Bradykinin-mediated Angioedema
Conditions
Keywords
Bradykinin, Angioedema, Smartphone, Application, Attack, Hereditary angioedema
Brief summary
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
Interventions
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.
Sponsors
Study design
Eligibility
Inclusion criteria
* Diagnosis of bradykinin-mediated angioedema * Age \> 18 * Having a smartphone * Non opposition to answer the questionnaire
Exclusion criteria
* Patients with other type (histaminergic) angioedema * Patient unable or unwilling to use a smartphone application * Opposition to the research * Minor or protected adult * Person deprived of liberty
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Average number of bradykinin-mediated angioedema attacks | Follow up during 1 year |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The average duration of an attack episode | Follow up during 1 year | — |
| The average duration between two attacks | Follow up during 1 year | — |
| The average number of attack by time of year (months, seasons) | Follow up during 1 year | — |
| Individual number of crisis | After a year of follow up | 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. |
| Type of attack triggering events | After a year of follow up | 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). |
| 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. | After a year of follow up | 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 |