Immunoglobulin replacement therapy in patients with immunodeficiencies: impact of infusion method on patient-reported outcomes
Bibliographic record
Abstract
BACKGROUND: Understanding the impact of different immunoglobulin (Ig) infusion methods (intravenous [IVIg] and subcutaneous [SCIg]) upon treatment experience can potentially facilitate optimization of patient outcomes. Here, the perspective of patients with primary and secondary immunodeficiency diseases (PID and SID, respectively) receiving IVIg and SCIg was evaluated, in terms of treatment satisfaction, accounting for treatment history, using Association des Patients Immunodéficients du Québec (APIQ) survey data. METHODS: The online APIQ survey (shared October 2020-March 2021) of patients with immunodeficiencies in Canada contained 101 questions on: Ig use, history, and detailed infusion characteristics; as well as structured patient-reported outcomes such as treatment satisfaction (via TSQM-9), symptom state (via PASS), general health perception (via GHP), and physical and mental function (via PROMIS). Adult respondents (≥ 18 years old) currently using Ig were compared by their current Ig infusion method (IVIg or SCIg cohort) overall, and in a sub-analysis, the IVIg cohort was compared with the SCIg cohort after stratification by respondents who started SCIg when naïve to Ig ('SCIg naïve') or with previous IVIg experience ('SCIg switch'). RESULTS: In total, 54 respondents currently used IVIg and 242 used SCIg. The average duration per infusion of a weekly SCIg infusion was significantly shorter compared with the average duration of a 3-4 weekly IVIg infusion (p < 0.001). The SCIg cohort was associated with significantly higher scores for the TSQM-9 effectiveness domain compared with the IVIg cohort. The scores for TSQM-9 convenience and global satisfaction domains were similar in the two cohorts. The SCIg cohort was also associated with a significantly higher proportion of respondents who were in an acceptable symptom state and a lower proportion who reported very poor or poor perception of health compared with the IVIg cohort. Further, the SCIg naïve subgroup was associated with significantly higher TSQM-9 effectiveness and convenience domain scores compared with the IVIg cohort, while there was no significant difference between the SCIg switch subgroup and the IVIg cohort in terms of convenience. CONCLUSIONS: A better understanding of how different IgRT administration methods impact treatment experience and satisfaction may assist with informed treatment decision making and ultimately further improvements in patient outcomes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".