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Enregistrement W3042987530 · doi:10.1111/acem.14086

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

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

Notice bibliographique

RevueAcademic Emergency Medicine · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueVenous Thromboembolism Diagnosis and Management
Établissements canadiensWestern UniversityMarkham Stouffville HospitalUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicinePulmonary embolismEmergency departmentRisk stratificationD-dimerEmergency medicineComputed tomographyRadiologyInternal medicine

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,022
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,077

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,022
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0040,003
Communication savante0,0030,005
Science ouverte0,0010,002
Intégrité de la recherche0,0030,005
Charge utile insuffisante (le modèle a refusé de juger)0,0230,007

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,055
Tête enseignante GPT0,317
Écart entre enseignants0,263 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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