Health System Research Articles

Abstract 4135352: Neighborhood-level Determinants and Disparities in Incident Type 2 Diabetes in a Population-Based Cohort, 2015–2020

Introduction: Living in an area with high Area Deprivation Index (ADI) is linked to worse cardiovascular disease (CVD) outcomes. However, the association of ADI with type 2 diabetes (T2D), a major CVD risk factor, is incompletely described. This knowledge may guide primary CVD prevention. Method: Rochester Epidemiology Project is a medical record linkage system of persons in southeast Minnesota and southwest Wisconsin. We identified 253,890 persons (aged 4–98 yrs on July 1, 2015) in 9 counties with >90% population coverage. ADI scores, from 2015 home addresses, were grouped into quartiles of low (Q1) to high (Q4) deprivation. We excluded persons with type 1 diabetes (T1D) or history of T2D and identified new T2D cases (2015-2020) using ICD codes, HbA1c and medications. We examined the association between ADI quartile and risk of incident T2D using Cox regression with persons right censored at death, T2D, T1D or last visit. Models were adjusted for age and gender, and reported adjusted hazard ratios (aHR [95% confidence interval]). Results: Age, gender, and race statistically differed across ADI quartiles (Table). Age × ADI interaction was significant ( P <.001). T2D incidence ranged from 6.3 (Q1) to 9.0 (Q4) per 1000 person-years ( P <.001). Women vs. men/non-binary persons (combined due to limited non-binary persons) had 30% lower risk of incident T2D (aHR 0.70 [0.67–0.72]). Risk of incident T2D was higher in persons from higher ADI areas. At median age of 48.6 yrs, the risk was 30% higher in Q2 (aHR 1.3 [1.22–1.38]), 38% higher in Q3 (aHR 1.38 [1.30–1.47]) and 53% higher in Q4 (aHR 1.53 [1.43–1.62]) vs. Q1. Higher ADI was associated with higher incident T2D in younger persons, with risk attenuation with increasing age (Figure). Conclusion: In this population cohort, persons, particularly of younger age, living in more deprived areas had a higher risk of incident T2D. ADI may help identify areas for targeted T2D screening, with implications for CVD screening and prevention.

Abstract 4136550: In-hospital cardiac arrest can be detected by Innovative smart healthcare syste

Background: Predicting in-hospital cardiac arrest (IHCA) is crucial for preventing patient fatalities. However, it remains challenging due to the irregularities in time series data across various features. Previous studies relied on traditional medical scoring systems, but their accuracy was limited as the models were overly simplistic. Recent research using machine learning methods has shown improved results. In our work, we employ deep learning and time series approaches, which are considered the most powerful models for addressing complex problems in recent years. Method/Research Design: The cohort study took place in a hospital in Taiwan from January 2016 to August 2022, involving 252,000 patients in general wards. Input data encompassed basic information, vital signs, Glasgow Coma Scale scores, medication, etc., to predict IHCA occurrences within 24 hours. Given the immediacy of IHCA events, we utilized left alignment to handle time series. Evaluation metrics included the area under the receiver operating characteristic curve (AUROC). Results: We compared three types of settings. The first was based on scoring systems, where the national early warning system (NEWS) achieved an AUROC of 0.756, and the modified early warning score (MEWS) reached 0.653. In the next setting, based on the data summary within a given time interval, the machine learning models XGBoost and deep learning model TabNet achieved AUROCs of 0.863 and 0.839, respectively. This indicates that machine learning outperforms traditional medical methods. The final setting involved time series analysis, where XGBoost and the Gate Recurrent Unit (GRU) network achieved 0.848 and 0.904, respectively, surpassing all previous settings. Conclusion: Our study demonstrates that learning-based methods outperform rule-based methods in predicting IHCA events. Machine learning algorithms offer more possibilities and have the potential to address complex problems effectively. Moreover, considering time series data rather than summarizing within fixed intervals preserves information and leads to better scores. Leveraging the time series deep learning method, GRU shows promising potential in significantly reducing the risk of IHCA events.

