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

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

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicinePulmonary embolismVenous thromboembolismAlgorithmThrombosisVenous thrombosisEmergency departmentDeep veinEmergency medicinePediatricsInternal medicine

Abstract

fetched live from 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.

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)0.008
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.288
Teacher spread0.258 · 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.

Study designObservational
DomainMethods
GenreEmpirical

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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Citations3
Published2005
Admission routes2
Has abstractyes

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