BACKGROUND Atrial fibrillation (AF) affects an estimated 33 million people worldwide, leading to increased mortality and an increased risk of heart failure and stroke. Many AF patient registries exist, but the ability to link and compare data across registries is hindered by differences in the outcome measures collected by each registry and a lack of harmonization. OBJECTIVES The purpose of this project was to develop a minimum set of standardized outcome measures that could be collected in AF patient registries and clinical practice. METHODS AF patient registries were identified through multiple sources and invited to join the workgroup and submit outcome measures. Additional measures were identified through literature searches and reviews of consensus statements. Outcome measures were categorized using the Agency for Healthcare Research and Quality's supported Outcome Measures Framework (OMF). A minimum set of broadly relevant measures was identified. Measure definitions were harmonized through in-person and virtual meetings. RESULTS One hundred twelve outcome measures, including those from thirteen registries, were curated according to the OMF and then harmonized into a minimum set of measures in the OMF categories of survival (3 measures), clinical response (3 measures), events of interest (9 measures), patient-reported outcomes (2 measures), and resource utilization (3 measures). The harmonized definitions build on existing consensus statements. CONCLUSIONS The harmonized measures represent a minimum set of outcomes that are relevant in AF research and clinical practice. Routine and consistent collection of these measures in registries and in other systems would support creation of a research infrastructure to efficiently address new questions and improve patient outcomes.
ObjectiveUse of the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) in routine clinical practice is inconsistent, and availability of clinician-recorded SLEDAI scores in real-world datasets is limited. This study aimed to validate a machine learning model to estimate SLEDAI score categories using clinical notes and to apply the model to a large, real-world dataset to generate estimated score categories for use in future research studies.MethodsA machine learning model was developed to estimate an individual patient’s SLEDAI score category (no activity, mild activity, moderate activity or high/very high activity) for a specific encounter date using clinical notes. A training cohort of 3504 encounters and a separate validation cohort of 1576 encounters were created from the OM1 SLE Registry. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calculated using a binarised version of the outcome that sets the positive class to be those records with clinician-recorded SLEDAI scores >5 and the negative class to be records with scores ≤5. Model performance was evaluated by categorising the scores into the four disease activity categories and by calculating the Spearman’s R value and Pearson’s R value.ResultsThe AUC for the two categories was 0.93 for the development cohort and 0.91 for the validation cohort. The model had a Spearman’s R value of 0.7 and a Pearson’s R value of 0.7 when calculated using the four disease activity categories.ConclusionThe model performs well when estimating SLEDAI score categories using unstructured clinical notes.
This project used a stakeholder-driven process to understand the factors that drive the selection of study designs for comparative effectiveness research (CER). The project assembled a diverse stakeholder committee to explore the basis of a translation framework and gathered input through surveys, interviews and an in-person meeting. Stakeholders recommended different study designs for the CER topic areas and identified different outcomes as the most important outcomes to study in each area. During the discussions, stakeholders described a variety of factors that influenced their study design recommendations. The stakeholder activities resulted in the identification of several key themes, including the need to have a highly specific detailed research question before discussing appropriate designs and the need to use multiple studies, potentially of different designs, to address the CER topic areas. The insights and themes from this project may inform efforts to develop a translation table.
Patient registries are important tools for health care research. The goal of this project, sponsored by the Agency for Healthcare Research and Quality (AHRQ), is to design and implement the Registry of Patient Registries (RoPR), the first searchable, public database designed specifically to provide information about registries. The RoPR is integrated with ClinicalTrials.gov, supports research collaboration, reduces redundancy, and improves transparency in observational clinical research. METHODS: The RoPR consists of a registration system and a public search Web site. The registration system collects over forty data elements which define a registry profile. The search site serves as a central listing of registries and includes options to filter for relevant profiles. RoPR registration is integrated with ClinicalTrials.gov: users registering a study on ClinicalTrials.gov who designate it as a patient registry are presented with a pop-up window displaying the RoPR registration system. Users complete and submit the requested data elements, creating a registry profile in the RoPR that is linked to the ClinicalTrials.gov listing through a unique identifier, the NCT ID. RESULTS: The RoPR was launched on December 1, 2012. As of January 11, 2013, 54 new patient registries are registered on ClinicalTrials.gov. Twelve of these have been fully published in the RoPR, representing 21 different condition areas. Most are classified as disease/disorder/condition (67%), drug (33%), and/or procedure (33%) registries. Reported registry purposes include effectiveness (50%), safety or harm (42%), natural history of disease (42%) and clinical practice assessment (33%). A total of 67% of registry sponsors are open to being contacted for collaboration, data access, investigator or patient participation, or for information requests. CONCLUSIONS: The RoPR is a searchable Web site used by registry sponsors to publish information about registries and by members of the public to search for information about existing registries. Integration with ClinicialTrials.gov presents a user-friendly interface to encourage registration.
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