Measurement of liver iron concentration by superconducting quantum interference device biomagnetic liver susceptometry validates serum ferritin as prognostic parameter for allogeneic stem cell transplantation
Bibliographic record
Abstract
INTRODUCTION: There are conflicting data regarding the role of serum ferritin (SF) as surrogate parameter for iron overload as an independent prognostic factor for outcome after allogeneic stem cell transplantation (SCT). Superconducting quantum interference device (SQUID) biomagnetic liver susceptometry, a noninvasive measurement of iron overload, allows measurement of the interference of an exteriorly applied small but highly constant magnetic field by the paramagnetic liver storage iron. By measuring the true iron load of patients through SQUID, we wanted to assess the effect of iron overload on patients undergoing SCT. METHODS: We conducted a single-center retrospective analysis (1994-2010), comparing the effect of SF and liver iron content measured by SQUID shortly before transplantation on overall survival (OS), event-free survival (EFS), and transplant-related mortality (TRM) in 142 patients (median age 54.5 yr, range 5.6-75 yr) undergoing SCT (80% reduced intensity regimen). Patients were subdivided into five groups: myelodysplastic syndrome, de novo acute myeloid leukemia (AML), secondary AML, primary myelofibrosis, and others. RESULTS: Correlation between SF and SQUID was significant (r = 0.6; P < 0.001; log function). The chance of infection was increased 2.4-fold (95% CI 1.22-4.71) when SQUID values ranged ≥1000 μg Fe/g liver (P = 0.012). We found similar results for SF >1000 ng/mL (P = 0.003). A significant association between SQUID and fungal infection was also seen (P = 0.004). For patients with SQUID ≥1000, the risk of proven fungal infection was increased 3.08-fold (95% CI 1.43-6.63). A similar association between SF >1000 and fungal infection was shown (P = 0.01). In univariate analysis, age was a prognostic factor for TRM (P = 0.034, HR 1.04, CI 1.00-1.08). SF ≥1000 was associated with OS (P = 0.033, HR 2.09, CI 1.06-4.11) and EFS (P = 0.016, HR 2.15, 95% CI 1.15-4.10). In multivariate analysis on EFS, only age and SF >1000 remained as independent factors (HR 1.027, P = 0.040, 95% CI 1.001-1.054 and HR 2.058, P = 0.034, 95% CI 1.056-4.008, respectively). The multivariate analysis on TRM left age and SQUID values ≥1000 in the final model (HR 1.045, P = 0.041, 95% CI 1.002-1.090 and HR 2.110, P = 0.103, 95% CI 0.859-5.183, respectively). CONCLUSION: Our data confirmed that SF ≥1000 increases the risk of infection, moreover fungal infection in transplant recipients. As SQUID values correlate well with SF, we could show that SF is indeed a good surrogate parameter for iron overload when measured shortly before SCT. Prospective trials are needed to investigate the effect of iron chelation before or during SCT on transplant outcome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".