P209 Personalised data-Linkage Understanding Treatment Optimisation (PLUTO) in the CFHealthHub Learning Health System: understanding how much is enough for normal life expectancy in the post-modulator era

Since starting CFTR modulators (CFTRm), people with CF are using less preventative inhaled therapy and the effect of this on prognosis is uncertain. CFTRm use, with an FEV1 decline ≤1% per year may offer people with CF the chance of a normal life expectancy. This opportunity may be squandered by reducing nebuliser use. Personalised data-Linkage Understanding Treatment Optimisation (PLUTO) helps people with CF to understand how much inhaled treatment is enough for them. We use CFHealthHub to structure pre-clinic meetings to pay attention to FEV1 decline, BMI trend, adherence to CFTRm and adherence to preventative inhaled therapy. The PLUTO structure identifies people with an annual FEV1 decline of >1% and explores how, by using CFHealthHub adherence data, we can support them to achieve a ≤1% annual decline. Results: For Person 1 and 2, despite differing adherence to inhaled therapy, the achievement of a <1% FEV1 decline suggests their current adherence to inhaled therapy is enough. Persons 3 and 4 have a >1% FEV1 decline. Person 3’s FEV1 decline might be attributed to 30% adherence to inhaled therapy and CFHealthHub intervention has the potential to increase adherence, which may reverse FEV1 decline. Person 4’s FEV1 is declining despite 100% adherence prompting the clinical team to look for new pathology, such as allergic bronchopulmonary aspergillosis or nontuberculous mycobacteria infection. For Person 5, both FEV1 and BMI are decreasing with 0% adherence to inhaled therapyand unsatisfactory adherence to CFTRm. They require attention around both CFTRm and nebuliser adherence (likely starting with CFTRm adherence). Annual FEV1 decline >1% can identify patients where current management is likely to fail to deliver a normal life expectancy. PLUTO offers a structured assessment that pays attention to objective adherence data using CFHealthHub to understand how much treatment is enough for each individual person with CF.