Impact of Hematopoeitic Cell Transplantation-Co-Morbidity Index (HCT-CI) and Its Individual Components on Allogeneic Transplant Outcomes
Notice bibliographique
Résumé
Background Allogeneic Stem Cell Transplantation (SCT) is potentially curative for many hematological diseases, however carries a high risk of mortality and morbidity. Multiple scoring systems has been developed to predict SCT outcomes and one of the more popular one id the Hematopoeitic Cell Transplantation - Co-Morbidity Index (HCT-CI). This study evaluates the value of HCT-CI score in predicting the outcomes of patients undergoing SCT at Princess Margaret Cancer Centre (PMCC). We also looked at the impact of the individual elements of HCT-CI in predicting SCT outcomes. Methods Two experienced physicians prospectively calculated the HCT-CI score for all patients transplanted at our center. Prospective calculation was performed during the patient's pre-transplant assessment before transplant admission using a pre-prepared form. All other patient and transplant characteristics were retrospectively collected from the EPR. Non-Relapse Mortality (NRM) and Overall survival (OS) were calculated to assess the prognostic power of the scores. This was correlated with the major SCT outcomes of Non-relapse mortality (NRM) and Overall survival (OS). Separately the impact of each components of HCT-CI was assessed in Univariate and multivariable analysis for NRM and OS. Results From August 2014 to April 2017, 299 patients underwent allogeneic HCT at the Princess Margaret Cancer Centre (PMCC), Toronto. Base line characteristics of the patients are shown in Table 1. HCT-CI scores were grouped as 0-2 as group 1 and ≥3 as group 2. Nearly two thirds belonged to group 1. (Table 1) The 2 year OS for the whole cohort was 51% (45%-56%) and NRM at 2 years was 25.1% (20%-31%). For the HCT-CI scores 0-2 vs ≥3, 2-year OS was 53% vs 46% respectively (p=0.29). The NRM at 2 years was 34% (29%-39%) for the whole cohort. For the HCT-CI scores 0-2 vs ≥3, 2-year NRM was 33% vs 35% respectively (p=0.75). (Figure 1) Univariate analysis of the impact of the independent components of HCT-CI score on OS and NRM was done. A p value of 0.2 was taken as cut off for selection for multivariable analysis. For NRM, cardiac co-morbidity, Diabetes and cerebrovascular accident were considered for multivariable analysis, where as cardiac co-morbidity, diabetes, severe pulmonary comorbidity, arrythmia and cerebrovascular accident were considered for OS multivariable analysis. On multivariable analysis, diabetes was the only factor found independently impacting both NRM [HR 2.2 (95%CI 1.4-3.4), p=0.0005] and OS [HR 1.6 (95%CI 1.1-2.4), p=0.025]. Conclusion HCT-CI was not found to predict OS or NRM accurately in our cohort of patients. Among the components of HCT-CI, diabetes was the only co-morbidity that significantly impacted both OS and NRM. Future prognostic scorings should incorporate only significant elements of comorbidities along with other transplant and disease related elements while developing prognostic scores. Disclosures Mattsson: Therakos: Honoraria; Celgene: Honoraria; Gilead: Honoraria. Michelis:CSL Behring: Other: Financial Support.
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 | 0,001 |
| 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 ».