Ignite Creation Date:
2024-05-05 @ 4:50 PM
Last Modification Date:
2024-10-26 @ 9:24 AM
Study NCT ID:
NCT00321282
Status:
TERMINATED
Last Update Posted:
2013-09-27
First Post:
2006-05-01
Brief Title:
A Retrospective Analysis of the Predictive Potential of Pre-operative
Sponsor:
Emory University
Organization:
Emory University
Study Overview
Official Title:
A Retrospective Analysis of the Predictive Potential of Pre-operative Data on Post-operative Atrial Fibrillation
Status:
TERMINATED
Status Verified Date:
2013-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
The study never started
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Roughly thirty percent of people that undergo open heart surgery get an abnormal heart beat afterwards known as atrial fibrillation AF While not life threatening this abnormal heart beat increases the likelihood of stroke and delays recovery There are strategies to prevent post-operative AF but they are costly and sometimes have undesirable side effects Therefore it would be best if we use these preventive treatments only in high risk patients
We intend to develop a risk prediction model based on demographic and electrocardiogram ECG findings that will predicted who is likely to get AF We will develop this model using data already available on patients who have undergone cardiac surgery The development of this model will use the latest mathematical algorithms similar to those used to study genetic evolution This type of model is capable of looking at many parameters in an unbiased way so that only the strongest independent predictors remain in the final model Once the model is developed we will validate the model by comparing our predictions to actual outcomes previously recorded in the database
We intend to develop a risk prediction model based on demographic and electrocardiogram ECG findings that will predicted who is likely to get AF We will develop this model using data already available on patients who have undergone cardiac surgery The development of this model will use the latest mathematical algorithms similar to those used to study genetic evolution This type of model is capable of looking at many parameters in an unbiased way so that only the strongest independent predictors remain in the final model Once the model is developed we will validate the model by comparing our predictions to actual outcomes previously recorded in the database
Detailed Description:
10 Background Currently roughly thirty percent of coronary artery bypass graft CABG patients develop atrial fibrillation AF in the five days following surgery increasing the risk of stroke prolonging hospital stay three to four days and increasing the overall cost of the procedure 1 2 According to some sources over 1 billion is spent annually on this problem in the US alone 2 Current pharmacologic and nonpharmacologic means of AF prevention are suboptimal and their side effects expense and inconvenience limit their widespread use in all patients 3
Though many methods have been presented touting high predictive value in terms of sensitivity and specificity for post-operative AF none are reliable enough for use in a clinical setting This may be due to the lack of a standardized method for the measurement of certain morphological P wave features in ECG analyses 4 Furthermore the medical community has relied on limited variable combination methods for much too long especially while there are advanced methods of data mining and decision-making to be harnessed A Bayesian network BN is an excellent tool for making decisions based on collected information and is even able to handle missing data points well 5 By combining more types of data and expert knowledge into a BN with a Bayesian statistical approach better accuracy is the likely result
20 Objectives
The main objective of this research is to develop a Bayesian network BN classifier which can modelpredictassign risk of the occurrence of atrial fibrillation in coronary artery bypass graft patients through the incorporation of different types of patient data Expert knowledge coming from doctors in the field will be combined using Bayesian statistics with patient data and electrocardiogram ECG analysis improving on the Frequentist methods currently used We intend to investigate profit or loss due to the inclusion of the following data types
Collected Data- Risk factors and other medical indicators recorded in the hospital
ECG Features- Time frequency wavelet and nonlinear domain features derived from the ECG signal showing AF prediction potential
Expert Knowledge- Cardiologist modified probability distribution and frequency beliefs of input data based on past experience This study will analyze data from patients who underwent cardiac surgery at Emory University Hospital Crawford Long Hospital or the Atlanta Veterans Affairs Medical Center The collected data will include demographic pre-operative operative and post-operative all taken from the patients chart ECG and available telemetry data will also be collected and analyzed for morphological features which may yield AF predisposition clues
Following development of the AF prediction algorithm this will be tested on a subset of the data points extracted from the database This study is a feasibility study for further funded research
30 Patient Selection 31 Eligibility criteria
1 All patients that patients who underwent cardiac surgery in the Emory University Hospital Crawford Long Hospital or the VA Medical Center
32 Ineligibility criteria
1 Emergent operations
2 The presence of AF or Aflut at the time of surgery
3 New York Heart Association NYHA class IV heart failure at the time of surgery
4 Hyperthyroidism
5 Implanted devices for designed for active management of atrial arrhythmias by pacing or defibrillation
6 Known illicit drug use
7 Known ethanol abuse
8 Electrophysiological ablation for atrial tachycardia within 6 months of the operation
40 Registration and randomization none
50 Therapy none
60 Pathology none
70 Patient assessment none
