CPAnet Registry-An International Chronic Pulmonary Aspergillosis Registry.
Christian B LaursenJesper Rømhild DavidsenLander Van AckerHelmut J F SalzerDanila SeidelOliver Andreas CornelyMartin HoeniglAna-Alastruey IzquierdoChristophe HennequinCendrine GodetAleksandra BaracHolger FlickOxana MunteanuEva Van BraeckelPublished in: Journal of fungi (Basel, Switzerland) (2020)
Chronic pulmonary aspergillosis (CPA) is a chronic fungal infection of the lung associated with high morbidity and mortality. The CPA Research network (CPAnet) registry established in 2018 is an international multicenter collaboration aiming to improve CPA knowledge and patient care. This study's aim was to describe the data collection process and content of CPAnet registry with preliminary clinical data. In the CPAnet registry, clinical data are collected through a web-based questionnaire. Data include CPA phenotype, comorbidities, treatment, outcome, and follow-up from several international centers. An exemplary descriptive analysis was performed on 74 patients, who were registered online before April 2020. CPA patients were predominantly (72%) male, 39% had chronic obstructive pulmonary disease, and 68% had a history of smoking. Chronic cavitary pulmonary aspergillosis was the most common CPA subtype (62%). In 32 patients (52%), voriconazole was the preferred first-line therapy. The multicenter multinational CPAnet registry is a valuable approach to gather comprehensive data on a large study population and reflects real-world clinical practice rather than focusing on specific patient populations in more specialized centers. Additional CPA reference centers are being encouraged to join this promising clinical research collaboration.
Keyphrases
- electronic health record
- end stage renal disease
- chronic obstructive pulmonary disease
- big data
- ejection fraction
- pulmonary hypertension
- newly diagnosed
- clinical practice
- cross sectional
- prognostic factors
- peritoneal dialysis
- healthcare
- mesenchymal stem cells
- patient reported
- palliative care
- machine learning
- data analysis
- case report
- artificial intelligence
- cystic fibrosis
- drug induced
- patient reported outcomes
- bone marrow
- lung function
- african american
- cell therapy
- genetic diversity