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Logical Analysis of Data and Cardiac Surgery Risk

Primary Purpose

Cardiovascular Diseases, Heart Diseases, Coronary Disease

Status
Completed
Phase
Locations
Study Type
Observational
Intervention
Sponsored by
National Heart, Lung, and Blood Institute (NHLBI)
About
Eligibility
Locations
Outcomes
Full info

About this trial

This is an observational trial for Cardiovascular Diseases

Eligibility Criteria

undefined - 100 Years (Child, Adult, Older Adult)All SexesDoes not accept healthy volunteers

No eligibility criteria

Sites / Locations

    Outcomes

    Primary Outcome Measures

    Secondary Outcome Measures

    Full Information

    First Posted
    April 19, 2004
    Last Updated
    July 28, 2016
    Sponsor
    National Heart, Lung, and Blood Institute (NHLBI)
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    1. Study Identification

    Unique Protocol Identification Number
    NCT00081666
    Brief Title
    Logical Analysis of Data and Cardiac Surgery Risk
    Study Type
    Observational

    2. Study Status

    Record Verification Date
    January 2008
    Overall Recruitment Status
    Completed
    Study Start Date
    July 2004 (undefined)
    Primary Completion Date
    June 2007 (Actual)
    Study Completion Date
    June 2007 (Actual)

    3. Sponsor/Collaborators

    Name of the Sponsor
    National Heart, Lung, and Blood Institute (NHLBI)

    4. Oversight

    5. Study Description

    Brief Summary
    To use a new statistical method, the Logical Analysis of Data (LAD), to predict cardiac surgery risk.
    Detailed 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.

    6. Conditions and Keywords

    Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
    Cardiovascular Diseases, Heart Diseases, Coronary Disease, Aortic Valve Stenosis, Mitral Valve Stenosis

    7. Study Design

    10. Eligibility

    Sex
    All
    Maximum Age & Unit of Time
    100 Years
    Accepts Healthy Volunteers
    No
    Eligibility Criteria
    No eligibility criteria
    Overall Study Officials:
    First Name & Middle Initial & Last Name & Degree
    Michael Lauer
    Organizational Affiliation
    Clevland Clinic Lerner College of Medicine

    12. IPD Sharing Statement

    Learn more about this trial

    Logical Analysis of Data and Cardiac Surgery Risk

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