Age-specific protective effects of statins against cirrhosis in patients with chronic liver enzyme elevation associated with metabolic dysfunction: a retrospective cohort study of electronic health records.

Background: The hepatoprotective effects of statins in chronic liver diseases are well-documented; however, variation by age, sex, and formulation remains unclear. The optimal regimen for cirrhosis prevention has yet to be defined. We aimed to address this knowledge gap.

Sustained Improvements After Intervention to Prevent Contrast-Associated Acute Kidney Injury: A Randomized Controlled Trial.

Background: In the IMPROVE AKI (A Cluster-Randomized Trial of Team-Based Coaching Interventions to Improve Acute Kidney Injury) trial, a combination of team-based coaching and data-driven surveillance dashboards reduced the odds of AKI following cardiac catheterization by 46%. The objective of this study was to determine if improvements in AKI outcomes would be sustained after completion of the active intervention.

Methods And Results: A 2×2 factorial cluster-randomized trial with an 18-month active intervention phase (October 2019-March 2021) and an 18-month sustainability phase (April 2021-September 2022) conducted among cardiac catheterization laboratories in 20 Veterans Affairs sites.

Emerging algorithmic bias: fairness drift as the next dimension of model maintenance and sustainability.

Objectives: While performance drift of clinical prediction models is well-documented, the potential for algorithmic biases to emerge post-deployment has had limited characterization. A better understanding of how temporal model performance may shift across subpopulations is required to incorporate fairness drift into model maintenance strategies.

Materials And Methods: We explore fairness drift in a national population over 11 years, with and without model maintenance aimed at sustaining population-level performance.

Augmenting the Hospital Score with social risk factors to improve prediction for 30-day readmission following acute myocardial infarction.

Background: Hospital Score is a well-known and validated tool for predicting readmission risk among diverse patient populations. Integrating social risk factors using natural language processing with the Hospital Score may improve its ability to predict 30-day readmissions following an acute myocardial infarction.

Methods: A retrospective cohort included patients hospitalized at Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary index diagnosis of acute myocardial infarction, who were discharged alive.

Factors Associated with Acute Telemental Health Consultations in Older Veterans.

Introduction: The United States Veterans Health Administration is a leader in the use of telemental health (TMH) to enhance access to mental healthcare amidst a nationwide shortage of mental health professionals. The Tennessee Valley Veterans Affairs (VA) Health System piloted TMH in its emergency department (ED) and urgent care clinic (UCC) in 2019, with full 24/7 availability beginning March 1, 2020. Following implementation, preliminary data demonstrated that veterans ≥65 years old were less likely to receive TMH than younger patients.

Natural language processing to identify lupus nephritis phenotype in electronic health records.

Background: Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis.

Helicobacter pylori Burden in the United States According to Individual Demographics and Geography: A Nationwide Analysis of the Veterans Healthcare System.

Background & Aims: There are no contemporary large-scale studies evaluating the burden of Helicobacter pylori in the United States according to detailed demographics. The primary objective was to evaluate H pylori positivity in a large national healthcare system according to individual demographics and geography.

Methods: We conducted a nationwide retrospective analysis of adults in the Veterans Health Administration who completed H pylori testing between 1999 and 2018.

Team-Based Coaching Intervention to Improve Contrast-Associated Acute Kidney Injury: A Cluster-Randomized Trial.

Background: Up to 14% of patients in the United States undergoing cardiac catheterization each year experience AKI. Consistent use of risk minimization preventive strategies may improve outcomes. We hypothesized that team-based coaching in a Virtual Learning Collaborative (Collaborative) would reduce postprocedural AKI compared with Technical Assistance (Assistance), both with and without Automated Surveillance Reporting (Surveillance).

The effect of telemental versus in-person mental health consults in the emergency department on 30-day utilization and processes of care.

Objectives: We sought to characterize how telemental health (TMH) versus in-person mental health consults affected 30-day postevaluation utilization outcomes and processes of care in Veterans presenting to the emergency department (ED) and urgent care clinic (UCC) with acute psychiatric complaints.

Methods: This exploratory retrospective cohort study was conducted in an ED and UCC located in a single Veterans Affairs system. A mental health provider administered TMH via iPad.

Predictive models for abdominal aortic aneurysms using polygenic scores and PheWAS-derived risk factors.

Abdominal aortic aneurysms (AAA) are common enlargements of the abdominal aorta which can grow larger until rupture, often leading to death. Detection of AAA is often by ultrasonography and screening recommendations are mostly directed at men over 65 with a smoking history. Recent large-scale genome-wide association studies have identified genetic loci associated with AAA risk.

Associations Between Gastrointestinal Symptoms and COVID-19 Severity Outcomes Based on a Propensity Score-Weighted Analysis of a Nationwide Cohort.

Background And Aims: Gastrointestinal (GI) symptoms are well-recognized manifestations of coronavirus disease 2019 (COVID-19). Our primary objective was to evaluate the association between GI symptoms and COVID-19 severity.

Methods: In this nationwide cohort of US veterans, we evaluated GI symptoms (nausea/vomiting/diarrhea) reported 30 days before and including the date of positive SARS-CoV-2 testing (March 1, 2020, to February 20, 2021).

Maintaining a National Acute Kidney Injury Risk Prediction Model to Support Local Quality Benchmarking.

Background: The utility of quality dashboards to inform decision-making and improve clinical outcomes is tightly linked to the accuracy of the information they provide and, in turn, accuracy of underlying prediction models. Despite recognition of the need to update prediction models to maintain accuracy over time, there is limited guidance on updating strategies. We compare predefined and surveillance-based updating strategies applied to a model supporting quality evaluations among US veterans.

Validating a Computable Phenotype for Nephrotic Syndrome in Children and Adults Using PCORnet Data.

Background: Primary nephrotic syndromes are rare diseases which can impede adequate sample size for observational patient-oriented research and clinical trial enrollment. A computable phenotype may be powerful in identifying patients with these diseases for research across multiple institutions.

Methods: A comprehensive algorithm of inclusion and exclusion ICD-9 and ICD-10 codes to identify patients with primary nephrotic syndrome was developed.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts.

Identifying Potential Therapeutic Applications and Diagnostic Harms of Increased Bilirubin Concentrations: A Clinical and Genetic Approach.

Bilirubin has antioxidant and anti-inflammatory properties in vitro and in animal studies and protects against inflammatory, cardiovascular, and other diseases in observational studies; therefore, bilirubin has potential as a therapeutic agent. However, observational studies could be confounded by many factors. We used a genetic (n = 61,281) and clinical (n = 234,670) approach to define the association between bilirubin and 19 conditions with a putative protective signal in observational studies.

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

Importance: In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models.

Objective: To compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model.