Identification of Adult Dermatomyositis Patients Using Real-World Data Sources.

Objective: Studying rare diseases like dermatomyositis (DM) in single-center cohorts is challenging due to small sample sizes and limited generalizability. This study develops and evaluates case identification algorithms for DM to enable coordinated analysis across multiple data sources.

Methods: Case identification algorithms were developed to identify adult DM patients within eleven independent electronic health record or claims databases, totaling over 800 million patients, using the Observational Medical Outcomes Partnership (OMOP) Common Data Model.

Variation in Phenotype Definitions in Observational Clinical Research: A Review of Three Conditions.

Our study examined the heterogeneity of phenotype algorithms (PA) in literature on Alzheimer's disease (AD), major depressive disorder (MDD), and pulmonary arterial hypertension (PAI), focusing on the impact of PA differences on patient overlap and incidence rate variability across conditions in six observational databases. We reviewed 49 replicated PAs (13 for AD, 23 for MDD, and 13 for PAI) and found significant heterogeneity. These varied PAs identified distinct patient cohorts, resulting in significant incidence rate heterogeneity.

A rapid cycle analytics framework for vaccine safety surveillance within a real-world data network: Experience with enhanced surveillance of the Janssen COVID-19 vaccine.

Objective: To complement and support routine pharmacovigilance, Janssen conducted rapid real-world data analyses for near real-time safety monitoring of the Janssen COVID-19 vaccine and to contextualize potential safety signals.

Methods: Analyses were performed in four U.S.

CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization.

Objective: This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.

Materials And Methods: The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date.

Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research.

The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared.

A spatial assessment of prostate cancer mortality-to-incidence ratios among South Carolina veterans: 1999-2015.

Purpose: To assess veteran-specific prostate cancer (PrCA) mortality-to-incidence ratios (MIR) in South Carolina's (SC) veteran population.

Methods: U.S.

Characterizing the incidence of adverse events of special interest for COVID-19 vaccines across eight countries: a multinational network cohort study.

Background: As large-scale immunization programs against COVID-19 proceed around the world, safety signals will emerge that need rapid evaluation. We report population-based, age- and sex-specific background incidence rates of potential adverse events of special interest (AESI) in eight countries using thirteen databases.

Methods: This multi-national network cohort study included eight electronic medical record and five administrative claims databases from Australia, France, Germany, Japan, Netherlands, Spain, the United Kingdom, and the United States, mapped to a common data model.

Deep phenotyping of 34,128 patients hospitalised with COVID-19 and a comparison with 81,596 influenza patients in America, Europe and Asia: an international network study.

Background In this study we phenotyped individuals hospitalised with coronavirus disease 2019 (COVID-19) in depth, summarising entire medical histories, including medications, as captured in routinely collected data drawn from databases across three continents. We then compared individuals hospitalised with COVID-19 to those previously hospitalised with influenza. Methods We report demographics, previously recorded conditions and medication use of patients hospitalised with COVID-19 in the US (Columbia University Irving Medical Center [CUIMC], Premier Healthcare Database [PHD], UCHealth System Health Data Compass Database [UC HDC], and the Department of Veterans Affairs [VA OMOP]), in South Korea (Health Insurance Review & Assessment [HIRA]), and Spain (The Information System for Research in Primary Care [SIDIAP] and HM Hospitales [HM]).

Patient- and area-level predictors of prostate cancer among South Carolina veterans: a spatial analysis.

Background: Racial and socio-economic status (SES) disparities exist in prostate cancer (PrCA) incidence and mortality. Less is known regarding how geographical factors, including neighborhood social vulnerability and distance traveled to receive care, affect PrCA risk. The purpose of this research was to use the Veterans Administration Medical System, which provides a unique means for studying PrCA epidemiology among diverse individuals with ostensibly equal access to healthcare, to determine whether area-level characteristics influence PrCA incidence while accounting for individual-level risk factors.

Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network.

Background And Purpose: Hemorrhagic transformation (HT) after cerebral infarction is a complex and multifactorial phenomenon in the acute stage of ischemic stroke, and often results in a poor prognosis. Thus, identifying risk factors and making an early prediction of HT in acute cerebral infarction contributes not only to the selections of therapeutic regimen but also, more importantly, to the improvement of prognosis of acute cerebral infarction. The purpose of this study was to develop and validate a model to predict a patient's risk of HT within 30 days of initial ischemic stroke.

Fluoroquinolone-related neuropsychiatric and mitochondrial toxicity: a collaborative investigation by scientists and members of a social network.

Background: The 3 fluoroquinolone (FQ) antibiotics - ciprofoxacin, levofoxacin, and moxifoxacin - are commonly administered to oncology patients. Although these oral antibiotics are approved by the US Food and Drug Administration (FDA) for treatment of urinary tract infections, acute bacterial sinusitis, or bacterial infection in patients with chronic obstructive pulmonary disease, they are commonly prescribed off-label to neutropenic cancer patients for the prevention and treatment of infections associated with febrile neutropenia. New serious FQ-associated safety concerns have been identified through novel collaborations between FQ-treated persons who have developed long-term neuropsychiatric (NP) toxicity, pharmacovigilance experts, and basic scientists.

The use of multiphase nonlinear mixed models to define and quantify long-term changes in serum prostate-specific antigen: data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

Purpose: To test the hypothesis that the pattern of prostate-specific antigen (PSA) change in men diagnosed with high-risk prostate cancer (PrCA) differs from the pattern evident in men diagnosed with low-risk PrCA or those with no evidence of PrCA.

Methods: A retrospective cohort study from which PSA measures were taken before PrCA diagnosis from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Data were fitted using a nonlinear regression model to estimate the adjusted absolute and relative (%) change of PSA.