{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["6(2)"],"submitter":["Weizman O"],"pubmed_abstract":["Aims
Although some scores based on traditional statistical methods are available for risk stratification in patients hospitalized in cardiac intensive care units (CICUs), the interest of machine learning (ML) methods for risk stratification in this field is not well established. We aimed to build an ML model to predict in-hospital major adverse events (MAE) in patients hospitalized in CICU.Methods and results
In April 2021, a French national prospective multicentre study involving 39 centres included all consecutive patients admitted to CICU. The primary outcome was in-hospital MAE, including death, resuscitated cardiac arrest, or cardiogenic shock. Using 31 randomly assigned centres as an index cohort (divided into training and testing sets), several ML models were evaluat"],"journal":["European heart journal. Digital health"],"pagination":["218-227"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11914730"],"repository":["biostudies-literature"],"pubmed_title":["Machine learning score to predict in-hospital outcomes in patients hospitalized in cardiac intensive care unit."],"pmcid":["PMC11914730"],"pubmed_authors":["Andrieu S","Chaussade AS","Delsarte L","Zakine C","Landemaine T","Mansencal N","Darmon A","Fabre J","Maitre Ballesteros L","Hauguel-Moreau M","Pezel T","Ohlmann P","El Ouahidi A","Ezzouhairi N","Rossanaly Vasram R","Swedsky F","Trimaille A","Dib JC","Roubille F","Boukertouta T","Sulman D","Weizman O","De Angelis E","Delmas C","Piliero N","Puymirat E","Azzakani S","Pasdeloup B","Thevenet E","Tron C","Schurtz G","Bouali N","Moine T","Toupin S","Chaib A","Levasseur T","Bouchot O","Martinez D","Grentzinger A","Huet F","Lim P","Marie B","Ramonatxo A","Boccara F","Merat B","Lemarchand L","Picard F","Goncalves T","Gerbaud E","Lattuca B","Noirclerc N","Brette JB","Stevenard M","Bochaton T","Elbaz M","Auvray S","Fard D","Boccara A","Amri N","El Hadad A","ADDICT-ICCU Investigators","Goralski M","Bonnet G","Coppens A","Yomi D","Azencot R","Dupasquier V","Bedossa M","Charbonnel C","Cottin Y","Fauvel C","Attou S","Vicaut E","Pommier T","Bouleti C","Docq C","Tea V","Meune C","Millischer D","Roule V","Canu M","Gilard M","Hamzi K","Bonnefoy-Cudraz E","Dillinger JG","Henry P","Thuaire C","Grinberg N","Nhan P","Viboud G","Albert E","Albert F","El Ouahidi Y","Aboyans V","Deney A","Alvain S"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning score to predict in-hospital outcomes in patients hospitalized in cardiac intensive care unit.","description":"Aims
Although some scores based on traditional statistical methods are available for risk stratification in patients hospitalized in cardiac intensive care units (CICUs), the interest of machine learning (ML) methods for risk stratification in this field is not well established. We aimed to build an ML model to predict in-hospital major adverse events (MAE) in patients hospitalized in CICU.Methods and results
In April 2021, a French national prospective multicentre study involving 39 centres included all consecutive patients admitted to CICU. The primary outcome was in-hospital MAE, including death, resuscitated cardiac arrest, or cardiogenic shock. Using 31 randomly assigned centres as an index cohort (divided into training and testing sets), several ML models were evaluat","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2026-06-01T06:04:37.471Z","creation":"2025-04-03T23:24:06.378Z"},"accession":"S-EPMC11914730","cross_references":{"pubmed":["40110223"],"doi":["10.1093/ehjdh/ztae098"]}}