Developing a Five-Biomarker Risk Score for Estimating Five-Year Risk of Myocardial Infarction or Death in a Sample of Subjects that Underwent Cardiac Catheterization Open Access

Naioti, Eric (Spring 2018)

Permanent URL: https://etd.library.emory.edu/concern/etds/td96k250z?locale=en
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Abstract

 

Biomarkers of heat-shock protein 70 (HSP70), fibrin degradation products (FDP), soluble

urokinase plasminogen activator receptor (suPAR), and C-reactive protein (CRP) have been

shown to be associated with heart disease. A biomarker risk score (BRS) based on measures of

circulating levels of these biomarkers has been shown to have utility in predicting adverse events

in patients with coronary artery disease (CAD). High-sensitivity cardiac troponin I (HS-Trop) has

been shown to indicate cardiomyocyte cell damage and could also be a predictor of heart disease.

We examined how well a new BRS that included HS-Trop could be used in predicting an

outcome of myocardial infarction (MI) or death in patients suspected of CAD. Two thousand

eight hundred eighty-six participants were recruited from three Emory healthcare sites in Atlanta

as part of the Emory Cardiovascular Biobank (EmCAB). Each participant had measures for each

of these biomarkers. The five biomarkers were shown to be independent of each other and useful

in predicting MI or all-cause death. It has been shown that using multiple cutoff points to stratify

patients into multiple risk groups can be highly favorable. Therefore, instead of only finding one

cutoff point for each biomarker, optimal numbers and locations of cutoff points were found using

the most significant splits of likelihood ratio tests. A BRS based on these stratified biomarkers

was found, and a model was constructed using this BRS along with traditional risk factors to

predict MI or all-cause death. In this cox proportional hazard model, BRS was found to be highly

significant (β = 0.60, SE = 0.10, Z=6.03, p<0.001). When this model was compared to a model

without BRS, we found using five-fold cross-validation that the model including our BRS

improved prediction ability, with a mean increase in the concordance statistic (C-statistic) of

0.0411 [95% CI = (0.0054, 0.0769)] and a mean net reclassification index (NRI) of 0.259 [95%

CI =(0.143, 0.376)].

Table of Contents

 

Introduction ......................................................................................................................... 1

Methods ............................................................................................................................... 2

Study Sample ................................................................................................................................ 2

Inflammatory Biomarkers ............................................................................................................ 3

Analysis Plan ................................................................................................................................ 3

Check whether the Biomarkers are Independent ...................................................................... 4

Deriving Optimal Cutoff Points ............................................................................................... 5

Visualizing Survival Curves ..................................................................................................... 6

Categorizing Biomarkers Using Cutoff Points ......................................................................... 6

Finding a Biomarker Risk Score (BRS) ................................................................................... 6

Developing a BRS-based Risk Prediction Model .................................................................... 7

Calibrating the Model ............................................................................................................... 7

Evaluating Discrimination of the Risk Prediction Models ....................................................... 7

Results ................................................................................................................................. 8

Baseline Characteristics of Study Participants ............................................................................. 8

Correlations of Biomarkers ........................................................................................................ 10

Deriving Optimal Cutoff Points ................................................................................................. 13

Visualizing Survival Curves ....................................................................................................... 14

Finding the BRS ......................................................................................................................... 16

Developing a New Model Using BRS ........................................................................................ 16

Finding the C-statistic and NRI for a New Model ..................................................................... 18

Discussion ......................................................................................................................... 19

References ......................................................................................................................... 21

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