Predicting lung function decline in cystic fibrosis: the impact of initiating ivacaftor therapy

BACKGROUND

Modulator therapies that seek to correct the underlying defect in cystic fibrosis (CF) have revolutionized the clinical landscape. Given the heterogeneous nature of lung disease progression in the post-modulator era, there is a need to develop prediction models that are robust to modulator uptake.

METHODS

We conducted a retrospective longitudinal cohort study of the CF Foundation Patient Registry (N = 867 patients carrying the G551D mutation who were treated with ivacaftor from 2003 to 2018). The primary outcome was lung function (percent predicted forced expiratory volume in 1 s or FEV1pp). To characterize the association between ivacaftor initiation and lung function, we developed a dynamic prediction model through covariate selection of demographic and clinical characteristics. The ability of the selected model to predict a decline in lung function, clinically known as an FEV1-indicated exacerbation signal (FIES), was evaluated both at the population level and individual level.

RESULTS

Based on the final model, the estimated improvement in FEV1pp after ivacaftor initiation was 4.89% predicted (95% confidence interval [CI]: 3.90 to 5.89). The rate of decline was reduced with ivacaftor initiation by 0.14% predicted/year (95% CI: 0.01 to 0.27). More frequent outpatient visits prior to study entry and being male corresponded to a higher overall FEV1pp. Pancreatic insufficiency, older age at study entry, a history of more frequent pulmonary exacerbations, lung infections, CF-related diabetes, and use of Medicaid insurance corresponded to lower FEV1pp. The model had excellent predictive accuracy for FIES events with 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. The root-mean-square errors of the FEV1pp predictions for these cohorts were 7.31% and 6.78% predicted, respectively, with standard deviations of 0.29 and 0.20. The predictive accuracy was robust across different covariate specifications.

CONCLUSIONS

The methods and applications of dynamic prediction models developed using data prior to modulator uptake have the potential to inform post-modulator projections of lung function and enhance clinical surveillance in the new era of CF care.

The impact of transforming healthcare delivery on cystic fibrosis outcomes: a decade of quality improvement at Cincinnati Children's Hospital

BACKGROUND

In 2001, Cincinnati Children's Hospital embarked on a journey to improve healthcare delivery to patients with cystic fibrosis (CF). Data from the Cystic Fibrosis Foundation National Patient Registry revealed our below-average clinical outcomes, prompting us to initiate improvement interventions.

OBJECTIVE

To improve clinical outcomes for patients with CF through a comprehensive quality-improvement approach directed at increasing patient centredness and improving healthcare delivery.

INTERVENTIONS

In 2001, we shared our below-average outcomes with patients, families and care providers. We instituted a quality-improvement steering committee with parental and hospital leadership, and our data-management support was restructured to provide real-time clinical data to monitor our progress. In 2002, our weekly chart conference changed to a prospective planning session and individualised daily schedules were created for inpatients. In 2003, an influenza vaccination campaign was initiated and our infection-control practices were redesigned. In 2005, best-practice guidelines were developed for airway-clearance therapy. In 2007, evidence-based clinical algorithms were designed and implemented and key care-team members were added.

MEASUREMENTS

Primary outcome measures were median forced expiratory volume in 1 s per cent predicted (age range 6-17 years) and median body mass index percentile (age range 2-20 years).

RESULTS

From 2000 to 2010, median forced expiratory volume in 1 s increased from 81.7% to 100.1% predicted and median body mass index increased from the 35th to the 55th centile.

DISCUSSION

By focusing on specific outcomes, empowering families and patients, effectively using data, and standardising care processes, we transformed the culture and delivery of care for our patients with CF and learned valuable lessons potentially translatable to other chronic-care providers.

Electronic monitoring reveals highly variable adherence patterns in patients prescribed ivacaftor

BACKGROUND

Previous studies of CF treatments have shown suboptimal adherence, though little has been reported regarding adherence patterns to ivacaftor. Electronic monitoring (EM) of adherence is considered a gold standard of measurement.

METHODS

Adherence rates by EM were prospectively obtained and patterns over time were analyzed. EM-derived adherence rates were compared to pharmacy refill history and self-report.

RESULTS

12 subjects (age 6-48 years; CFTR-G551D mutation) previously prescribed ivacaftor were monitored for a mean of 118 days. Overall adherence by EM was 61% (SD=28%) and decreased over time. Median duration between doses was 16.9 hours (IQR 13.9-24.1 hours) and increased over time. There was no correlation between EM-derived adherence and either refill history (84%, r=0.26, p=0.42) or self-report (100%, r=0.40, p=0.22).

CONCLUSIONS

Despite the promising nature of ivacaftor, our data suggest adherence rates are suboptimal and comparable to other prescribed CF therapies, and more commonly used assessments of adherence may be unreliable.