80 Data collection Data will be collected from review of the patients hospital charts from telemetry recordings and ECGs The presence or absence of atrial fibrillation will be diagnosed on the basis of an electrocardiographic recording and confirmed by a cardiologist Data collected will include age race sex body mass index blood pressure NYHA classification Killip classification and the history of previous myocardial infarction hypertension diabetes smoking alcohol use antiarrhythmic drug use presence and type of pacemaker if any history of AF or Aflut previous cardiovascular events type of operation and length of operation Patients enrolled in this study will be given unique study numbers and all identifying information will be removed after correlation between telemetry and chart data are established No follow up data will be required from patients
90 Statistical considerations This study design is a retrospective chart review of a cohort of patients undergoing cardiac surgery to determine variable combinations that predict atrial fibrillation following cardiac surgery Within the cohort those patients with AF will be compared to those without these atrial arrhythmias in the post-operative period Probability tables will be developed using both groups thereby populating a predictive Bayesian network structure Many different structures will be evaluated using an artificial intelligence technique genetic algorithms which will evolve the structure to its optimal form K-fold cross-validation will then be used to validate the predictive ability of the Bayesian network structure Sensitivities specificities and positive predictive values will be reported on the results of this validation
Though many methods have been presented touting high predictive value in terms of sensitivity and specificity for post-operative AF none are reliable enough for use in a clinical setting This may be due to the lack of a standardized method for the measurement of certain morphological P wave features in ECG analyses 4 Furthermore the medical community has relied on limited variable combination methods for much too long especially while there are advanced methods of data mining and decision-making to be harnessed A Bayesian network BN is an excellent tool for making decisions based on collected information and is even able to handle missing data points well 5 By combining more types of data and expert knowledge into a BN with a Bayesian statistical approach better accuracy is the likely result
20 Objectives
The main objective of this research is to develop a Bayesian network BN classifier which can modelpredictassign risk of the occurrence of atrial fibrillation in coronary artery bypass graft patients through the incorporation of different types of patient data Expert knowledge coming from doctors in the field will be combined using Bayesian statistics with patient data and electrocardiogram ECG analysis improving on the Frequentist methods currently used We intend to investigate profit or loss due to the inclusion of the following data types
Collected Data- Risk factors and other medical indicators recorded in the hospital
ECG Features- Time frequency wavelet and nonlinear domain features derived from the ECG signal showing AF prediction potential
Expert Knowledge- Cardiologist modified probability distribution and frequency beliefs of input data based on past experience This study will analyze data from patients who underwent cardiac surgery at Emory University Hospital Crawford Long Hospital or the Atlanta Veterans Affairs Medical Center The collected data will include demographic pre-operative operative and post-operative all taken from the patients chart ECG and available telemetry data will also be collected and analyzed for morphological features which may yield AF predisposition clues
Following development of the AF prediction algorithm this will be tested on a subset of the data points extracted from the database This study is a feasibility study for further funded research
30 Patient Selection 31 Eligibility criteria
1 All patients that patients who underwent cardiac surgery in the Emory University Hospital Crawford Long Hospital or the VA Medical Center
32 Ineligibility criteria
1 Emergent operations
2 The presence of AF or Aflut at the time of surgery
3 New York Heart Association NYHA class IV heart failure at the time of surgery
4 Hyperthyroidism
5 Implanted devices for designed for active management of atrial arrhythmias by pacing or defibrillation
6 Known illicit drug use
7 Known ethanol abuse
8 Electrophysiological ablation for atrial tachycardia within 6 months of the operation
40 Registration and randomization none
50 Therapy none
60 Pathology none
70 Patient assessment none
80 Data collection Data will be collected from review of the patients hospital charts from telemetry recordings and ECGs The presence or absence of atrial fibrillation will be diagnosed on the basis of an electrocardiographic recording and confirmed by a cardiologist Data collected will include age race sex body mass index blood pressure NYHA classification Killip classification and the history of previous myocardial infarction hypertension diabetes smoking alcohol use antiarrhythmic drug use presence and type of pacemaker if any history of AF or Aflut previous cardiovascular events type of operation and length of operation Patients enrolled in this study will be given unique study numbers and all identifying information will be removed after correlation between telemetry and chart data are established No follow up data will be required from patients
90 Statistical considerations This study design is a retrospective chart review of a cohort of patients undergoing cardiac surgery to determine variable combinations that predict atrial fibrillation following cardiac surgery Within the cohort those patients with AF will be compared to those without these atrial arrhythmias in the post-operative period Probability tables will be developed using both groups thereby populating a predictive Bayesian network structure Many different structures will be evaluated using an artificial intelligence technique genetic algorithms which will evolve the structure to its optimal form K-fold cross-validation will then be used to validate the predictive ability of the Bayesian network structure Sensitivities specificities and positive predictive values will be reported on the results of this validation
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None