Pre-Transplant C-Reactive Protein (CRP), Ferritin and Albumin As Biomarkers to Predict Transplant Related Mortality (TRM) after Allogeneic Hematopoietic Cell Transplant (HCT)
Notice bibliographique
Résumé
Abstract HCT entails substantial TRM justifying continued efforts to better risk stratify patients. Single institutional studies have suggested several clinically available biomarkers of CRP, ferritin and albumin influence TRM if not overall survival (OS). We sought to confirm the independent prognostic value of these biomarkers in a large multi-institutional cohort. The study population consisted of 784 adults with AML in remission (80%) or MDS (20%) undergoing unrelated donor HCT between 2008 and 2010 and available cryopreserved samples through the Center for International Blood and Marrow Transplant Research (CIBMTR) repository. CRP and ferritin were centrally quantified by ELISA from cryopreserved plasma whereas albumin levels obtained from center reported data. Correlation studies for biomarkers required log transformation of CRP and ferritin to produce a normal distribution. Multivariate models were fitted separately for each biomarker applying protocol specified thresholds generated from the literature of CRP > 10 mg/L, ferritin > 2500 ng/mL, albumin < 3.5 g/dL on TRM. Further analysis explored optimal cutpoints for this cohort for all significant clinical variables for TRM. HCT characteristics included a median age of 50 (range 18-78) years, HCT-CI (co-morbidity index) 3 or more in 35%, single allele/antigen mismatch (7/8) in 23%, PB as stem cell source in 83%, and myeloablative conditioning in 72%. Biomarker data were available in 783, 781 and 695 cases for CRP, ferritin and albumin respectively. The median values and ranges for each biomarker were as follows: 5.0 mg/L (0.3 - 316) for CRP, 1148 ng/mL (51 – 14,298) for ferritin and 3.6 g/dL (0.6-5.3) for albumin. Log transformed CRP and ferritin showed a modest correlation (r=0.35, P<0.001), and log CRP was marginally associated with albumin (r=-0.12, P=0.002). HCT-CI had no correlation to CRP and albumin. Higher ferritin was associated with HCT-CI (P=0.014) and disease (P<.001). In the entire population, TRM and overall survival (OS) at 2 years were 23% and 54%, respectively. The table shows the independent association of each biomarker with TRM and OS. Ferritin had no association with these outcomes but only 12% had levels above 2500 by ELISA. The optimal cutpoints for TRM were defined as follows: CRP > 3.67 (HR=1.75, P=0.001); albumin < 3.4 (HR 1.70, P <0.001); and ferritin as a continuous variable (HR 1.30, P = 0.008). A risk score was created modeling optimal biomarker thresholds to predict TRM based on the following formula: risk score = 0.44633*I(CRP>3.67)+0.18330*ln(Ferritin)+0.44730*I(albumin<3.4), where “I” is an indicator function and represent 1 or 0 depending on the presence of the abnormal biomarker level in parenthesis. The risk score included adjustment for HCT-CI. The biomarkers did not influence the incidence of relapse or graft-versus-host disease with any of these models. Elevated CRP and ferritin and decreased albumin prior to HCT were associated with impaired transplant survival, primarily through higher TRM. Integration of a biomarker panel in clinical practice once validated can enhance risk-stratification, improve adjustment for comparative studies and point towards the design of biomarker driven trials. Abstract 422. Table: Multivariate analysis of TRM and OS for biomarkers, and biomarker panel risk score* Abnormal, N (%) Transplant related mortality Overall Survival Biomarker HR 95% CI P HR 95% CI P Protocol defined CRP >10 mg/L 184 (24) 1.52 1.11 – 2.07 0.008 1.22 0.98 – 1.53 0.072 Ferritin >2500 ng/mL 93 (12) 1.26 0.84 – 1.88 0.27 1.15 0.86 – 1.54 0.35 Albumin < 3.5 g/dL 290 (42) 1.48 1.10 – 2.0 0.010 1.39 1.13 – 1.72 0.002 Optimal threshold, combined model ** CRP >3.67 mg/L 497 (64) 1.56 1.11 – 2.19 0.010 1.24 1.00 – 1.55 0.054 Log(Ferritin), linear n/a 1.20 0.99 – 1.46 0.07 1.20 1.04 – 1.38 0.010 <3.4 g/dL 203 (29.5) 1.58 1.16 – 2.15 0.004 1.37 1.10 – 1.72 0.005 Biomarker Score ** N Low (<1.5) 200 1.0 - - 1.0 Intermediate (1.5-2.0) 331 1.66 1.10-2.49 0.015 1.38 1.06-1.80 0.017 High (>2.0) 159 2.72 1.77-4.19 <0.001 2.01 1.51-2.70 <0.001 * biomarkers adjusted for significant covariates of BMI, disease, mismatch, and CMV serostatus and stratified on conditioning regimen ** combined model includes all biomarkers and HCT-CI Disclosures No relevant conflicts of interest to declare.
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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) | 0,001 | 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 | 0,001 | 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 tête enseignante, pas un consensus.
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