ITP
Conditions
Brief summary
Prospective, observational study to explore the significance of gut microbiome in the diagnosis of ITP, and to identify the predictive value of baseline gut microbiome for GC resistance/relapse.
Detailed description
A prospective, observational study to (1) collect fecal samples from ITP patients at initial diagnosis (baseline) and after first-line GC treatment, (2) detect the composition of gut microbiome and related metabolites using metagenomic sequencing combined with metabolomics, (3) observe the impact of first-line treatment on gut microbiome, (4) explore the significance of gut microbiome in the diagnosis of ITP, and (5) identify the predictive value of baseline gut microbiome for GC resistance/relapse, thus to provide new ideas for clinical diagnosis and treatment of ITP.
Interventions
None listed
Sponsors
Beijing Hospital
Peking University People's Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 18 years or older; * Isolated thrombocytopenia (platelet count of less than 100×10\^9/L); * Normal leukocyte and erythrocyte counts according to routine blood tests; * Did not receive any medication for thrombocytopenia for 6 months.
Exclusion criteria
* Secondary ITP such as drug-associated thrombocytopenia; * Thrombocytopenia caused by viral infection (HIV, Hepatitis B virus or Hepatitis C virus); * Nursing or pregnant women; * Severe dysfunction of the heart, kidney, lung or liver; * Active or previous malignancy; * Patients with other diseases were undergoing treatment with immunosuppressants; * Myelodysplastic disorder or myelofibrosis; * Patients who were undergoing first - or second-line therapy for thrombocytopenia within 6 months.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUC value of 1-month drug resistance/relapse using baseline gut microbiota efficacy prediction model | 1 month | The prediction model is constructed and calculated using machine learning methods |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| AUC value of 3-month drug resistance/relapse using baseline gut microbiota efficacy prediction model | 3 months | The prediction model is constructed and calculated using machine learning |
| AUC value of 6-month drug resistance/relapse using baseline gut microbiota | 6 months | The prediction model is constructed and calculated using machine learning |
| Time to response | 6 months | The time to achieve platelet count ≥ 30×10\^9/L and at least 2-fold increase of the baseline count and absence of bleeding since start of treatment. |
| Duration of response | 6 months | The duration of achieve platelet count ≥ 30×10\^9/L and at least 2-fold increase of the baseline count and absence of bleeding since start of treatment |
Countries
China
Contacts
Primary ContactXiao-Hui Zhang, Dr.
Backup ContactFeng-Qi Liu, MD.
Outcome results
None listed