BACKGROUNDThe appropriate dose of aspirin to lower the risk of death, myocardial infarction, and stroke and to minimize major bleeding in patients with established atherosclerotic cardiovascular disease is a subject of controversy. METHODSUsing an open-label, pragmatic design, we randomly assigned patients with established atherosclerotic cardiovascular disease to a strategy of 81 mg or 325 mg of aspirin per day. The primary effectiveness outcome was a composite of death from any cause, hospitalization for myocardial infarction, or hospitalization for stroke, assessed in a time-to-event analysis. The primary safety outcome was hospitalization for major bleeding, also assessed in a time-to-event analysis. RESULTSA total of 15,076 patients were followed for a median of 26.2 months (interquartile range [IQR], 19.0 to 34.9). Before randomization, 13,537 (96.0% of those with available information on previous aspirin use) were already taking aspirin, and 85.3% of these patients were previously taking 81 mg of daily aspirin. Death, hospitalization for myocardial infarction, or hospitalization for stroke occurred in 590 patients (estimated percentage, 7.28%) in the 81-mg group and 569 patients (estimated percentage, 7.51%) in the 325-mg group (hazard ratio, 1.02; 95% confidence interval [CI], 0.91 to 1.14). Hospitalization for major bleeding occurred in 53 patients (estimated percentage, 0.63%) in the 81-mg group and 44 patients (estimated percentage, 0.60%) in the 325-mg group (hazard ratio, 1.18; 95% CI, 0.79 to 1.77). Patients assigned to 325 mg had a higher incidence of dose switching than those assigned to 81 mg (41.6% vs. 7.1%) and fewer median days of exposure to the assigned dose (434 days [IQR, 139 to 737] vs. 650 days [IQR, 415 to 922]). CONCLUSIONSIn this pragmatic trial involving patients with established cardiovascular disease, there was substantial dose switching to 81 mg of daily aspirin and no significant differences in cardiovascular events or major bleeding between patients assigned to 81 mg and those assigned to 325 mg of aspirin daily. (Funded by the Patient-Centered Outcomes Research Institute; ADAPTABLE ClinicalTrials.gov number, NCT02697916.

Clinicians need to target high-risk women with phase-delayed rhythms, higher symptoms, and lower physical functioning for intervention.

Care transitions, defined as hospital discharge or movement from one health care setting to another, are currently a major concern of health care providers and policy makers. Extensive empirical research has been conducted on care transitions, but the theoretical foundations are rarely made explicit. We propose that integrating concepts on complex adaptive systems from complexity science with classic theory on transitions in nursing provides a powerful new lens through which to study care transitions and improve transition outcomes. We summarize concepts from both theoretical approaches, propose an expanded model of transitions, and apply the model to the transition from hospital to home.

Growing interest is evident in longitudinal mixed methods research, particularly fully longitudinal mixed methods designs in which both quantitative and qualitative data are collected concurrently for the duration of the study. Fully longitudinal mixed methods designs are particularly relevant for research on dynamic phenomena because of their ability to illuminate both quantitative and qualitative dimensions of change in real time as the phenomenon of interest changes. However, these are complex research designs and their data-intense nature makes them potentially burdensome for study participants, challenging for research teams, and costly for funding agencies. Despite growing use, the methodological literature on fully longitudinal mixed methods research is sparse and little guidance is available for researchers considering this approach. We address this gap by describing our experience with the design and implementation of a fully longitudinal mixed methods study of a dynamic phenomenon, namely, family caregiving during cancer treatment. We describe important questions and key decisions confronted while developing the research proposal, proactive strategies for study implementation, and implementation realities encountered while the study was in progress. On the basis of insights gained through real-world experience, we offer three guiding principles for researchers undertaking such a study. First, align the study design with the nature of the dynamics in the phenomenon of interest. Second, plan from the start when and how the integration of the longitudinal quantitative and qualitative data will occur. Third, employ implementation strategies that take into account the practical aspects of repeated contacts with study participants for an extended period.

The goal of this study was to assess the utility of participatory needs assessment processes for continuous improvement of developing clinical and translational research (CTR) networks. Our approach expanded on evaluation strategies for CTR networks, centers, and institutes, which often survey stakeholders to identify infrastructure or resource needs, using the case example of the Great Plains IDeA-CTR Network. Our 4-stage approach (i.e., pre-assessment, data collection, implementation of needs assessment derived actions, monitoring of action plan) included a member survey (n = 357) and five subsequent small group sessions (n = 75 participants) to better characterize needs identified in the survey and to provide actionable recommendations. This participatory, mixed-methods needs assessment and strategic action planning process yielded 11 interrelated recommendations. These recommendations were presented to the CTR steering committee as inputs to develop detailed, prioritized action plans. Preliminary evaluation shows progress towards improved program capacity and effectiveness of the network to respond to member needs. The participatory, mixed-methods needs assessment and strategic planning process allowed a wide range of stakeholders to contribute to the development of actionable recommendations for network improvement, in line with the principles of team science.