AJC Publication

Multiple biomarkers for predicting major adverse cardiac events in patients undergoing diagnostic coronary angiography: results from the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA) Study.

March 2017

Highlights

  • In a moderate- to high-risk population undergoing diagnostic coronary angiography, a multiple biomarker score with high accuracy for predicting incident major adverse cardiac events (MACE) to at least one year of follow up was evaluated.

  • The final score is comprised of four biomarkers: Kidney Injury Molecule-1 (KIM-1), NT-proBNP, osteopontin and Tissue Inhibitor of Metalloproteinase-1 (TIMP-1).

  • Lower risk patients (score 0 - 4.5) had a NPV of 97% for predicting MACE at 3-365 days, while higher risk patients (score 6-10) had a PPV of 36% for MACE at 3-365 days.

  • The present risk score may be particularly useful for those with suspected or known stable coronary artery disease, for which there are very few prognostic risk models and also has a role in enriching clinical trials of therapies to prevent MACE.

  • In such a cohort, the score could both identify higher-risk patients who merit consideration for more aggressive medical therapy while avoiding such measures in lower-risk patients.

Study Overview

BACKGROUND

With a prevalence of cardiovascular disease as high as 30% in American adults, prevention of major adverse cardiac events (MACE) is a major public health objective. Central to this preventative process is risk assessment.  Clinical risk factors are commonly used to predict MACE, however, they often lack sufficient positive and negative predictive value.

 

GOAL

In an at-risk population, to develop and validate a robust, non-invasive, multi-biomarker based score for predicting incident MACE within one year of follow up.

 

HYPOTHESIS

A multiple biomarker approach would provide accurate, biologically-based predictive information regarding risk for MACE, additive to or superior to clinical variables and individual biomarkers alone in and at-risk population referred for coronary angiography.

 

METHODS

The CASABLANCA study was a prospective, observational cohort study of 1251 subjects undergoing coronary and peripheral angiography ± intervention between 2008 and 2011 at Massachusetts General Hospital.

 

The initial 927 patients undergoing coronary angiography (with or without peripheral angiography) between 2008 and 2011 were included in this study and were randomly split into a training set (n=649) and a holdout validation set (n=278)

 

MACHINE LEARNING & BIG DATA

Candidate panels of proteins and clinical features were generated via least-angle regression (LARs), a method similar to forward stepwise regression. In this method, factors are included in the model one at a time, with their coefficients determined by their correlation with the outcome.  This is repeated until all factors are included in the model, and the step at which the performance plateaued resulted in an initial panel of interest.

 

With this panel of interest, predictive analyses were run on the training set using least absolute shrinkage and selection operator (LASSO) with logistic regression, predicting the outcome of one-year MACE using only the variables in the panel of interest.  The ultimate result of this process was a final panel of four proteins.

 

Age- and score-adjusted Cox proportional hazards analyses were performed.  Hazard ratios (HR) for an elevated prognostic score as well as per-unit score increase with 95% CI were estimated.  Time-to-first MACE event as a function of elevated prognostic score was calculated, displayed as Kaplan-Meier survival curves, and compared using log-rank testing.  Lastly, to facilitate interpretation, raw output of the prognostic model was linearly transformed into a scaled score of 0-10.

Key Findings

  • During the model-building process, no clinical variables were retained and the final score is comprised of four biomarkers: KIM-1, NT-proBNP, osteopontin and TIMP-1.

 

  • Lower risk patients (score 0 - 4.5) had a NPV of 97% for predicting MACE at 3-365 days, while higher risk patients (score 6-10) had a PPV of 36% for MACE at 3-365 days.

Conclusions

  • The present risk score may be particularly useful for those with suspected or known stable coronary artery disease, for which there are very few prognostic risk models and also has a role in enriching clinical trials of therapies to prevent MACE.
     

  • In such a cohort, the score could both identify higher-risk patients who merit consideration for more aggressive medical therapy while avoiding such measures in lower-risk patients.
     

