Blood Coagulation Disorder
Conditions
Keywords
Tigecycline, Blood Coagulation Disorder, multi-omics, machine learning techbology
Brief summary
This study is to screen out the biomarkers and establish the model to predict coagulation dysfunction induced tigecycline
Detailed description
The critically ill patients treated with tigecycline in in tensive care unit will be recruited and divided into tigecycline-induced coagulation dysfunction group and non-coagulation dysfunction group. The multi-omics will be used to screen out biomarkers for early prediction of coagulation dysfunction caused by tigecycline. Afterwards, machine learning methods will be adopted to establish the the early prediction model of tigecycline-induced coagulation dysfunction.
Interventions
None listed
Sponsors
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Inpatients receiving tigecycline treatment in the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School * Intravenous tigecycline ≥ 3 days * Monitoring the plasma concentration of tigecycline
Exclusion criteria
* Missing clinical data
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Biomarkers associated with the coagulation dysfunction induced tigecycline | January 2023-December 2025 | Multi-omics will be adopted to screen out biomarkers associated with the coagulation dysfunction induced by tigecycline. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Prediction model for coagulation dysfunction induced by tigecycline | January 2023-December 2025 | Machine learning techbology will be adopted to establish the prediction model for coagulation dysfunction induced by tigecycline |
Outcome results
None listed