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Utility of Transient Elastography (Fibroscan) in Estimating Hepatic Iron Concentration in Comparison to MRI in Patients with Transfusion Dependent Hemoglobinopathies

2014· article· en· W2540318517 on OpenAlexaffabout
Hatoon Ezzat

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of British ColumbiaProvidence Health Care
Fundersnot available
KeywordsMedicineTransient elastographyCirrhosisInternal medicineGastroenterologyLiver diseaseProspective cohort studyHemochromatosisLiver transplantationSurgeryTransplantationLiver fibrosis

Abstract

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Abstract Background Patients with severe hereditary anemias (e.g. β-Thalassemia Major) are transfusion-dependent for survival. Current guidelines suggest monitoring serum ferritin every three months and annual MRI to assess hepatic and cardiac iron load1. However, MRI, particularly the R2 sequence (FerriScan) which has high specificity and sensitivity in estimating the liver iron concentration, is expensive and not always readily available. Transient elastography (FibroScan) measures liver's stiffness and predicts fibrosis. Previous studies have suggested its utility in other conditions that increase liver stiffness, such as amyloidosis2and perhaps iron overload. Aim To determine if FibroScan value correlates with hepatic iron concentration estimated using R2 MRI (FerriScan), and/or serum ferritin level. Methods A prospective cross-sectional study was conducted at a university-affiliated tertiary care center (St. Paul’s Hospital, Vancouver, BC) in 2013 and 2014. Inclusion criteria: Age ≥ 19 years with transfusion-dependent hereditary anemias. Exclusion criteria: liver cirrhosis, primary liver disease (e.g. Wilson’s disease, hereditary hemochromatosis), and chronic viral hepatitis (e.g. Hepatitis B, C and HIV). In addition to having annual MRI and ferritin levels monitored every three months, subjects underwent FibroScan within six months of MRI in 2013. In 2014, participants were invited to undergo repeat FibroScan within three months of the annual MRI. Linear regression analysis was used to determine if there is any correlation/linear fit between FibroScan result, MRI result, and ferritin levels. This study was approved by the University of British Columbia Research Ethics Board. Results 20 subjects have been recruited as of August 1, 2014, with 35 and 33 complete FibroScan and MRI results, respectively. 14 (70%) were female. Mean age was 30.7±9.8 years. Most common primary diagnosis was transfusion-dependent beta-thalassemia (Major and intermedia) (n=17). Linear regression analysis showed a weakly positive correlation between hepatic iron concentrations estimated with R2 MRI (FerriScan) and ferritin levels (R2=0.29; p=0.004), when they are performed within four weeks apart. The correlation remained statistically significant when all subjects were included regardless of time lapse between the two investigations (R2=0.30; p=0.001). However, FibroScan values did not appear to correlate with MRI, regardless of whether the scans are performed within six months (R2=0.011; p=0.58) or three months apart (R2=0.035; p=0.44). Similarly, there was no correlation between FibroScan and Ferritin (R2=0.022; p=0.49) when the investigations were performed within 4 weeks part. Conclusion Interim analysis did not demonstrate any correlation between FibroScan result and MRI-estimated hepatic iron concentration. A final analysis will be performed upon complete formal evaluation of the remaining MRI and FibroScan data. References Remacha A, Sanz C, Contreras E, et al. Guidelines on haemovigilance of post-transfusional iron overload. Blood Transfus. 2013; 11(1): 128-139Loustaud-Ratti V, Cypierre A, Rousseau A, et al. Non-invasive detection of hepatic amyloidosis: Fibroscan, a new tool. Amyloid 2013; 18(1): 19-24 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.223
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2014
Admission routes2
Has abstractyes

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