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This study is to screen out the biomarkers and establish the model to predict coagulation dysfunction induced tigecycline
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| coagulation dysfunction group | The critically ill patients in intensive care unit showed coagulation dysfunction after treated with tigecycline. | ||
| non-coagulation dysfunction group | The critically ill patients in intensive care unit showed normal coagulation function after treated with tigecycline. |
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| Measure | Description | Time Frame |
|---|---|---|
| Biomarkers associated with the coagulation dysfunction induced tigecycline | Multi-omics will be adopted to screen out biomarkers associated with the coagulation dysfunction induced by tigecycline. | January 2023-December 2025 |
| Measure | Description | Time Frame |
|---|---|---|
| Prediction model for coagulation dysfunction induced by tigecycline | Machine learning techbology will be adopted to establish the prediction model for coagulation dysfunction induced by tigecycline | January 2023-December 2025 |
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Inclusion Criteria:
Exclusion Criteria:
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The critically ill patients treated with tigecycline in the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
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| ID | Term |
|---|---|
| D001778 | Blood Coagulation Disorders |
| ID | Term |
|---|---|
| D006402 | Hematologic Diseases |
| D006425 | Hemic and Lymphatic Diseases |
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