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The following is a summary of “Predicting lung function decline in cystic fibrosis: the impact of initiating ivacaftor therapy,” published in the April 2024 issue of Pulmonology by Zhou et al.
The advent of modulator therapies aimed at rectifying the underlying defect in cystic fibrosis (CF) represents a pivotal advancement in clinical management. However, the variable trajectory of lung disease progression in the post-modulator era underscores the necessity for robust prediction models that account for modulator utilization.
Conducted as a retrospective longitudinal cohort study utilizing data from the CF Foundation Patient Registry, the investigation encompassed 867 patients bearing the G551D mutation who received ivacaftor treatment between 2003 and 2018. Focusing on lung function, quantified as percent predicted forced expiratory volume in 1 second (FEV1pp), the analysis endeavored to delineate the impact of ivacaftor initiation on lung function by crafting a dynamic prediction model. This model integrated demographic and clinical characteristics to forecast lung function decline, encapsulated by FEV1-indicated exacerbation signals (FIES) at the population and individual levels.
The resultant model unveiled a noteworthy enhancement in FEV1pp following ivacaftor initiation, with an estimated improvement of 4.89% predicted (95% CI: 3.90 to 5.89). Furthermore, the rate of decline in lung function exhibited a reduction of 0.14% predicted/year (95% CI: 0.01 to 0.27) post-ivacaftor initiation. Noteworthy predictors of overall FEV1pp encompassed variables such as pancreatic insufficiency, age at study entry, history of pulmonary exacerbations, and CF-related comorbidities. Importantly, the model demonstrated robust predictive accuracy for FIES events, validated by an area under the receiver operating characteristic curve of 0.83 (95% CI: 0.83 to 0.84) for the independent testing cohort and 0.90 (95% CI: 0.89 to 0.90) for 6-month forecasting with the masked cohort.
In conclusion, the dynamic prediction model, anchored in pre-modulator era data, offers a potent tool for projecting post-modulator lung function trajectories and bolstering clinical surveillance in the contemporary landscape of CF care.
Source: respiratory-research.biomedcentral.com/articles/10.1186/s12931-024-02794-2