Experience With Elexacaftor/Tezacaftor/Ivacaftor in Patients With Cystic Fibrosis and Advanced Disease.

INTRODUCTION: Elexacaftor/tezacaftor/ivacaftor (ETI) was used through the early access programme in Spain from December 2019 in cystic fibrosis (CF) patients with homozygous or heterozygous F508del mutation with advanced lung disease. METHODOLOGY: Multicentre, ambispective, observational, study in which 114 patients in follow-up in 16 national CF units were recruited. Clinical data, functional tests, nutritional parameters, quality of life questionnaires, microbiological isolates, number of exacerbations, antibiotic treatments and side effects were collected. The study also compared patients with homozygous and heterozygous F508del mutations. RESULTS: Of the 114 patients, 85 (74.6%) were heterozygous for F508del mutation, and the mean age was 32.2±9.96 years. After 30 months of treatment, lung function measured by FEV1% showed improvement from 37.5 to 48.6 (p<0.001), BMI increased from 20.5 to 22.3 (p<0.001), and all isolated microorganisms decreased significantly. The total number of exacerbations was also significantly reduced from 3.9 (±2.9) to 0.9 (±1.1) (p<0.001). All items in the CFQ-R questionnaire showed improvement, except for the digestive domain. Oxygen therapy use decreased by 40%, and only 20% of patients referred for lung transplantation remained on the active transplant list. ETI was well-tolerated, with only 4 patients discontinuing treatment due to hypertransaminemia. CONCLUSIONS: ETI decreases the number of exacerbations, increases lung function and nutritional parameters, decrease in all isolated microorganisms, for 30 months of treatment. There is an improvement in the CFQ-R questionnaire score except for the digestive item. It is a safe and well-tolerated drug.

Trajectories of Severe Exacerbations of Chronic Obstructive Pulmonary Disease and Their Relationship with Mortality Risk.

PURPOSE: Acute exacerbations of COPD (AECOPD) are important factors contributing to mortality risk. The rate of exacerbations varies overtime. An inconsistent pattern of exacerbation occurrence is a common finding. The mortality risk associated with such a pattern is not entirely clear. Our objective was to assess the risk of mortality associated with various possible patterns of AECOPD trajectories. METHODS: This is a multicenter historical cohort study. Four different exacerbation trajectories were defined according to the incidence of severe AECOPD requiring hospital admission 2 years before and after the date of the first visit to the respiratory clinic-Consistent non-exacerbators (NEx): no AECOPD before or after the index date; consistent exacerbators (Ex): at least one AECOPD both before and after the index date; converters to exacerbators (CONV-Ex): no exacerbations before and at least one AECOPD after the index date; converters to non-exacerbators (CONV-NEx): at least one AECOPD before the index date, and no exacerbations after said date. All-cause mortality risk for these trajectories was assessed. RESULTS: A total of 1713 subjects were included in the study: NEx: 1219 (71.2%), CONV-NEx: 225 (13.1%), CONV-Ex: 148 (8.6%), Ex: 121 (7.1%). After correcting for confounding variables, the group with the highest mortality risk was Ex. The CONV-Ex and CONV-Nex groups had a mortality risk between Ex and NEx, with no significant differences between them. CONCLUSION: Different possible trajectories of severe AECOPD before and after a first specialized consultation are associated with different mortality risks. An inconsistent pattern of exacerbations has a mortality risk between Ex and NEx, with no clear differences between CONV-Ex and CONV-NEx.

Evaluation of the multimorbidity network and its relationship with clinical phenotypes in chronic obstructive pulmonary disease: The GALAXIA study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous condition, in which taking into consideration clinical phenotypes and multimorbidity is relevant to disease management. Network analysis, a procedure designed to study complex systems, allows to represent connections between the distinct features found in COPD. METHODS: Network analysis was applied to a cohort of patients with COPD in order to explore the degree of connectivity between different diseases, taking into account the presence of two phenotypic traits commonly used to categorize patients in clinical practice: chronic bronchitis (CB+ /CB- ) and the history of previous severe exacerbations (Ex+ /Ex- ). The strength of association between diseases was quantified using the correlation coefficient Phi (ɸ). RESULTS: A total of 1726 patients were included, and 91 possible links between 14 diseases were established. Although the four phenotypically defined groups presented a similar underlying comorbidity pattern, with special relevance for cardiovascular diseases and/or risk factors, classifying patients according to the presence or absence of CB implied differences between groups in network density (mean ɸ: 0.098 in the CB- group and 0.050 in the CB+ group). In contrast, between-group differences in network density were small and of questionable significance when classifying patients according to prior exacerbation history (mean ɸ: 0.082 among Ex- subjects and 0.072 in the Ex+ group). The degree of connectivity of any given disease with the rest of the network also varied depending on the selected phenotypic trait. The classification of patients according to the CB- /CB+ groups revealed significant differences between groups in the degree of conectivity between comorbidities. On the other side, grouping the patients according to the Ex- /Ex+ trait did not disclose differences in connectivity between network nodes (diseases). CONCLUSIONS: The multimorbidity network of a patient with COPD differs according to the underlying clinical characteristics, suggesting that the connections linking comorbidities between them vary for different phenotypes and that the clinical heterogeneity of COPD could influence the expression of latent multimorbidity. Network analysis has the potential to delve into the interactions between COPD clinical traits and comorbidities and is a promising tool to investigate possible specific biological pathways that modulate multimorbidity patterns.