Predictors of Venous Thmromboembolism in Colorectal Cancer: Results from a Global Prospective Study
Notice bibliographique
Résumé
Abstract BACKGROUND Venous thromboembolism (VTE) is an important complication among patients with colorectal cancer but its prevalence and predictors are incompletely understood. The Khorana Score (KS), comprising primary site, baseline hemoglobin, leukocyte and platelet counts, and body mass index [Khorana et al, Blood, 2008], has been demonstrated to predict VTE in various cancer settings. We evaluated the value of this Score and other key prognostic variables in a global prospective cohort study to define the incidence, predictors and consequences of VTE in patients undergoing adjuvant or palliative chemotherapy for colorectal cancer. METHODS CANTARISK was a prospective, non-interventional, international cohort study in patients with lung and colorectal cancer on chemotherapy; data for the colorectal cohort are presented here. Clinical data were collected at baseline, and at 2, 4 and 6 months. All data were compiled centrally and analyzed after the study had closed. KS categories were as defined previously [Blood, 2008]. Statistically significant univariable associations and a priori variables were tested in multivariable models; adjusted odds ratios (OR) with confidence intervals (CIs) are presented. RESULTS A total of 1,789 patients with colorectal cancer were enrolled from 2011 to 2012. Median age was 62 years; 61% were male; 71% were Caucasian; 18% were Asian; 37% were from Europe, 28% from North America, 23% from Asia, and 12% from South America. During the six-month follow-up period, 92 (5.1%) patients experienced VTE events; 18 patients experienced 2 events each, and 2 patients experienced 3 events each, for a total of 112 VTE events. Of these 112 events, 69 (61.6%) were deep venous thromboses (DVT), 22 (19.6%) were pulmonary emboli (PE), 14 (12.5%) were catheter-associated thrombi, and 7 (6.3%) were visceral thrombi. The majority (n=94, 83.9%) were symptomatic. For low, intermediate, and high KS, there were 4.3% (n=42), 6.8% (n=39), and 12.5% (n=5) VTE events, respectively (N=1596 due to some missing values for KS components). In adjusted multivariable analyses, KS (OR for high/intermediate vs. low = 1.82, 95% CI = 1.15-2.87), ECOG performance score (OR for 2/3/4 vs. 0/1 = 2.17, 95% CI=1.07-4.43), and central venous catheter (OR for yes vs. no = 4.01, 95% CI=2.32-6.94) were independent predictors of VTE. Notably, age, metastatic (vs. non-metastatic) disease, surgery or immobilization within the prior 6 months, current smoking, and history of VTE were not associated with new VTE events. CONCLUSIONS This global prospective study demonstrates that VTE events are prevalent among patients with colorectal cancer receiving systemic chemotherapy. Khorana Score category is a strong predictor of risk. Ongoing clinical trials are focusing on the benefit of prophylactic anticoagulation in high-risk patients in these settings. Disclosures Kuderer: Janssen Scientific Affairs, LLC: Consultancy, Honoraria. Lyman:Amgen: Research Funding. Khorana:Amgen: Consultancy, Honoraria, Research Funding; Bayer: Consultancy, Honoraria; Halozyme: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Leo: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Janssen Scientific Affairs, LLC: Consultancy, Honoraria, Research Funding.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».