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Record W3042987530 · doi:10.1111/acem.14086

Hot Off the Press: Tell Me How To Diagnose a Pulmonary Embolism

2020· letter· en· W3042987530 on OpenAlexaff
Christopher Bond, Justin Morgenstern, Corey Heitz, William K. Milne

Bibliographic record

VenueAcademic Emergency Medicine · 2020
Typeletter
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsWestern UniversityMarkham Stouffville HospitalUniversity of Calgary
Fundersnot available
KeywordsMedicinePulmonary embolismEmergency departmentRisk stratificationD-dimerEmergency medicineComputed tomographyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Pulmonary embolism (PE) is a common ED diagnosis, with an estimated 1% to 2% of all patients presenting to U.S. EDs undergoing computed tomography (CT) for suspected PE.1 However, less than 10% of these scans show PE.2-4 There are multiple validated risk stratification tools to evaluate for PE and reduce inappropriate testing, including the Pulmonary Embolism Rule Out Criteria (PERC), Wells score, YEARS algorithm, and D-dimer testing.5-7 There have also been more recent adjustments to D-dimer threshold based on clinical probability as calculated by a trichotomized Wells score.8 Unfortunately, clinician uptake of these validated tools has been incomplete, with some ED studies finding that 25% of patients who warranted no laboratory or imaging studies still received testing.4, 9-12 Low-value testing increases costs, increases ED length of stay, and subjects patients to both unnecessary ionizing radiation and the risk of anaphylaxis from intravenous contrast dye.13, 14 Moreover, false-positive CT scans are common (estimated to be between 10 and 26%), resulting in unnecessary anticoagulation and risk to patients.15-17 The result is overtesting, overdiagnosing, and overtreating of PE. This qualitative study evaluated emergency physician perspectives on the barriers and facilitators to the uptake of evidence-based practices in the ED evaluation for acute PE. Barriers were primarily at the clinician level, including knowledge of decision instruments, emotions, and beliefs about consequences of missed PE. Facilitators were primarily at the institutional level. Clinicians felt that institutional support and a clear, easy-to-follow algorithm endorsed by their hospital or group would facilitate their use of evidence-based approaches. This qualitative study was performed in a total of 12 academic and community hospitals in New England in the northeastern United States. It asks a sensible and valuable question for which a qualitative methodology is appropriate. There was a clear statement of findings and data analysis was sufficiently rigorous. The primary limitations of this study are sample size and recruitment. There were only 23 participating physicians in the study and the primary investigator was a colleague with six of them. Participating physicians were selected by emailing a purposive sample of physicians, many of whom were colleagues of the principal investigators. Thus, the researchers may have been able to anticipate their colleagues’ practice patterns and responses, facilitating selection bias. The external validity of this study is also in question due to the small sample size and practice location being a single area of the United States. This may limit its generalizability outside of the United States. Twenty-three physicians from a total of 12 academic and community hospitals in New England were interviewed. Two potential participants declined. Participants had a median of 14 years in practice, 48% practiced solely in an academic setting, 20% practiced exclusively in a community ED, and the remaining 32% practiced in a combination of academic and community EDs. All clinicians reported some familiarity and some use of risk stratification tools, particularly PERC in the workup of PE. Barriers were at the clinician level and included knowledge of decision tools and beliefs about the consequences of missed PE. There was a lack of knowledge regarding validated cutoffs for the Wells score and a lack of knowledge of a trichotomized Wells threshold, and most providers would only use a D-dimer for patients with a Wells score of less than or equal to 3. Providers reported more confidence in their gestalt than risk stratification tools. They commonly reported that if a patient satisfied “PE is the most likely diagnosis” or there was a prior history of venous thromboembolism or had active malignancy, the patient would automatically be too high risk to order a D-dimer. Beliefs about consequences of using the tools, particularly risk avoidance and fear of missing PE, were also common provider-level barriers. Nearly all participants were unaware of existing professional guidelines on PE. Facilitators were primarily at the institutional level. Clinicians felt that institutional support and a clear, easy-to-follow algorithm endorsed by their hospital or group would facilitate their use of evidence-based approaches. This would also need to be easily accessible on shift. They also felt that this would provide perceived medicolegal protection and establish a cultural norm of practice and cited peer pressure as a root cause to motivate them to change practice. Clinicians felt that simplicity of PERC facilitated its use, while the element of gestalt incorporated into Wells made it more challenging to use. Audit and feedback also emerged as an implementation strategy, noting that they would not want to be an outlier among their colleagues. This study shows that there remain multiple barriers in the ED evaluation of PE despite high-quality literature on the subject. More knowledge translation in this area is still required, as are institutional support and guidelines to help physicians feel more comfortable in more liberal use of D-dimer testing and reducing unnecessary CTPA use. What are your barriers to using decision tools in the workup of PE? Medicolegal? Knowledge? Fear/anxiety? Cardiology ordering D-Dimers and requesting CTPE after I decided with my decision tool and clinical assessment that this is not what they needed. Agree. Interdisciplinary tension can be a barrier and when this came up we sampled internal med docs and PCPs (but did not include them in this study at the request of the reviewers)..turns out their determinants of practice are largely very similar. Some of us recently have started electronic ED documentation and I wonder if this can help address this issue. If I document my rationale, clinical decision rule usage, etc to justify why I don’t pursue a certain Dx – consultants will more clearly see that the Dx was considered. Use clinical decision rules in the workup of PE in the ED. Ensure you are familiar with Wells cutoffs for which a D-dimer can be ordered and consider creating or adopting institutional guidelines for PE risk stratification.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.003
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0230.007

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.

Opus teacher head0.055
GPT teacher head0.317
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2020
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