Predictors of relapse and efficacy of rituximab in immune thrombotic thrombocytopenic purpura
Bibliographic record
Abstract
Abstract Patients with immune-mediated thrombotic thrombocytopenic purpura (iTTP) often experience life-threatening relapses of the disease, and rituximab (RTX) can be used to mitigate relapse risk. However, the predictors of relapse in iTTP and the magnitude and duration of effect of RTX remain key unanswered questions. Using a multi-institutional cohort of consecutive adult patients with iTTP, we used survival analysis to compare relapse rates between patients who received RTX during the index presentation with acute iTTP and those who did not. Of 124 patients, 60 (48%) received RTX and 34 (27%) experienced relapse. Median time to relapse was 3.71 (interquartile range, 1.75-4.9) and 1.33 (interquartile range, 0.43-2.35) years for RTX-treated and untreated patients, respectively. RTX conferred protection from relapse at 1 year of follow-up (P = .01) but not at 5 years of follow-up. Extended Cox regression was then used to identify predictors of relapse and to estimate the protective effect of RTX. The following parameters were independently associated with increased risk for subsequent relapse: presenting in iTTP relapse (hazard ratio [HR], 2.97; 95% confidence interval [CI], 1.4-6.4), age younger than 25 years (HR, 2.94; 95% CI, 1.2-7.2), and non-O blood group (HR, 2.15; 95% CI, 1.06-4.39). RTX initially provided protection from relapse (HR, 0.16; 95% CI, 0.04-0.70), but this effect gradually diminished, returning to the baseline risk for untreated patients at approximately 2.6 years. Patients who are young, have non-O blood group, or present with relapsed iTTP are at increased risk for subsequent relapse. RTX appears to confer short-term protection from relapse.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".