Prediction of Lead Molecule Esculetin of Tridax procumbens against Zika Virus Envelope Protein

In the existing field of science, it is necessary for Siddha, Ayurveda therapists, to understand the interactions and to treat the target regions of the human system effectively. One of the most significant nightmares for scientists and medical professionals has been the Zika virus. Since the bite of the Aedes mosquito species has infected people worldwide with the Zika virus (Flavivirus). The human mechanism and infection control interactions of the Zika virus are still unclear. In addition, some herbal products may give a pathway to the treat the zika virus. Many herbs aim to regulate various diseases, but the herb Tridax procumbens has a key collection of pharmacological values for diseases. Traditional health practitioners have struggled to grasp the precise mechanism of linking existence and how to treat the disease, so the current Ayurinformatics approach and the evolving field are an idea of a modern medical science system. Here we examine the interaction of flavonoids procumbenetin and esculetin of Tridax procumbens with zika virus in silico docking and molecular simulation and ADME and toxicity profile for the drug candidate. Hereby we conclude that these two compounds have the property to suppress the ZIKA virus and are stable and active with ZIKA virus. Molecular docking of Esculetin and Procumbenetin with ZIKV 5JHM, they bind with each other on active site pockets ASP98, ARG99, GLY100, TRP101, GLY102, ASN103, GLY104, CYS105, GLY106, LEU107, PHE108, GLY109.

Abstract 4125252: Pre-Hematopoietic Stem Cell Transplantation Echocardiographic Indices and Post-Transplant Cardiovascular Outcomes

Introduction: Hematopoietic stem cell transplantation (HSCT) is associated with adverse cardiovascular (CV) events including the development of heart failure (HF) and arrythmias. While transthoracic echocardiogram (TTE) is routinely obtained prior to HSCT, its role in predicting the incidence of HSCT related CV events is poorly understood. Methods: We used data from the Cardiovascular Registry in Bone Marrow Transplantation (CARE-BMT) study, a multicenter observational study of adult patients (aged ≥18 years) who underwent autologous/allogeneic HSCT for malignant or nonmalignant bone marrow disorders at the University of Michigan Health System (UMHS) and Rush University Medical Center from 2008-2019. In this analysis, we included patients from UMHS with a baseline TTE. Data on pre-HSCT TTE parameters and post-HSCT CV outcomes were collected through manual chart review. Left ventricular (LV) function and dimensions were categorized into normal, mildly abnormal, and moderately/severely abnormal based on American Society of Echocardiography guidelines. The primary outcomes were new-onset HF and atrial fibrillation/flutter post-HSCT. Analyses were conducted using a Fine-Gray model adjusted for the pre-HSCT CARE-BMT CV risk score. Results: Of the 2071 patients (mean age at HSCT 55.5 + 12.9 years; 59.5% male) with a pre-HSCT TTE (median 25 days pre-HSCT), 116 (5.6%) and 128 (6.2%) patients experienced HF and atrial fibrillation/flutter, respectively, over a median period of 2.2 years. Greater abnormalities in left ventricular internal diameter at end-diastole (LVIDd) and end-systole (LVIDs) were linearly associated with a higher risk of HF (P-trend 0.018 and 0.004, respectively) (Table). Similarly, moderately/severely abnormal LVIDd was associated with a 2.41-fold (95% CI: 1.07, 5.43) increase in risk of atrial fibrillation/flutter (Table). Pre-HSCT ejection fraction (EF) was not associated with either endpoint. Conclusion: LV dilation, even when mild, was notably associated with increased risk of developing new HF or atrial arrythmias post-HSCT, regardless of EF. Whether evidence of LV dilation should prompt the initiation of guideline directed medical therapy to minimize the risk of incident HF warrants further study.

Abstract 4118668: Understanding Medication Adherence to Oral Anticoagulants in Atrial Fibrillation Management: Patient and Provider Perspectives in a Mixed Methods Study

Background: Atrial fibrillation (AF) increases risk of stroke. National guidelines recommend oral anticoagulant (OAC) use to reduce this risk. Medication adherence is pivotal for effective AF management, necessitating an understanding of adherence barriers. Aims: This mixed-methods study aimed to identify patient and provider factors influencing OAC adherence. Methods: Patients with nonvalvular AF and OAC prescriptions were sampled from a large community health system serving seven states. A stratified random sample of 2,000 patients was surveyed by mail, achieving a 13.3% response rate (266 patients). Twenty-eight semi-structured interviews were conducted with a purposive sample of patient survey respondents. Eligible providers were surveyed by email, resulting in a 3% response rate (78 providers). Survey data were analyzed descriptively and using multivariable regression models. Interview data were analyzed thematically. Results: Of 213 patients with an adherence score available, 31.5% reported non-adherence. Nonadherent patients were more likely to report poor healthcare experiences, including discrimination in a healthcare setting and poorer mental/emotional health. Receiving information from providers was significantly associated with adherence, especially among younger patients and those with fewer chronic conditions. High self-efficacy score was associated with adherence, particularly among older and rural patients. Interview analyses showed provider communication about AF medications, perceptions of AF as a serious illness, and a sense that medication was helping manage AF were all important for medication adherence. In contrast, most providers (87%) perceived high non-adherence among AF patients, citing forgetfulness, cost, and side effects as the main reasons. Providers’ responses indicate a need for improving communication and support for addressing adherence barriers. Conclusion: Most patients reported adherence to their medications, with adherence being associated with positive healthcare experiences and better mental/emotional health. Providers perceived relatively low adherence among their patients but aligned with patients on the need for improved communication and shared decision making. Tools that encourage discussion with patients and open communication between providers and patients are pivotal for optimizing patient care and enhancing medication adherence and treatment outcomes.

