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.

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 2015-2019. 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 versus MA), trial arm (intervention versus control), and STRIDE's ten participating healthcare systems. RESULTS: Both reference standard data and Medicare data were available for 4941 (of 5451) participants. The reference standard and algorithm identified 2054 and 2067 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 or trial arm, but showed variation among STRIDE healthcare systems (AUC range by healthcare 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.

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.

Unresolved issues with noninferiority pragmatic trials: Results of a literature survey.

BACKGROUND: Issues with specification of margins, adherence, and analytic population can potentially bias results toward the alternative in randomized noninferiority pragmatic trials. To investigate this potential for bias, we conducted a targeted search of the medical literature to examine how noninferiority pragmatic trials address these issues. METHODS: An Ovid MEDLINE database search was performed identifying publications in New England Journal of Medicine, Journal of the American Medical Association, Lancet, or British Medical Journal published between 2015 and 2021 that included the words "pragmatic" or "comparative effectiveness" and "noninferiority" or "non-inferiority." Our search identified 14 potential trials, 12 meeting our inclusion criteria (11 individually randomized, 1 cluster-randomized). RESULTS: Eleven trials had results that met the criteria established for noninferiority. Noninferiority margins were prespecified for all trials; all but two trials provided justification of the margin. Most trials did some monitoring of treatment adherence. All trials conducted intent-to-treat or modified intent-to-treat analyses along with per-protocol analyses and these analyses reached similar conclusions. Only two trials included all randomized participants in the primary analysis, one used multiple imputation for missing data. The percentage excluded from primary analyses ranged from ∼2% to 30%. Reasons for exclusion included randomization in error, nonadherence, not receiving assigned treatment, death, withdrawal, lost to follow-up, and incomplete data. CONCLUSION: Specification of margins, adherence, and analytic population require careful consideration to prevent bias toward the alternative in noninferiority pragmatic trials. Although separate guidance has been developed for noninferiority and pragmatic trials, it is not compatible with conducting a noninferiority pragmatic trial. Hence, these trials should probably not be done in their current format without developing new guidelines.

Changes in Older Adult Trauma Quality When Evaluated Using Longer-Term Outcomes vs In-Hospital Mortality.

Importance: Lack of knowledge about longer-term outcomes remains a critical blind spot for trauma systems. Recent efforts have expanded trauma quality evaluation to include a broader array of postdischarge quality metrics. It remains unknown how such quality metrics should be used. Objective: To examine the utility of implementing recommended postdischarge quality metrics as a composite score and ascertain how composite score performance compares with that of in-hospital mortality for evaluating associations with hospital-level factors. Design, Setting, and Participants: This national hospital-level quality assessment evaluated hospital-level care quality using 100% Medicare fee-for-service claims of older adults (aged ≥65 years) hospitalized with primary diagnoses of trauma, hip fracture, and severe traumatic brain injury (TBI) between January 1, 2014, and December 31, 2015. Hospitals with annual volumes encompassing 10 or more of each diagnosis were included. The data analysis was performed between January 1, 2021, and December 31, 2022. Exposures: Reliability-adjusted quality metrics used to calculate composite scores included hospital-specific performance on mortality, readmission, and patients' average number of healthy days at home (HDAH) within 30, 90, and 365 days among older adults hospitalized with all forms of trauma, hip fracture, and severe TBI. Main Outcomes and Measures: Associations with hospital-level factors were compared using volume-weighted multivariable logistic regression. Results: A total of 573 554 older adults (mean [SD] age, 83.1 [8.3] years; 64.8% female; 35.2% male) from 1234 hospitals were included. All 27 reliability-adjusted postdischarge quality metrics significantly contributed to the composite score. The most important drivers were 30- and 90-day readmission, patients' average number of HDAH within 365 days, and 365-day mortality among all trauma patients. Associations with hospital-level factors revealed predominantly anticipated trends when older adult trauma quality was evaluated using composite scores (eg, worst performance was associated with decreased older adult trauma volume [odds ratio, 0.89; 95% CI, 0.88-0.90]). Results for in-hospital mortality showed inverted associations for each considered hospital-level factor and suggested that compared with nontrauma centers, level 1 trauma centers had a 17 times higher risk-adjusted odds of worst (highest quantile) vs best (lowest quintile) performance (odds ratio, 17.08; 95% CI, 16.17-18.05). Conclusions and Relevance: The study results challenge historical notions about the adequacy of in-hospital mortality as the single measure of older adult trauma quality and suggest that, when it comes to older adults, decisions about how quality is evaluated can profoundly alter understandings of what constitutes best practices for care. Composite scores appear to offer a promising means by which postdischarge quality metrics could be used.

