## Protocol Section
### Identification Module
**NCT ID:** NCT00872079

**Brief Title:** Personalized Warfarin Dosing by Genomics and Computational Intelligence

**Official Title:** Personalized Warfarin Dosing Using Genomics and Computational Intelligence

#### Organization Study ID Info
**ID:** CAMM-04-07S
#### Organization
**Class:** FED
**Full Name:** VA Office of Research and Development
### Status Module
#### Completion Date
**Date:** 2011-09

**Type:** ACTUAL

#### Expanded Access Info
#### Last Update Post Date
**Date:** 2014-02-24

**Type:** ESTIMATED

**Last Update Submit Date:** 2014-02-21

**Overall Status:** TERMINATED

#### Primary Completion Date
**Date:** 2011-09

**Type:** ACTUAL

#### Results First Post Date
**Date:** 2014-02-24

**Type:** ESTIMATED

**Results First Submit Date:** 2014-02-21

**Results First Submit QC Date:** 2014-02-21

#### Start Date
**Date:** 2008-09

**Status Verified Date:** 2014-02

#### Study First Post Date
**Date:** 2009-03-31
**Type:** ESTIMATED
**Study First Submit Date:** 2009-03-27
**Study First Submit QC Date:** 2009-03-27
**Why Stopped:** Lack of Funding
### Sponsor Collaborators Module

#### Lead Sponsor

**Class:** FED

**Name:** US Department of Veterans Affairs

#### Responsible Party

**Type:** SPONSOR

### Oversight Module

**Oversight Has DMC:** False

### Description Module

**Brief Summary:** This study will create a computer program that can be used to help dose a drug called warfarin for the prevention of blood clotting. The study will collected specific information about those patients receiving this drug and use that information to create a computer program that will predict the effects of the drug. With this prediction program in place, the investigators can perform a series of "what if I gave this amount of drug" simulations to determine the best dose of drug for that patient. Once the computer programs are developed, the investigators will test the program in patients that actually need this drug. They will also include genetic information into the prediction since it has been shown that this information can affect how well the drug works. Patients will have this genetic information determined during this study.

**Detailed Description:** The objective of this project is to develop new techniques to incorporate genomic data into pharmacodynamic models to improve the dosing of chronically administered drugs. Specifically, the investigators look to improve warfarin therapy by decreasing the variability in the effect of this drug using information about the subjects genotype and computational intelligence. The investigators propose to achieve our objectives using a prospective, randomized, controlled clinical trial of a computer program that they will develop from both historical and prospective data. This computer program will be tested against a control group using standard warfarin dosing, and a group using standard dosing plus subject genotype. Warfarin dose and response data will be obtained from patients seen in the Louisville VA anticoagulation clinic. Following informed consent, subject genotype for cytochrome P450 allele 2C9 (2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1) will be determined. Other data routinely obtained to aid in warfarin dosing will also be recorded. Using this information, the investigators will develop many different models for warfarin dosing using incrementally more information. Each of these models will be tested using computer simulation until they have obtained the best model. This model will be used in a pilot study to test performance in real time. The results of the pilot study will then be used to power a final clinical trial of standard dosing, standard dosing and genetic information, computer dosing, and computer dosing plus genetic information.

The specific aims of this research are:

1. Determine the structure and the type of neural network model for predictions from historically obtained data. (Computer Model)
2. Prospectively develop an individualized neural network and nonlinear mixed effect modelling (NONMEM) model capable of predicting erythropoietin dosing for chronic in-center hemodialysis patients using adaptive techniques.
3. Develop computer programs based on neural computing that can be used in a clinical setting. (Computer Model)
4. Determine the utility of the computer programs prospectively in the clinical setting.

### Conditions Module

**Conditions:**

- Venous Thrombosis

- Atrial Fibrillation

- Myocardial Infarction

**Keywords:**

- warfarin

- pharmacokinetics

- pharmacodynamics

### Design Module

#### Design Info

**Allocation:** NA

**Intervention Model:** SINGLE_GROUP

##### Masking Info

**Masking:** NONE

**Primary Purpose:** HEALTH_SERVICES_RESEARCH

#### Enrollment Info

**Count:** 175

**Type:** ACTUAL

**Phases:**

- NA

**Study Type:** INTERVENTIONAL

### Arms Interventions Module

#### Arm Group 1

**Description:** Aim 1: Collect historical data on warfarin dosing in subjects at the VA. Aim 2: Collect genotype information on up to 300 subjects receiving warfarin anticoagulation.

