Real-World Data of Crizanlizumab in Sickle Cell Disease: A Single-Center Analysis
Bibliographic record
Abstract
Background: Crizanlizumab was approved by the United States Food and Drug Administration agency in 2019 for decreasing vaso-occlusive events (VOEs) in sickle cell disease (SCD). Data regarding the use of crizanlizumab in the real-world setting are limited. Our goal was to identify patterns of crizanlizumab prescriptions in our SCD program and evaluate the benefits and identify barriers to its use in our SCD clinic. Methods: We conducted a retrospective analysis of patients who received crizanlizumab at our institution between July 2020 and January 2022. We compared acute care usage patterns before and after initiation of crizanlizumab, adherence to treatment, discontinuation and reasons for discontinuation. High utilizers of hospital-based services were defined as those with more than one visit to the emergency department (ED) per month or more than three visits to the day infusion program per month. Results: Fifteen patients received at least one dose of crizanlizumab 5 mg/kg of actual body weight during the study period. The average number of acute care visits decreased following crizanlizumab initiation but was not statistically significant (20 visits vs. 10 visits, P = 0.07). Among high users of hospital-based services, the average number of acute care visits decreased after initiation of crizanlizumab (40 vs. 16, P = 0.005). Only five patients included in this study remained on crizanlizumab 6 months after initiation. Conclusion: Our study suggests that crizanlizumab use may be helpful in decreasing acute care visits in SCD, particularly among high utilizers of hospital-based acute care services. However, the discontinuation rate in our cohort was extremely high, and further evaluation of efficacy and causes contributing to discontinuation in larger cohorts is warranted.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".