Clearance of Mucus Plugs Drives Functional Improvement in Pediatric CF After Highly-effective CFTR Therapy
Bibliographic record
Abstract
Abstract Purpose: Lung MRI presents an opportunity to track Cystic Fibrosis (CF) progression compared to traditional global functional parameters such as forced expiratory volume (FEV1) by providing localized measurements of underlying abnormalities. Developments in ultrashort echo-time magnetic resonance imaging (UTE MRI) have improved image resolution and quality to near-CT levels without ionizing radiation (1), and Xe MRI is now a clinically-accepted modality for regional ventilation (2). CF transmembrane conductance regulator (CFTR) modulator drugs have dramatically improved CF patients’ prognosis and quality of life; however, their full impact on underlying CF pathophysiology is not completely understood (3). We hypothesized that CFTR modulators produced greater functional improvements in patients with higher mucus burden. Methods: MRI and spirometry were performed at baseline and 1+ months after clinical therapy initiation in 30 patients. Images were visually scored via an established system: each lobar region was scored on a 0-2 scale for each abnormality (e.g. bronchiectasis) by two blinded, independent readers and averaged (4). Ventilation defect percentage (VDP) was calculated via established thresholds (5). Changes in structure (UTE MRI) and function (Xe MRI and FEV1) were evaluated using Wilcoxon signed rank tests. Results: Eleven patients initiated lumacaftor/ivacaftor (LI), ages 9±4, and 19 patients initiated elexacaftor/tezacaftor/ivacaftor (ETI), ages 13±3. LI patients saw no significant improvements in UTE MRI score but small, non-significant improvements in VDP (3±8% p=.20). Figure 1A shows ETI mucus sub-scores changed from 1.5 to 0.8 (p<.05). Consolidation, ground-glass opacities, and air-trapping improved (p<.05), but bronchiectasis and wall-thickening did not. Mean VDP and FEV1 improved by 6±6% (p<.001) and 9±12% (p=.01) respectively in ETI patients. This change was driven by mucus improvements: only ETI patients whose mucus scores improved saw significant improvements in FEV1 (12±14%, p<.01) and VDP (8±4%, p<.01, Figure 1B). Notably, the subject with the largest mucus score improvement (4.5) had the largest improvement in VDP (14%) and FEV1 (34%), as shown in Figure 1C,D. Conclusions: Regional structural and functional MRI scoring provides additional information about the underlying abnormalities that change with CFTR modulators and provides new insight on what makes ETI clinically effective (6). This small study shows that the clearance of mucus plugs in pediatric CF patients post-ETI treatment is associated functional improvement. References: 1) Woods et al. JMRI 2020. 2) Niedbalski et al. MagnResonMed 2021.3) Turcios et al. RespCare 2020. 4) Eichinger et al. EJR 2012. 5) Kirby et al. AcadRadiol 2012. 6) Tice et al. JManagCare&Spec 2021.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".