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.

Characteristics and Outcomes of Over a Million Patients with Inflammatory Bowel Disease in Seven Countries: Multinational Cohort Study and Open Data Resource.

Background And Aims: Observational healthcare data are an important tool for delineating patients' inflammatory bowel disease (IBD) journey in real-world settings. However, studies that characterize IBD cohorts typically rely on a single resource, apply diverse eligibility criteria, and extract variable sets of attributes, making comparison between cohorts challenging. We aim to longitudinally describe and compare IBD patient cohorts across multiple geographic regions, employing unified data and analysis framework.

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.

Comparing broad and narrow phenotype algorithms: differences in performance characteristics and immortal time incurred.

When developing phenotype algorithms for observational research, there is usually a trade-off between definitions that are sensitive or specific. The objective of this study was to estimate the performance characteristics of phenotype algorithms designed for increasing specificity and to estimate the immortal time associated with each algorithm. We examined algorithms for 11 chronic health conditions.

The Epidemiology of Palmoplantar Pustulosis: An Analysis of Multiple Health Insurance Claims and Electronic Health Records Databases.

Background: Palmoplantar pustulosis (PPP) is a chronic inflammatory condition characterized by sterile pustules on the palms and soles. This study evaluated the epidemiology of PPP using claims and electronic health record (EHR) databases.

Methods: Patients coded for PPP in the United States (US) and Japan from 2016 to 2020 were identified.

Phenotype Algorithms to Identify Hidradenitis Suppurativa Using Real-World Data: Development and Validation Study.

Background: Hidradenitis suppurativa (HS) is a potentially debilitating, chronic, recurring inflammatory disease. Observational databases provide opportunities to study the epidemiology of HS.

Objective: This study's objective was to develop phenotype algorithms for HS suitable for epidemiological studies based on a network of observational databases.

Using a data-driven approach for the development and evaluation of phenotype algorithms for systemic lupus erythematosus.

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown origin. The objective of this research was to develop phenotype algorithms for SLE suitable for use in epidemiological studies using empirical evidence from observational databases.

Methods: We used a process for empirically determining and evaluating phenotype algorithms for health conditions to be analyzed in observational research.

Development and Evaluation of the Algorithm CErtaInty Tool (ACE-IT) to Assess Electronic Medical Record and Claims-based Algorithms' Fit for Purpose for Safety Outcomes.

Introduction: Electronic health record (EHR) or medical claims-based algorithms (i.e., operational definitions) can be used to define safety outcomes using real-world data.

Performance characteristics of code-based algorithms to identify urinary tract infections in large United States administrative claims databases.

Background: In real-world evidence research, reliability of coding in healthcare databases dictates the accuracy of code-based algorithms in identifying conditions such as urinary tract infection (UTI). This study evaluates the performance characteristics of code-based algorithms to identify UTI.

Methods: Retrospective observational study of adults contained within three large U.

Development and performance characteristics of novel code-based algorithms to identify invasive Escherichia coli disease.

Purpose: Evaluation of novel code-based algorithms to identify invasive Escherichia coli disease (IED) among patients in healthcare databases.

Methods: Inpatient visits with microbiological evidence of invasive bacterial disease were extracted from the Optum© electronic health record database between January 1, 2016 and June 30, 2020. Six algorithms, derived from diagnosis and drug exposure codes associated to infectious diseases and Escherichia coli, were developed to identify IED.

Incidence rate of hospitalization and mortality in the first year following initial diagnosis of cardiac amyloidosis in the US claims databases.

Objective: This study aimed to determine rates of hospitalization and in-hospital mortality in the first year following amyloidosis diagnosis with cardiac involvement using observational databases.

Methods: Three administrative claims databases, IBM MarketScan Commercial Claims and Encounters (CCAE), IBM MarketScan Multi-State Medicare Database (MDCR), and Optum's de-identified Clinformatics Data Mart Database (Optum) were analyzed. Adults ≥18 years old, with a diagnosis of amyloidosis and evidence of cardiac involvement (i.

