Bradykinin-mediated angioedema is a rare and disabling disease, characterized by the occurrence of attacks marked by localized swelling of skin, but also of the airways, which can be life-threatening. The unpredictable nature of attacks is a key feature of angioedema, placing patients under constant threat. It seems that there are different patterns of yearly distribution for these attacks, but this is poorly described in the literature. The objectives of the study are to establish different rhythmicity profiles of patients according to the frequency of the attacks; and to identify factors potentially triggering the attacks. For this purpose, patients with bradykinin-mediated angioedema will be monitored daily using a smartphone application. Each day, the application will ask the patient if he or she is having an attack and, if so, the characteristics of the attack and the events preceding it
Study Type
OBSERVATIONAL
Each day, the smartphone application will ask the patient if he or she is having an angioedema attack. It not, the questionnaire will stop. If the patient is having an attack, a series of 5 short questions is asked about the attack characteristics and the events that preceded it.
Average number of bradykinin-mediated angioedema attacks
Time frame: Follow up during 1 year
The average number of attack by time of year (months, seasons)
Time frame: Follow up during 1 year
The average duration of an attack episode
Time frame: Follow up during 1 year
The average duration between two attacks
Time frame: Follow up during 1 year
Number of stable clusters of patients that may be drawn out of the data based on attack rhythmicity measures and characteristics of these clusters in each and every cluster.
Number of stable clusters of patients that may be drawn out of the data based on attack rhythmicity measures (based on the number of attacks, the average duration of an attack episode, and the average duration between two attacks) and characteristics of these clusters (mean and standard deviation of rhythmicity measures) in each and every cluster. Stability and information metrics grounding the choice of the number of clusters and their analysis: * Bootstrapped Jaccard stability index of clusters, and its 95% confidence interval * Silhouette value of the clustering * Share of intra-cluster variance
Time frame: After a year of follow up
Individual number of crisis
Descriptif between patient's characteristics ( age, sex, disease onset and treatments) and Individual attack rhythmicity measures(secondary outcome measures n°2) Coefficients (with 95% intervals and Wald test p-values) and pseudo-R² of a Poisson regression modeling the individual number of crisis based on age, sex, disease onset and treatments.
Time frame: After a year of follow up
Type of attack triggering events
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In the application ,question is : "In the 48 hours preceding the onset of the crisis, did you identify one or more of the following events? (several answers possible) Multiple choice can be one or more between Physical activity; Local trauma (blow, injury ...); Dental or surgical procedure; Psychological stress or emotion; Infection; Periods / menstruation; Medication; Other (specify).
Time frame: After a year of follow up