Costs of fall injuries in the STRIDE study: an economic evaluation of healthcare system heterogeneity and heterogeneity of treatment effect.

OBJECTIVES: The Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) Study cluster-randomized 86 primary care practices in 10 healthcare systems to a patient-centered multifactorial fall injury prevention intervention or enhanced usual care, enrolling 5451 participants. We estimated total healthcare costs from participant-reported fall injuries receiving medical attention (FIMA) that were averted by the STRIDE intervention and tested for healthcare-system-level heterogeneity and heterogeneity of treatment effect (HTE). METHODS: Participants were community-dwelling adults age ≥ 70 at increased fall injury risk. We estimated practice-level total costs per person-year of follow-up (PYF), assigning unit costs to FIMA with and without an overnight hospital stay. Using independent variables for treatment arm, healthcare system, and their interaction, we fit a generalized linear model with log link, log follow-up time offset, and Tweedie error distribution. RESULTS: Unadjusted total costs per PYF were $2,034 (intervention) and $2,289 (control). The adjusted (intervention minus control) cost difference per PYF was -$167 (95% confidence interval (CI), -$491, $216). Cost heterogeneity by healthcare system was present (p = 0.035), as well as HTE (p = 0.090). Adjusted total costs per PYF in control practices varied from $1,529 to $3,684 for individual healthcare systems; one system with mean intervention minus control costs of -$2092 (95% CI, -$3,686 to -$944) per PYF accounted for HTE, but not healthcare system cost heterogeneity. CONCLUSIONS: We observed substantial heterogeneity of healthcare system costs in the STRIDE study, with small reductions in healthcare costs for FIMA in the STRIDE intervention accounted for by a single healthcare system. TRIAL REGISTRATION: Clinicaltrials.gov (NCT02475850).

Simulating time-to-event data subject to competing risks and clustering: A review and synthesis.

Simulation studies play an important role in evaluating the performance of statistical models developed for analyzing complex survival data such as those with competing risks and clustering. This article aims to provide researchers with a basic understanding of competing risks data generation, techniques for inducing cluster-level correlation, and ways to combine them together in simulation studies, in the context of randomized clinical trials with a binary exposure or treatment. We review data generation with competing and semi-competing risks and three approaches of inducing cluster-level correlation for time-to-event data: the frailty model framework, the probability transform, and Moran's algorithm. Using exponentially distributed event times as an example, we discuss how to introduce cluster-level correlation into generating complex survival outcomes, and illustrate multiple ways of combining these methods to simulate clustered, competing and semi-competing risks data with pre-specified correlation values or degree of clustering.

Intervention Fidelity in Pain Pragmatic Trials for Nonpharmacologic Pain Management: Nuanced Considerations for Determining PRECIS-2 Flexibility in Delivery and Adherence.