Evaluation of code-based algorithms to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients in large administrative databases.

Large administrative healthcare (including insurance claims) databases are used for various retrospective real-world evidence studies. However, in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension, identifying patients retrospectively based on administrative codes remains challenging, as it relies on code combinations (algorithms) and the accuracy for patient identification of most of them is unknown. This study aimed to assess the performance of various algorithms in correctly identifying patients with pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension in administrative databases.

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]).

Channeling Bias in the Analysis of Risk of Myocardial Infarction, Stroke, Gastrointestinal Bleeding, and Acute Renal Failure with the Use of Paracetamol Compared with Ibuprofen.

Introduction: Observational studies estimating severe outcomes for paracetamol versus ibuprofen use have acknowledged the specific challenge of channeling bias. A previous study relying on negative controls suggested that using large-scale propensity score (LSPS) matching may mitigate bias better than models using limited lists of covariates.

Objective: The aim was to assess whether using LSPS matching would enable the evaluation of paracetamol, compared to ibuprofen, and increased risk of myocardial infarction, stroke, gastrointestinal (GI) bleeding, or acute renal failure.

Acute pancreatitis risk in type 2 diabetes patients treated with canagliflozin versus other antihyperglycemic agents: an observational claims database study.

Observational evidence suggests that patients with type 2 diabetes mellitus (T2DM) are at increased risk for acute pancreatitis (AP) versus those without T2DM. A small number of AP events were reported in clinical trials of the sodium glucose co-transporter 2 inhibitor canagliflozin, though no imbalances were observed between treatment groups. This observational study evaluated risk of AP among new users of canagliflozin compared with new users of six classes of other antihyperglycemic agents (AHAs).

Developing Predictive Models to Determine Patients in End-of-Life Care in Administrative Datasets.

Introduction: In observational studies with mortality endpoints, one needs to consider how to account for subjects whose interventions appear to be part of 'end-of-life' care.

Objective: The objective of this study was to develop a diagnostic predictive model to identify those in end-of-life care at the time of a drug exposure.

Methods: We used data from four administrative claims datasets from 2000 to 2017.

Comparison of First-Line Dual Combination Treatments in Hypertension: Real-World Evidence from Multinational Heterogeneous Cohorts.

Background And Objectives: 2018 ESC/ESH Hypertension guideline recommends 2-drug combination as initial anti-hypertensive therapy. However, real-world evidence for effectiveness of recommended regimens remains limited. We aimed to compare the effectiveness of first-line anti-hypertensive treatment combining 2 out of the following classes: angiotensin-converting enzyme (ACE) inhibitors/angiotensin-receptor blocker (A), calcium channel blocker (C), and thiazide-type diuretics (D).

PheValuator: Development and evaluation of a phenotype algorithm evaluator.

Background: The primary approach for defining disease in observational healthcare databases is to construct phenotype algorithms (PAs), rule-based heuristics predicated on the presence, absence, and temporal logic of clinical observations. However, a complete evaluation of PAs, i.e.

Channeling in the Use of Nonprescription Paracetamol and Ibuprofen in an Electronic Medical Records Database: Evidence and Implications.

Introduction: Over-the-counter analgesics such as paracetamol and ibuprofen are among the most widely used, and having a good understanding of their safety profile is important to public health. Prior observational studies estimating the risks associated with paracetamol use acknowledge the inherent limitations of these studies. One threat to the validity of observational studies is channeling bias, i.

Ischemic Stroke Rate Increases in Young Adults: Evidence for a Generational Effect?

Background: The incidence rates of ischemic stroke and ST-segment elevation myocardial infarction (STEMI) have decreased significantly in the United States since 1950. However, there is evidence of flattening of this trend or increasing rates for stroke in patients younger than 50 years. The objective of this study was to examine the changes in incidence rates of stroke and STEMI using an age-period-cohort model with statewide data from New Jersey.