Racial and Ethnic Disparities in Sublingual Immunotherapy Prescriptions for Allergic Rhinitis in the United States.

Black patients with allergic rhinitis are under-prescribed sublingual immunotherapy tablets. Barriers to sublingual immunotherapy need to be explored and mitigated to ensure equitable access.

Systematic review of omalizumab for refractory clonal and non-clonal mast cell activation syndrome.

Patients with mast cell activation syndrome (MCAS) can be refractory to standard antimediator therapy. Alternative treatment options to reduce disease burden and improve quality of life are needed. To compile the evidence that supports the use of omalizumab for patients with refractory MCAS.

Predicting Nephrotoxic Acute Kidney Injury in Hospitalized Adults: A Machine Learning Algorithm.

Rationale And Objective: Acute kidney injury (AKI) is a common complication among hospitalized adults, but AKI prediction and prevention among adults has proved challenging. We used machine learning to update the nephrotoxic injury negated by just-in time action (NINJA), a pediatric program that predicts nephrotoxic AKI, to improve accuracy among adults.

Study Design: A retrospective cohort study.

A machine learning framework to adjust for learning effects in medical device safety evaluation.

Objectives: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects.

Upper tract urothelial carcinoma: epidemiology, presentation, and high-risk endemic populations.

Purpose Of Review: Upper tract urothelial carcinoma (UTUC) only accounts for 5-10% of all urothelial cancers but these patients present with high stage disease and 2 out of 3 patients have evidence of muscle-invasion at time of diagnosis. Furthermore, 10% of UTUC patients have associated Lynch syndrome and therefore diagnosis of UTUC should prompt timely evaluation and familial counseling. The purpose of this review is to outline the current evidence on the epidemiology, presentation, and high-risk endemic populations of UTUC through review of contemporary publications occurring over the preceding 18 months.

Combining electronic health records data from a clinical research network with registry data to examine long-term outcomes of interventions and devices: an observational cohort study.

Objectives: To assess the feasibility of assessing long-term outcomes of peripheral vascular intervention (PVI) by linking data from a clinical registry to electronic health records (EHR) data from a clinical research network.

Design: Observational cohort study.

Setting: Vascular Quality Initiative registry linked to INSIGHT Clinical Research Network, which aggregated EHR data from multiple institutions in New York City.

High-fidelity teleportation of a logical qubit using transversal gates and lattice surgery.

Quantum state teleportation is commonly used in designs for large-scale quantum computers. Using Quantinuum's H2 trapped-ion quantum processor, we demonstrate fault-tolerant state teleportation circuits for a quantum error correction code-specifically the Steane code. The circuits use up to 30 qubits at the physical level and employ real-time quantum error correction.

Sodium-Glucose Cotransporter-2 Inhibitor and Glucagon-Like Peptide-1 Receptor Agonist Discontinuation in Patients with CKD.

Key Points: Treatment discontinuation is common among patients with CKD prescribed sodium-glucose cotransporter-2 (SGLT2) inhibitors (discontinued in 37%) or glucagon-like peptide-1 receptor agonists (GLP-1 RA; discontinued in 47%). Discontinuation of SGLT2 inhibitors and GLP-1 RA was associated with recent hospitalizations, Black race, Hispanic ethnicity, and vascular disease. Discontinuation of both agents was associated with death and cardiovascular events.

Enhancing Postmarketing Surveillance of Medical Products With Large Language Models.

Importance: The Sentinel System is a key component of the US Food and Drug Administration (FDA) postmarketing safety surveillance commitment and uses clinical health care data to conduct analyses to inform drug labeling and safety communications, FDA advisory committee meetings, and other regulatory decisions. However, observational data are frequently deemed insufficient for reliable evaluation of safety concerns owing to limitations in underlying data or methodology. Advances in large language models (LLMs) provide new opportunities to address some of these limitations.

Fault-tolerant one-bit addition with the smallest interesting color code.

Fault-tolerant operations based on stabilizer codes are the state of the art in suppressing error rates in quantum computations. Most such codes do not permit a straightforward implementation of non-Clifford logical operations, which are necessary to define a universal gate set. As a result, implementations of these operations must use either error-correcting codes with more complicated error correction procedures or gate teleportation and magic states, which are prepared at the logical level, increasing overhead to a degree that precludes near-term implementation.

Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem.

The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically intractable problems remains unclear. Here, we perform an extensive numerical investigation of QAOA on the low autocorrelation binary sequences (LABS) problem, which is classically intractable even for moderately sized instances.

Heart failure subtype after acute kidney injury.

Introduction: Acute kidney injury (AKI) is associated with increased risk of heart failure (HF). Determining the type of HF experienced by AKI survivors (heart failure with preserved or reduced ejection fraction, HFpEF or HFrEF) could suggest potential mechanisms underlying the association and opportunities for improving post-AKI care.

Methods: In this retrospective study of adults within the Vanderbilt University health system with a diagnosis of HF, we tested whether AKI events in the two years preceding incident HF associated more with HFpEF or HFrEF while controlling for known predictors.

Determinants of appropriate antibiotic and NSAID prescribing in unscheduled outpatient settings in the veterans health administration.

Background: Despite efforts to enhance the quality of medication prescribing in outpatient settings, potentially inappropriate prescribing remains common, particularly in unscheduled settings where patients can present with infectious and pain-related complaints. Two of the most commonly prescribed medication classes in outpatient settings with frequent rates of potentially inappropriate prescribing include antibiotics and nonsteroidal anti-inflammatory drugs (NSAIDs). In the setting of persistent inappropriate prescribing, we sought to understand a diverse set of perspectives on the determinants of inappropriate prescribing of antibiotics and NSAIDs in the Veterans Health Administration.

Use of noisy labels as weak learners to identify incompletely ascertainable outcomes: A Feasibility study with opioid-induced respiratory depression.

Objective: Assigning outcome labels to large observational data sets in a timely and accurate manner, particularly when outcomes are rare or not directly ascertainable, remains a significant challenge within biomedical informatics. We examined whether noisy labels generated from subject matter experts' heuristics using heterogenous data types within a data programming paradigm could provide outcomes labels to a large, observational data set. We chose the clinical condition of opioid-induced respiratory depression for our use case because it is rare, has no administrative codes to easily identify the condition, and typically requires at least some unstructured text to ascertain its presence.

Sustainable deployment of clinical prediction tools-a 360° approach to model maintenance.

Background: As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting from evolving clinical environments strain the longevity of AI models as predictive accuracy and associated utility deteriorate over time.

Objective: Responsible practice thus necessitates the lifecycle of AI models be extended to include ongoing monitoring and maintenance strategies within health system algorithmovigilance programs.