Nonpharmacological treatments are considered first-line pain management strategies, but they remain clinically underused. For years, pain-focused pragmatic clinical trials (PCTs) have generated evidence for the enhanced use of nonpharmacological interventions in routine clinical settings to help overcome implementation barriers. The Pragmatic Explanatory Continuum Indicator Summary (PRECIS-2) framework describes the degree of pragmatism across 9 key domains. Among these, "flexibility in delivery" and "flexibility in adherence," address a key goal of pragmatic research by tailoring approaches to settings in which people receive routine care. However, to maintain scientific and ethical rigor, PCTs must ensure that flexibility features do not compromise delivery of interventions as designed, such that the results are ethically and scientifically sound. Key principles of achieving this balance include clear definitions of intervention core components, intervention monitoring and documentation that is sufficient but not overly burdensome, provider training that meets the demands of delivering an intervention in real-world settings, and use of an ethical lens to recognize and avoid potential trial futility when necessary and appropriate. PERSPECTIVE: This article presents nuances to be considered when applying the PRECIS-2 framework to describe pragmatic clinical trials. Trials must ensure that patient-centered treatment flexibility does not compromise delivery of interventions as designed, such that measurement and analysis of treatment effects is reliable.

Beyond In-hospital Mortality: Use of Postdischarge Quality-Metrics Provides a More Complete Picture of Older Adult Trauma Care.

OBJECTIVE: To identify the distributions of and extent of variability among 3 new sets of postdischarge quality-metrics measured within 30/90/365 days designed to better account for the unique health needs of older trauma patients: mortality (expansion of the current in-hospital standard), readmission (marker of health-system performance and care coordination), and patients' average number of healthy days at home (marker of patient functional status). BACKGROUND: Traumatic injuries are a leading cause of death and loss of independence for the increasing number of older adults living in the United States. Ongoing efforts seek to expand quality evaluation for this population. METHODS: Using 100% Medicare claims, we calculated hospital-specific reliability-adjusted postdischarge quality-metrics for older adults aged 65 years or older admitted with a primary diagnosis of trauma, older adults with hip fracture, and older adults with severe traumatic brain injury. Distributions for each quality-metric within each population were assessed and compared with results for in-hospital mortality, the current benchmarking standard. RESULTS: A total of 785,867 index admissions (305,186 hip fracture and 92,331 severe traumatic brain injury) from 3692 hospitals were included. Within each population, use of postdischarge quality-metrics yielded a broader range of outcomes compared with reliance on in-hospital mortality alone. None of the postdischarge quality-metrics consistently correlated with in-hospital mortality, including death within 1 year [ r =0.581 (95% CI, 0.554-0.608)]. Differences in quintile-rank revealed that when accounting for readmissions (8.4%, κ=0.029) and patients' average number of healthy days at home (7.1%, κ=0.020), as many as 1 in 14 hospitals changed from the best/worst performance under in-hospital mortality to the completely opposite quintile rank. CONCLUSIONS: The use of new postdischarge quality-metrics provides a more complete picture of older adult trauma care: 1 with greater room for improvement and better reflection of multiple aspects of quality important to the health and recovery of older trauma patients when compared with reliance on quality benchmarking based on in-hospital mortality alone.

Engagement of older adults in STRIDE's multifactorial fall injury prevention intervention.

BACKGROUND: Evidence-based multifactorial fall prevention interventions in clinical practice have been less effective than expected. One plausible reason is that older adults' engagement in fall prevention care is suboptimal. METHODS: This was a post-hoc analysis of 2403 older adults' engagement in a multifactorial fall prevention intervention in the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) pragmatic trial. Based on the direct clinical care level of the Patient and Family Continuum of Engagement (CE) framework, three indicators of progressively interactive engagement were assessed: (1) Consultation (receiving information), (2) Involvement (prioritizing risks), and (3) Partnership (identifying prevention actions). Drop off at each step was determined as well as predictors of engagement. RESULTS: The participants' engagement waned with increasingly interactive CE domains. Although all participants received information about their positive fall risk factors (consultation) and most (51%-96%) prioritized them (involvement), fewer participants (33%-55%) identified fall prevention actions (partnership) for most of their risk factors, except for strength gait or balance problems (95%). More participants (70%) identified home exercises than other actions. Finally, fall prevention actions were identified more commonly among participants who received two visits compared to one (OR = 2.33 [95% CI, 2.06-2.64]), were ≥80 years old (OR = 1.83 [95% CI, 1.51-2.23]), and had fewer fall risk factors (OR = 0.90 [95% CI, 0.83-0.99]). CONCLUSIONS: The drop-off in participants' engagement based on the level of their interaction with clinicians suggests that future multifactorial fall prevention interventions need to be more focused on interactive patient-clinician partnerships that help older adults increase and maintain fall prevention actions. Our analyses suggest that more frequent contact with clinicians and more monitoring of the implementation and outcomes of Fall Prevention Care Plans could potentially improve engagement and help older adults maintain fall prevention actions.

