Evaluating the Impact of Interstitial Lung Diseases Nursing Support on Antifibrotic Treatment Adherence in Patients With Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
Abstract Background: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive disease with a median survival of 3-5 years without treatment. We now have antifibrotic drugs that can slow down the progression of the disease and decrease mortality in patients with IPF. However, adherence to antifibrotics is a challenge, even in randomized control trials, where about 20% of patients abandon treatment within a 1-year study period. We report the impact of ILD nursing support on adherence to antifibrotic treatment in IPF patients in the modern era, where ancillary resources are now available to help patients stay on treatment. Objective: To evaluate the impact of an ILD nurse on the adherence to antifibrotic medications in patients with IPF. Methods: We evaluated the number of patients diagnosed with IPF in our ILD center that were started on pirfenidone or nintedanib between January 2020 and December 2023 with the support of two different ILD Nurses during the study period. We assessed patient adherence rates to antifibrotics, the proportion of patients on full or reduced dose, and the proportion requiring switching antifibrotics during the study period. We also compared the adherence rates achieved by our ILD Nurse to those reported in other studies. Results: A total of 118 patients started treatment with either pirfenidone or nintedanib during the study period. There were 91 males and 27 females with a mean age of 76 years (minimum 46 years – maximum 92 years old). The average treatment duration was 15.9 months, ranging from 8 days to 3.75 years. By the end of the study, 109 patients (92%) remained on antifibrotics; 90 (83%) were on the full dose of either drug, while 19 (17%) were on a reduced dose. Among the 87 patients initially on pirfenidone, 10 (11%) were switched to nintedanib due to side effects. Of the 50 patients initially on nintedanib, 9 (18%) were switched to pirfenidone. The antifibrotic treatment abandonment rate of 8% in our center over almost 4 years of the study period is lower than that reported in randomized control trials (abandonment rate ∼20%) over 1 year. Conclusion: Having ILD nurse support greatly enhances patient adherence to antifibrotic treatment in patients with IPF.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".