Deep learning for rare disease: A scoping review.

Although individually rare, collectively more than 7,000 rare diseases affect about 10% of patients. Each of the rare diseases impacts the quality of life for patients and their families, and incurs significant societal costs. The low prevalence of each rare disease causes formidable challenges in accurately diagnosing and caring for these patients and engaging participants in research to advance treatments.

A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data.

Background: Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population.

Objectives: This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage.

Published models that predict hospital readmission: a critical appraisal.

Introduction: The number of readmission risk prediction models available has increased rapidly, and these models are used extensively for health decision-making. Unfortunately, readmission models can be subject to flaws in their development and validation, as well as limitations in their clinical usefulness.

Objective: To critically appraise readmission models in the published literature using Delphi-based recommendations for their development and validation.

Clinical comparison between trial participants and potentially eligible patients using electronic health record data: A generalizability assessment method.

Objective: To present a generalizability assessment method that compares baseline clinical characteristics of trial participants (TP) to potentially eligible (PE) patients as presented in their electronic health record (EHR) data while controlling for clinical setting and recruitment period.

Methods: For each clinical trial, a clinical event was defined to identify patients of interest using available EHR data from one clinical setting during the trial's recruitment timeframe. The trial's eligibility criteria were then applied and patients were separated into two mutually exclusive groups: (1) TP, which were patients that participated in the trial per trial enrollment data; (2) PE, the remaining patients.

Comparison of Clinical Characteristics Between Clinical Trial Participants and Nonparticipants Using Electronic Health Record Data.

Importance: Assessing generalizability of clinical trials is important to ensure appropriate application of interventions, but most assessments provide minimal granularity on comparisons of clinical characteristics.

Objective: To assess the extent of underlying clinical differences between clinical trial participants and nonparticipants by using a combination of electronic health record and trial enrollment data.

Design, Setting, And Participants: This cross-sectional study used data obtained from a single academic medical center between September 1996 and January 2019 to identify 1645 clinical trial participants from a diverse set of 202 available trials conducted at the center.

Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials.

Objective: This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data.

Materials And Methods: On June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate.

Contemporary use of real-world data for clinical trial conduct in the United States: a scoping review.

Objective: Real-world data (RWD), defined as routinely collected healthcare data, can be a potential catalyst for addressing challenges faced in clinical trials. We performed a scoping review of database-specific RWD applications within clinical trial contexts, synthesizing prominent uses and themes.

Materials And Methods: Querying 3 biomedical literature databases, research articles using electronic health records, administrative claims databases, or clinical registries either within a clinical trial or in tandem with methodology related to clinical trials were included.

Understanding the nature and scope of clinical research commentaries in PubMed.

Scientific commentaries are expected to play an important role in evidence appraisal, but it is unknown whether this expectation has been fulfilled. This study aims to better understand the role of scientific commentary in evidence appraisal. We queried PubMed for all clinical research articles with accompanying comments and extracted corresponding metadata.

Ensembles of natural language processing systems for portable phenotyping solutions.

Background: Manually curating standardized phenotypic concepts such as Human Phenotype Ontology (HPO) terms from narrative text in electronic health records (EHRs) is time consuming and error prone. Natural language processing (NLP) techniques can facilitate automated phenotype extraction and thus improve the efficiency of curating clinical phenotypes from clinical texts. While individual NLP systems can perform well for a single cohort, an ensemble-based method might shed light on increasing the portability of NLP pipelines across different cohorts.

Stepped-wedge randomised trial to evaluate population health intervention designed to increase appropriate anticoagulation in patients with atrial fibrillation.

Background: Clinical guidelines recommend anticoagulation for patients with atrial fibrillation (AF) at high risk of stroke; however, studies report 40% of this population is not anticoagulated.

Objective: To evaluate a population health intervention to increase anticoagulation use in high-risk patients with AF.

Methods: We used machine learning algorithms to identify patients with AF from electronic health records at high risk of stroke (CHADS-VASc risk score ≥2), and no anticoagulant prescriptions within 12 months.

A Data Element-Function Conceptual Model for Data Quality Checks.

Introduction: In aggregate, existing data quality (DQ) checks are currently represented in heterogeneous formats, making it difficult to compare, categorize, and index checks. This study contributes a data element-function conceptual model to facilitate the categorization and indexing of DQ checks and explores the feasibility of leveraging natural language processing (NLP) for scalable acquisition of knowledge of common data elements and functions from DQ checks narratives.

Methods: The model defines a "data element", the primary focus of the check, and a "function", the qualitative or quantitative measure over a data element.

Comparative effectiveness of generic and brand-name medication use: A database study of US health insurance claims.

