Pragmatic Evaluation of an Algorithm Using D-Dimer Adjusted to Clinical Probability in the Diagnosis of Pulmonary Embolism
Notice bibliographique
Résumé
Abstract BACKGROUND: Diagnosis of pulmonary embolism (PE) using clinical decision rules in combination with D-dimer (DD) values is a standard practice. The Wells score is the most commonly used rule, either in its original (3-category) or modified (2-category) versions, and in conjunction with a (DD) <500 ng/mL allows to exclude a PE in approximately 30% of patients. The recent PEGeD study (Kearon et al. 2019) concluded that a PE can be safely excluded by using a DD threshold adjusted to the clinical pre-test probability (C-PTP). In that study PE was excluded in patients with low C-PTP and a DD <1000 ng/mL or a moderate C-PTP and a DD <500 ng/mL In the present study we aimed to evaluate the performance of the PEGeD algorithm in daily practice. METHODS: We conducted a retrospective cohort study involving all adult patients who presented at London Health Sciences Centre or St. Joseph's Health Care Emergency Departments in London, Ontario, Canada between November 1, 2018 and December 31, 2020 with signs or symptoms suggestive of a pulmonary embolism and for whom a DD was ordered electronically. They were excluded if they did not have complete follow-up information for at least 90 days from the initial visit, they were pregnant, they were on long term anticoagulation for other indications, or had chest imaging prior to DD order. Using the electronic hospital chart, we extracted demographics, imaging results, and the Wells score with all its individual components. In our center, information about the Wells score and its components is routinely and prospectively collected when ordering DD. Since the PEGeD algorithm is not routinely used in our hospital, data of the C-PTP was utilized to determine which DD cut-off should be applied to the patient. Decision to perform imaging studies was taken by the ED physician at the time of assessment. The outcome of interest was the proportion of a PE or DVT at 90 days after the visit to the ED in patients with a low or intermediate C-PTP and who did not receive an initial diagnosis of PE and 99% confidence intervals (CI) were estimated using the Wilson's score method. RESULTS: A total of 2769 patient charts were reviewed and 1070 were included (Table 1, Figure 1). Of the 1070 patients, 71 (7%) of patients had a pulmonary embolism on initial presentation to the emergency department. At 90 days of follow up none (99% CI 0, 0.84) of the 787 patients who had a low C-PTP or a moderate C-PTP score and a DD <1000 ng/mL or <500 ng/mL, respectively, were positive for a PE . This included 194 patients who had a low C-PTP and a DD level of 500-999 ng/mL and 26 patients who had an intermediate C-PTP and a DD level of <500 ng/mL. Notably, 8 (1.02%, 99% CI 0.42-2.43) PEs would have been missed using the PEGeD protocol when using DD cut-off levels of <1000 ng/mL in the low C-PTP group, or <500 ng/mL in the intermediate C-PTP. CONCLUSIONS: In this cohort we found that if the PEGeD algorithm had been used, it would have resulted in a low risk of VTE during follow up in patients without an initial diagnosis of PE and who had either a low C-PTP and a DD <1000 ng/mL or a moderate C-PTP and a DD <500 ng/mL. We also found it would have been associated with 194 (48%) less diagnostic imaging studies in the low C-PTP range and 2 (6%) less studies in the intermediate C-PTP range. Despite this, 1% of patients with PE (99% upper confidence limit 2.43%) would have been missed. This study is limited by its retrospective nature with an inherent risk of misclassification. Further studies are needed before recommending the use of this algorithm in clinical practice. Work Cited Kearon, C., de Wit, K., Parpia, S., Schulman, S., Afilalo, M., Hirsch, A., Spencer, F. A., Sharma, S., D'Aragon, F., Deshaies, J.-F., Le Gal, G., Lazo-Langner, A., Wu, C., Rudd-Scott, L., Bates, S. M., & Julian, J. A. (2019). Diagnosis of Pulmonary Embolism with d -Dimer Adjusted to Clinical Probability. New England Journal of Medicine, 381(22), 2125-2134. https://doi.org/10.1056/NEJMoa1909159 Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,037 | 0,121 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».