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Time Trends in the Usefulness of Pretest Prediction and D-Dimer Testing for the Diagnosis of Venous Thromboembolism (VTE).

2006· article· en· W2589121350 on OpenAlexaff
Normand Blais, Jacques Morais, Nicolas Sauvé, St-Onge Louise, Nathalie Aucoin

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsCentre Integre de Sante et de Services Sociaux de LavalHôpital Notre-Dame
Fundersnot available
KeywordsMedicineD-dimerContext (archaeology)Venous thromboembolismCohortPre- and post-test probabilityClinical trialInternal medicineEmergency medicinePediatricsThrombosis

Abstract

fetched live from OpenAlex

Abstract Objectives: Emergency room (ER) evaluation may differ when physicians see patients in the context of clinical trials compared to routine care. We aimed to determine whether patterns in the use of a systemic pretest probability (PTP) stratification tool and D-dimer testing change over time and whether this influences their clinical utility. Methods: Charts were reviewed of 974 cases that had D-dimer testing for VTE exclusion in two time periods; 474 consecutive patients evaluated between 07/02 and 10/02 and 500 between 07/04 and 10/04. The former cohort was managed by ER physicians that had just participated in a clinical trial on VTE diagnosis including PTP assessment and D-dimer testing, whereas the latter group had received no formal training after 2002. In both cohorts, pretest scoring as low, intermediate or high risk according to Wells criteria for DVT and PE was performed in every case since this was requested from the laboratory. D-dimer testing (Vidas® D-Dimer, Biomerieux) was performed only in the low and moderate risk group after reception of the PTP assessment form. Physicians were also asked to check out a form detailing individual Wells criteria leading to the overall assessment but were not mandated to do so in order to obtain the D-dimer result from the laboratory. Results: Pretest probability was evaluated as low, moderate or high in 66,7%, 31,9% and 1,3% vs. 78,4%, 21,2% and 0% (all comparisons are 2002 vs. 2004). There was a significant increase in proportion of undetailed evaluations of PTP (32,7% vs. 61%). Detailed forms in both cohorts had similar risk distribution (low/moderate risk 51,7/46,4% vs. 59,2/40,8%) whereas undetailed forms were usually quoted as low risk (97,4% vs. 90.5%). Number of D-dimer tests performed per month was stable over the three year period of observation and in the two evaluated cohorts (119/mth vs. 125/mth). D-dimer results were negative in 299/474 cases (63,1%) in 2002 and 359/500 (71,8%) in 2004 (p=0,003), although this difference was less apparent when analysed according to the individual PTP risk groups (low/moderate risk 77,3/51,9% vs. 71,2/48,3%). Incidence of VTE events decreased over time from 5,3 to 1,6% (p=0,002). Incidence in the low/moderate risk groups was 1,6%/10,6% vs. 0,3%/6,6%. Only one false negative result (popliteal vein DVT) was observed in the two cohorts (NPP = 99,9%). Conclusion: Our results show a decreasing incidence in VTE over time in an ER population screened by D-dimer testing and PTP even though the number of tests performed were stable over time. This was accompanied by a decreasing number of cases considered to have an intermediate PTP. These findings suggest a change of practice over time resulting in an increasing use of D-dimer testing for very low risk patients and a decrease in their use for intermediate risk patients. A decrease in the proper use of the PTP tool over time might result in overestimation of the physician perceived risk and therefore lead to an increase in imaging resource utilisation. Broader studies including imaging prescription trends over time will be needed to confirm this hypothesis.

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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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.253
Teacher spread0.224 · 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 designObservational
Domainnot available
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".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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