Background: To the extent that outcomes are mediated through negative perceptions of generics (the nocebo effect), observational studies comparing brand-name and generic drugs are susceptible to bias favoring the brand-name drugs. We used authorized generic (AG) products, which are identical in composition and appearance to brand-name products but are marketed as generics, as a control group to address this bias in an evaluation aiming to compare the effectiveness of generic versus brand medications.

Methods And Findings: For commercial health insurance enrollees from the US, administrative claims data were derived from 2 databases: (1) Optum Clinformatics Data Mart (years: 2004-2013) and (2) Truven MarketScan (years: 2003-2015).

Characteristics of early sacubitril/valsartan patients and considerations for studies in electronic health record data.

Aim: We examined characteristics of early sacubitril/valsartan users in a large US electronic health records database.

Patients & Methods: We identified three cohorts of patients with heart failure (HF): sacubitril/valsartan patients with a prior HF diagnosis; patients with HF with reduced ejection fraction; and patients with HF treated with an angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker and a β-blocker.

Results: Sacubitril/valsartan patients were younger than patients in the other cohorts; the mean age of sacubitril/valsartan patients increased by 2 years in the first 15 months of marketing.

Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic.

The tree-based scan statistic is a statistical data mining tool that has been used for signal detection with a self-controlled design in vaccine safety studies. This disproportionality statistic adjusts for multiple testing in evaluation of thousands of potential adverse events. However, many drug safety questions are not well suited for self-controlled analysis.

Effect of Lawyer-Submitted Reports on Signals of Disproportional Reporting in the Food and Drug Administration's Adverse Event Reporting System.

Introduction: Lawyer-submitted reports may have unintended consequences on safety signal detection in spontaneous adverse event reporting systems.

Objective: Our objective was to assess the impact of lawyer-submitted reports primarily for one adverse event (AE) on the ability to detect a signal of disproportional reporting for another AE for the same drug in the US FDA Adverse Event Reporting System (FAERS).

Methods: FAERS reports from January 2004 to September 2015 were used to estimate yearly cumulative proportional reporting ratios (PRRs) for three known drug-AE pairs-isotretinoin-birth defects, atorvastatin-rhabdomyolysis, and rosuvastatin-rhabdomyolysis-with and without lawyer-submitted reports.

No difference in cardiovascular risk of tocilizumab versus abatacept for rheumatoid arthritis: A multi-database cohort study.

Objectives: While tocilizumab may increase serum lipid levels, recent studies do not suggest a link between tocilizumab use and clinical cardiovascular risk in patients with rheumatoid arthritis (RA).

Methods: To compare cardiovascular safety of tocilizumab with abatacept, we conducted a cohort study using data from Medicare (2010-2013), IMS PharMetrics (2011-2014) and MarketScan (2011-6/2015). RA patients aged ≥18 years who newly started tocilizumab or abatacept entered the cohort on the day of their first use of tocilizumab or abatacept after a continuous enrollment period for ≥365 days.

Differences in rates of switchbacks after switching from branded to authorized generic and branded to generic drug products: cohort study.

Objectives: To compare rates of switchbacks to branded drug products for patients switched from branded to authorized generic drug products, which have the same active ingredients, appearance, and excipients as the branded product, with patients switched from branded to generic drug products, which have the same active ingredients as the branded product but may differ in appearance and excipients.

Design: Observational cohort study.

Setting: Private (a large commercial health plan) and public (Medicaid) insurance programs in the US.

The relative benefits of claims and electronic health record data for predicting medication adherence trajectory.

Background: Healthcare providers are increasingly encouraged to improve their patients' adherence to chronic disease medications. Prediction of adherence can identify patients in need of intervention, but most prediction efforts have focused on claims data, which may be unavailable to providers. Electronic health records (EHR) are readily available and may provide richer information with which to predict adherence than is currently available through claims.

Assessment of coronary vascular function with cardiac PET in relation to serum uric acid.

Background: Elevated serum uric acid (SUA) levels have been independently associated with cardiovascular disease. Stress myocardial perfusion positron emission tomography (PET) allows for measurement of absolute myocardial blood flow (MBF) and quantification of global left ventricular coronary flow reserve (CFR). A CFR <2.

Survey of Living Organ Donors' Experience and Directions for Process Improvement.

Introduction: Understanding living organ donors' experience with donation and challenges faced during the process is necessary to guide the development of effective strategies to maximize donor benefit and increase the number of living donors.

Methods: An anonymous self-administered survey, specifically designed for this population based on key informant interviews, was mailed to 426 individuals who donated a kidney or liver at our institution. Quantitative and qualitative methods including open and axial coding were used to analyze donor responses.