{"doi":"10.1093/ehjacc/zuab030","title":"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.","journal":"European Heart Journal Acute Cardiovascular Care","year":2021,"id":177366,"datarank":1.0016730103269684,"base_score":3.044522437723423,"endowment":3.044522437723423,"self_citation_contribution":0.4566783656585135,"citation_network_contribution":0.5449946446684549,"self_endowment_contribution":0.4566783656585135,"citer_contribution":0.5449946446684549,"corpus_percentile":null,"corpus_rank":null,"citation_count":20,"citer_count":17,"citers_with_citation_signal":13,"citers_with_endowment":13,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6255,"is_data_producer":true,"deposit_databanks":{"ClinicalTrials.gov":["NCT01000701"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":721617,"name":"Alessandro Candreva","orcid":"0000-0002-6676-7541","position":1,"is_corresponding":false},{"id":633558,"name":"Rebekka Burkholz","orcid":null,"position":2,"is_corresponding":false},{"id":721618,"name":"Roland Klingenberg","orcid":"0000-0002-7726-4421","position":3,"is_corresponding":false},{"id":50036,"name":"Lorenz Räber","orcid":"0000-0003-0824-3026","position":4,"is_corresponding":false},{"id":686305,"name":"Dik Heg","orcid":"0000-0002-8766-7945","position":5,"is_corresponding":false},{"id":493474,"name":"Robert Manka","orcid":"0000-0002-3383-4998","position":6,"is_corresponding":false},{"id":80353,"name":"Bariş Gencer","orcid":"0000-0002-8954-9694","position":7,"is_corresponding":false},{"id":635087,"name":"François Mach","orcid":"0000-0003-3178-9131","position":8,"is_corresponding":false},{"id":721619,"name":"David Nanchen","orcid":"0000-0002-2493-3505","position":9,"is_corresponding":false},{"id":481657,"name":"Nicolas Rodondi","orcid":"0000-0001-9083-6896","position":10,"is_corresponding":false},{"id":2652,"name":"Stephan Windecker","orcid":"0000-0003-2653-6762","position":11,"is_corresponding":false},{"id":258673,"name":"Reijo Laaksonen","orcid":"0000-0001-9888-4278","position":12,"is_corresponding":false},{"id":108895,"name":"Stanley L. 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Matter","orcid":"0000-0002-8124-1767","position":18,"is_corresponding":false},{"id":721616,"name":"Sebastian Weichwald","orcid":"0000-0003-0169-7244","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-18T23:47:32.096934Z","pmid":"34015112","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}