Outcomes and Health Resource Utilization Among Second-Line Therapies for Immune Thrombocytopenia: A Provincial Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Introduction: Immune thrombocytopenia (ITP) is an immune-mediated disorder characterized by increased platelet destruction. Current guidelines recommend either rituximab, splenectomy, or thrombopoietin receptor agonist (TPO-RA) in chronic ITP patients who are unresponsive to first-line corticosteroids or who are corticosteroid-dependent. Though there are known practice variations in choice and timing of second-line therapy in Canada, there are scarce data comparing the outcomes and resource utilization in patients who received these second-line treatment strategies. In this multi-centre retrospective cohort study, we aim to identify differences in health services utilization and ITP-related outcomes in patients who received different second-line ITP therapies. Methods: Adults who received rituximab, splenectomy or TPO-RA as second-line therapy for ITP during 2012-2019 in the province of Alberta, Canada were identified via the provincial special drug access database. Institutional ethics board approval was obtained. We examined treatment patterns including sequencing of therapies and predictors of second-line therapies. Major outcomes documented included ITP-related hospitalizations (bleeding or infections), blood product utilization, major bleeding and thromboembolism, and all-cause mortality. Kaplan-Meier survival curves were used to estimate overall survival and the cumulative incidence of hospitalizations. Log-rank test was used to assess for differences between groups. Results: At the time of interim analysis, 189 adults with chronic ITP received second-line therapy. The median age at the time of second-line therapy was 58 years; 102 (54%) were female. Patients who received TPO-RA were significantly older than those who received rituximab or splenectomy (median 65 years vs 53 vs 49 years; P=0.0008). Rituximab was the most prescribed second-line therapy (108; 57%) followed by splenectomy (45; 24%) and TPO-RA (36; 19%) (Figure 1). Compared to recipients of rituximab, those who received TPO-RA had higher rates of hypertension (64% vs 33%, P=0.004), dyslipidemia (58% vs 20%, P <0.001), and prior history of myocardial infarction (22% vs 6%, P=0.02). On multivariable logistic regression, age (adjusted odds ratio [aOR] 1.03, 95% confidence interval [CI] 1.01-1.05, P=0.01) and a history of thromboembolism prior to second-line therapy (aOR 2.7, 95% 1.04-7.1, P=0.04) were significantly associated with TPO-RA prescription. Sex, rural residence, ITP etiology, major bleeding prior to second-line therapy, and abnormal bone marrow findings were not significant predictors of second-line therapy. During 773 person-years of follow-up, 23 deaths occurred, due to cardiac events (n=6), infections (n=5), malignancies (n=4), bleeding (n=3), and other or unclear causes (n=5). The 5-year overall survival was 88% (95% CI 82-94%), significantly shorter in recipients of second-line TPO-RA (80%) than rituximab (90%) and splenectomy (94%; log-rank P=0.04; Figure 2). The 5-year relapse-free survival was 55% (95% CI 46-65%), with no significant difference between treatment groups (log-rank P=0.8). Overall, 59 (31%) patients required ITP-related hospitalizations following second-line therapy. The median times to hospitalization were 5.1 years, 12.2 years, and not reached in patients receiving rituximab, splenectomy, and TPO-RA, respectively (log-rank P=0.1). Intravenous immunoglobulins and platelet transfusions were frequently utilized across all treatment groups (Table 2). DISCUSSION: Despite a myriad of therapeutic options for chronic ITP, patients still experience a high burden of hospitalization for bleeding or infections, and transfusion requirements. Patients with advanced age and a history of thromboembolism were more likely to receive TPO-RA as second-line ITP therapy. We did not observe a significant difference in relapse-free survival or ITP-related hospitalizations across different second-line therapies, although this may be limited by our sample size and duration of follow-up. At present, significant cost of TPO-RAs limits their availability as second-line therapy in many publicly funded healthcare systems. Real-world data are important in guiding future cost-effectiveness analysis to assess the impact of second-line therapies on the overall healthcare system. Figure 1 Figure 1. Disclosures Sun: Shire: Consultancy; Octapharma: Consultancy, Research Funding; Pfizer: Consultancy; Novo Nordisk: Consultancy; Bayer: Consultancy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".