Aim 3: Develop a computer model incorporating the information from Aim 1 and 2. Aim 4: Conduct randomized clinical trial.

**Intervention Names:**

- Device: Genomics

**Label:** Genomics

**Type:** ACTIVE_COMPARATOR

### Interventions

#### Intervention 1

**Arm Group Labels:**

- Genomics

**Description:** Model predictive control is a computer based algorithm that can be applied to drug dosing. This computer tool uses a model of how a patient will respond to a drug dose based on demographic and historical dosing information to predict a new drug response. A drug dose controller applies all possible doses to the response model and selects the one dose that best meets the stated goals of the drug therapy. In the case of warfarin, we will calculate an international normalized ratio (INR) value within a specific target range.

**Name:** Genomics

**Type:** DEVICE

### Outcomes Module

#### Primary Outcomes

**Description:** During Aim 2, Determined Patient Genotypes: CYP2C9 and VKORC1.

**Measure:** Patient Genomics

**Time Frame:** Baseline

### Eligibility Module

**Eligibility Criteria:** Inclusion Criteria:

* Warfarin therapy
* Attend anticoagulation clinic
* Warfarin therapy for 6 months

Exclusion Criteria:

* History of non-compliance

**Minimum Age:** 18 Years

**Sex:** ALL

**Standard Ages:**

- ADULT

- OLDER_ADULT

### Contacts Locations Module

#### Locations

**Location 1:**

**City:** Louisville

**Country:** United States

**Facility:** VA Medical Center, Louisville

**State:** Kentucky

**Zip:** 40206

#### Overall Officials

**Official 1:**

**Affiliation:** VA Medical Center, Louisville

**Name:** Michael E. Brier, PhD

**Role:** PRINCIPAL_INVESTIGATOR

## Derived Section

### Condition Browse Module - Ancestors

- ID: D000001145

- Term: Arrhythmias, Cardiac

- ID: D000006331

- Term: Heart Diseases

- ID: D000002318

- Term: Cardiovascular Diseases

- ID: D000010335

- Term: Pathologic Processes

- ID: D000007511

- Term: Ischemia

- ID: D000009336

- Term: Necrosis

- ID: D000017202

- Term: Myocardial Ischemia

- ID: D000014652

- Term: Vascular Diseases

- ID: D000016769

- Term: Embolism and Thrombosis

### Condition Browse Module - Browse Branches

- Abbrev: BC14

- Name: Heart and Blood Diseases

- Abbrev: BC23

- Name: Symptoms and General Pathology

- Abbrev: All

- Name: All Conditions

### Condition Browse Module - Browse Leaves

- ID: M12155

- Name: Myocardial Infarction

- Relevance: HIGH

- As Found: Myocardial Infarction

- ID: M10282

- Name: Infarction

- Relevance: HIGH

- As Found: Infarction

- ID: M4586

- Name: Atrial Fibrillation

- Relevance: HIGH

- As Found: Atrial Fibrillation

- ID: M16686

- Name: Thrombosis

- Relevance: HIGH

- As Found: Thrombosis

- ID: M22071

- Name: Venous Thrombosis

- Relevance: HIGH

- As Found: Venous Thrombosis

- ID: M4453

- Name: Arrhythmias, Cardiac

- Relevance: LOW

- As Found: Unknown

- ID: M9419

- Name: Heart Diseases

- Relevance: LOW

- As Found: Unknown

- ID: M10543

- Name: Ischemia

- Relevance: LOW

- As Found: Unknown

- ID: M12284

- Name: Necrosis

- Relevance: LOW

- As Found: Unknown

- ID: M6546

- Name: Coronary Artery Disease

- Relevance: LOW

- As Found: Unknown

- ID: M19506

- Name: Myocardial Ischemia

- Relevance: LOW

- As Found: Unknown

- ID: M17400

- Name: Vascular Diseases

- Relevance: LOW

- As Found: Unknown

- ID: M7784

- Name: Embolism

- Relevance: LOW

- As Found: Unknown

- ID: M19128

- Name: Embolism and Thrombosis

- Relevance: LOW

- As Found: Unknown

### Condition Browse Module - Meshes

- ID: D000001281

- Term: Atrial Fibrillation

- ID: D000009203

- Term: Myocardial Infarction

- ID: D000013927

- Term: Thrombosis

- ID: D000020246

- Term: Venous Thrombosis

- ID: D000007238

- Term: Infarction

### Intervention Browse Module - Browse Branches

- Abbrev: AnCoag

- Name: Anticoagulants

- Abbrev: All

- Name: All Drugs and Chemicals

### Intervention Browse Module - Browse Leaves

- ID: M17602

- Name: Warfarin

- Relevance: LOW

- As Found: Unknown

### Misc Info Module

- Version Holder: 2024-05-24

## Results Section

### Adverse Events Module

#### Event Groups

**Group ID:** EG000

**Title:** Genomics

**Description:** Model Predictive Control: Model predictive control is a computer based algorithm that can be applied to drug dosing. This computer tool uses a model of how a patient will respond to a drug dose based on demographic and historical dosing information to predict a new drug response. A drug dose controller applies all possible doses to the response model and selects the one dose that best meets the stated goals of the drug therapy. In the case of warfarin, we will calculate an INR value within a specific target range.

**ID:** EG000

**Title:** Genomics

**Frequency Threshold:** 0

## Results Section - Baseline Characteristics Module

### Denomination Counts

**Group ID:** BG000

**Value:** 175

**Units:** Participants

### Group

**ID:** BG000

**Title:** Genomics

**Description:** The specific aims of this research are:

1. Determine the structure and the type of neural network model for predictions from historically obtained data. (Computer Model)
2. Prospectively develop an individualized neural network and NONMEM model capable of predicting erythropoietin dosing for chronic in-center hemodialysis patients using adaptive techniques.
3. Develop computer programs based on neural computing that can be used in a clinical setting. (Computer Model)
4. Determine the utility of the computer programs prospectively in the clinical setting.

