The COVID-19 Pandemic's Effect on Preventive Imaging.

Introduction: This study assessed the effect of the COVID-19 pandemic on preventive care imaging and potential disparities because preventive care may be perceived as nonurgent. The objective was to identify the associations between the COVID-19 pandemic and changes in preventive imaging volumes for patients in general and as affected by race and ethnicities.

Methods: The authors performed a retrospective observational study by extracting the weekly volumes of all imaging studies between January 7, 2019 and May 1, 2022 from a radiology data warehouse at a tertiary care medical center (=92,105 preventive imaging studies and 3,493,063 total radiology imaging studies) and compared preshutdown with postshutdown periods using a 2-sample -test.

Electronic Health Record Alert With Heart Failure Risk and Sodium Glucose Cotransporter 2 Inhibitor Prescriptions in Diabetes: A Randomized Clinical Trial.

Background: Sodium glucose cotransporter 2 inhibitors (SGLT2i) prevent heart failure (HF) in patients with type 2 diabetes mellitus (T2DM) but prescription rates are low. The effect of an electronic health record (EHR) alert notifying providers of patients' estimated risk of developing HF on SGTL2i prescriptions is unknown.

Methods: This was a pragmatic, randomized clinical trial that compared an EHR alert and usual care among patients with T2DM and no history of HF or SGLT2i use at a single center.

Development and validation of a machine learning-based approach to identify high-risk diabetic cardiomyopathy phenotype.

Aims: Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abnormalities that precede the development of clinical heart failure (HF), is lacking. In this study, we developed and validated a machine learning-based clustering approach to identify the high-risk DbCM phenotype based on echocardiographic and cardiac biomarker parameters.

Impact of team performance on the surgical safety checklist on patient outcomes: an operating room black box analysis.

Background: Surgical safety checklists reduce adverse events, but monitoring adherence to checklists is confounded by observation bias. The ORBB platform can monitor checklist compliance and correlate compliance with outcomes. This study aims to evaluate the association between checklist compliance and patient outcomes using the ORBB platform.

Specific Gravity Improves Identification of Clinically Significant Quantitative Proteinuria from the Dipstick Urinalysis.

Key Points: Urine albumin-to-creatinine ratio and urine protein-to-creatinine ratio are frequently obtained and represent possible tools for screening for proteinuria and thus early CKD. Adding specific gravity to dipstick proteinuria improves the ability to screen patients with clinically significant proteinuria and can be used to identify patients with early CKD.

Background: CKD is often underdiagnosed during early stages when GFR is preserved because of underutilization of testing for quantitative urine albumin-to-creatinine ratio (UACR) or urine protein-to-creatinine ratio (UPCR).

A Phenomapping Tool and Clinical Score to Identify Low Diuretic Efficiency in Acute Decompensated Heart Failure.

Background: Individuals with acute decompensated heart failure (ADHF) have a varying response to diuretic therapy. Strategies for the early identification of low diuretic efficiency to inform decongestion therapies are lacking.

Objectives: The authors sought to develop and externally validate a machine learning-based phenomapping approach and integer-based diuresis score to identify patients with low diuretic efficiency.

An Integrated Process for Co-Developing and Implementing Written and Computable Clinical Practice Guidelines.

The goal of this article is to describe an integrated parallel process for the co-development of written and computable clinical practice guidelines (CPGs) to accelerate adoption and increase the impact of guideline recommendations in clinical practice. From February 2018 through December 2021, interdisciplinary work groups were formed after an initial Kaizen event and using expert consensus and available literature, produced a 12-phase integrated process (IP). The IP includes activities, resources, and iterative feedback loops for developing, implementing, disseminating, communicating, and evaluating CPGs.

Validation of a predictive model for hospital-acquired acute kidney injury with emergence of SARS-CoV-2 variants.

We previously developed and validated a model to predict acute kidney injury (AKI) in hospitalized coronavirus disease 2019 (COVID-19) patients and found that the variables with the highest importance included a history of chronic kidney disease and markers of inflammation. Here, we assessed model performance during periods when COVID-19 cases were attributable almost exclusively to individual variants. Electronic Health Record data were obtained from patients admitted to 19 hospitals.

Real-Time Patient Portal Use Among Emergency Department Patients: An Open Results Study.

Objectives: We characterized real-time patient portal test result viewing among emergency department (ED) patients and described patient characteristics overall and among those not enrolled in the portal at ED arrival.

Methods: Our observational study at an academic ED used portal log data to trend the proportion of adult patients who viewed results during their visit from May 04, 2021 to April 04, 2022. Correlation was assessed visually and with Kendall's τ.

Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19.

Rationale & Objective: Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, but validated, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI in hospitalized patients with coronavirus disease 2019 and assess its performance over time with the emergence of vaccines and the Delta variant.

Study Design: Longitudinal cohort study.

Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19.

Background: Acute kidney injury (AKI) is a common complication in patients hospitalized with COVID-19 and may require renal replacement therapy (RRT). Dipstick urinalysis is frequently obtained, but data regarding the prognostic value of hematuria and proteinuria for kidney outcomes is scarce.

Methods: Patients with positive severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) PCR, who had a urinalysis obtained on admission to one of 20 hospitals, were included.