Abstract Sa1103: Utility of early Computed Tomography after out-of-hospital cardiac arrest resuscitation

Background: Initial care after out-of-hospital cardiac arrest (OHCA) poses major challenges to clinical teams due to diverse arrest etiologies and the potential need for time-sensitive interventions. Emergency department-based evaluation of these patients is crucial for identifying treatable causes of arrest. Recent data from one academic hospital highlights the potential importance of initial broad computed tomography (CT) immediately following resuscitation. Objective: To assess the ability of early post-arrest CT imaging to diagnose potential etiologies and sequelae of OHCA. Methods: We employed a registry of consecutive OHCA cases admitted to 3 hospitals within the University of Pennsylvania Health System between 1/2019-10/2020, collecting demographic, arrest, and post-arrest clinical data. Inclusion criteria included age >18 y, OHCA with return of spontaneous circulation (ROSC). We abstracted CT data including findings, contrast use, and timing, and subsequent clinical notes to assess for interventions performed based on CT findings from the EHR. KDIGO definition was used for acute kidney injury (AKI). SAS software was used for data analysis. Results: A total of 161 OHCA patients were included (mean age 60.8±15.6y; 42% female). Initial shockable rhythms were present in 16%, with 70% witnessed and 39% receiving bystander CPR. 154 patients survived to admission, and 41 survived to discharge. 117 patients underwent CT imaging in the ED: 117 underwent head CT, 87 chest CT, and 64 abdomen/pelvis CT. Median time to any first CT was 2.0h (IQR:1.4,2.8). Intracranial hemorrhage was diagnosed in 5/117 patients (4.3%); 3 received conservative therapy. Cerebral edema was found in 30/117 patients (25.6%), 10 received treatment. Pulmonary embolism was diagnosed in 8/87 cases (9.2%), 7 of which were acute and treated pharmacologically and/or mechanically. Aspiration and/or pneumonia, pneumonitis, bronchitis, bronchiolitis was found on 65/84 cases (74.7%), 30 received antibiotics. Deep venous thrombosis was detected in 3/64 patients (4.7%), 2 received anticoagulation. We found no significant differences in AKI rates with the use of contrast compared to patients receiving no contrast (p=0.93). Conclusion: These results confirm and extend other reports emphasizing the potential value for routine use of CT imaging post-ROSC. Implementing larger scale prospective studies comparing such protocols and standard-of-care is important to determine impact on OHCA outcomes.

Tobacco use, cirrhosis, and age are predictors of readiness to change and continued drinking following brief alcohol intervention in Veterans.

Alcohol-use disorder (AUD) is prevalent within the Veterans Health System, especially in patients being seen in hepatology clinics, and needs a point-of-care strategy. A brief alcohol intervention based on AUDIT-C (Alcohol Use Disorders Identification Test) may be needed for management of hazardous alcohol intake, but feasibility is unclear. We aimed to define predictors of readiness to cease alcohol intake (0-10, 10 being ready to quit now) and continued drinking after using a brief alcohol intervention in Veterans seen in hepatology clinics cross-sectionally and longitudinally over 6 months. A total of 414 men with liver disease (average age of 61 years old, 48% with cirrhosis, 31% with concomitant psychiatric conditions, 44% with tobacco use, 14% with prior AUD therapy) were approached. 144 patients (35%) had a positive AUDIT-C (8.9±4.0) and were given the brief intervention. 80 of these patients (56%) had a high readiness to change, listing "wanting to improve health" and "prior success at reducing alcohol intake" as their most frequent reasoning. On regression analysis, concomitant tobacco use was associated with a significantly lower readiness to quit alcohol score. On longitudinal follow-up at 6 months, 34 (23%) patients were still drinking. These patients were older, and more likely to have concomitant diagnoses of cirrhosis and anxiety. Overall, we found that in a large cohort of Veterans seen in hepatology clinics that in routine practice,a brief intervention targeted at alcohol use is feasible. Older patients with concomitant tobacco use, cirrhosis, and anxiety may need more longitudinal attention to ensure that the initial interest in reducing problem drinking is sustained.

