Experience with expanded use of oritavancin in a tertiary hospital: indications, tolerability and outcomes.

Background: Oritavancin is a lipoglycopeptide antibacterial agent used to treat infections caused by Gram-positive organisms. It is FDA-approved for the treatment of acute bacterial skin and soft tissue infections (ABSSIs) but is increasingly being used off-label to treat invasive bacterial infections such as osteomyelitis, prosthetic joint infection and infective endocarditis.

Objectives: This study describes the clinical outcomes and adverse reactions related to oritavancin.

Predicting 30-Day and 1-Year Mortality in Heart Failure with Preserved Ejection Fraction (HFpEF).

Objectives: To develop and compare prediction models for 30-day and 1-year mortality in Heart failure with preserved ejection fraction (HFpEF) using EHR data, utilizing both traditional and machine learning (ML) techniques.

Background: HFpEF represents 1 in 2 heart failure patients. Predictive models in HFpEF, specifically those derived from electronic health record (EHR) data, are less established.

National Patterns of Remote Patient Monitoring Service Availability at US Hospitals and their Readmission Performance for Cardiovascular Conditions.

Background: Digital remote patient monitoring (RPM) enables longitudinal care outside traditional healthcare settings, especially in the vulnerable period after hospitalizations, with broad coverage of the service by payers. We sought to evaluate patterns of RPM service availability at US hospitals and the association of these services with 30-day readmissions for two key cardiovascular conditions, heart failure (HF) and acute myocardial infarction (AMI).

Methods: We used contemporary national data from the American Hospital Association (AHA) Annual Survey to ascertain US hospitals offering RPM services for post-discharge or chronic care and used census-based county-level data to define the characteristics of the communities they serve.

Factors Associated With Surgical Management in Gallbladder Cancer-A Surveillance, Epidemiology, and End Results Medicare-Based Study.

Introduction: Gallbladder cancer (GBC) incidence is rising, yet prognosis remains poor. Oncological resection of stage T1b or higher improves survival, yet many patients do not receive appropriate resection. This study aims to evaluate factors that may attribute to this discrepancy using the Surveillance, Epidemiology, and End Results-Medicare (SEER-Medicare) database.

Adoption of the voluntary conflict of interest statement on PubMed.

In 2017, the National Library of Medicine (NLM) added a voluntary field for conflict of interest (COI) statements ("posted COI") on the abstract page of PubMed, but the extent to which it is used is unknown. This repeated cross-sectional study examined journals and articles indexed on PubMed from 2016 through 2021. We described the proportion of all journals with at least one article that included a posted COI and the percentage of all articles that included a posted COI over time.

Early, Individualized Recommendations for Hospitalized Patients With Acute Kidney Injury: A Randomized Clinical Trial.

Importance: Acute kidney injury (AKI) is a common complication during hospitalization and is associated with adverse outcomes.

Objective: To evaluate whether diagnostic and therapeutic recommendations sent by a kidney action team through the electronic health record improve outcomes among patients hospitalized with AKI compared with usual care.

Design, Setting, And Participants: Randomized clinical trial conducted at 7 hospitals in 2 health systems: in New Haven, Bridgeport, New London, and Waterbury, Connecticut, and Westerly, Rhode Island; and in Baltimore, Maryland.

Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing.

Background: The lack of automated tools for measuring care quality limits the implementation of a national program to assess guideline-directed care in heart failure with reduced ejection fraction (HFrEF).

Objectives: The authors aimed to automate the identification of patients with HFrEF at hospital discharge, an opportunity to evaluate and improve the quality of care.

Methods: The authors developed a novel deep-learning language model for identifying patients with HFrEF from discharge summaries of hospitalizations with heart failure at Yale New Haven Hospital during 2015 to 2019.

'Show me the money': An analysis of US global health funding from 1995 to 2019.

Background: Historically, the US has been the largest contributor to development assistance for health (DAH), although its allocation has shifted in response to outside forces. This included, for example, the establishment of the Millennium Development Goals (MDGs) in 2000, which emphasised child mortality, maternal health, HIV/AIDS, and malaria. This led to funds being earmarked for disease-specific interventions rather than health system strengthening (HSS).

Safety and Efficacy of Switching Patients With Type 2 Diabetes From Glucagon-Like Peptide-1 Receptor Agonists to Tirzepatide: A Case Series.

