Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.

BACKGROUND: This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose. METHODS: In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST). RESULTS: The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low. CONCLUSIONS: Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

Treatments for Acute Nonarteritic Central Retinal Artery Occlusion: Findings From a Cochrane Systematic Review.

Many interventions for nonarteritic central retinal artery occlusion (CRAO) are associated with serious complications and little effect on visual outcomes. We report on the findings of a Cochrane systematic review that searched seven databases for peer-reviewed articles reporting on treatments for acute nonarteritic CRAO. We assessed six randomized controlled trials, including interventions such as tissue plasminogen activator (t-PA), isovolumic hemodilution, eyeball massage, intraocular pressure reduction, anticoagulation, vasodilation, oxygen inhalation, laser embolysis, transcorneal electrical stimulation, thrombolysis, pentoxifylline, and enhanced external counterpulsation. However, none of the randomized controlled trials demonstrated significant improvement in visual acuity at 1 month compared to observation, and some patients treated with t-PA experienced serious adverse effects including intracranial hemorrhage. Proposed interventions for acute nonarteritic CRAO may not be better than observation, but the evidence is uncertain. Larger, well-designed studies are necessary to determine the most effective management option for acute nonarteritic CRAO. [Ophthalmic Surg Lasers Imaging Retina 2023;54:650-653.].

Behavioral Health Decision Support Systems and User Interface Design in the Emergency Department.

OBJECTIVE: The objective of this qualitative study is to gauge physician sentiment about an emergency department (ED) clinical decision support (CDS) system implemented in multiple adult EDs within a university hospital system. This CDS system focuses on predicting patients' likelihood of ED recidivism and/or adverse opioid-related events. METHODS: The study was conducted among adult emergency physicians working in three EDs of a single academic health system in Rhode Island. Qualitative, semistructured interviews were conducted with ED physicians. Interviews assessed physicians' prior experience with predictive analytics, thoughts on the alert's placement, design, and content, the alert's overall impact, and potential areas for improvement. Responses were aggregated and common themes identified. RESULTS: Twenty-three interviews were conducted (11 preimplementation and 12 postimplementation). Themes were identified regarding each physician familiarity with predictive analytics, alert rollout, alert appearance and content, and on alert sentiments. Most physicians viewed these alerts as a neutral or positive EHR addition, with responses ranging from neutral to positive. The alert placement was noted to be largely intuitive and nonintrusive. The design of the alert was generally viewed positively. The alert's content was believed to be accurate, although the decision to respond to the alert's call-to-action was physician dependent. Those who tended to ignore the alert did so for a few reasons, including already knowing the information the alert contains, the alert offering information that is not relevant to this particular patient, and the alert not containing enough information to be useful. CONCLUSION: Ultimately, this alert appears to have a marginally positive effect on ED physician workflow. At its most beneficial, the alert reminded physicians to deeply consider the care provided to high-risk populations and to potentially adjust their care and referrals. At its least beneficial, the alert did not affect physician decision-making but was not intrusive to the point of negatively impacting workflow.

Cross-cultural Comparison of an American and a Taiwanese Medical School with Longstanding Institutional Ties.

Cross-cultural medical education has been suggested to train students to care for diverse patient populations and reform medical education systems. In this article, the authors conduct a cross-cultural comparison between two medical schools with a long-standing relationship - the Warren Alpert Medical School of Brown University in the United States and the School of Medicine of National Cheng Kung University in Taiwan - focusing on history, admissions, and curriculum.

Theoretical Frameworks in Medical Education: Using a Systematic Review of Ophthalmology Education Research to Create a Theory of Change Model.

Background: Theoretical frameworks provide a lens to examine questions and interpret results; however, they are underutilized in medical education. Objective: To systematically evaluate the use of theoretical frameworks in ophthalmic medical education and present a theory of change model to guide educational initiatives. Methods: Six electronic databases were searched for peer-reviewed, English-language studies published between 2016 and 2021 on ophthalmic educational initiatives employing a theoretical framework. Quality of studies was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach; risk of bias was evaluated using the Medical Education Research Study Quality Instrument (MERSQI) and the Accreditation Council for Graduate Medical Education (ACGME) guidelines for evaluation of assessment methods. Abstracted components of the included studies were used to develop a theory of change model. Results: The literature search yielded 1661 studies: 666 were duplicates, 834 studies were excluded after abstract review, and 132 after full-text review; 29 studies (19.2%) employing a theoretical framework were included. The theories used most frequently were the Dreyfus model of skill acquisition and Messick's contemporary validity framework. GRADE ratings were predominantly "low," the average MERSQI score was 10.04, and the ACGME recommendation for all assessment development studies was the lowest recommendation. The theory of change model outlined how educators can select, apply, and evaluate theory-based interventions. Conclusions: Few ophthalmic medical education studies employed a theoretical framework; their overall rigor was low as assessed by GRADE, MERSQI, and ACGME guidelines. A theory of change model can guide integration of theoretical frameworks into educational initiatives.

A Graphical Toolkit for Longitudinal Dataset Maintenance and Predictive Model Training in Health Care.

