Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network.

INTRODUCTION: Traditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks to advance diabetes surveillance efforts in youth and young adults through the use of large-volume electronic health record (EHR) data. The network has two primary aims, namely: (1) to refine and validate EHR-based computable phenotype algorithms for accurate identification of type 1 and type 2 diabetes among youth and young adults and (2) to estimate the incidence and prevalence of type 1 and type 2 diabetes among youth and young adults and trends therein. The network aims to augment diabetes surveillance capacity in the USA and assess performance of EHR-based surveillance. This paper describes the DiCAYA Network and how these aims will be achieved. METHODS AND ANALYSIS: The DiCAYA Network is spread across eight geographically diverse US-based centres and a coordinating centre. Three centres conduct diabetes surveillance in youth aged 0-17 years only (component A), three centres conduct surveillance in young adults aged 18-44 years only (component B) and two centres conduct surveillance in components A and B. The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. Prevalence and incidence rates will be presented as unadjusted estimates and as race/ethnicity, sex and age-adjusted estimates using Poisson regression. ETHICS AND DISSEMINATION: The DiCAYA Network is well positioned to advance diabetes surveillance methods. The network will disseminate EHR-based surveillance methodology that can be broadly adopted and will report diabetes prevalence and incidence for key demographic subgroups of youth and young adults in a large set of regions across the USA.

Systematic data quality assessment of electronic health record data to evaluate study-specific fitness: Report from the PRESERVE research study.

Study-specific data quality testing is an essential part of minimizing analytic errors, particularly for studies making secondary use of clinical data. We applied a systematic and reproducible approach for study-specific data quality testing to the analysis plan for PRESERVE, a 15-site, EHR-based observational study of chronic kidney disease in children. This approach integrated widely adopted data quality concepts with healthcare-specific evaluation methods. We implemented two rounds of data quality assessment. The first produced high-level evaluation using aggregate results from a distributed query, focused on cohort identification and main analytic requirements. The second focused on extended testing of row-level data centralized for analysis. We systematized reporting and cataloguing of data quality issues, providing institutional teams with prioritized issues for resolution. We tracked improvements and documented anomalous data for consideration during analyses. The checks we developed identified 115 and 157 data quality issues in the two rounds, involving completeness, data model conformance, cross-variable concordance, consistency, and plausibility, extending traditional data quality approaches to address more complex stratification and temporal patterns. Resolution efforts focused on higher priority issues, given finite study resources. In many cases, institutional teams were able to correct data extraction errors or obtain additional data, avoiding exclusion of 2 institutions entirely and resolving 123 other gaps. Other results identified complexities in measures of kidney function, bearing on the study's outcome definition. Where limitations such as these are intrinsic to clinical data, the study team must account for them in conducting analyses. This study rigorously evaluated fitness of data for intended use. The framework is reusable and built on a strong theoretical underpinning. Significant data quality issues that would have otherwise delayed analyses or made data unusable were addressed. This study highlights the need for teams combining subject-matter and informatics expertise to address data quality when working with real world data.

Implementing a pragmatic clinical trial to tailor opioids for chronic pain on behalf of the IGNITE ADOPT PGx investigators.

Chronic pain is a prevalent condition with enormous economic burden. Opioids such as tramadol, codeine, and hydrocodone are commonly used to treat chronic pain; these drugs are activated to more potent opioid receptor agonists by the hepatic CYP2D6 enzyme. Results from clinical studies and mechanistic understandings suggest that CYP2D6-guided therapy will improve pain control and reduce adverse drug events. However, CYP2D6 is rarely used in clinical practice due in part to the demand for additional clinical trial evidence. Thus, we designed the ADOPT-PGx (A Depression and Opioid Pragmatic Trial in Pharmacogenetics) chronic pain study, a multicenter, pragmatic, randomized controlled clinical trial, to assess the effect of CYP2D6 testing on pain management. The study enrolled 1048 participants who are taking or being considered for treatment with CYP2D6-impacted opioids for their chronic pain. Participants were randomized to receive immediate or delayed (by 6 months) genotyping of CYP2D6 with clinical decision support (CDS). CDS encouraged the providers to follow the CYP2D6-guided trial recommendations. The primary study outcome is the 3-month absolute change in the composite pain intensity score assessed using Patient-Reported Outcomes Measurement Information System (PROMIS) measures. Follow-up will be completed in July 2024. Herein, we describe the design of this trial along with challenges encountered during enrollment.

Evolving availability and standardization of patient attributes for matching.

Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes were highly available (>90%) across health care organizations and time. SSN declined slightly in the last years of the study period. Birth sex, gender identity, language, country full name, country abbreviation, health insurance number, ethnicity, cell phone number, email address, and weight increased over 50% from 2010 to 2020. Understanding the wide variation in available patient attributes across care settings in the United States can guide selection and standardization efforts for improved patient matching in the United States.