### Measure

#### Measurement

**Group ID:** BG000

**Value:** 0

**Category Title:** <=18 years

#### Measurement

**Group ID:** BG000

**Value:** 50

**Category Title:** Between 18 and 65 years

#### Measurement

**Group ID:** BG000

**Value:** 125

**Category Title:** >=65 years

**Class Title:** 

### Measure

#### Measurement

**Group ID:** BG000

**Value:** 10

**Category Title:** Female

#### Measurement

**Group ID:** BG000

**Value:** 165

**Category Title:** Male

**Class Title:** 

### Measure

#### Measurement

**Group ID:** BG000

**Value:** 0

**Category Title:** American Indian or Alaska Native

#### Measurement

**Group ID:** BG000

**Value:** 0

**Category Title:** Asian

#### Measurement

**Group ID:** BG000

**Value:** 1

**Category Title:** Native Hawaiian or Other Pacific Islander

#### Measurement

**Group ID:** BG000

**Value:** 17

**Category Title:** Black or African American

#### Measurement

**Group ID:** BG000

**Value:** 157

**Category Title:** White

#### Measurement

**Group ID:** BG000

**Value:** 0

**Category Title:** More than one race

#### Measurement

**Group ID:** BG000

**Value:** 0

**Category Title:** Unknown or Not Reported

**Class Title:** 

**Denomination Units Selected:** 

## Results Section - Baseline Characteristics Module

### Measure 1
**Parameter Type:** COUNT_OF_PARTICIPANTS
**Title:** Age, Categorical
**Unit of Measure:** Participants
### Measure 2
**Parameter Type:** COUNT_OF_PARTICIPANTS
**Title:** Sex: Female, Male
**Unit of Measure:** Participants
### Measure 3
**Parameter Type:** COUNT_OF_PARTICIPANTS
**Title:** Race (NIH/OMB)
**Unit of Measure:** Participants
## Results Section - More Information Module
### Certain Agreement
**PI Sponsor Employee:** True
### Point of Contact
**Email:** mbrier@louisville.edu
**Organization:** University of Louisville
**Phone:** 502-852-0246
**Title:** Michael Brier
## Results Section - Outcome Measures Module
### Outcome Measure 1
## Results Section
### Outcome Measures Module
#### Outcome Measure 1
**Class:**
##### Category
**Measurements:**
- **Comment:** 
- **Group ID:** OG000
- **Lower Limit:** 
- **Spread:** 
- **Upper Limit:** 
- **Value:** 112
**Title:** 
**Class:**
##### Category
**Measurements:**
- **Comment:** 
- **Group ID:** OG000
- **Lower Limit:** 
- **Spread:** 
- **Upper Limit:** 
- **Value:** 23
**Title:** 
**Class:**
##### Category
**Measurements:**
- **Comment:** 
- **Group ID:** OG000
- **Lower Limit:** 
- **Spread:** 
- **Upper Limit:** 
- **Value:** 19
**Title:** 
**Class:**
##### Category
**Measurements:**
- **Comment:** 
- **Group ID:** OG000
- **Lower Limit:** 
- **Spread:** 
- **Upper Limit:** 
- **Value:** 3
**Title:** 
**Class:**
##### Category
**Measurements:**
- **Comment:** 
- **Group ID:** OG000
- **Lower Limit:** 
- **Spread:** 
- **Upper Limit:** 
- **Value:** 3
**Title:** 
**Class:**
##### Category
**Measurements:**
- **Comment:** 
- **Group ID:** OG000
- **Lower Limit:** 
- **Spread:** 
- **Upper Limit:** 
- **Value:** 2
**Title:** 
**Class:**
##### Category
**Measurements:**
- **Comment:** 