BACKGROUND: Predictive analytic models, including machine learning (ML) models, are increasingly integrated into electronic health record (EHR)-based decision support tools for clinicians. These models have the potential to improve care, but are challenging to internally validate, implement, and maintain over the long term. Principles of ML operations (MLOps) may inform development of infrastructure to support the entire ML lifecycle, from feature selection to long-term model deployment and retraining. OBJECTIVES: This study aimed to present the conceptual prototypes for a novel predictive model management system and to evaluate the acceptability of the system among three groups of end users. METHODS: Based on principles of user-centered software design, human-computer interaction, and ethical design, we created graphical prototypes of a web-based MLOps interface to support the construction, deployment, and maintenance of models using EHR data. To assess the acceptability of the interface, we conducted semistructured user interviews with three groups of users (health informaticians, clinical and data stakeholders, chief information officers) and evaluated preliminary usability using the System Usability Scale (SUS). We subsequently revised prototypes based on user input and developed user case studies. RESULTS: Our prototypes include design frameworks for feature selection, model training, deployment, long-term maintenance, visualization over time, and cross-functional collaboration. Users were able to complete 71% of prompted tasks without assistance. The average SUS score of the initial prototype was 75.8 out of 100, translating to a percentile range of 70 to 79, a letter grade of B, and an adjective rating of "good." We reviewed persona-based case studies that illustrate functionalities of this novel prototype. CONCLUSION: The initial graphical prototypes of this MLOps system are preliminarily usable and demonstrate an unmet need within the clinical informatics landscape.

Statin Use Is Associated with Decreased Risk of Invasive Mechanical Ventilation in COVID-19 Patients: A Preliminary Study.

COVID-19 disproportionately affects patients with medical comorbidities such as cardiovascular disease (CVD). Patients with CVD are widely prescribed 3-hydroxy-3-methyl-glutayl-CoA (HMG-CoA) reductase inhibitors (statins), a class of lipid-lowering medications known for their pleiotropic anti-inflammatory and immunomodulatory effects. However, the relationship between statin use and COVID-19 outcomes is not fully understood. In this preliminary study, we explored the association between statin use and severe COVID-19 outcomes in hospitalized patients, including intensive care unit (ICU) admission, the need for invasive mechanical ventilation (IMV), and in-hospital death. We performed a retrospective cohort study of 249 patients hospitalized with COVID-19 from 3 March 2020 to 10 April 2020 in Rhode Island, USA. Patient demographics, past medical history, current medications, and hospital course were recorded and analyzed. A multivariate logistic regression analysis was conducted to examine associations. After adjusting for age, sex, race, cardiovascular disease, chronic pulmonary disease, diabetes, and obesity, statin use was significantly associated with decreased risk for IMV (adjusted Odds Ratio (aOR) = 0.45, 95% Confidence Interval (CI): 0.20-0.99). Our results support the continued use of statins among COVID-19 patients and could have implications for future prospective studies on the management of COVID-19.

Circuits that encode and guide alcohol-associated preference.

A powerful feature of adaptive memory is its inherent flexibility. Alcohol and other addictive substances can remold neural circuits important for memory to reduce this flexibility. However, the mechanism through which pertinent circuits are selected and shaped remains unclear. We show that circuits required for alcohol-associated preference shift from population level dopaminergic activation to select dopamine neurons that predict behavioral choice in Drosophila melanogaster. During memory expression, subsets of dopamine neurons directly and indirectly modulate the activity of interconnected glutamatergic and cholinergic mushroom body output neurons (MBON). Transsynaptic tracing of neurons important for memory expression revealed a convergent center of memory consolidation within the mushroom body (MB) implicated in arousal, and a structure outside the MB implicated in integration of naïve and learned responses. These findings provide a circuit framework through which dopamine neuronal activation shifts from reward delivery to cue onset, and provide insight into the maladaptive nature of memory.

Automated real-time quantification of group locomotor activity in Drosophila melanogaster.

Recent advances in neurogenetics have highlighted Drosophila melanogaster as an exciting model to study neural circuit dynamics and complex behavior. Automated tracking methods have facilitated the study of complex behaviors via high throughput behavioral screening. Here we describe a newly developed low-cost assay capable of real-time monitoring and quantifying Drosophila group activity. This platform offers reliable real-time quantification with open source software and a user-friendly interface for data acquisition and analysis. We demonstrate the utility of this platform by characterizing ethanol-induced locomotor activity in a dose-dependent manner as well as the effects of thermo and optogenetic manipulation of ellipsoid body neurons important for ethanol-induced locomotor activity. As expected, low doses of ethanol induced an initial startle and slow ramping of group activity, whereas high doses of ethanol induced sustained group activity followed by sedation. Advanced offline processing revealed discrete behavioral features characteristic of intoxication. Thermogenetic inactivation of ellipsoid body ring neurons reduced group activity whereas optogenetic activation increased activity. Together, these data establish the fly Group Activity Monitor (flyGrAM) platform as a robust means of obtaining an online read out of group activity in response to manipulations to the environment or neural activity, with an opportunity for more advanced post-processing offline.