Dataset Information

Improving 1-year mortality prediction in ACS patients using machine learning.

ABSTRACT:

Background

The Global Registry of Acute Coronary Events (GRACE) score is an established clinical risk stratification tool for patients with acute coronary syndromes (ACS). We developed and internally validated a model for 1-year all-cause mortality prediction in ACS patients.

Methods

Between 2009 and 2012, 2'168 ACS patients were enrolled into the Swiss SPUM-ACS Cohort. Biomarkers were determined in 1'892 patients and follow-up was achieved in 95.8% of patients. 1-year all-cause mortality was 4.3% (n = 80). In our analysis we consider all linear models using combinations of 8 out of 56 variables to predict 1-year all-cause mortality and to derive a variable ranking.

Results

1.3% of 1'420'494'075 models outperformed the GRACE 2.0 Score. The SPUM-ACS Score includes age, plasma glucose, NT-proBNP, left ventricular ejection fraction (LVEF), Killip class, history of peripheral artery disease (PAD), malignancy, and cardio-pulmonary resuscitation. For predicting 1-year mortality after ACS, the SPUM-ACS Score outperformed the GRACE 2.0 Score which achieves a 5-fold cross-validated AUC of 0.81 (95% CI 0.78-0.84). Ranking individual features according to their importance across all multivariate models revealed age, trimethylamine N-oxide, creatinine, history of PAD or malignancy, LVEF, and haemoglobin as the most relevant variables for predicting 1-year mortality.

Conclusions

The variable ranking and the selection for the SPUM-ACS Score highlight the relevance of age, markers of heart failure, and comorbidities for prediction of all-cause death. Before application, this score needs to be externally validated and refined in larger cohorts.

Clinical trial registration

NCT01000701.

SUBMITTER: Weichwald S

PROVIDER: S-EPMC8557454 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improving 1-year mortality prediction in ACS patients using machine learning.

Weichwald Sebastian S Candreva Alessandro A Burkholz Rebekka R Klingenberg Roland R Räber Lorenz L Heg Dik D Manka Robert R Gencer Baris B Mach François F Nanchen David D Rodondi Nicolas N Windecker Stephan S Laaksonen Reijo R Hazen Stanley L SL von Eckardstein Arnold A Ruschitzka Frank F Lüscher Thomas F TF Buhmann Joachim M JM Matter Christian M CM

European heart journal. Acute cardiovascular care 20211001 8

<h4>Background</h4>The Global Registry of Acute Coronary Events (GRACE) score is an established clinical risk stratification tool for patients with acute coronary syndromes (ACS). We developed and internally validated a model for 1-year all-cause mortality prediction in ACS patients.<h4>Methods</h4>Between 2009 and 2012, 2'168 ACS patients were enrolled into the Swiss SPUM-ACS Cohort. Biomarkers were determined in 1'892 patients and follow-up was achieved in 95.8% of patients. 1-year all-cause m ...[more]

PMID: 34015112

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Dataset Information

Improving 1-year mortality prediction in ACS patients using machine learning.

Background

Methods

Results

Conclusions

Clinical trial registration

Publications

Improving 1-year mortality prediction in ACS patients using machine learning.

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