Effect of montelukast and rupatadine on rituximab infusion time, rate, severity of reactions, and cost of administration.
Bibliographic record
Abstract
6500 Background: Rituximab is associated with frequent infusion reactions which carry significant burden to patients and health care practitioners. Standard pre-medications (SP) do not prevent reactions sufficiently. Rupatidine (R) and Montelukast (M) are used for symptomatic treatment of urticaria and allergic rhinitis. We assessed addition of R, M and their combination on Rituximab infusion, rate, severity of reactions and cost of administration. Methods: Adult patients with lymphoproliferative disorders treated at our cancer center between Jan 2018 to Jan 2019 were evaluated with Rituximab-containing regiments. Since the majority of reactions occur during the first infusion, our study was limited to the initial Rituximab treatment. Patients received either SP with diphenhydramine/acetaminophen and additional R, M or R+M combination. Comparative analysis of infusion time/rate, severity of infusion reactions, number of rescue medications and cost of Rituximab infusions among groups was performed using one-way ANOVA with Tukey post-hoc or chi-square. The study was approved by our institutional IRB. Results: Patients received either: 1) SP; 2) SP + Rupatadine (R) 10 mg; 3) SP + Montelukast (M) 10 mg; or 4) both (SP+R+M). Patient demographics are shown in the table. Compared to SP, the R, M and R+M groups had greater improvement in Rituximab delivery. Mean infusion time was 306 [range 235-441] min. in SP, 254 [105-390] in M, 265 [193-350] in R and 248 [196-342] in R+M groups, (p=0.0001). Infusion reactions occurred in 92% in SP vs. 38, 45, 33% in M, R and R+M groups (p=0.0001). Median reaction grade was 2 in SP, 1 (M), 0 (R and R+M). Median number of rescue medications was 3 [0-10] in SP vs 0 [0-7] in M, R and R+M groups. Cost of rescue medications (US$) was 38 [0-63] (SP), 11 [0-50] (M), 17 [0-63] (R), 12 [0-58] (R+M) groups (p<0.0001). Mean nursing cost (US$) per patient infusion was calculated as 269 [207-388] in SP vs 222 [92-343] (M), 233 [170-308] (R), 218 [174-301] (R+M) group. Conclusions: Addition of R, M and particularly R+M significantly improved Rituximab delivery, lowered the rate and severity of infusion reactions, and lowered the cost of Rituximab administration. [Table: see text]
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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 | 0.002 |
| 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".