Predictors of Blood Transfusion Requirements in Patients Undergoing Autologous Stem Cell Transplantation for Multiple Myeloma.
Notice bibliographique
Résumé
Abstract INTRODUCTION: The majority of patients with multiple myeloma undergoing autologous stem cell transplant require transfusion support during the engraftment period. A retrospective study was conducted to identify factors predicting need for blood transfusion in this population: METHODS: Charts were reviewed for all patients undergoing their first autologous stem cell transplantation for multiple myeloma (MM) at the Princess Margaret Hospital (PMH) from July 1, 1999 to Oct 31, 2002. In order to ensure all transfusion needs were tracked, patients who did not receive all follow-up care at PMH were excluded from analysis. Use of blood products during the 90-day period following stem cell infusion (Day 0–90) was determined by review of the hospital blood bank laboratory information system. An RBC transfusion was defined as a single unit, and a platelet transfusion as either one apheresis unit or 3–5 pooled whole-blood derived units. The following factors were evaluated as predictors for receipt of an RBC and/or platelet transfusion: patient age and sex, myeloma subtype (IgG vs. other) and stage (Salmon-Durie I/II vs. III); presence of hematologic comorbidity (active blood loss and being on therapeutic anticoagulation) in the post-transplant period; previous treatment with alkylating agent(s) >1 month and > 4 months; pre-transplant pelvic or lumbar radiation therapy; hemoglobin at transplant admission (Day -2), white blood cell count, platelet count, and creatinine; number of CD34 cells infused. Results were analyzed by both univariate and multivariate logistic regression analysis. RESULTS: Over the 40-month period of review, 280 transplants for MM were performed with 128 cases identified with complete transfusion and follow-up data available. The overall transfusion rate in the post-transplant period was 54.0 % for RBCs, 67.6% for platelets. By univariate analysis, significant predictors for RBC transfusion were female sex (OR 2.03, p =0.046), previous treatment with alkylating agent(s) >1 month (OR 3.29, p =0.013) or > 4 months (OR 4.83, p =0.018) and decreased admission hemoglobin (OR 0.90, p<0.001). In the multivariate model, admission hemoglobin and > 4 months alkylator treatment remained as independent predictors. If hemoglobin was dichotomized as ≥110 vs. <110 g/L, this variable remained the only significant multivariate predictor of RBC transfusion (p < 0.001). In the univariate model, no predictors for platelet transfusion were identified. CONCLUSIONS: Hemoglobin at time of transplant and prior alkylator therapy were predictive of transfusion needs in myeloma patients post-transplant. Although prolonged alkylator exposure can slow engraftment by damaging stem cells for autologous collection, we were not able to consistently identify total stem cell collection volume as an independent predictor for transfusion. Further data collection is planned with aims to develop approaches to blood product conservation in this transplant population.
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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 | 0,000 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,001 | 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 ».