Effect of the STRIDE fall injury prevention intervention on falls, fall injuries, and health-related quality of life.

BACKGROUND: Falls are common in older adults and can lead to severe injuries. The Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial cluster-randomized 86 primary care practices across 10 health systems to a multifactorial intervention to prevent fall injuries, delivered by registered nurses trained as falls care managers, or enhanced usual care. STRIDE enrolled 5451 community-dwelling older adults age ≥70 at increased fall injury risk. METHODS: We assessed fall-related outcomes via telephone interviews of participants (or proxies) every 4 months. At baseline, 12 and 24 months, we assessed health-related quality of life (HRQOL) using the EQ-5D-5L and EQ-VAS. We used Poisson models to assess intervention effects on falls, fall-related fractures, fall injuries leading to hospital admission, and fall injuries leading to medical attention. We used hierarchical longitudinal linear models to assess HRQOL. RESULTS: For recurrent event models, intervention versus control incidence rate ratios were 0.97 (95% confidence interval [CI], 0.93-1.00; p = 0.048) for falls, 0.93 (95% CI, 0.80-1.08; p = 0.337) for self-reported fractures, 0.89 (95% CI, 0.73-1.07; p = 0.205) for adjudicated fractures, 0.91 (95% CI, 0.77-1.07; p = 0.263) for falls leading to hospital admission, and 0.97 (95% CI, 0.89-1.06; p = 0.477) for falls leading to medical attention. Similar effect sizes (non-significant) were obtained for dichotomous outcomes (e.g., participants with ≥1 events). The difference in least square mean change over time in EQ-5D-5L (intervention minus control) was 0.009 (95% CI, -0.002 to 0.019; p = 0.106) at 12 months and 0.005 (95% CI, -0.006 to 0.015; p = 0.384) at 24 months. CONCLUSIONS: Across a standard set of outcomes typically reported in fall prevention studies, we observed modest improvements, one of which was statistically significant. Future work should focus on patient-, practice-, and organization-level operational strategies to increase the real-world effectiveness of interventions, and improving the ability to detect small but potentially meaningful clinical effects. CLINICALTRIALS: gov identifier: NCT02475850.

A comparison of analytical strategies for cluster randomized trials with survival outcomes in the presence of competing risks.

While statistical methods for analyzing cluster randomized trials with continuous and binary outcomes have been extensively studied and compared, little comparative evidence has been provided for analyzing cluster randomized trials with survival outcomes in the presence of competing risks. Motivated by the Strategies to Reduce Injuries and Develop Confidence in Elders trial, we carried out a simulation study to compare the operating characteristics of several existing population-averaged survival models, including the marginal Cox, marginal Fine and Gray, and marginal multi-state models. For each model, we found that adjusting for the intraclass correlations through the sandwich variance estimator effectively maintained the type I error rate when the number of clusters is large. With no more than 30 clusters, however, the sandwich variance estimator can exhibit notable negative bias, and a permutation test provides better control of type I error inflation. Under the alternative, the power for each model is differentially affected by two types of intraclass correlations-the within-individual and between-individual correlations. Furthermore, the marginal Fine and Gray model occasionally leads to higher power than the marginal Cox model or the marginal multi-state model, especially when the competing event rate is high. Finally, we provide an illustrative analysis of Strategies to Reduce Injuries and Develop Confidence in Elders trial using each analytical strategy considered.

Sample size calculation in hierarchical 2×2 factorial trials with unequal cluster sizes.

