Risk Stratification for the Development of Venous Thromboembolism in Hospitalized Patients with Cancer
Notice bibliographique
Résumé
Abstract Introduction: No method of venous thromboembolism (VTE) risk stratification exists for hospitalized cancer patients. The Khorana score is a validated tool in outpatients with cancer. The objective of this study was to assess the Khorana score for predicting development of VTE in cancer patients during admission to hospital. Methods: We conducted a retrospective analysis of data collected from healthcare records of consecutive, medically-ill cancer patients hospitalized between January and June 2010 in 3 academic medical centers in Canada. Objectively diagnosed symptomatic VTE during hospitalization, anticoagulant thromboprophylaxis (TP), and Khorana score variables were collected for every patient. Patients receiving therapeutic anticoagulation at admission, and those with incomplete data were excluded. The risk of VTE based on Khorana score category was evaluated using logistic regression. Continuous data were compared using a Student's t-test and expressed using the means and standard deviations. Categorical data were compared using the Pearson Chi-square test and were expressed as percentages. Statistical significance was defined as alpha less than 0.05. Results: 1398 patients were included. Mean age was 61.6 years, 51.2% were male, and mean BMI was 25.9 kg/m2. The most frequent tumor types were non-small cell lung carcinoma (12.7%) followed by lymphoma (10.9%). The median length of stay was 6 days (range 0-114 days). The most frequent reasons for hospitalization were chemotherapy (22.3%) followed by pain and palliation (21.4%). 34.5% received anticoagulant TP (n = 483/1398). The incidence of VTE was 2.9% (41/1398) overall, 5.4% (9/166) in high, 3.2% (26/817) in moderate, and 1.4% (6/415) in low Khorana score risk groups. High risk patients were significantly more likely than low risk patients to have VTE (p=0.016; OR 3.9, 95% CI 1.4-11.2). There was no difference in VTE incidence between patients who received anticoagulant TP and those who did not (3.5% vs 2.6%, p = 0.345). Patients with high risk Khorana score were more likely to receive anticoagulant TP than those with low risk Khorana score (46.4% vs. 23.9%, p <0.001, OR 2.8, 95% CI 1.9-4.0). Total incidence of major bleeding was 1.8% (25/1398). There was no difference in major bleeding between patients who received anticoagulant TP and those who did not (1.7% vs. 1.9%, p = 0.787). Conclusion: The Khorana score is predictive of VTE development in cancer patients who are hospitalized for medical illness and may be a useful tool for tailoring inpatient anticoagulant prophylaxis. Disclosures Lee: LEO: Consultancy, Honoraria; Bayer: Consultancy, Honoraria; Bristol Myers-Squibb: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria. Carrier:BMS: Research Funding; Leo Pharma: Research Funding. Wu:Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Leo Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».