{"doi":"10.2217/bmm-2019-0584","title":"Application of A Machine Learning-Driven, Multibiomarker Panel for Prediction of Incident Cardiovascular Events in Patients with Suspected Myocardial Infarction","abstract":null,"journal":"Biomarkers in Medicine","year":2020,"id":610069,"datarank":0.29188652235829704,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.0,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1568356,"name":"Nils A Sörensen","orcid":null,"position":1,"is_corresponding":false},{"id":362178,"name":"Tanja Zeller","orcid":"0000-0003-3379-2641","position":2,"is_corresponding":false},{"id":1568357,"name":"Craig A Magaret","orcid":null,"position":3,"is_corresponding":false},{"id":1568358,"name":"Grady Barnes","orcid":null,"position":4,"is_corresponding":false},{"id":1568359,"name":"Rhonda F Rhyne","orcid":null,"position":5,"is_corresponding":false},{"id":1568360,"name":"Celine Peters","orcid":null,"position":6,"is_corresponding":false},{"id":1568361,"name":"Alina Goßling","orcid":null,"position":7,"is_corresponding":false},{"id":1568362,"name":"Tau S Hartikainen","orcid":null,"position":8,"is_corresponding":false},{"id":1568363,"name":"Paul M Haller","orcid":null,"position":9,"is_corresponding":false},{"id":1420370,"name":"Jonas Lehmacher","orcid":null,"position":10,"is_corresponding":false},{"id":1568365,"name":"Sarina Schäfer","orcid":null,"position":11,"is_corresponding":false},{"id":1568367,"name":"James L Januzzi","orcid":null,"position":12,"is_corresponding":false},{"id":781387,"name":"Dirk Westermann","orcid":"0000-0002-7542-1956","position":13,"is_corresponding":false},{"id":732597,"name":"Franz–Josef Neumann","orcid":"0000-0002-9478-2757","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Application of A Machine Learning-Driven, Multibiomarker Panel for Prediction of Incident Cardiovascular Events in Patients with Suspected Myocardial Infarction","abstract":"Background: In patients with suspected myocardial infarction (MI), we sought to validate a machine learning-driven, multibiomarker panel for prediction of incident major adverse cardiovascular events (MACE). Methodology & results: A previously described prognostic panel for MACE consisting of four biomarkers was measured in 748 patients with suspected MI. The investigated end point was incident MACE within 1 year. The prognostic value of a continuous score and an optimal cut-off was investigated. The area under the curve was 0.86 for the overall model. Using the optimal cut-off resulted in a negative predictive value of 99.4% for incident MACE. Patients with an elevated prognostic score were at high risk for MACE. Conclusion: Among patients with suspected MI, we validated a multibiomarker panel for predicting 1-year MACE. Clinical Trial Registration: NCT02355457 (ClinicalTrials.gov)","is_dataset_classified":null,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"32462911","pmcid":null,"openalex_id":"https://openalex.org/W3029853307","authors":[],"funders":[{"funder_name":"Prevencio and Abbott Diagnostics","grant_id":"BACC","title":null},{"funder_name":"Else Kröner Fresenius Stiftung and the DZHK","grant_id":"NE 2165/1-1","title":null},{"funder_name":"German Heart Foundation/German Foundation of Heart Research","grant_id":"","title":null}],"total_grants":3,"fwci":0.6882,"citation_percentile":0.71684605,"influential_citations":0,"citation_trend":[{"year":2021,"count":1},{"year":2022,"count":1},{"year":2023,"count":3}],"oa_status":"closed","license":null,"oa_locations":[{"url":"https://www.tandfonline.com/doi/pdf/10.2217/bmm-2019-0584","host_type":"publisher"},{"url":"https://doi.org/10.2217/bmm-2019-0584","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/32462911","host_type":"repository"}],"fields_of_study":["Acute Myocardial Infarction Research","Cardiac Imaging and Diagnostics","ECG Monitoring and Analysis"],"mesh_terms":["Machine Learning","Aged","Cardiovascular Diseases","Female","Humans","Male","Middle Aged","Myocardial Infarction","Predictive Value of Tests","Prognosis","Biomarkers","Risk Assessment"],"keywords":["Medicine","Myocardial infarction","Internal medicine","Cardiology","Secondary prevention","Prediction","Artificial intelligence","Biomarkers","ACS","Outcome","Machine Learning","Major Adverse Cardiac Events","Noninvasive Risk Assessment"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"nct"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-31T19:04:39.901649Z","pmid":null,"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":[]}