The dementia care study (D-CARE): Recruitment strategies and demographic characteristics of participants in a pragmatic randomized trial of dementia care.

INTRODUCTION: Pragmatic research studies that include diverse dyads of persons living with dementia (PLWD) and their family caregivers are rare. METHODS: Community-dwelling dyads were recruited for a pragmatic clinical trial evaluating three approaches to dementia care. Four clinical trial sites used shared and site-specific recruitment strategies to enroll health system patients. RESULTS: Electronic health record (EHR) queries of patients with a diagnosis of dementia and engagement of their clinicians were the main recruitment strategies. A total of 2176 dyads were enrolled, with 80% recruited after the onset of the pandemic. PLWD had a mean age of 80.6 years (SD 8.5), 58.4% were women, and 8.8% were Hispanic/Latino, and 11.9% were Black/African American. Caregivers were mostly children of the PLWD (46.5%) or spouses/partners (45.2%), 75.8% were women, 9.4% were Hispanic/Latino, and 11.6% were Black/African American. DISCUSSION: Health systems can successfully enroll diverse dyads in a pragmatic clinical trial.

Validation of a Rule-Based ICD-10-CM Algorithm to Detect Fall Injuries in Medicare Data.

BACKGROUND: Diagnosis-code-based algorithms to identify fall injuries in Medicare data are useful for ascertaining outcomes in interventional and observational studies. However, these algorithms have not been validated against a fully external reference standard, in ICD-10-CM, or in Medicare Advantage (MA) data. METHODS: We linked self-reported fall injuries leading to medical attention (FIMA) from the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial (reference standard) to Medicare fee-for-service (FFS) and MA data from 2015-19. We measured the area under the receiver operating characteristic curve (AUC) based on sensitivity and specificity of a diagnosis-code-based algorithm against the reference standard for presence or absence of ≥1 FIMA within a specified window of dates, varying the window size to obtain points on the curve. We stratified results by source (FFS vs MA), trial arm (intervention vs control), and STRIDE's 10 participating health care systems. RESULTS: Both reference standard data and Medicare data were available for 4 941 (of 5 451) participants. The reference standard and algorithm identified 2 054 and 2 067 FIMA, respectively. The algorithm had 45% sensitivity (95% confidence interval [CI]: 43%-47%) and 99% specificity (95% CI: 99%-99%) to identify reference standard FIMA within the same calendar month. The AUC was 0.79 (95% CI: 0.78-0.81) and was similar by FFS or MA data source and by trial arm but showed variation among STRIDE health care systems (AUC range by health care system, 0.71 to 0.84). CONCLUSIONS: An ICD-10-CM algorithm to identify fall injuries demonstrated acceptable performance against an external reference standard, in both MA and FFS data.

Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials - A case study.

BACKGROUND: Variable data quality poses a challenge to using electronic health record (EHR) data to ascertain acute clinical outcomes in multi-site clinical trials. Differing EHR platforms and data comprehensiveness across clinical trial sites, especially if patients received care outside of the clinical site's network, can also affect validity of results. Overcoming these challenges requires a structured approach. METHODS: We propose a framework and create a checklist to assess the readiness of clinical sites to contribute EHR data to a clinical trial for the purpose of outcome ascertainment, based on our experience with the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) study, which enrolled 5451 participants in 86 primary care practices across 10 healthcare systems (sites). RESULTS: The site readiness checklist includes assessment of the infrastructure (i.e., size and structure of the site's healthcare system or clinical network), data procurement (i.e., quality of the data), and cost of obtaining study data. The checklist emphasizes the importance of understanding how data are captured and integrated across a site's catchment area and having a protocol in place for data procurement to ensure consistent and uniform extraction across each site. CONCLUSIONS: We suggest rigorous, prospective vetting of the data quality and infrastructure of each clinical site before launching a multi-site trial dependent on EHR data. The proposed checklist serves as a guiding tool to help investigators ensure robust and unbiased data capture for their clinical trials. ORIGINAL TRIAL REGISTRATION NUMBER: NCT02475850.