Abstract 4124531: Digital Health Intervention to Enhance Optimization of Heart Failure Care: From Reciprocal Innovation using Human-Centered Design to Pilot Study

Introduction: Guideline-directed medical therapy (GDMT) for Heart Failure with reduced ejection fraction (HFrEF) reduces adverse events by 70%, but is underutilized. The Human Centered Design (HCD) methodology can be useful to build a strategy to optimize GDMT centered on patients’ demands and perspectives. We aimed to reciprocally innovate a telephone-based digital health intervention (DHI) from a Brazilian trial to an app co-developed for a US trial and iterate it again to the Brazilian context using HCD. We will evaluate the app’s usability and utility to facilitate HF GDMT optimization, considering barriers of the Brazilian public health system users, such as low literacy and socioeconomic status. Methods: Mixed methods study. Steps were carried out according to HCD method: (1) empathizing and identifying challenges, (2) suggesting solutions, (3) prototype development, (4) ranking features for feasibility and utility (5) engineering work and (6) prototype testing. In stages 1-3, semi-structured interviews were held with 10 HF patients and 2 health professionals. Steps 4 and 5 were done by the research team. In stage 6, we conducted a pilot study with 10 HFrEF patients, with at least one GDMT drug with an optimization gap. The primary outcome was the app’s usability assessed by an engagement score, and the app’s utility at 4 weeks. Secondary outcomes included change in self-care score using the European Self-Care Behavior Scale. After step 6, new interviews were carried out to capture the user experience and identify new iterations. Interviews were recorded and transcribed for content analysis. Results: The average engagement score was 80% (SD 16) and the app’s utility score was 82% (SD 8). The average self-care score increased by 32% (SD 25) at the end of the pilot. The main challenges were lack of knowledge about HF and self-care. Technical difficulties and lack of trust in the app to handle health data were mentioned. In the pilot, patients reported the app was easy to use and contributed to self-care and understanding of HF. Among the DHI components, teleconsultation was best evaluated, followed by the educational videos. The greatest challenge was connecting the blood pressure cuff and scale via Bluetooth. Conclusion: The app was well accepted and considered useful by patients. User contributions were fundamental to create tools that facilitate engagement and utility. The final product will be evaluated in a randomized, multicenter, clinical trial.

Abstract 4137423: Can Machine Learning Help Prioritise Who to Screen for Elevated Lipoprotein(a) (Lp[a]) in the General Population vs a Screen all Approach? An Analysis from UK Biobank

Background: Elevated lipoprotein(a) [Lp(a)] is an inherited, currently non-modifiable risk marker that increases lifetime ASCVD risk. Guidance vary on Lp(a) levels at which risk increases; hence prevalence of “elevated” Lp(a) depends on putative thresholds e.g. >1.3 billion people globally have Lp(a)≥125 nmol/L. Lp(a) levels are >90% genetically determined and stable throughout life; hence measurement once in adulthood is recommended. Awareness of Lp(a) levels may change patient management with more intensive control of traditional risk factors. However, testing all adults is costly and the test is not universally available. Research Question: Can Machine Learning (ML) models reduce the number needed to screen (NNS) compared to population universal screening for identifying individuals with elevated Lp(a)? Aims&Objectives: To derive a model from ML to help prioritise individuals likely to have high levels for Lp(a) testing and compare its yield to universal screening at different Lp(a) cut-points. This approach could enable automatic screening of large databases like EHRs for Lp(a) testing. Method: We conducted a cross-sectional predictive analysis using UK Biobank, including individuals ≥40 years old with Lp(a) measurements, split into feature importance, derivation, and validation datasets. Eight ML classification algorithms were used for feature importance analysis and model derivation. Models' performance was evaluated in the validation set using sensitivity and NNS in comparison with the discrimination ability of the following guidelines across different populations: The 2019’s Heart UK and European Atherosclerosis Society (EAS) and Society of Cardiology guidelines, the 2022 EAS Consensus Statement, and threshold used in clinical trial —respective cut-offs: 90,430,125,200nmol/L. Results: 438,579 patients were included. The best ML models were neural networks with different weights. Regardless of the Lp(a) threshold used, ML models resulted in higher rates of high Lp(a) cases identified per million tests with lower NNS compared to universal screening (Table 1). Using higher Lp(a) thresholds (200-430nmol/L) increased models sensitivity with far fewer tests required to identify those with high Lp(a). Conclusion: ML models could reduce the number of tests needed to identify individuals with high Lp(a), increasing efficiency and potentially helping to prioritize Lp(a) testing, with a potentially scalable cost-effective option for health systems. Work supported by Novartis

