Impact of Concomitant Medication Burden on Tolerability of Disease-targeted Therapy and Survival in Interstitial Lung Disease
Bibliographic record
Abstract
Abstract Rationale Multimorbidity is common and leads to substantial concomitant medication burden in patients with interstitial lung disease (ILD), which may affect tolerability of ILD-targeted medications and health outcomes. Objectives To determine the associations of concomitant medication burden with tolerability of ILD-targeted medications and survival in patients with idiopathic pulmonary fibrosis (IPF) and non-IPF ILD. Methods Patients with IPF receiving nintedanib or pirfenidone and patients with non-IPF ILD receiving azathioprine or mycophenolate were identified from two Australian and Canadian registries. Baseline concomitant medication burden was evaluated using three measures: medication count, polypharmacy (⩾5 medications), and the medication regimen complexity index (MRCI). Medication intolerance and discontinuation were evaluated at 6 months and 1 year after initiation of ILD-targeted medications, respectively. Cox regression models and likelihood ratio tests were used to determine the prognostic significance of medication burden on transplant-free survival. Results In 645 treated patients with IPF, 43% experienced adverse reactions leading to intolerance (defined as dose reduction, temporary dose interruption, or permanent drug discontinuation) of antifibrotic medications within 6 months of initiation, with high baseline concomitant medication burden being consistently associated with intolerance (medication count: P = 0.005; polypharmacy: P = 0.006; MRCI: P = 0.004). This association was not observed for immunosuppressive medications in 1,255 treated patients with non-IPF ILD, who also had a lower intolerance (18%). Baseline concomitant medication burden was not independently associated with permanent discontinuation of antifibrotic (29%) and immunosuppressive medications (20%) at 1 year. The MRCI was the only measure of concomitant medication burden associated with transplant-free survival in both cohorts (P < 0.01 for both), which improved prognostication beyond common clinical factors and the ILD-GAP index (P < 0.001 for both). Conclusions Concomitant medication burden is associated with intolerance of antifibrotic medications in patients with IPF. Medication regimen complexity is superior to simpler evaluation of concomitant medication burden for predicting prognosis in ILD.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".