Risk Factors Predictive of Occult Cancer Detection in Patients with Unprovoked Venous Thromboembolism
Notice bibliographique
Résumé
Abstract Background: Venous thromboembolism (VTE) may be the earliest sign of cancer. Risk factors associated with the presence of an occult cancer in patients with a first acute unprovoked VTE are unknown. We sought to assess the risk factors predictive of occult cancer detection in patients with a first unprovoked symptomatic VTE. Methods: Post-hoc, pre-defined analyses of the multicenter open-label randomized controlled trial - Screening for Occult Malignancy in Patients with Idiopathic Venous Thromboembolism (SOME) trial (Carrier M et al. N Engl J Med 2015). The trial compared comprehensive computed tomography (cCT) of the abdomen and pelvis in addition to limited occult-cancer screening (complete history and examination, basic laboratory testing, chest radiography, and breast, cervical and prostate cancer screening) with limited occult-cancer screening alone in patients with a first unprovoked episode of VTE. Cox proportional hazard models were used to analyze the effect of specific risk factors on the outcome of occult cancer within 12 months of a diagnosis of unprovoked VTE. Multivariable analysis was performed using Cox proportional hazard models that included all variables that achieved a p value of < 0.20 in univariate analyses. Results: A total of 854 patients were randomized to limited occult cancer screening only, or limited occult cancer screening in combination with a cCT. The mean age was 54 years and 67.4% were males. A total of 33 (3.9%; 95% C.I. 2.8-5.4) patients received a new diagnosis of cancer at 12 months follow-up. Age ≥ 60 years, compared to age < 60 years, was a predictor of cancer with a corresponding hazard ratio (HR) of 2.90 (95% C.I. 1.44-5.83, p=0.003). A previous provoked VTE in patients was also associated with a higher risk of developing cancer (HR=3.57, 95% C.I. 1.38-9.25, p=0.009). Patients with an unprovoked deep vein thrombosis (DVT), compared to either those with a pulmonary embolism (PE) only or both DVT and PE, seemed more likely to have a diagnosis of cancer. However, this trend was not statistically significant. (Table 1) These results were confirmed on multivariable analysis. Patients exhibiting one of these characteristics had a three-fold higher risk of occult cancer compared with patients without these characteristics. (Table 1) Conclusion: Age at unprovoked VTE diagnosis (≥ 60 years) and prior provoked VTE are predictors of occult cancer, and could potentially be used to identify a group of patients with unprovoked VTE at high risk of underlying cancer. Table 1.Risk factors of occult malignancy among patients with a first unprovoked symptomatic VTE.Patients without cancer (%) (n = 821)Patients with cancer (%) (n = 33)Univariate analysis Hazard Ratio (95% C.I.)P valueMultivariable analysis Hazard Ratio (95% C.I.)P valueAge at diagnosis ≥ 60 years288 (35.1)20 (60.6)2.90 (1.44-5.83)0.0033.0 (1.47-5.99)0.002Male sex555 (67.6)21 (63.6)0.72 (0.35-1.46)0.358--Prior provoked VTE42 (5.1)5 (15.2)3.57 (1.38-9.25)0.0093.8 (1.46-10.03)0.006Type of current VTEDVT only444 (54.3)24 (72.7)1.91 (0.89-4.12)0.0972.1 (0.97-4.51)0.061PE only271 (33.1)7 (21.2)0.60 (0.26-1.38)0.229--DVT + PE103 (12.6)2 (6.1)0.54 (0.13-2.24)0.392--Baseline medicationsOral contraceptive pill48 (5.8)0 (0.0)----Exogenous estrogen18 (2.2)1 (3.0)1.51 (0.21-11.07)0.685--Antiplatelet agent39 (4.8)1 (3.0)0.62 (0.09-4.56)0.641--Oral anticoagulant688 (83.8)26 (78.8)0.66 (0.29-1.53)0.337--LMWH391 (47.7)15 (45.5)0.68 (0.34-1.36)0.275--VTE, venous thromboembolism; DVT, deep vein thrombosis; PE, pulmonary embolism; LMWH, low molecular weight heparin Disclosures Lazo-Langner: Pfizer: Honoraria, Other: Participated in studies funded by this organization, Speakers Bureau; LEO Pharma: Honoraria, Other: Participated in studies funded by this organization; Boehringer Ingelheim: Honoraria, Other: Participated in studies funded by this organization; Bayer: Honoraria, Other: Participated in studies funded by this organization; Daiichi-Sankyo: Other: Participated in studies funded by this organization; Novartis: Other: Participated in studies funded by this organization; Celgene: Other: Participated in studies funded by this organization; Alexion: Research Funding. Shivakumar:Bayer: Honoraria. Routhier:Sanofi-Aventis: Research Funding. Douketis:Janssen: Consultancy; Bristol-Myers Squibb: Consultancy, Honoraria; Pfizer: Honoraria; Sanofi-Aventis: Honoraria; Daiichi-Sankyo: Consultancy; Actelion: Consultancy; Biotie: Other: Advisory board; The Medicines Company: Other: Advisory board; Bayer: Consultancy; Boehringer Ingelheim: Consultancy, Honoraria. Carrier:LEO Pharma: Consultancy, Research Funding; BMS: Research Funding; Bayer: Consultancy; Pfizer: Consultancy.
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 | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».