  • In a moderate- to high-risk population undergoing diagnostic coronary angiography, a multiple biomarker score has been developed with high accuracy for predicting incident MACE to at least one year of follow up.

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Highlights

  • In a moderate- to high-risk population undergoing diagnostic coronary angiography, a multiple biomarker score with high accuracy for predicting incident major adverse cardiac events (MACE) to at least one year of follow up was evaluated. A composite of myocardial infarct (MI), stroke, or cardiovascular (CV) death were evaluated as MACE.

 

  • The final score is comprised of four biomarkers: Kidney Injury Molecule-1 (KIM-1), NT-proBNP, osteopontin and Tissue Inhibitor of Metalloproteinase-1 (TIMP-1).

 

  • Lower risk patients (score 0 - 4.5) had a NPV of 97% for predicting MACE at 3-365 days, while higher risk patients (score 6-10) had a PPV of 36% for MACE at 3-365 days. Both the NPV and PPV are highly acceptable targets for prognostic tests. Overall, the panel AUC was 0.79.

 

  • The present risk score may be particularly useful for those with suspected or known stable coronary artery disease, for which there are very few prognostic risk models and also has a role in enriching clinical trials of therapies to prevent MACE.

 

  • In such a cohort, the score could both identify higher-risk patients who merit consideration for more aggressive medical therapy while avoiding such measures in lower-risk patients.

Study Overview

BACKGROUND

With a prevalence of cardiovascular disease as high as 30% in American adults, prevention of major adverse cardiac events (MACE) is a major public health objective. Central to this preventative process is risk assessment.  Clinical risk factors are commonly used to predict MACE, however, they often lack sufficient positive and negative predictive value.

GOAL

In an at-risk population, to develop and validate a robust, non-invasive, multi-biomarker based score for predicting incident MACE within one year of follow up.

HYPOTHESIS

A multiple biomarker approach would provide accurate, biologically-based predictive information regarding risk for MACE, additive to or superior to clinical variables and individual biomarkers alone in and at-risk population referred for coronary angiography.

METHODS

The CASABLANCA study was a prospective, observational cohort study of 1251 subjects undergoing coronary and peripheral angiography ± intervention between 2008 and 2011 at Massachusetts General Hospital.

The initial 927 patients undergoing coronary angiography (with or without peripheral angiography) between 2008 and 2011 were included in this study and were randomly split into a training set (n=649) and a holdout validation set (n=278).
 

MACHINE LEARNING & BIG DATA

Candidate panels of biomarkers and clinical variables were generated and evaluated with machine learning statistical techniques, a subset of Artificial Intelligence (AI). The result of this process was a final test panel and scoring system predicting the outcome of one-year MACE using only the variables in the panel of interest. The ultimate result of this process was a final panel of four proteins.

Age- and score-adjusted Cox proportional hazards analyses were performed. Hazard ratios (HR) for an elevated prognostic score as well as per-unit score increase with 95% CI were estimated. Time-tofirst MACE event as a function of elevated prognostic score was calculated, displayed as Kaplan-Meier survival curves, and compared using log-rank testing. Lastly, to facilitate interpretation, raw output of the prognostic model was linearly transformed into a scaled score of 0-10.

Key Findings

  • During the model-building process, no clinical variables were retained and the final score is comprised of four biomarkers: KIM-1, NT-proBNP, osteopontin and TIMP-1.

 

  • Lower risk patients (score 0 - 4.5) had a NPV of 97% for predicting MACE at 3-365 days, while higher risk patients (score 6-10) had a PPV of 36% for MACE at 3-365 days.

Conclusions

  • The present risk score may be particularly useful for those with suspected or known stable coronary artery disease, for which there are very few prognostic risk models and also has a role in enriching clinical trials of therapies to prevent MACE.
     

  • In such a cohort, the score could both identify higher-risk patients who merit consideration for more aggressive medical therapy while avoiding such measures in lower-risk patients.
     

  • In a moderate- to high-risk population undergoing diagnostic coronary angiography, a multiple biomarker score has been developed with high accuracy for predicting incident MACE to at least one year of follow up.

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