Diagnostic Accuracy of D-Dimer for Pulmonary Embolism and Lower Limb Deep Vein Thrombosis Testing in People with Cancer: A Meta-Analysis
Notice bibliographique
Résumé
Abstract Introduction There are evidence-based protocols for the diagnosis and exclusion of pulmonary embolism (PE) and deep vein thrombosis (DVT) which include clinical probability scoring along with selective D-dimer testing and diagnostic imaging. D-dimer assessment in VTE (venous thromboembolism) testing tends to be omitted in patients with cancer, partly because of perceived D-dimer lack of sensitivity and specificity. The aim of this systematic review and meta-analysis was to report the diagnostic accuracy of D-dimer for PE and lower limb DVT in patients with cancer. This study was part of a research program to set International Society of Thrombosis and Haemostasis standards for VTE testing in patients with cancer. Methods This systematic review and meta-analysis followed the MOOSE guidelines and was registered in PROSPERO, CRD42020181007. We searched Medline via OVID from conception to 12 th March 2020 for diagnostic PE and DVT studies reporting on people with cancer, or a subgroup of people with cancer. Researchers in the field were contacted for information on unpublished studies. All languages were included. Two researchers screened the titles and abstracts. Four researchers reviewed the selected full texts to determine which studies fulfilled inclusion criteria. Two researchers assessed risk of bias using QUADAS-2, extracted data on the true positive, false positive, true negative and false negative results for D-dimer alone, and D-dimer combined with clinical probability estimation. We used the bivariate random effects method to meta-analyze sensitivity and specificity values. We used a random effects model to estimate pooled false negative rates and efficiency for combining a negative D-dimer (manufacturer recommended cutoff) with a low clinical probability to exclude PE or DVT in patients with cancer. Results were displayed on a Forest plot. Heterogeneity was assessed using I 2. Results From 7947 titles and abstracts, we reviewed 49 full text manuscripts, including 13 studies for analysis. Risk of bias was low across all domains for only 5/13 studies. Figures 1 shows the Forest plots grouped by sensitivity and specificity for PE and DVT. The pooled estimates for D-dimer in the diagnosis of VTE in cancer patients (regardless of clinical probability) were 96.4% (95% confidence interval (CI) 94.8 to 97.5%) sensitivity and 26.4% (95% CI 18.1 to 37.0%) specificity. For PE, D-dimer was 96.9% (96.1 to 97.5%) sensitive (I 2 0%, N=2,299) and 14.0% (12.1 to 16.0%) specific, (I 2 69%, N=11,455). For DVT, D-dimer was 94.3% (89.8 to 97.6%) sensitive, (I 2 61%, N=546) and 46.4% (39.8 to 53.3%) specific, (I 2 59%, N=724). The efficiency of combining a low D-dimer (using the manufacturer recommended cutoff) and low clinical probability to exclude DVT or PE was 9.3%, (95% CI 6.9 to 11.9%), N=1,347. There were only 122 patients in the false negative rate analysis of whom 1 patient was diagnosed with VTE in follow up. A pooled analysis was not performed for the false negative rate. Conclusions D-dimer is a sensitive test for both PE and lower limb DVT in people who have cancer. Approximately 10% of patients with cancer and suspected VTE can have VTE excluded with D-dimer and clinical probability prior to ordering diagnostic imaging. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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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 | 0,027 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,022 | 0,074 |
| Bibliométrie | 0,008 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».