Dataset Information

Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long-Term Mortality Risk.

ABSTRACT:

Background

We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high-sensitivity cardiac troponin T, N-terminal pro-B-type natriuretic peptide, high-sensitivity C-reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long-term mortality risk.

Methods and results

BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high-frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker-based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all-cause death were evaluated using accelerated failure time models (median follow-up, 9.1 years; 141 deaths). Three biomarker-based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long-term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44-0.93; P=0.018) compared with cluster 1, and 0.71 (95% CI, 0.39-1.32; P=0.281) compared with patients with a repeat ACS.

Conclusions

Patients with subphenotypes of post-ACS with different all-cause mortality risks during long-term follow-up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they experienced a repeat ACS in the first year.

SUBMITTER: de Bakker M

PROVIDER: S-EPMC10926784 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long-Term Mortality Risk.

de Bakker Marie M Scholte Niels T B NTB Oemrawsingh Rohit M RM Umans Victor A VA Kietselaer Bas B Schotborgh Carl C Ronner Eelko E Lenderink Timo T Aksoy Ismail I van der Harst Pim P Asselbergs Folkert W FW Maas Arthur A Oude Ophuis Anton J AJ Krenning Boudewijn B de Winter Robbert J RJ The S Hong Kie SHK Wardeh Alexander J AJ Hermans Walter W Cramer G Etienne GE van Schaik Ron H RH de Rijke Yolanda B YB Akkerhuis K Martijn KM Kardys Isabella I Boersma Eric E

Journal of the American Heart Association 20240112 2

<h4>Background</h4>We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high-sensitivity cardiac troponin T, N-terminal pro-B-type natriuretic peptide, high-sensitivity C-reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long-term mortality risk.<h4>Methods and results</h4>BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) ...[more]

PMID: 38214281

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Project description:BackgroundThe formal risk assessment is essential in the management of acute coronary syndrome (ACS). In this study, we develop a risk model for the prediction of 3-year mortality for Chinese ACS patients with machine learning algorithms.MethodsA total of 2174 consecutive patients who underwent angiography with ACS were enrolled. The missing data among baseline characteristics were imputed using the MissForest algorithm based on random forest method. In model development, a least absolute shrinkage and selection operator (LASSO) derived Cox regression with internal tenfold cross-validation was used to identify the predictors for 3-year mortality. The clinical performance was assessed with decision curve analysis.ResultsThe average follow-up period was 27.82 ± 13.73 months; during the 3 years of follow up, 193 patients died (mortality rate 8.88%). The Kaplan-Meier estimate of 3-year mortality was 0.91 (95% confidence interval (CI): 0.890.92). After feature selection, 6 predictors were identified: Age," "Creatinine," "Hemoglobin," "Platelets," "aspartate transaminase (AST)" and "left ventricular ejection fraction (LVEF)". At tenfold internal validation, our risk model performed well in both discrimination (area under curve (AUC) of receiver operating characteristic (ROC) analysis was 0.768) and calibration (calibration slope was approximately 0.711). As a comparison, the AUC and calibration slope were 0.701 and 0.203 in Global Registry of Acute Coronary Events (GRACE) risk score, respectively. Additionally, the highest net benefit of our model within the entire range of threshold probability for clinical intervention by decision curve analysis demonstrated the superiority of it in daily practice.ConclusionOur study developed a prediction model for 3-year morality in Chinese ACS patients. The methods of missing data imputation and model derivation base on machine learning algorithms improved the ability of prediction. . Trial registration ChiCTR, ChiCTR-OOC-17010433. Registered 17 February 2017-Retrospectively registered.

Dataset Information

Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long-Term Mortality Risk.

Background

Methods and results

Conclusions

Publications

Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long-Term Mortality Risk.

Similar Datasets

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