Provider practices and preparedness for gene therapy in pediatric sickle cell disease: A national survey of pediatric hematologists
Bibliographic record
Abstract
Abstract Introduction: Sickle cell disease (SCD) is a severe, life-limiting genetic disorder affecting over 100,000 individuals in the U.S. While allogeneic hematopoietic stem cell transplantation from an HLA-matched donor remains the only established cure, most patients lack a suitable donor. In 2023, the FDA approved two autologous gene therapies for patients ≥12 years old with recurrent vaso-occlusive crises (VOCs): lovotibeglogene autotemcel (Bluebird Bio) and exagamglogene autotemcel (Vertex/CRISPR Therapeutics). Despite their promise, integration into routine care remains challenging due to strict trial criteria and limited implementation guidance. We sought to assess provider knowledge, referral patterns, and comfort discussing gene therapy (GT) to guide equitable adoption across pediatric SCD practices. Method: We distributed a 35-question REDCap survey to pediatric hematology providers in North America. Eligible participants were board-eligible/certified pediatric hematologists who care for individuals with SCD. The study was approved by the IRB of Baylor College of Medicine. This interim analysis includes responses collected from June 6 to July 24, 2025. Stratified analyses based on provider practice type, clinical experience, and SCD center volume were conducted using two-sided t-tests. Results: In addition to posting the survey link on the American Society of Pediatric Hematology and Oncology (ASPHO) Hemoglobinopathy Special Interest Group discussion board and ASPHO Clinical Forum, we distributed email invitations to approximately 500 pediatric hematology/oncology providers identified through pediatric hematology directories. A total of 102 providers who met inclusion criteria completed the survey, yielding a response rate of ~20%. We captured a representative sample of pediatric hematologists caring for individuals with SCD and pediatric SCD centers across the United States and Canada. Practice types included general hematology/oncology (38.3%), SCD-focused (47.1%), and HSCT-focused (14.7%). Clinical experience ranged from <10 years (31.4%) to >20 years (29.4%). Practice sizes ranged from centers caring for <100 (22.5%), 100–400 (36.3%), and >400 (41.2%) individuals with SCD. Most providers (91.6%) reported they would likely discuss GT with patients lacking a matched donor who either had recurrent VOCs or expressed interest. However, only 40% were likely to discuss GT in the absence of VOCs. Stratification revealed that SCD-focused providers were significantly less likely than generalists to recommend GT for patients with prior SCD related neurological complications including: prior stroke (44.8% vs. 69.2%, p=0.028); CNS vasculopathy (39.6% vs. 69.2%, p=0.005); abnormal TCD (60.4% vs. 74.4%, p=0.1). Comfort with gene therapy-related discussions also varied significantly. SCD focused hematologists reported significantly greater comfort with GT counseling including: initiating conversations (100% vs. 76.9%, p=0.002); differentiating products (97.9% vs. 30.8%, p<0.001); discussing risks/benefits (97.9% vs. 51.3%, p<0.001). Physicians were also asked whether, based on currently available information, they would be inclined to recommend one gene therapy product over another when referring patients. Among respondents, 26.7% of HSCT-focused providers indicated they would make such a recommendation, compared to 12.5% of SCD-focused providers and 7.7% of general hematology/oncology providers. Notably, all who expressed a preference favored exagamglogene autotemcel over lovotibeglogene autotemcel, citing concerns about insertional mutagenesis and secondary malignancies, a preference for gene-editing approaches, and considerations related to cost. Conclusions: This national survey highlights substantial variability in gene therapy referral practices for pediatric sickle cell disease, particularly in cases involving neurologic complications and across different provider types. While hematologists with a SCD-focused practice reported greater comfort with GT counseling, they were less likely to recommend GT in certain high-risk scenarios. These findings emphasize the urgent need for standardized, evidence-based guidelines and targeted provider education to ensure equitable access and appropriate integration of GT into pediatric SCD care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".