Data analysis protocol for early autonomic dysfunction characterization after severe traumatic brain injury.

Background: Traumatic brain injury (TBI) disrupts normal brain tissue and functions, leading to high mortality and disability. Severe TBI (sTBI) causes prolonged cognitive, functional, and multi-organ dysfunction. Dysfunction of the autonomic nervous system (ANS) after sTBI can induce abnormalities in multiple organ systems, contributing to cardiovascular dysregulation and increased mortality.

Distinguishing Among Causes of Death for Patients with Kidney Failure on Hemodialysis.

Background: Patients treated with maintenance hemodialysis (HD) are at high risk of death from a variety of causes.

Methods: To identify markers (i.e.

Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

Objectives: This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretability, and implementation of ML-based CPMs among multiple constituent groups.

Materials And Methods: We collected and analyzed qualitative data from focus groups with varied end users, including: dialysis support providers (clinical providers and additional dialysis support providers such as dialysis clinic staff and social workers); patients; patients' caregivers (n = 52).

Breast and Prostate Cancer Screening by Life Expectancy in Patients with Kidney Failure on Dialysis.

Key Points: Breast and prostate cancer screening were more frequent among patients on hemodialysis waitlisted for kidney transplant. Among patients not waitlisted for transplant, we found that screening rates were generally higher among patients with higher predicted 5-year survival. Among patients not waitlisted for transplant and with the highest predicted 5-year survival, there was a deficit of screening compared with waitlisted patients.

Interactive visualization tool to understand and monitor health disparities in diabetes care and outcomes.

Objective: Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities.

A conditional multi-label model to improve prediction of a rare outcome: An illustration predicting autism diagnosis.

Objective: This study aimed to develop a novel approach using routinely collected electronic health records (EHRs) data to improve the prediction of a rare event. We illustrated this using an example of improving early prediction of an autism diagnosis, given its low prevalence, by leveraging correlations between autism and other neurodevelopmental conditions (NDCs).

Methods: To achieve this, we introduced a conditional multi-label model by merging conditional learning and multi-label methodologies.

Addressing cost barriers to healthy eating with Eat Well, a prescription produce subsidy, for patients with diabetes and at risk for food insecurity: Study protocol for a type 1 hybrid effectiveness-implementation pragmatic randomized controlled trial.

Background: Patients with diabetes at risk of food insecurity face cost barriers to healthy eating and, as a result, poor health outcomes. Population health management strategies are needed to improve food security in real-world health system settings. We seek to test the effect of a prescription produce program, 'Eat Well' on cardiometabolic health and healthcare utilization.

Incorporating informatively collected laboratory data from EHR in clinical prediction models.

Background: Electronic Health Records (EHR) are widely used to develop clinical prediction models (CPMs). However, one of the challenges is that there is often a degree of informative missing data. For example, laboratory measures are typically taken when a clinician is concerned that there is a need.

Enhancing Clinical Decision Support in Nephrology: Addressing Algorithmic Bias Through Artificial Intelligence Governance.

There has been a steady rise in the use of clinical decision support (CDS) tools to guide nephrology as well as general clinical care. Through guidance set by federal agencies and concerns raised by clinical investigators, there has been an equal rise in understanding whether such tools exhibit algorithmic bias leading to unfairness. This has spurred the more fundamental question of whether sensitive variables such as race should be included in CDS tools.

Impact of preeclampsia on infant and maternal health among women with rheumatic diseases.

Objectives: We sought to identify the impact of preeclampsia on infant and maternal health among women with rheumatic diseases.

Methods: A retrospective single-center cohort study was conducted to describe pregnancy and infant outcomes among women with systemic lupus erythematosus (SLE) with and without preeclampsia as compared to women with other rheumatic diseases with and without preeclampsia.

Results: We identified 263 singleton deliveries born to 226 individual mothers (mean age 31 years, 35% non-Hispanic Black).

Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare.

Objective: The complexity and rapid pace of development of algorithmic technologies pose challenges for their regulation and oversight in healthcare settings. We sought to improve our institution's approach to evaluation and governance of algorithmic technologies used in clinical care and operations by creating an Implementation Guide that standardizes evaluation criteria so that local oversight is performed in an objective fashion.

