Impact of Cystic Fibrosis Transmembrane Conductance Regulator Modulating Therapies on Liver Transplant Outcomes
Bibliographic record
Abstract
Background and Aims Up to 40% of patients with cystic fibrosis (CF) develop CF-related liver disease (CFrLD), which can progress to the point of requiring liver transplantation (LT). Advances in CF transmembrane conductance regulator (CFTR) modulator therapies, especially triple therapy modulators, have significantly improved pulmonary outcomes, but their impact on LT for CFrLD remains unclear. Methods Using data from the Scientific Registry of Transplant Recipients in 2000-2023, we analyzed trends in LT waitlisting for CFrLD pre- and post-U.S. Food and Drug Administration (FDA) approval of CFTR modulators: ivacaftor (January 31, 2012; single therapy), ivacaftor-lumacaftor (July 2, 2015; dual therapy), and ivacaftor-tezacaftor-elexacaftor (October 21, 2019; triple therapy). We compared the waitlist characteristics and post-LT outcomes of pre- and post-FDA approval eras. Results Of 258,090 patients waitlisted for LT, 551 (0.2%) had CFrLD. The proportion of CFrLD patients on the LT waitlist decreased after FDA approval of triple CFTR modulators (0.23% to 0.14%; P < .0004). Patients waitlisted after FDA approval of single and dual CFTR modulators were, on average, older (17.4 vs 20.6 years; P < .001 and 18.0 vs 20.8 years; P = .004). Median model for end-stage liver disease-sodium scores were higher among individuals waitlisted following the approval of dual (9 [6–14] vs 10 [8–15], P < .013) and triple (9 [6–14] vs 12.5 [8–17], P < .003) CFTR modulators. There were no significant differences in post-LT survival pre- and post-FDA approval of single, dual, or triple CFTR therapy. Conclusion These findings suggest that CFTR modulators may mitigate CFrLD complications and delay the need for waitlisting as physicians await the patient's response to therapy and reassess the need for LT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".