Tirzepatide is a novel glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptor agonist (RA) that is approved for the treatment of type 2 diabetes mellitus (T2D). Despite its similarities to GLP-1 RA, there is a lack of data on how to switch between GLP-1 RA and GIP/GLP-1 RA. The objective of this study is to evaluate the efficacy and safety of switching from GLP-1 RA to tirzepatide in patients with T2D and provide guidance for switching between the two classes.

Asynchronous education improves emergency medical services clinician confidence and knowledge in caring for patients near the end-of-life.

Objective: To evaluate the impact of a nationally available continuing education online curriculum on Emergency Medical Services (EMS) clinician confidence and knowledge in caring for end-of-life (EOL) patients.

Methods: This is a prospective observational study of EMS clinicians (emergency medical technicians [EMTs], advanced EMTs [AEMTs], and paramedics). EMS clinicians and physicians with both EMS and palliative care expertise developed two 20-min modules regarding: (1) communication skills (including death notification) and (2) hospice knowledge.

Using Panel Management to Identify Adult Patients With High-Risk Metabolic Dysfunction-Associated Steatotic Liver Disease/Metabolic Dysfunction-Associated Steatohepatitis Fibrosis in a Primary Care Clinic: A Pilot Study.

Background: As rates of metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) rise, national organizations have released new guidance for primary care-driven detection of patients with advanced fibrosis who are most likely to have clinically relevant morbidity. Yet time constraints, workflow, and practitioner awareness limit integration of risk identification into clinical care.

Materials And Methods: At the authors' primary care clinic, they implemented a panel management strategy that utilized the electronic health record to identify patients older than 35 years of age at risk for MASLD fibrosis with abnormal Fibrosis-4 (Fib-4) scores.

Analyzing determinants of premature trial discontinuation in leukemia clinical trials.

Clinical trials are crucial for improving patient outcomes. Although a significant number of trials are discontinued prematurely, our understanding of factors influencing early termination is limited. We conducted a comprehensive search of ClinicalTrials.

Opioid use disorder's impact on asthma hospitalizations: A propensity-matched nationwide study.

Background: Patients with asthma and opioid use disorder (OUD) experience higher rates of acute exacerbation, but the effects of OUD on asthma hospitalizations have been poorly described.

Objective: To explore how concurrent OUD may affect the clinical outcomes of adult patients hospitalized for asthma.

Methods: Using the National Inpatient Sample, adult patients admitted for asthma with concomitant OUD were identified and compared with those without OUD.

Venous Thromboembolism after Hospital Discharge: Temporal Trends in Baseline Characteristics, Prevention, Treatment, and 90-day Outcomes.

Venous thromboembolism (VTE) after hospital discharge poses a serious health risk. Assessments of patient characteristics, prophylaxis, treatment, outcomes, and over time changes lack consistency. Data on 16,901 hospitalized patients in the Registro Informatizado Enfermedad TromboEmbolica registry (2003-2022) were analyzed to evaluate trends in baseline characteristics, prophylaxis, treatments, and 90-day outcomes among medical (6,218) and surgical (10,683) patient cohorts.

Enhancing stroke knowledge among youth: Insights from Stroke Busters.

Introduction: Community stroke education has shown promising results with sustained stroke knowledge and behavioral changes; however less is known about the effects of targeted education towards youth. We developed an interactive educational program for high school students, Stroke Busters, that focuses on stroke prevention, recognizing warning signs, and the importance of seeking prompt care.

Methods: This is a prospective cohort study where students are offered a stroke education program, 'Stroke Busters', through the Yale Pathways to Science Program, a Science, Technology, Engineering and Math (STEM) pipeline program for high school students living near New Haven, Connecticut.

Development and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms.

Background And Aims: AI-enhanced 12-lead ECG can detect a range of structural heart diseases (SHDs) but has a limited role in community-based screening. We developed and externally validated a noise-resilient single-lead AI-ECG algorithm that can detect SHD and predict the risk of their development using wearable/portable devices.

Methods: Using 266,740 ECGs from 99,205 patients with paired echocardiographic data at Yale New Haven Hospital, we developed ADAPT-HEART, a noise-resilient, deep-learning algorithm, to detect SHD using lead I ECG.

An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.

Background: Identifying structural heart diseases (SHDs) early can change the course of the disease, but their diagnosis requires cardiac imaging, which is limited in accessibility.

Objective: To leverage images of 12-lead ECGs for automated detection and prediction of multiple SHDs using an ensemble deep learning approach.

Methods: We developed a series of convolutional neural network models for detecting a range of individual SHDs from images of ECGs with SHDs defined by transthoracic echocardiograms (TTEs) performed within 30 days of the ECG at the Yale New Haven Hospital (YNHH).