Data Integration of Health Financing Systems as a Critical Enabler for Objective-Oriented Health System Reform: A Scoping Review from India

ABSTRACT Health financing fragmentation poses a challenge to reforms intended to address system-wide objectives vis-à-vis universal health coverage (UHC). India’s experience with publicly subsidized health insurance schemes (PSHIs), such as Rashtriya Swasthya Bima Yojana (RSBY) and its state adaptations, testify to the challenges inherent in effecting objective-oriented health systems reforms, particularly owing to wide variation in programmatic and operational design. Recent efforts to defragment PSHIs under the aegis of a new government initiative called Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PMJAY) using, inter alia, a unified information and communication technology (ICT) interface provide important policy lessons. This paper presents a theory of change for the role that ICT systems can play in promoting the objectives of UHC and highlights the early effects of ICT reforms in India on UHC. Holistic and defragmented ICT systems have a positive effect on the processes and operations of government health programs, according to the literature reviewed. Streamlined ICT systems promote equity through the introduction of portability modules, which increase access to services and facilitate stronger transparency and accountability measures by using big data and machine learning for fraud detection. Although reliability issues persist on certain fronts, India’s experience with homegrown, incremental reforms to defragment ICT systems for health financing have proven of paramount importance for progressing toward UHC.

Using a behaviour-change approach to support uptake of population genomic screening and management options for breast or prostate cancer.

As the possibility of implementing population genomic screening programs for the risk of developing hereditary cancers in health systems increases, understanding how to support individuals who wish to have genomic screening is essential. This qualitative study aimed to link public perceived barriers to a) taking up the offer of population genomic screening for breast or prostate cancer risk and b) taking up risk-management options following their result, with possible theory-informed behaviour-change approaches that may support implementation. Ten focus groups were conducted with a total of 25 members of the Australian public to identify and then categorise barriers within the behaviour-change Capability, Opportunity, Motivation - Behaviour (COM-B) model. Ten COM-B categorised barriers were identified as perceived influences on an individual's intentions to take-up the offer, including Capability (e.g., low public awareness), Opportunity (e.g., inconvenient sample collection procedure) and Motivation (e.g., genomic screening not perceived as relevant to an individual). Ten barriers for taking up risk-management options included Motivation (e.g., concerns about adverse health impact) and Opportunity (e.g., social opportunity and cost incurred to the individual). Our findings demonstrate that a nuanced approach is required to support people to take-up the offer of population genomic screening and, where appropriate, to adopt risk-management options. Even amongst participants who were enthusiastic about a population genomic screening program, needs were varied, demanding a range of implementation strategies. Promulgating equitable uptake of genomic screening and management options for breast and prostate cancer risk will require a needs-based approach.

Abstract 4136273: A Machine Learning Approach to Simplify Risk Stratification of Patients with Atherosclerotic Cardiovascular Disease

Introduction: The 2018 AHA/ACC/Multisociety Blood Cholesterol Guideline recommends that patients with atherosclerotic cardiovascular disease (ASCVD) be classified as very high-risk (VHR) or not very high-risk (NVHR). This designation is based on the presence of > 2 major ASCVD events (recent acute coronary syndrome [ACS], history of myocardial infarction [MI], history of ischemic stroke, symptomatic peripheral artery disease [PAD]) or 1 major ASCVD event and > 2 high-risk conditions (age ≥65 years, heterozygous familial hypercholesterolemia, history of coronary revascularization, diabetes, hypertension, chronic kidney disease, current smoking, persistently elevated LDL-cholesterol [LDL-C], history of heart failure). While this approach was initially instituted to better predict future ASCVD events alone, more intensive LDL-C lowering is now recommended for those at VHR (LDL-C treatment threshold of 55 mg/dL for VHR patients vs 70 mg/dL for NVHR patients). We aimed to simplify ASCVD risk stratification in patients with clinical ASCVD using machine learning models. Methods: We used electronic health record data (Providence Health system) from 2022 to identify patients with ASCVD based on ICD-10 codes. Patient demographics, comorbidities, and laboratory data stratified patients as VHR or NVHR. Then, a classification and regression tree analysis was performed, with the outcome being VHR classification. Patients were randomly assigned to either the testing or training dataset in equal proportion. Variables in the model included age, sex, race, ethnicity, and the Guideline criteria. Model performance was assessed using misclassification rate and area under the receiver operative curve (AUC). Results: A total of 491,514 patients with ASCVD were included, of which 56% were male, 77% were white, and the mean (SD) age was 72.5 (13.0) years. Over half of patients (59%) were VHR. Those with recent ACS, history of MI, history of ischemic stroke, or PAD were classified as VHR 93%, 92%, 84%, and 91%, respectively. Presence of hypertension in these patients increased the likelihood of being VHR to 97%, 97%, 94%, and 98%, respectively. The misclassification rate and AUC for the testing set were 6.8% and 96.2%, respectively. Conclusions: Most patients with a major ASCVD event and hypertension were found to be VHR. Relying on these two factors alone can help simplify risk assessment. This approach may also help increase use of guideline-recommended LDL-C lowering therapy.

