Biomarkers and Prediction of Tigecycline Induced Coagulation Dysfunction

Biomarkers and Prediction of Tigecycline Induced Coagulation Dysfunction Based on Multi-omics and Machine Learning Techbology

This study is to screen out the biomarkers and establish the model to predict coagulation dysfunction induced tigecycline

Study Overview

• Status: Not yet recruiting
• Conditions: Blood Coagulation Disorder

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.

• Study Type: Observational
• Enrollment (Anticipated): 200

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

The critically ill patients treated with tigecycline in the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Prospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	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

Secondary Outcome Measures

Outcome Measure	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

Collaborators and Investigators

• Sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

Study record dates

Study Major Dates

• Study Start (Anticipated): May 1, 2023
• Primary Completion (Anticipated): December 31, 2025
• Study Completion (Anticipated): December 31, 2025

Study Registration Dates

• First Submitted: January 16, 2023
• First Submitted That Met QC Criteria: January 16, 2023
• First Posted (Actual): January 26, 2023

Study Record Updates

• Last Update Posted (Actual): April 19, 2023
• Last Update Submitted That Met QC Criteria: April 17, 2023
• Last Verified: January 1, 2023

More Information

Terms related to this study

• Keywords: Tigecycline; Blood Coagulation Disorder; multi-omics; machine learning techbology
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Vascular Diseases; Hematologic Diseases; Hemorrhagic Disorders; Hemostatic Disorders; Blood Coagulation Disorders
• Other Study ID Numbers: 2022-LCYJ-PY-14

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No