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Published on: January 28, 2020
Computationally generated cardiac biomarkers for risk stratification after acute coronary syndrome
Zeeshan Syed1, Collin M Stultz, Benjamin M Scirica
1University of Michigan, Ann Arbor, MI 48109, USA. zhs@umich.edu
Insights
New cardiac biomarkers, including morphologic variability (MV), symbolic mismatch (SM), and heart rate motifs (HRM), significantly improve risk prediction for cardiovascular death after acute coronary syndrome.
Area of Science:
- Cardiology
- Biomarkers
- Computational Medicine
Background:
- Current risk stratification tools for acute coronary syndrome (ACS) patients, such as echocardiography and clinical risk scores, identify only a small subset of high-risk individuals.
- These existing methods account for a minority of post-ACS deaths, indicating a need for improved risk assessment strategies.
Purpose of the Study:
- To investigate the utility of three novel computationally generated cardiac biomarkers: morphologic variability (MV), symbolic mismatch (SM), and heart rate motifs (HRM).
- To assess the effectiveness of these biomarkers in improving risk stratification for cardiovascular death in patients following acute coronary syndrome.
Main Methods:
- Biomarkers (MV, SM, HRM) were derived from time-series analyses of continuous electrocardiographic data using machine learning and data mining.
- These biomarkers were evaluated in a large cohort (>4500 patients) from the MERLIN-TIMI36 clinical trial.
- The study employed a blinded, prespecified, and fully automated approach.
Main Results:
- All three computationally generated biomarkers demonstrated a strong association with cardiovascular death over a 2-year period post-ACS.
- The information provided by MV, SM, and HRM was independent of existing clinical risk scores, electrocardiographic metrics, and echocardiography.
- Incorporating these biomarkers significantly enhanced model discrimination and improved the precision and recall of prediction rules.
Conclusions:
- Computationally derived cardiac biomarkers (MV, SM, HRM) offer a powerful new approach for accurate risk stratification in acute coronary syndrome patients.
- These biomarkers can be extracted from routinely collected electrocardiographic data, facilitating their clinical implementation.
- Improved risk stratification has the potential to lead to more personalized and effective patient treatment strategies.
Abstract:
The existing tools for estimating the risk of death in patients after they experience acute coronary syndrome are commonly based on echocardiography and clinical risk scores (for example, the TIMI risk score). These identify a small group of high-risk patients who account for only a minority of the deaths that occur in patients after acute coronary syndrome. Here, we investigated the use of three computationally generated cardiac biomarkers for risk stratification in this population: morphologic variability (MV), symbolic mismatch (SM), and heart rate motifs (HRM). We derived these biomarkers from time-series analyses of continuous electrocardiographic data collected from patients in the TIMI-DISPERSE2 clinical trial through machine learning and data mining methods designed to extract information that is difficult to visualize directly in these data. We evaluated these biomarkers in a blinded, prespecified, and fully automated study on more than 4500 patients in the MERLIN-TIMI36 (Metabolic Efficiency with Ranolazine for Less Ischemia in Non-ST-Elevation Acute Coronary Syndrome-Thrombolysis in Myocardial Infarction 36) clinical trial. Our results showed a strong association between all three computationally generated cardiac biomarkers and cardiovascular death in the MERLIN-TIMI36 trial over a 2-year period after acute coronary syndrome. Moreover, the information in each of these biomarkers was independent of the information in the others and independent of the information provided by existing clinical risk scores, electrocardiographic metrics, and echocardiography. The addition of MV, SM, and HRM to existing metrics significantly improved model discrimination, as well as the precision and recall of prediction rules based on left ventricular ejection fraction. These biomarkers can be extracted from data that are routinely captured from patients with acute coronary syndrome and will allow for more accurate risk stratification and potentially for better patient treatment.
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