- **Group ID:** OG000
- **Lower Limit:** 
- **Spread:** 
- **Upper Limit:** 
- **Value:** 13
**Title:** 
**Class:**
##### Category
**Measurements:**
- **Comment:** 
- **Group ID:** OG000
- **Lower Limit:** 
- **Spread:** 
- **Upper Limit:** 
- **Value:** 23
**Title:** 
**Class:**
##### Category
**Measurements:**
- **Comment:** 
- **Group ID:** OG000
- **Lower Limit:** 
- **Spread:** 
- **Upper Limit:** 
- **Value:** 72
**Title:** 
**Class:**
##### Category
**Measurements:**
- **Comment:** 
- **Group ID:** OG000
- **Lower Limit:** 
- **Spread:** 
- **Upper Limit:** 
- **Value:** 67
**Title:** 
**Class:**
##### Category
**Measurements:**
- **Comment:** 
- **Group ID:** OG000
- **Lower Limit:** 
- **Spread:** 
- **Upper Limit:** 
- **Value:** 13
**Title:** 
## Results Section
### Outcome Measures Module
#### Outcome Measure 1
**Description:** During Aim 2, Determined Patient Genotypes: CYP2C9 and VKORC1.
**Parameter Type:** NUMBER
**Reporting Status:** POSTED
**Time Frame:** Baseline
**Title:** Patient Genomics
**Type:** PRIMARY
**Unit of Measure:** participants
##### Group
**Description:** Model Predictive Control: Model predictive control is a computer based algorithm that can be applied to drug dosing. This computer tool uses a model of how a patient will respond to a drug dose based on demographic and historical dosing information to predict a new drug response. A drug dose controller applies all possible doses to the response model and selects the one dose that best meets the stated goals of the drug therapy. In the case of warfarin, we will calculate an INR value within a specific target range.

There are 4 Aims in this study.

1. To collect historical data on warfarin dosing in subjects.
2. To collect genotype information on up to 300 subjects receiving warfarin anticoagulation.
3. to develop a computer model incorporating the information from aim 1 and aim 2.
4. To conduct a randomized clinical trial.
**ID:** OG000
**Title:** Genomics
### Participant Flow Module
#### Group
**Description:** Model Predictive Control: Model predictive control is a computer based algorithm that can be applied to drug dosing. This computer tool uses a model of how a patient will respond to a drug dose based on demographic and historical dosing information to predict a new drug response. A drug dose controller applies all possible doses to the response model and selects the one dose that best meets the stated goals of the drug therapy. In the case of warfarin, we will calculate an INR value within a specific target range.
**ID:** FG000
**Title:** Genomics
#### Period
**Title:** Overall Study
##### Milestone
**Type:** STARTED
###### Achievement
**Group ID:** FG000
**Number of Subjects:** 175
##### Milestone
**Type:** COMPLETED
###### Achievement
**Group ID:** FG000
**Number of Subjects:** 175
##### Milestone
**Type:** NOT COMPLETED
###### Achievement
**Group ID:** FG000
**Number of Subjects:** 0
**Pre-Assignment Details:** Participants were not enrolled into the randomized clinical trial for Aim 4 due to lack of funding.
**Has Results:** True