Motivated by a suicide prevention trial with hierarchical treatment allocation (cluster-level and individual-level treatments), we address the sample size requirements for testing the treatment effects as well as their interaction. We assume a linear mixed model, within which two types of treatment effect estimands (controlled effect and marginal effect) are defined. For each null hypothesis corresponding to an estimand, we derive sample size formulas based on large-sample z-approximation, and provide finite-sample modifications based on a t-approximation. We relax the equal cluster size assumption and express the sample size formulas as functions of the mean and coefficient of variation of cluster sizes. We show that the sample size requirement for testing the controlled effect of the cluster-level treatment is more sensitive to cluster size variability than that for testing the controlled effect of the individual-level treatment; the same observation holds for testing the marginal effects. In addition, we show that the sample size for testing the interaction effect is proportional to that for testing the controlled or the marginal effect of the individual-level treatment. We conduct extensive simulations to validate the proposed sample size formulas, and find the empirical power agrees well with the predicted power for each test. Furthermore, the t-approximations often provide better control of type I error rate with a small number of clusters. Finally, we illustrate our sample size formulas to design the motivating suicide prevention factorial trial. The proposed methods are implemented in the R package H2x2Factorial.

Impact of complex, partially nested clustering in a three-arm individually randomized group treatment trial: A case study with the wHOPE trial.

BACKGROUND/AIMS: When participants in individually randomized group treatment trials are treated by multiple clinicians or in multiple group treatment sessions throughout the trial, this induces partially nested clusters which can affect the power of a trial. We investigate this issue in the Whole Health Options and Pain Education trial, a three-arm pragmatic, individually randomized clinical trial. We evaluate whether partial clusters due to multiple visits delivered by different clinicians in the Whole Health Team arm and dynamic participant groups due to changing group leaders and/or participants across treatment sessions during treatment delivery in the Primary Care Group Education arm may impact the power of the trial. We also present a Bayesian approach to estimate the intraclass correlation coefficients. METHODS: We present statistical models for each treatment arm of Whole Health Options and Pain Education trial in which power is estimated under different intraclass correlation coefficients and mapping matrices between participants and clinicians or treatment sessions. Power calculations are based on pairwise comparisons. In practice, sample size calculations depend on estimates of the intraclass correlation coefficients at the treatment sessions and clinician levels. To accommodate such complexities, we present a Bayesian framework for the estimation of intraclass correlation coefficients under different participant-to-session and participant-to-clinician mapping scenarios. We simulated continuous outcome data based on various clinical scenarios in Whole Health Options and Pain Education trial using a range of intraclass correlation coefficients and mapping matrices and used Gibbs samplers with conjugate priors to obtain posteriors of the intraclass correlation coefficients under those different scenarios. Posterior means and medians and their biases are calculated for the intraclass correlation coefficients to evaluate the operating characteristics of the Bayesian intraclass correlation coefficient estimators. RESULTS: Power for Whole Health Team versus Primary Care Group Education is sensitive to the intraclass correlation coefficient in the Whole Health Team arm. In these two arms, an increased number of clinicians, more evenly distributed workload of clinicians, or more homogeneous treatment group sizes leads to increased power. Our simulation study for the intraclass correlation coefficient estimation indicates that the posterior mean intraclass correlation coefficient estimator has less bias when the true intraclass correlation coefficients are large (i.e. 0.10), but when the intraclass correlation coefficient is small (i.e. 0.01), the posterior median intraclass correlation coefficient estimator is less biased. CONCLUSION: Knowledge of intraclass correlation coefficients and the structure of clustering are critical to the design of individually randomized group treatment trials with partially nested clusters. We demonstrate that the intraclass correlation coefficient of the Whole Health Team arm can affect power in the Whole Health Options and Pain Education trial. A Bayesian approach provides a flexible procedure for estimating the intraclass correlation coefficients under complex scenarios. More work is needed to educate the research community about the individually randomized group treatment design and encourage publication of intraclass correlation coefficients to help inform future trial designs.

