Acquired Thrombotic Thrombocytopenic Purpura
Conditions
Keywords
caplacizumab, platelet responses, aTTP, iTTP
Brief summary
The interpretation of platelet counts has to be revaluated in the light of caplacizumab. By effectively blocking platelet binding sites on VWF-multimers, the nanobody leads to a rapid normalization of the platelet count within 3 to 4 days. Most importantly, caplacizumab uncouples platelet counts from ADAMTS13 activity and thereby launches unprecedented thrombocyte dynamics, with potential pitfalls for over- and undertreatment. A relevant number of patients responds to caplacizumab with a brisk increase in platelet count, followed by a marked dip of platelets (patient on the left). This may mislead treating physicians into re-intensifying therapy, with a respective risk for adverse side-effects and complications. Taken together, these observations call for reliable descriptions and the identification of predictive parameters to predict the platelet response upon administration of caplacizumab in a large patient cohort. Here, PREDICT-2020 is designed as a retrospective study to specifically address the following aspects: * Identifying and describing clusters of platelet responses to caplacizumab * Identifying potential pitfalls for treating physicians * Predicting the individual thrombocyte response * Correlating platelet responses with individual patient outcome
Interventions
DRUGCablivi
Patients with immune Thrombotic Thrombocytopenic Purpura, who have been treated with caplacizumab (Cablivi®)
Sponsors
University of Cologne
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Confirmed diagnosis of autoimmune thrombotic thrombocytopenic purpura * Treatment with at least a single dose of caplacizumab, either i.v. or s.c. * Male or female patients older than 18 years of age
Exclusion criteria
* Hereditary thrombotic thrombocytopenic purpura
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Reliable description and prediction of platelet responses to caplacizumab | Enrollment | Reliable description and prediction of platelet responses to caplacizumab employing mathematic modelling algorithms |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Determination of different clusters of platelet responses to caplacizumab | Enrollment | It will be hypothesized the existence of different clusters of caplacizumab responders during the first weeks of therapy, when ADAMTS13 activity typically is still \<10%. A detailed cluster analysis and description of thrombocyte responses to caplacizumab in a large cohort will reliably identify these different responders |
| Correlation of platelet responses to caplacizumab with patient outcome | Enrollment | It will be hypothesized the existence of different clusters of caplacizumab responders during the first weeks of therapy, when ADAMTS13 activity typically is still \<10%. A detailed cluster analysis and description of thrombocyte responses to caplacizumab in a large cohort will reliably identify these different responders |
| Risk stratification of iTTP patients based on their platelet response to caplacizumab | Enrollment | Description: Predicting the individual thrombocyte response to caplacizumab improves risk stratification of iTTP patients after initiation of caplacizumab therapy. An early risk stratification allows an optimal timing of monitoring intervals during the first weeks after diagnosis, which are often critical |
Countries
Germany
Outcome results
None listed