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Record W4405046305 · doi:10.1182/blood-2024-200196

Large Scale Analysis of the Real-World Association between Fetal Hemoglobin and Vaso-Occlusive Crises in Sickle Cell Disease

2024· article· en· W4405046305 on OpenAlexaboutno aff
Peter Bruun‐Rasmussen, Elena Dudukina, L. Korsholm, Julie Derving Karsbøl, Inga Hegemann, Maarten Jan Wensink

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsVaso-occlusive crisisFetal hemoglobinMedicineSickle cell anemiaDiseaseInternal medicineHemoglobinFetusPregnancyBiologyGenetics

Abstract

fetched live from OpenAlex

Background Fetal hemoglobin (HbF) attenuates the rate of vaso-occlusive crises (VOCs) in sickle cell disease (SCD), but exact quantification is lacking. Methods We analyzed two large historic datasets with contemporary statistical methods. Data, acquired through BioLINCC, were the Cooperative Study of SCD (CSSCD; USA, 1979-1988) and the Multicenter Study on Hydroxyurea (MSH; USA and Canada, 1992-1995). The CSSCD had 3 planned HbF assessments: baseline, 1st annual visit, 2nd annual visit. We included those aged ≥12 years with at least one HbF measurement at baseline, 1st, or 2nd annual visit (1395 unique individuals, median age 22 years). Follow-up was up to 8 years post baseline. We analyzed the data using one of three approaches: baseline HbF and all follow-up; three HbF readings and one year of follow-up after each visit; three HbF readings and all follow-up up to next visit or follow-up end. For the MSH (HbSS genotype, age ≥18 years, ≥3 VOCs within the pre-trial year, 299 unique individuals, median age 29 years), we used HbF at randomization in the placebo arm and after titration in the hydroxyurea (HU) arm. Placebo participants were offered HU after early trial termination and their follow-up was censored then. HbF was analyzed as a percentage of total hemoglobin. In the MSH data, F-cells (as a percentage of red blood cells [RBC]) were additionally analyzed. F-cells were averaged over follow-up. We used directed acyclic graphs for confounder identification and negative binomial generalized additive models to allow the data to suggest the functional relationship between HbF and the VOC rate. A straight line suggests a linear effect of HbF% on log VOC rate and implies that each percentage point (%pt) increase in HbF or F-cells reduces the VOC rate by a fixed percentage. We regressed VOC rate during follow-up on HbF and adjusted models for age at baseline, sex, ethnicity, and when applicable for β-globin haplotype, assigned treatment (HU/placebo), and received HU dose. Results HbF showed a linear protective effect on log VOC rate in two of the CSSCD analyses and a non-linear progressive effect for the remaining analysis: each additional %pt increase in HbF reduced the VOC rate by a slightly larger percentage. When fitting linear models, each %pt increase in HbF was associated with a 4% (95% CI: 2-6%) to 6% (95% CI: 3-8%) reduction in VOC rate. For MSH data, each %pt increase in HbF was associated with an 8% (95% CI: 4-11%) reduction in VOC rate. The model suggested that the reduction per %pt increase in HbF depended on the level of HbF%, with a larger reduction in VOC rate the higher the HbF%. We found no evidence of change in VOC rate up to 35% F-cells. Above 35% F-cells, there was increasing protection against VOCs. Individuals with ≥70% F-cells had less than half the VOC rate of those with 35% F-cells. Results did not change when the models were adjusted for total hemoglobin. Interpretation The results show that each %pt increase in HbF has a protective effect on VOC rate, which was larger for higher HbF% in two analyses. Since HbF intercalates in HbS polymerization, VOC rate may be driven by HbF/HbS ratio. Hence, increasing HbF% would be progressively more effective through the numerator effect in HbS (like odds). These data further highlight the importance of HbF distribution across RBC, as VOCs occurred at a progressively lower rate for F-cells >35%. HbF content varies also among RBC that fail the F-cell threshold (6pg), and a small number of sickle RBC may suffice to cause a VOC. Translation of these data from its geographic reach to a current, global setting may be limited. However, data from a recent small study on HU in India suggested an upper limit of 9% VOC reduction for every %pt increase in HbF, if all protective effect of HU comes from HbF induction. This corresponds well with the 4-8% reduction in VOC rate found here. The CSSCD remains the largest real-world study on SCD, which contributes pre-treatment data, and the MSH yielded comparable results. Conclusion This large real-world study on prospectively collected data confirms the direct relationship between HbF and VOC occurrence. Further, our results suggest that one %pt increase in HbF% may be more beneficial for patients with a higher starting HbF%. These results highlight the importance of HbF distribution across the RBC population with a focus on HbF as an important SCD therapy target. Repeating the study on data of greater geographic reach would strengthen the interpretation.

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)0.007
metaresearch head score (Gemma)0.018
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 score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.248
Teacher spread0.242 · 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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Citations2
Published2024
Admission routes1
Has abstractyes

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