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Best Markers for Cardiac "Died Suddenly"

It's never one thing but a couple of markers rise to the top when predicting cardiovascular disease.


The usual suspects - not those at the top of the list - also provide powerful predictive power. Leading that list, especially when considering costs include: CRP, NLR, cystatin C, fasting insulin, and Fibrinogen.


In setting up our lab panels 20 years ago, I reviewed essentially every important biomarker. Two, in particular, have very high sensitivity for cardiac health - namely, Troponin T and NT-proBNP. I don't run these regularly due to cost. However, they may help zero in on higher risk individuals. We can run these markers on anyone interested. Just contact us at info@healthrevivalpartners.com


Again, CRP and the other markers provide strong predictive power as well.


 

Here is a paper that explains the predictive power of these two markers. I show you the contributors because it's an unusually large number. I think one person can study the literature and derive the information presented in the paper - just saying.....




Background

Currently, there is no generally accepted model to predict outcomes in stable coronary heart disease (CHD).


Objectives

This study evaluated and compared the prognostic value of biomarkers and clinical variables to develop a biomarker-based prediction model in patients with stable CHD.


Methods

In a prospective, randomized trial cohort of 13,164 patients with stable CHD, we analyzed several candidate biomarkers and clinical variables and used multivariable Cox regression to develop a clinical prediction model based on the most important markers. The primary outcome was cardiovascular (CV) death, but model performance was also explored for other key outcomes. It was internally bootstrap validated, and externally validated in 1,547 patients in another study.


Results

During a median follow-up of 3.7 years, there were 591 cases of CV death. The 2 most important biomarkers were N-terminal pro–B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT), where NT-proBNP and hs-cTnT had greater prognostic value than any other biomarker or clinical variable. The final prediction model included age (A), biomarkers (B) and NT-proBNP, hs-cTnT, and clinical variables (C) (smoking, diabetes mellitus, and peripheral arterial disease). This “ABC-CHD” model had high discriminatory ability for CV death (c-index 0.81 in derivation cohort, 0.78 in validation cohort), with adequate calibration in both cohorts.


Conclusions

This model provided a robust tool for the prediction of CV death in patients with stable CHD. As it is based on a small number of readily available biomarkers and clinical factors, it can be widely employed to complement clinical assessment and guide management based on CV risk. (The Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy Trial [STABILITY]; NCT00799903)


This image shows the log-linear (gaussian) change in risk with the level of the biomarkers.





Figure 3. Variable Importance

The relative importance of variables included in the large model for the prediction of CV death is shown, where importance is measured as chi-square statistic minus the predictor degrees of freedom (df). BMI = body mass index; CHD = coronary heart disease; CHF = congestive heart failure; CKD-EPI = Chronic Kidney Disease Epidemiology Collaboration formula; CRP = C-reactive protein; GDF= growth differentiation factor; HDL = high-density lipoprotein; IL = interleukin; PAD = peripheral arterial disease; Prev = previous; Revasc = revascularization; WBC = white blood cells;


Conclusions

In patients with stable CHD, the cardiac biomarkers NT-proBNP and hs-cTnT had greater prognostic value than any clinical variable or any other currently used biomarker for CV outcomes; plus, they provided incremental predictive information in addition to clinical variables and other biomarkers. Based on these findings, we developed and internally and externally validated a novel biomarker-based model for the prediction of CV death in patients with stable CHD containing age (A); the biomarkers (B) NT-proBNP, hs-cTnT; and the clinical variables (C) smoking, DM, and prior PAD. The ABC-CHD score showed excellent discriminatory ability for CV death and other CV outcomes and was validated and well-calibrated for CV death in an external cohort. Therefore, this novel biomarker-based ABC-CHD risk score might serve as a clinically useful decision-support tool in patients with stable CHD.

 

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