S1642 DEVELOPMENT OF A CLINICAL PREDICTION RULE FOR VENOUS THROMBOEMBOLISM IN PATIENTS WITH ACUTE LEUKEMIA
Notice bibliographique
Résumé
Background: Risk factors for venous thromboembolism (VTE) in patients with solid tumors are well studied, however studies in patients with acute leukemia (AL) are lacking. Identifying risk factors for VTE in leukemia patients would help to develop tailored VTE prophylaxis or surveillance strategies. Aims: To develop a clinical prediction model for VTE in AL patients. Methods: We conducted a retrospective cohort study of adult patients diagnosed with acute myeloid leukemia and acute lymphoblastic leukemia diagnosed between June 2006 and June 2017 at a tertiary care center in Canada. Outcome of interest was occurrence of imaging-confirmed VTE including proximal upper and lower extremity deep vein thrombosis, pulmonary embolism or thrombosis of unusual sites, including cerebral and splanchnic. Participants were followed until VTE occurrence, death or last follow up. Groups’ characteristics were compared using chi-square, Fisher's exact, or Student's T-tests as appropriate. Potential predictors were evaluated using single variable logistic regression and confirmed with multiple variable logistic regression. The final risk score was derived based on weighed variables and compared using survival analysis. Internal validation was conducted using non-parametric bootstrapping. Results: A total of 501 leukemia patients (427 myeloid and 74 lymphoblastic; 260 males) were included. Venous thromboembolism occurred in 77(15.3%) patients (44 upper extremity DVT, 28 lower extremity DVT or PE, 5 cerebral vein thrombosis. Median time from AL diagnosis to VTE was 64 days (interquartile range 22–130). The cumulative incidence of VTE was 9.6% (n = 48) (95%CI: 7.30–12.47) at 3 months, 12.8% (n = 64) (95%CI: 10.13–15.98) at 6 months, and 14% (n = 71) (95%CI: 11.39–17.50) at 12 months from the AL diagnosis. Of a total of 20 potential predictors, 7 were included in the multi-variable model. The final prediction score that was derived and validated included: previous history of venous thromboembolism (3 points), lymphoblastic leukemia (2 points), and platelet count > 50 x 109/L at the time of diagnosis (1 point). The overall cumulative incidence of venous thromboembolism was 44% in the high risk group (≥ 3 points) versus 10.5% in the low risk group (0–2 points) and it was consistent at 3 (28.8% vs 6.3%), 6 (41.1% vs 7.9%), and 12 (42.5% vs 9.3%) months (Figure; Log-rank p < 0.001).Summary/Conclusion: We derived and internally validated a predictive score of venous thromboembolism risk in acute leukemia patients. FA-A is a fellow of the Canadian Venous Thromboembolism Clinical Trials and Outcomes Research (CanVECTOR) Network; ALL-L is an investigator of the CanVECTOR Network. This study was funded by the CanVECTOR Network which receives grant funding from the Canadian Institutes of Health Research (Funding Reference: CDT-142654).
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».