Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT00081666 |
| Other study ID # |
1246 |
| Secondary ID |
R01HL072771 |
| Status |
Completed |
| Phase |
N/A
|
| First received |
April 19, 2004 |
| Last updated |
July 28, 2016 |
| Start date |
July 2004 |
| Est. completion date |
June 2007 |
Study information
| Verified date |
January 2008 |
| Source |
National Heart, Lung, and Blood Institute (NHLBI) |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
United States: Federal Government |
| Study type |
Observational
|
Clinical Trial Summary
To use a new statistical method, the Logical Analysis of Data (LAD), to predict cardiac
surgery risk.
Description:
BACKGROUND:
One of the most important tasks that cardiovascular clinicians perform is risk
stratification, as that enables appropriate targeting of aggressive treatments to patients
that are most likely to benefit from them. Contemporary risk stratification strategies
include clinical scoring systems along with performance of noninvasive tests. Although these
approaches are commonly used, clinicians still find themselves needing to incorporate
multiple pieces of clinical information into a cohesive global risk assessment. The concept
of utilizing data from large observational data sets to develop complex risk scores and to
encourage their use in routine practice is therefore gradually evolving and gaining
acceptance. The Logical Analysis of Data (LAD) is a potentially useful approach for
systematically analyzing large databases for the purpose of developing and validating
clinically useful risk prediction schemes. Unlike standard regression techniques, LAD does
not primarily focus on individual risk factors and two-way interactions between them.
Rather, LAD is designed to identify complex patterns of findings, or syndromes, that predict
outcomes. This method has been applied to problems in economics, seismology and oil
exploration, but not to medicine.
DESIGN NARRATIVE:
The study has three specific aims: 1). to apply LAD to develop and validate risk prediction
instruments among patients undergoing different types of cardiac surgery. 2. to compare the
predictive value of LAD predictive instruments with predictive instruments developed using
standard statistical methods, including multiple time-phase parametric modeling. 3. to
develop predictive instruments using relative risk forests, a new Monte Carlo method for
estimating risk values in large survival data settings with large numbers of correlated
variables. Relative risk forests are an adaptation of random forests introduced by Breiman.
When possible these methods will be compared to LAD. Internal estimates for the
generalization error, a measure of how well the method will generalize to other data
settings, will be computed and will be used in the development of the predictive instrument.
Relative risk forests will also be compared to several other non-deterministic methods,
including boosting and spike and slab variable selection. All of these techniques can be
used to develop complex models while maintaining good prediction error and are ideal for
high dimensional problems where traditional methods breakdown. Although this project will
focus on risk assessment among patients undergoing cardiac surgery, it is important to
recognize that we are primarily interested in the value of LAD as a means of analyzing very
large and complex data sets within a medical sphere. Hence, the applicability of this work
goes beyond determination of risk of patients undergoing cardiac surgery.
Data used for this study will consist of cardiac surgery data from the Cleveland Clinic
Foundation Cardiovascular Information Registry (CVIR). Four cohorts of data will be
assembled; Cohort I: 18,914 CABG patients between 1990 and 2000; Cohort II: 6952 patients
undergoing aortic valve replacement; Cohort III: 2979 patients undergoing mitral valve
replacement; Cohort IV: 10,482 patients undergoing mitral valve repair. The primary endpoint
will be long term total mortality; for valve surgery patients it will be active follow-up.