Abstract 4136975: Lipoprotein (a) Discovery Project: Assessing Patient and Professional Education Needs

Introduction: It is estimated that 1 in 5 Americans have high lipoprotein (a) [Lp(a)] levels. High levels of Lp(a) are an independent, predominantly inherited, and causal risk factor for atherosclerotic cardiovascular disease (ASCVD). More investigation is needed to better understand the gaps in both professional and patient education on Lp(a) and its associated risk with ASCVD. Goals: The American Heart Association (AHA) is implementing a national 3-year initiative called the Lp(a) Discovery Project to understand system-level practice patterns for patients screened for high Lp(a) and develop models for national education. We aim to gauge an understanding of healthcare professional (HCP) and patient awareness, perceptions, knowledge, and behaviors of Lp(a) to develop a comprehensive educational approach that ultimately encourages patient-provider shared decision-making. Methods: To assess patient education needs, an online survey was conducted from October 11 to November 6, 2023, by Harris Insights&Analytics LLC with a nationally representative sample of 3,006 U.S. adults 18 years+. To assess professional education needs, the AHA engaged 10 champions from diverse health systems with established Lp(a) screening processes and workflows to participate in a Learning Healthcare System model to share best practices, inform on models for Lp(a) screening, and provide input on professional education topics. Results: The patient survey revealed that 34% of adults do not perceive high Lp(a) to pose an increased risk of heart attack, peripheral artery disease or stroke. If told that high Lp(a) does increase this risk, 63% are motivated to have their Lp(a) level checked and 88% would be motivated to reduce their risk for these conditions if they had high Lp(a). Ultimately, HCP recommendation was the most significant driver in getting screened among those who had their Lp(a) measured (61%). AHA-led champion interviews revealed that primary and specialty care HCPs need education on the importance of Lp(a) testing and cascade screening for family members as well as care considerations for patients with high Lp(a). Conclusion: Given that patients are most likely to be screened for Lp(a) if their HCP recommends it, we will focus on 1) educating patients and HCPs on the connection between Lp(a) and risk for cardiovascular disease and stroke and 2) providing HCPs with guidance on care considerations for those with high Lp(a) including cascade screening.

Impact of a social media post on improving an existing residency program.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. A social media post prompted reflection and changes to a longstanding pharmacy residency program. Pharmacy leadership utilized consultants with expertise in pharmacy residency training to help facilitate change and introduce new ideas to tenured staff. Other health systems working to continuously improve their residency programs may benefit from our example. The guiding principles of the changes were to align with the new harmonized residency program standards and reduce preceptor burden and potential burnout. Changes were made to the entire program's design, including new leadership, changes to committee structure, reduced requirements for preceptors that were not required in the standards, and resident recruitment and onboarding. We encourage residency programs to regularly evaluate program structure, design, and conduct to ensure alignment with the organization's evolving priorities as well as its mission and vision.

PUBLIC POLICIES ON DRUGS: ORIGINS AND DEVELOPMENTS OF THE BRAZILIAN PUNITIVIST MODEL

This article presents a critical analysis of the evolution of drug policies in Brazil, from the colonial period to the present day. The research demonstrates how the prohibitionist model, present since the Philippine Ordinances, has consolidated itself as the only strategy for combating drugs over the centuries, undergoing few changes only in the last three decades. The historical analysis reveals a hardening of laws, especially during the Civil-Military Dictatorship, with the criminalization of drug use and the strengthening of the logic of the “War on Drugs”. With the promulgation of the 1988 Constitution, there was a paradigm shift, with the recognition of health as a fundamental right and the creation of the Unified Health System. Despite this, the prohibitionist model persisted, albeit with some nuances. The article reflects on the ineffectiveness of the prohibitionist model in reducing consumption and organized crime, defending the need for a more humanitarian approach based on scientific evidence. Finally, this work also proposed to critically analyze current public policies, unraveling the characteristics of each of them.