Optimizing the Impact of Pragmatic Clinical Trials for Veteran and Military Populations: Lessons From the Pain Management Collaboratory.

Pragmatic clinical trials (PCTs) are well-suited to address unmet healthcare needs, such as those arising from the dual public health crises of chronic pain and opioid misuse, recently exacerbated by the COVID-19 pandemic. These overlapping epidemics have complex, multifactorial etiologies, and PCTs can be used to investigate the effectiveness of integrated therapies that are currently available but underused. Yet individual pragmatic studies can be limited in their reach because of existing structural and cultural barriers to dissemination and implementation. The National Institutes of Health, Department of Defense, and Department of Veterans Affairs formed an interagency research partnership, the Pain Management Collaboratory. The partnership combines pragmatic trial design with collaborative tools and relationship building within a large network to advance the science and impact of nonpharmacological approaches and integrated models of care for the management of pain and common co-occurring conditions. The Pain Management Collaboratory team supports 11 large-scale, multisite PCTs in veteran and military health systems with a focus on team science with the shared aim that the "whole is greater than the sum of the parts." Herein, we describe this integrated approach and lessons learned, including incentivizing all parties; proactively offering frequent opportunities for problem-solving; engaging stakeholders during all stages of research; and navigating competing research priorities. We also articulate several specific strategies and their practical implications for advancing pain management in active clinical, "real-world," settings.

Assessing the impact of the COVID-19 pandemic on pragmatic clinical trial participants.

Characterizing the impacts of disruption attributable to the COVID-19 pandemic on clinical research is important, especially in pain research where psychological, social, and economic stressors attributable to the COVID-19 pandemic may greatly impact treatment effects. The National Institutes of Health - Department of Defense - Department of Veterans Affairs Pain Management Collaboratory (PMC) is a collective effort supporting 11 pragmatic clinical trials studying nonpharmacological approaches and innovative integrated care models for pain management in veteran and military health systems. The PMC rapidly developed a brief pandemic impacts measure for use across its pragmatic trials studying pain while remaining broadly applicable to other areas of clinical research. Through open discussion and consensus building by the PMC's Phenotypes and Outcomes Work Group, the PMC Coronavirus Pandemic (COVID-19) Measure was iteratively developed. The measure assesses the following domains (one item/domain): access to healthcare, social support, finances, ability to meet basic needs, and mental or emotional health. Two additional items assess infection status (personal and household) and hospitalization. The measure uses structured responses with a three-point scale for COVID-19 infection status and four-point ordinal rank response for all other domains. We recommend individualized adaptation as appropriate by clinical research teams using this measure to survey the effects of the COVID-19 pandemic on study participants. This can also help maintain utility of the measure beyond the COVID-19 pandemic to characterize impacts during future public health emergencies that may require mitigation strategies such as periods of quarantine and isolation.

Comparing a Multimedia Digital Informed Consent Tool With Traditional Paper-Based Methods: Randomized Controlled Trial.

BACKGROUND: The traditional informed consent (IC) process rarely emphasizes research participants' comprehension of medical information, leaving them vulnerable to unknown risks and consequences associated with procedures or studies. OBJECTIVE: This paper explores how we evaluated the feasibility of a digital health tool called Virtual Multimedia Interactive Informed Consent (VIC) for advancing the IC process and compared the results with traditional paper-based methods of IC. METHODS: Using digital health and web-based coaching, we developed the VIC tool that uses multimedia and other digital features to improve the current IC process. The tool was developed on the basis of the user-centered design process and Mayer's cognitive theory of multimedia learning. This study is a randomized controlled trial that compares the feasibility of VIC with standard paper consent to understand the impact of interactive digital consent. Participants were recruited from the Winchester Chest Clinic at Yale New Haven Hospital in New Haven, Connecticut, and healthy individuals were recruited from the community using fliers. In this coordinator-assisted trial, participants were randomized to complete the IC process using VIC on the iPad or with traditional paper consent. The study was conducted at the Winchester Chest Clinic, and the outcomes were self-assessed through coordinator-administered questionnaires. RESULTS: A total of 50 participants were recruited in the study (VIC, n=25; paper, n=25). The participants in both groups had high comprehension. VIC participants reported higher satisfaction, higher perceived ease of use, higher ability to complete the consent independently, and shorter perceived time to complete the consent process. CONCLUSIONS: The use of dynamic, interactive audiovisual elements in VIC may improve participants' satisfaction and facilitate the IC process. We believe that using VIC in an ongoing, real-world study rather than a hypothetical study improved the reliability of our findings, which demonstrates VIC's potential to improve research participants' comprehension and the overall process of IC. TRIAL REGISTRATION: ClinicalTrials.gov NCT02537886; https://clinicaltrials.gov/ct2/show/NCT02537886.