Rolling Up the Sleeve: Equitable, Efficient, and Safe COVID-19 Mass Immunization for Academic Medical Center Employees.

Background: Novel coronavirus disease 2019 (COVID-19) vaccine administration has faced distribution barriers across the United States. We sought to delineate our vaccine delivery experience in the first week of vaccine availability, and our effort to prioritize employees based on risk with a goal of providing an efficient infrastructure to optimize speed and efficiency of vaccine delivery while minimizing risk of infection during the immunization process.

Objective: This article aims to evaluate an employee prioritization/invitation/scheduling system, leveraging an integrated electronic health record patient portal framework for employee COVID-19 immunizations at an academic medical center.

Incorporation of natriuretic peptides with clinical risk scores to predict heart failure among individuals with dysglycaemia.

Aims: To evaluate the performance of the WATCH-DM risk score, a clinical risk score for heart failure (HF), in patients with dysglycaemia and in combination with natriuretic peptides (NPs).

Methods And Results: Adults with diabetes/pre-diabetes free of HF at baseline from four cohort studies (ARIC, CHS, FHS, and MESA) were included. The machine learning- [WATCH-DM(ml)] and integer-based [WATCH-DM(i)] scores were used to estimate the 5-year risk of incident HF.

Electronic Health Records-Based Cardio-Oncology Registry for Care Gap Identification and Pragmatic Research: Procedure and Observational Study.

Background: Professional society guidelines are emerging for cardiovascular care in cancer patients. However, it is not yet clear how effectively the cancer survivor population is screened and treated for cardiomyopathy in contemporary clinical practice. As electronic health records (EHRs) are now widely used in clinical practice, we tested the hypothesis that an EHR-based cardio-oncology registry can address these questions.

Development and validation of optimal phenomapping methods to estimate long-term atherosclerotic cardiovascular disease risk in patients with type 2 diabetes.

Aims/hypothesis: Type 2 diabetes is a heterogeneous disease process with variable trajectories of CVD risk. We aimed to evaluate four phenomapping strategies and their ability to stratify CVD risk in individuals with type 2 diabetes and to identify subgroups who may benefit from specific therapies.

Methods: Participants with type 2 diabetes and free of baseline CVD in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial were included in this study (N = 6466).

Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements.

Objective: Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately.

Coupled Effect of Electronic Medical Record Modifications and Lean Six Sigma Methodology on Rheumatoid Arthritis Disease Activity Measurement and Treat-to-Target Outcomes.

Objective: Rheumatoid arthritis (RA) disease activity assessment is critical for treatment decisions and treat to target (T2T) outcomes. Utilization of the electronic medical record (EMR) and techniques to improve the routine capture of disease activity measures in clinical practice are not well described. We leveraged a Lean Six Sigma (LSS) approach, a data-driven five-step process improvement and problem-solving methodology, coupled with EMR modifications to evaluate improvement in disease activity documentation and patient outcomes.

Impact of a problem-oriented view on clinical data retrieval.

Objective: The electronic health record (EHR) data deluge makes data retrieval more difficult, escalating cognitive load and exacerbating clinician burnout. New auto-summarization techniques are needed. The study goal was to determine if problem-oriented view (POV) auto-summaries improve data retrieval workflows.

The use of automated data extraction tools to develop a solid organ transplant registry: Proof of concept study of bloodstream infections.

Background: We created an electronic health record-based registry using automated data extraction tools to study the epidemiology of bloodstream infections (BSI) in solid organ transplant recipients. The overarching goal was to determine the usefulness of an electronic health record-based registry using data extraction tools for clinical research in solid organ transplantation.

Methods: We performed a retrospective single-center cohort study of adult solid organ transplant recipients from 2010 to 2015.

Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis.

Aim: To identify distinct phenotypic subgroups in a highly-dimensional, mixed-data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis.

Methods And Results: The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model-based clustering analysis on 61 mixed-data phenotypic variables.

Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score.

Objective: To develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM).

Research Design And Methods: Using data from 8,756 patients free at baseline of HF, with <10% missing data, and enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, we used random survival forest (RSF) methods, a nonparametric decision tree machine learning approach, to identify predictors of incident HF. The RSF model was externally validated in a cohort of individuals with T2DM using the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT).

User stories as lightweight requirements for agile clinical decision support development.

Objective: We sought to demonstrate applicability of user stories, progressively elaborated by testable acceptance criteria, as lightweight requirements for agile development of clinical decision support (CDS).

Materials And Methods: User stories employed the template: As a [type of user], I want [some goal] so that [some reason]. From the "so that" section, CDS benefit measures were derived.

"D/C the CC (Carbon Copy)" - Improving the EHR Signal-to-Noise Ratio for Clinicians by Selective Feature De-Implementation.

De-implementation of a 10-year EHR configuration resulted in over 50% decrease in the volume of the most-common InBasket message type received by PCPs. Pro-actively seeking out ways to not only (a) implement helpful new EHR features but (b) de-implement detrimental ones offers an opportunity to accelerate improvement in the S/N ratio and reduce clinician frustration and dissatisfaction with the EHR. Balancing governance decision agendas with de-implementation opportunities can enhance the clinician experience.