Abstract 4138602: Effect of Left Ventricular Ejection Fraction On Computed Tomography-Derived Fractional Flow Reserve Diagnostic Accuracy For Significant Coronary Artery Disease

Background: Coronary Computed Tomography Angiography (CCTA) and CT-derived fractional flow reserve (CT-FFR) accurately assess the severity of coronary artery disease. However, the diagnostic accuracy of CT-FFR in patients with low left ventricular ejection fraction (LVEF) is unknown. Goal: Describe the per-vessel diagnostic performance of CT-FFR to detect significant coronary disease in patients with normal and reduced LVEF by echocardiogram. Methods: This was a retrospective study of 441 patients who underwent CCTA and CT-FFR at the UMass Memorial Health System between 2020 and 2023. Those with echocardiogram and invasive coronary angiography were analyzed and divided in two groups based on two-dimensional LVEF: those with LVEF < 55%, and with LVEF ≥ 55%. We excluded those with prior coronary stents, incomplete CCTA, CT-FFR data due to artifacts, or poor-quality echocardiographic images. CT-FFR ≤ 0.8 was considered abnormal. Significant coronary disease by invasive angiography (reference standard) was defined as lumen stenosis ≥ 70% (left main ≥ 50%), or abnormal physiologic characteristics by FFR, resting full-cycle ratio, or instantaneous wave-free ratio. Results: 222 coronary vessels (102 patients) were analyzed. Of 102 vessels (44%) with significant coronary artery disease, 55 (53.9%) were treated with percutaneous coronary intervention, and 25 (24.5%) with coronary artery bypass grafting. Overall, CT-FFR sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 86.7% (95%CI 78.3-92.7), 75.0% (95%CI 66.4-82.3), 73.2% (95%CI 66.6-78.9), 87.7% (95%CI 81.0-92.3), and 80.1% (95%CI 74.3-85.2), respectively. 37 vessels corresponded to patients with LVEF < 55%, and 185 to those with LVEF ≥ 55%. Diagnostic accuracy in both groups is seen in table 1. Conclusions: Low-normal and reduced LVEF adversely affects the diagnostic performance of CT-FFR, by reducing sensitivity, and slightly increasing specificity.

Abstract 4135582: Sociodemographic Trends in Diabetes Medications after Metformin: the BESTMED (oBservational Evaluation of Second line Therapy MEdications in Diabetes) Study

Objective: Though updated American Diabetes Association (ADA) guidelines recommend initiating GLP1 receptor agonists (GLP1) or SGLT2 inhibitors (SGLT2i) for persons with type 2 diabetes mellitus (PWT2DM) with high CV risk, metformin was previously recommended as 1st-line treatment. For PWT2DM at moderate CV risk, it is unknown which 2nd-line drug optimizes CV outcomes. Our multicenter observational study describes US-wide trends in 2nd-line T2DM medication use (DPP4 inhibitors [DPP4i], GLP1s, SGLT2i and sulfonylureas [SU]) overall and in key sociodemographic subgroups between 2014-2023. Methods: This is a secondary analysis of the oBservational Evaluation of Second line Therapy Medications in Diabetes (BESTMED) study, which used a new-user design to evaluate associations between 2nd-line medications and CV events in PWT2DM at moderate CV risk. The database comprised EHRs and insurance claims for 75,224 PWT2DM from 10 health systems and 2 insurance plans on metformin who initiated a 2nd medication from 1/2013 to 1/2023. Data were obtained from orders or prescription fills. Prescription class was regressed on calendar time via multinomial logistic regression to model trends in prescribing patterns, adjusted for sociodemographic variables. Subgroup analyses were performed by adjustment factors; CIs and p-values for differences were calculated via nonparametric bootstrap (B=200). Due to large sample size, statistical significance was interpreted at p<0.05 and annual rate of change >0.5%. Results: We included 75,224 T2DM adults (median A1c 7.8%), of whom 18%,17%, 18%, and 47% initiated DPP4i, GLP1, SGLT2i, SU respectively. From 2014 - 2023, SGLT2i and GLP1 increased: 5.7 to 28.8%, and 3.5 to 30.3%, and DPP4i and SUs decreased: 25.3 to 11.6%, and 65.6 to 29.3%, respectively. Annual rate of change for SGLT2i was higher in men(3.0% vs 2.1%/year; p<0.001) and in > 65 y (3.0% vs 2.4%; p<0.001), and for GLP1s was higher in those <65 y (3.6% vs 2.1%; p<0.001), women (3.5% vs 2.5%; p<0.001), Hispanic ethnicity (3.2% vs 2.7%; p=0.005), and with class II/III obesity (4.1% versus 2.6% per year; p<0.001). There was no evidence that these trends differed by race, insurance, or SDI. Conclusion: In this nationwide study of prescribing patterns PWT2DM at moderate CV risk, prevalences of 2nd-line T2DM therapies are approaching current ADA guidelines for initial therapy for those at high CV risk. Differences between sociodemographic groups appear aligned with clinical expectations.