Utility of a short, telephone-administered version of the Montreal Cognitive Assessment.

BACKGROUND: Because of the COVID-19 pandemic, the ongoing D-CARE pragmatic trial of two models of dementia care management needed to transition to all data collection by telephone. METHODS: For the first 1069 D-CARE participants, we determined the feasibility of administering a short 3-item version of the Montreal Cognitive Assessment (MoCA) to persons with dementia by telephone and examined the correlation with the full 12-item version. RESULTS: The 3-item version could be administered by telephone in approximately 6 min and was highly correlated with the full MoCA (r = 0.78, p < 0.0001). CONCLUSIONS: This brief version of the MoCA was feasible to collect by telephone and could be used as an alternative to the full MoCA, particularly if the purpose of cognitive assessment is characterization of study participants.

A case study of ascertainment bias for the primary outcome in the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial.

BACKGROUND/AIM: In clinical trials, there is potential for bias from unblinded observers that may influence ascertainment of outcomes. This issue arose in the Strategies to Reduce Injuries and Develop Confidence in Elders trial, a cluster randomized trial to test a multicomponent intervention versus enhanced usual care (control) to prevent serious fall injuries, originally defined as a fall injury leading to medical attention. An unblinded nurse falls care manager administered the intervention, while the usual care arm did not involve contact with a falls care manager. Thus, there was an opportunity for falls care managers to refer participants reporting falls to seek medical attention. Since this type of observer bias could not occur in the usual care arm, there was potential for additional falls to be reported in the intervention arm, leading to dilution of the intervention effect and a reduction in study power. We describe the clinical basis for ascertainment bias, the statistical approach used to assess it, and its effect on study power. METHODS: The prespecified interim monitoring plan included a decision algorithm for assessing ascertainment bias and adapting (revising) the primary outcome definition, if necessary. The original definition categorized serious fall injuries requiring medical attention into Type 1 (fracture other than thoracic/lumbar vertebral, joint dislocation, cut requiring closure) and Type 2 (head injury, sprain or strain, bruising or swelling, other). The revised definition, proposed by the monitoring plan, excluded Type 2 injuries that did not necessarily require an overnight hospitalization since these would be most subject to bias. These injuries were categorized into those with (Type 2b) and without (Type 2c) medical attention. The remaining Type 2a injuries required medical attention and an overnight hospitalization. We used the ratio of 2b/(2b + 2c) in intervention versus control as a measure of ascertainment bias; ratios > 1 indicated the likelihood of falls care manager bias. We determined the effect of ascertainment bias on study power for the revised (Types 1 and 2a) versus original definition (Types 1, 2a, and 2b). RESULTS: The estimate of ascertainment bias was 1.14 (95% confidence interval: 0.98, 1.30), providing evidence of the likelihood of falls care manager bias. We estimated that this bias diluted the hazard ratio from the hypothesized 0.80 to 0.86 and reduced power to under 80% for the original primary outcome definition. In contrast, adapting the revised definition maintained study power at nearly 90%. CONCLUSION: There was evidence of ascertainment bias in the Strategies to Reduce Injuries and Develop Confidence in Elders trial. The decision to adapt the primary outcome definition reduced the likelihood of this bias while preserving the intervention effect and study power.