Pre-Transplant C-Reactive Protein (CRP), Ferritin and Albumin As Biomarkers to Predict Transplant Related Mortality (TRM) after Allogeneic Hematopoietic Cell Transplant (HCT)
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".