were asked to complete a survey concerning their opinions about this specific AKI alert system. Individual approval of the alert system was defined by a provider's desire to continue receiving the alert after termination of the trial.ResultsA total of 98 individuals completed the survey, including 62 physicians, 27 pharmacists and 7 non-physician providers. Sixty-nine percent of responders approved the alert, with no significant difference among the various professions (P = 0.28). Alert approval was strongly correlated with the belief that the alerts improved patient care (P < 0.0001), and negatively correlated with the belief that alerts did not provide novel information (P = 0.0001). With each additional 30 days of trial duration, odds of approval decreased by 20% (3–35%) (P = 0.02).ConclusionsThe alert system was generally well received, although approval waned with time. Approval was correlated with the belief that this type of alert improved patient care. These findings suggest that perceived efficacy is critical to the success of future alert trials.