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Enregistrement W2584085060 · doi:10.1182/blood.v106.11.2243.2243

The Diagnosis of Venous Thromboembolism (VTE) in the Emergency Room: Poor Compliance with Recommended Diagnostic Algorithms.

2005· article· en· W2584085060 sur OpenAlexaffabout
Heather Racz, K L G Mills, Lan Vu, Michael J. Kovacs

Notice bibliographique

RevueBlood · 2005
Typearticle
Langueen
DomaineMedicine
ThématiqueVenous Thromboembolism Diagnosis and Management
Établissements canadiensLondon Health Sciences CentreWestern University
Organismes subventionnairesnon disponible
Mots-clésMedicinePulmonary embolismVenous thromboembolismAlgorithmThrombosisVenous thrombosisEmergency departmentDeep veinEmergency medicinePediatricsInternal medicine

Résumé

récupéré en direct d'OpenAlex

Abstract Background: The diagnosis of VTE remains problematic in the emergency room. There are a number of algorithms, for both pulmonary embolism (PE) and deep venous thrombosis (DVT), that have been validated for use. These incorporate clinical assessment of pretest probability, D-Dimer measurement, and non-invasive diagnostic imaging. The purpose of this study was to evaluate adherence to the VTE diagnostic algorithms (according to Wells, Anderson et al.) at Victoria Hospital, London Ontario (a tertiary centre). Methods: The charts for all patients who presented with suspected DVT or PE during a two-month period were retrospectively reviewed. Patients were identified by those who had D-dimer testing or diagnostic imaging for VTE (D-dimers are only indicated for use in our hospital for the investigation of VTE). Patients were excluded if they were currently anticoagulated, pregnant, had upper limb DVT or were <18 years of age. All charts were reviewed by 3 persons to assign the Wells criteriae and to review the adherence to the recommended algorithms - determination was by consensus. Results: There were 289 patients included, 157 women and 132 men with an average age of 57. Of 289 patients included, 242 were investigated for PE with 6 events occurring, for an event rate of 2.5%. Forty-seven patients were investigated for DVT, with 4 events, for an event rate of 8.5%. The algorithms were seemingly correctly applied at superficial analysis in 64.4% of all patients and incorrectly applied in 20.8% of all patients. In 14.9% of patients, the algorithms were not followed to completion due to admission to hospital or an alternative diagnosis developing. Consultants correctly managed in 68.37% of cases and Residents in 59.8%. Although 71% of patients with suspected PE were seemingly managed according to algorithm - the vast majority of these patients had a low pretest probability (217) and were investigated with D-dimer only, which was negative. There were 25 patients in the moderate to high probability group and 17 were incorrectly managed with either unnecessary D-dimer assessments or a lack of leg imaging after non-diagnostic V/Q scans or CT scans. Eight patients in the low probability group had imaging despite negative D-dimer tests. With an event rate of only 2.5%, a disproportionately large number of patients were investigated for PE. Only 30% of patients with suspected DVT were managed correctly according to algorithm, 41% for “unlikely” and 7% “likely”. Of the 32 patients in the “unlikely” group, 9 patients had U/S performed despite negative D-dimer and 5 patients had U/S without D-dimer assessment. For the 15 patients who were “likely”, 6 had D-dimers performed upfront and 6 did not have D-dimers done after initial negative imaging. Overall, of 46 patients who had chest imaging for PE, 13 V/Q scans and 1 CT scan were done that were not indicated and 7 patients who should have been imaged were not. In the DVT group, 39 U/S were done and 10 of these were not indicated. Conclusions: The disproportionate amount of PE patients, and the overall low event rate for PE, suggest that D-dimers are being used indiscriminately as a screening test for the diagnosis of chest symptoms, an approach that has not been validated by studies. The algorithms for the diagnosis of DVT and PE are not being applied appropriately at our centre. The implications of this include the potential for missed diagnoses as well as the cost and potential clinical consequences of over investigation.

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)0,008
score de la tête « metaresearch » (Gemma)0,054
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,992
Score d'incertitude au seuil0,042

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,054
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,030
Tête enseignante GPT0,288
Écart entre enseignants0,258 · 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.

Devis d'étudeObservationnel
DomaineMéthodes
GenreEmpirique

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

En bref

Citations3
Publié2005
Routes d'admission2
Résumé présentoui

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