Real-World Healthcare Resource Utilization (HRU) and Costs of Patients with Paroxysmal Nocturnal Hemoglobinuria (PNH) Receiving Eculizumab in a US Population
Bibliographic record
Abstract
INTRODUCTION: To evaluate the economic burden and treatment patterns of patients with paroxysmal nocturnal hemoglobinuria (PNH) treated with eculizumab, a C5 inhibitor, who were defined as blood transfusion-dependent (TD) versus blood transfusion-free (TF) in the US population. METHODS: Research Databases (April 1, 2014-September 30, 2019). The overall PNH eculizumab user cohort was stratified into the TD cohort (i.e., at least one claim for blood transfusion within 6 months following any eculizumab infusion, including on the infusion date) or the TF cohort (i.e., all non-TD patients). Treatment patterns, healthcare resource utilization (HRU), and costs were evaluated and compared during follow-up (i.e., index date to end of enrollment or data availability). RESULTS: Of 151 patients in the overall cohort (mean age 36.7 years; 55.6% female), 55 were TD (mean age 35.1 years; 67.3% female) and 96 were TF (mean age 37.6 years; 49.0% female). A total of 61% of patients (TD, 66%; TF, 58%) discontinued eculizumab, with TD patients having a shorter median time to discontinuation (TD, 0.5 years; TF, 0.9 years). TD patients had more all-cause hospitalizations than TF patients (p < 0.05). TD patients incurred higher all-cause direct medical costs (adjusted cost difference = $247,848) and medical-related absenteeism costs (adjusted cost difference = $4186) than TF patients (all p < 0.05), largely driven by hospitalizations. Similar trends were observed for PNH-related HRU and costs. CONCLUSIONS: The economic burden of patients with PNH treated with eculizumab is greater among those dependent on blood transfusions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".