Trial · NCT02535949
Registered outcomes14
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
1 to 14 of 14
Download this page as JSON| List | Outcome | Time frame | Description |
|---|---|---|---|
| Primary | Change in HLA-DR Expression on Monocytes 72 Hours After Drug or Placebo Administration in Patient Groups (0g TXA (Placebo); 2g TXA; 4g TXA)." | Samples Drawn through 72 hours after study initiation | Blood was drawn from patients at baseline (0 h, just before placebo or drug administration) and at 72 hours post placebo or drug administration. Leukocytes in these blood samples were stained with fluroescent antibodies specific for CD45, CD14, and HLA-DR, analyzed by flow cytometry, and the median fluorescen intensity (MFI) of HLA-DR signal was recorded for monocytes (CD45+CD14+). The fold change in HLA-DR expression from prior to placebo/drug administration to 72 h after placebo/drug administration ("0 h : 72 h") was calculated as HLA-DR MFI72hours ÷ HLA-DR CD14 MFI0hours. Non-paramteric one-way ANOVA (Kruskal-Wallis test) was performed between each treatment group at the given time pont, and the p-value reported. |
| Secondary | Differences in Cytokine Profiles Between the Three Study Groups | Samples Drawn through 72 hours after study initiation | To evaluate the effects of TXA on immune function parameters we will, in a RCT, analyze samples from 150 patients (50 in each study group), at multiple time points. Parameters are: a. Cytokines measured from time 0 to 72 hours. |
| Secondary | Differences in Leukocyte Function Parameters Between the Three Study Groups | Samples Drawn through 72 hours after study initiation | To evaluate the effects of TXA on immune function parameters we will, in a RCT, analyze samples from 150 patients (50 in each study group), at multiple time points. Parameters are: a. Flow cytometric analyses on leukocytes measured from time 0 to 72 hours. |
| Secondary | Total Transfusion Volume CL | 24 hours | Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109\*((WT/70)\*\*0.75) \* (SCRint\^-0.084) \* ((NIRSInt)/96)\^ -0.27 ) \* ((PLTint)/130)\^0.45) V1=1,160\*(WT/70) \* (TxTot)\^0.03) Q=174\*((WT/70)\*\*0.75) V2=1080 \*(WT/70) "Total Transfusion Volume CL" equals clearance (CL) affected by the covariate of Total Transfusion Volume (TxTot). This value is unitless per NONMEM reporting. |
| Secondary | Determine the Incidence of Thromboembolic Events (DVT, MI, PE, Stroke) in All Three Study Groups. | Hospital Discharge (average 10 days) | The number of events per group for the incidence of thromboembolic events (DVT, MI, PE, Stroke) in all three study groups. |
| Secondary | Determine the Incidence of Seizures at 24 Hours in All Three Study Groups. | 24 hours following TXA | The incidence of seizures at 24 hours in all three study groups. Number of participants with seizures are reported |
| Secondary | Determine the Incidence of All Adverse Events in All Three Study Groups | Hospital Discharge (average 10 days) | All adverse events were totaled for each of the three study groups based on the number of incidents. |
| Secondary | Platelet Count CL | 24 hours | Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109\*((WT/70)\*\*0.75) \* (SCRint\^-0.084) \* ((NIRSInt)/96)\^ -0.27 ) \* ((PLTint)/130)\^0.45) V1=1,160\*(WT/70) \* (TxTot)\^0.03) Q=174\*((WT/70)\*\*0.75) V2=1080 \*(WT/70) "Platelet Count CL" equals clearance (CL) affected by the covariate of Platelet Count (PLTint). This value is unitless per NONMEM reporting. |
| Secondary | Near Infrared Spectroscopy CL | 24 hours | Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109\*((WT/70)\*\*0.75) \* (SCRint\^-0.084) \* ((NIRSInt)/96)\^ -0.27 ) \* ((PLTint)/130)\^0.45) V1=1,160\*(WT/70) \* (TxTot)\^0.03) Q=174\*((WT/70)\*\*0.75) V2=1080 \*(WT/70) "Near Infrared Spectroscopy CL" equals clearance (CL) affected by the covariate of Near Infrared Spectroscopy (NIRSint). This value is unitless per NONMEM reporting. |
| Secondary | Creatinine Count CL | 24 hours | Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109\*((WT/70)\*\*0.75) \* (SCRint\^-0.084) \* ((NIRSInt)/96)\^ -0.27 ) \* ((PLTint)/130)\^0.45) V1=1,160\*(WT/70) \* (TxTot)\^0.03) Q=174\*((WT/70)\*\*0.75) V2=1080 \*(WT/70) "Creatinine Count CL" equals clearance (CL) affected by the covariate of Creatinine levels (SCRint). This value is unitless per NONMEM reporting. |
| Secondary | V2- Peripheral Volume (L/70kg) | 24 hours | Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109\*((WT/70)\*\*0.75) \* (SCRint\^-0.084) \* ((NIRSInt)/96)\^ -0.27 ) \* ((PLTint)/130)\^0.45) V1=1,160\*(WT/70) \* (TxTot)\^0.03) Q=174\*((WT/70)\*\*0.75) V2=1080 \*(WT/70) "V2" equals Peripheral Volume in L/70kg. |
| Secondary | Q- Intercompartmental Clearance (L/70kg) | 24 hours | Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109\*((WT/70)\*\*0.75) \* (SCRint\^-0.084) \* ((NIRSInt)/96)\^ -0.27 ) \* ((PLTint)/130)\^0.45) V1=1,160\*(WT/70) \* (TxTot)\^0.03) Q=174\*((WT/70)\*\*0.75) V2=1080 \*(WT/70) "Q" equals intercompartmental clearance in L/70kg. |
| Secondary | V1- Central Volume (L/70kg) | 24 hours | Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109\*((WT/70)\*\*0.75) \* (SCRint\^-0.084) \* ((NIRSInt)/96)\^ -0.27 ) \* ((PLTint)/130)\^0.45) V1=1,160\*(WT/70) \* (TxTot)\^0.03) Q=174\*((WT/70)\*\*0.75) V2=1080 \*(WT/70) "V1" equals central volume in L/70kg. |
| Secondary | CL- Clearance of TXA (mL/(Min*70kg)) | 24 hours | Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109\*((WT/70)\*\*0.75) \* (SCRint\^-0.084) \* ((NIRSInt)/96)\^ -0.27 ) \* ((PLTint)/130)\^0.45) V1=1,160\*(WT/70) \* (TxTot)\^0.03) Q=174\*((WT/70)\*\*0.75) V2=1080 \*(WT/70) "CL" equals clearance of TXA in mL/(min\*70kg). |
Source ClinicalTrials.govtrials-2026-11-12-0.5.0
Sources 3
- ClinicalTrials.gov's posted results, through CTTI's AACTFreely available. Acknowledgement requested
- ClinicalTrials.govNo license stated
- NIH RePORTER's clinical studies and project filesPublic domain
Each source's terms
- ClinicalTrials.gov's posted results, through CTTI's AACTClinicalTrials.gov data via Aggregate Analysis of ClinicalTrials.gov (AACT), CTTI. CTTI makes its materials freely available and requests acknowledgement. https://aact.ctti-clinicaltrials.org/documentation/299
- ClinicalTrials.govnone stated
- NIH RePORTER's clinical studies and project filesPublic domain (NIH RePORTER)
ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
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