Improved Prediction of CD34+ Cell Yield before Peripheral Blood Hematopoietic Progenitor Cell Collection Using a Modified Target Value–Tailored Approach
Notice bibliographique
Résumé
The most commonly used stem cell source for both autologous and allogeneic transplantation is mobilized peripheral blood hematopoietic progenitor cells collected by apheresis. In the 1990s, an Italian group used the correlation between the preapheresis peripheral blood CD34+ cell count and the final number of CD34+ cells collected to devise a formula for "target value-tailored" (TVT) apheresis. Using local patient data, the Canadian Blood Services Stem Cell Laboratory created a similar model to determine the blood volume to process during apheresis collection. The objectives of this study were to determine the correlation between the number of CD34+ cells predicted by the TVT formula and the actual number of CD34+ cells collected and to determine whether the TVT formula remains predictive when applied to an external data set. All apheresis collections performed at the Ottawa Hospital between January 1, 2003 and October 2, 2013 were reviewed. The primary outcome was the correlation between the number of CD34+ cells predicted by the TVT formula and the actual number of CD34+ cells collected on day 1 of apheresis. For the external data set, all autologous collections performed at the London Health Sciences Centre between December 1, 2008 and December 1, 2013 were reviewed. The external data set was divided into test and validation sets to determine whether a model could be created to predict the final number of CD34+ cells collected on day 1 based on the preapheresis CD34+ count. A total of 1252 collections were included in the analysis. The Ottawa data set included 1012 collections, 836 of which were autologous and 176 of which were from donors. Of the autologous collections in Ottawa, 764 (92.5%) were first collections. In 759 (91%) collections, chemotherapy plus granulocyte colony-stimulating factor (G-CSF) was used as the mobilization regimen. In 747 collections (89%), only 1 collection day was required to achieve the desired number of CD34+ cells. The TVT estimate was highly predictive of the number of CD34+ cells × 10(6)/kg actually collected on apheresis day 1 (r = .90, P < .0001). The London data set included 240 autologous collections. All mobilizations were with G-CSF alone. For the test set, the precollection CD34+ count was highly predictive of the number of CD34+ cells × 10(6)/kg collected on day 1 of apheresis. Applying this model to the validation set, the correlation between the predicted and final and day 1 CD34+ cells × 10(6)/kg count was .9186 (P < .0001). Using a modified TVT approach, the preapheresis CD34+ count can be used to accurately predict the number of CD34+ cells × 10(6)/kg collected on day 1. This approach can be applied at other centers and for different diseases and mobilization regimens. This method can be used to individualize the blood volume processed and, thus, optimize resource utilization.
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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 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 ».