Materials And Methods: Building on a framework that applies key ethical and quality principles (clinical value and safety, fairness and equity, usability and adoption, transparency and accountability, and regulatory compliance), we created concrete guidelines for evaluating algorithmic technologies at our institution.

Comparing Natural Language Processing and Structured Medical Data to Develop a Computable Phenotype for Patients Hospitalized Due to COVID-19: Retrospective Analysis.

Background: Throughout the COVID-19 pandemic, many hospitals conducted routine testing of hospitalized patients for SARS-CoV-2 infection upon admission. Some of these patients are admitted for reasons unrelated to COVID-19 and incidentally test positive for the virus. Because COVID-19-related hospitalizations have become a critical public health indicator, it is important to identify patients who are hospitalized because of COVID-19 as opposed to those who are admitted for other indications.

Impact of state legislation and institutional protocols on opioid prescribing practices following pediatric tonsillectomy.

Objectives: Tonsillectomy is a common pediatric surgery, and pain is an important consideration in recovery. Due to the opioid epidemic, individual states, medical societies, and institutions have all taken steps to limit postoperative opioids, yet few studies have examined the effect of these interventions on pediatric otolaryngology practices. The primary aim of this study was to characterize opioid prescribing practices following North Carolina state opioid legislation and targeted institutional changes.

Predictive Modeling to Identify Children With Complex Health Needs At Risk for Hospitalization.

Background: Identifying children at high risk with complex health needs (CCHN) who have intersecting medical and social needs is challenging. This study's objectives were to (1) develop and evaluate an electronic health record (EHR)-based clinical predictive model ("model") for identifying high-risk CCHN and (2) compare the model's performance as a clinical decision support (CDS) to other CDS tools available for identifying high-risk CCHN.

Methods: This retrospective cohort study included children aged 0 to 20 years with established care within a single health system.

Use of Structured Electronic Health Records Data Elements for the Development of Computable Phenotypes to Identify Potential Adverse Events Associated with Intravenous Immunoglobulin Infusion.

Introduction: Detection of adverse reactions to drugs and biologic agents is an important component of regulatory approval and post-market safety evaluation. Real-world data, including insurance claims and electronic health records data, are increasingly used for the evaluation of potential safety outcomes; however, there are different types of data elements available within these data resources, impacting the development and performance of computable phenotypes for the identification of adverse events (AEs) associated with a given therapy.

Objective: To evaluate the utility of different types of data elements to the performance of computable phenotypes for AEs.

Utilization and Outcomes of Extracorporeal Membrane Oxygenation Following Traumatic Brain Injury in the United States.

Describe contemporary ECMO utilization patterns among patients with traumatic brain injury (TBI) and examine clinical outcomes among TBI patients requiring ECMO. Retrospective cohort study. Premier Healthcare Database (PHD) between January 2016 to June 2020.

Association between Gentrification and Health and Healthcare Utilization.

There is tremendous interest in understanding how neighborhoods impact health by linking extant social and environmental drivers of health (SDOH) data with electronic health record (EHR) data. Studies quantifying such associations often use static neighborhood measures. Little research examines the impact of gentrification-a measure of neighborhood change-on the health of long-term neighborhood residents using EHR data, which may have a more generalizable population than traditional approaches.

Transition from pediatric to adult care in type 1 diabetes mellitus: a longitudinal analysis of age at transfer and gap in care.

Introduction: Adolescents and young adults (AYAs) with type 1 diabetes (T1D) are at risk of suboptimal glycemic control and high acute care utilization. Little is known about the optimal age to transfer people with T1D to adult care, or time gap between completing pediatric care and beginning adult endocrinology care.

Research Design And Methods: This retrospective, longitudinal study examined the transition of AYAs with T1D who received endocrinology care within Duke University Health System.

Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer.

Dealing with severe class imbalance poses a major challenge for many real-world applications, especially when the accurate classification and generalization of minority classes are of primary interest. In computer vision and NLP, learning from datasets with long-tail behavior is a recurring theme, especially for naturally occurring labels. Existing solutions mostly appeal to sampling or weighting adjustments to alleviate the extreme imbalance, or impose inductive bias to prioritize generalizable associations.