Judge rules against former employee

Case name: Maxius v. Mount Sinai Health Systems Inc., et al., No. 21 Civ. 10422 (S.D. N.Y. 09/12/24).

Abstract 4121840: Contemporary Safety of Ultrasound Enhancing Agents in a Nationwide Analysis

Background: Ultrasound enhancing agents (UEAs) are an important diagnostic tool for transthoracic or stress echocardiography (TTE/SE) but recent concerns have been raised about their safety in reports from individual health systems. Objectives: To identify if UEAs for TTE/SE are associated with serious adverse events within 48 hours of receipt in nationwide claims data. Methods: All-payor nationwide claims from 320 million insured individuals across the US, 2018-2022 from the Komodo Health database were used to evaluate rates of death or the composite of death, anaphylaxis, MI, coronary angiography, PCI, CABG, ventricular tachycardia or cardiac arrest within 48 hours of UEA receipt for TTE/SE among adults. Propensity score matching (PSM) was used to account for baseline imbalances. Results: Of the 11,421,463 individuals included (mean age 57.5 ± 16.2, 54.0% female, 46.2% White), a total of 500,073 (4.4%) received TTE/SE with UEAs. Individuals receiving Lumason™ or Definity™ more frequently had TTEs/SEs performed in the emergency department/inpatient setting and had more comorbidities than those receiving Optison™. After PSM, the odds of death were lower in those receiving UEAs (receipt vs. non-receipt, 0.02% vs. 0.14%, OR 0.23, 95% CI 0.19-0.28, p < 0.001) and were similar across agents ( Table; Definity™: 0.02%, OR 0.22, 95% CI 0.18-0.28; Lumason™: 0.03%, OR 0.33, 95% CI 0.18-0.28; Optison™: 0.01%, OR 0.17, 95% CI 0.08-0.38; all p < 0.001). Rates of the composite outcome were essentially unchanged after excluding claims for anaphylaxis. Rates of all outcomes were stable across study years, including considering pre-COVID (2018-2019) and post-COVID (2020-2022) periods. Conclusions: In this large nationwide claims analysis from 2018-2022, serious adverse events from UEAs for TTE/SE were uncommon and overall similar across agents and years of study. Receipt of UEAs was associated with a lower odds of death at 48 hours.

Recommendations for Clinicians, Technologists, and Healthcare Organizations on the Use of Generative Artificial Intelligence in Medicine: A Position Statement from the Society of General Internal Medicine.

Generative artificial intelligence (generative AI) is a new technology with potentially broad applications across important domains of healthcare, but serious questions remain about how to balance the promise of generative AI against unintended consequences from adoption of these tools. In this position statement, we provide recommendations on behalf of the Society of General Internal Medicine on how clinicians, technologists, and healthcare organizations can approach the use of these tools. We focus on three major domains of medical practice where clinicians and technology experts believe generative AI will have substantial immediate and long-term impacts: clinical decision-making, health systems optimization, and the patient-physician relationship. Additionally, we highlight our most important generative AI ethics and equity considerations for these stakeholders. For clinicians, we recommend approaching generative AI similarly to other important biomedical advancements, critically appraising its evidence and utility and incorporating it thoughtfully into practice. For technologists developing generative AI for healthcare applications, we recommend a major frameshift in thinking away from the expectation that clinicians will "supervise" generative AI. Rather, these organizations and individuals should hold themselves and their technologies to the same set of high standards expected of the clinical workforce and strive to design high-performing, well-studied tools that improve care and foster the therapeutic relationship, not simply those that improve efficiency or market share. We further recommend deep and ongoing partnerships with clinicians and patients as necessary collaborators in this work. And for healthcare organizations, we recommend pursuing a combination of both incremental and transformative change with generative AI, directing resources toward both endeavors, and avoiding the urge to rapidly displace the human clinical workforce with generative AI. We affirm that the practice of medicine remains a fundamentally human endeavor which should be enhanced by technology, not displaced by it.