Standardized Data Elements for Patients with Acute Pulmonary Embolism: A Consensus Report from the Pulmonary Embolism Research Collaborative
Bibliographic record
Abstract
ABSTRACT Recent advances in therapy and the promulgation of multidisciplinary pulmonary embolism teams (PERTs) show great promise to improve management and outcomes of acute pulmonary embolism (PE). However, the absence of randomized evidence and lack of consensus leads to tremendous variations in treatment and compromises the wide implementation of new innovations. Moreover, the changing landscape of healthcare, where quality, cost, and accountability are increasingly relevant, dictates that a broad spectrum of outcomes of care must be routinely monitored to fully capture the impact of modern PE treatment. We set out to standardize data collection in PE patients undergoing evaluation and treatment, and thus establish the foundation for an expanding evidence base that will address gaps in evidence and inform future care for acute PE. To do so, over 100 international PE thought leaders convened in Washington, DC in April 2022 to form the Pulmonary Embolism Research Collaborative (PERC™). Participants included physician experts, key members of the United States Food and Drug Administration (FDA), patient representatives, and industry leaders. Recognizing the multi-disciplinary nature of PE care, the Pulmonary Embolism Research Collaborative (PERC™) was created with representative experts from stakeholder medical subspecialties, including cardiology, pulmonology, vascular medicine, critical care, hematology, cardiac surgery, emergency medicine, hospital medicine, and pharmacology. A list of critical evidence gaps was composed with a matching comprehensive set of standardized data elements; these data points will provide a foundation for productive research, knowledge enhancement, and advancement of clinical care within the field of acute PE, and contribute to answering urgent unmet needs in PE management. Evidence produced through PERC™, as it is applied to data collection, promises to provide crucial knowledge that will ultimately produce a robust evidence base that will lead to standardization and harmonization of PE management and improved outcomes. CLINICAL PERSPECTIVE 1) What is new? Recent advances have increased options for treatment of acute pulmonary embolism, yet there remain wide variations in management due to the lack of a reliable evidence base upon which to base therapeutic decisions. The PERT Consortium TM is a strong advocate of evidence based care for PE patients and therefore initiated the Pulmonary Embolism Research Collaborative (PERC TM ) to establish a foundation for advancing high quality research and improving clinical care. A novel comprehensive set of standardized data elements is proposed for collection in patients with acute pulmonary embolism, to provide a foundation for expanding the evidence base and enhancing care. 2) What are the clinical implications? Standardizing collection of data for acute pulmonary embolism will enable analyses that will inform optimal risk stratification, treatment, and follow-up of patients with pulmonary embolism, and provide evidence-based treatment algorithms that will improve outcomes. Registries created using the proposed standardized elements will enable benchmarking and quality assurance for clinicians caring for pulmonary embolism patients. Incorporation of comprehensive standardized data elements into FDA IDE trials will enable the Agency to better assess the safety and effectiveness of investigational devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.528 | 0.576 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.013 | 0.015 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".