{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Fritz BA"],"funding":["NINR NIH HHS"],"pubmed_abstract":["Background
Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown.Methods
This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operati"],"journal":["medRxiv : the preprint server for health sciences"],"pagination":["2024.05.22.24307754"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11142290"],"repository":["biostudies-literature"],"pubmed_title":["Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial."],"pmcid":["PMC11142290"],"funding_grant_id":["R01 NR017916"],"pubmed_authors":["Kronzer A","Wildes TS","King CR","Abdelhack M","Mehta D","Abraham J","Tripathi S","Higo O","Avidan MS","Montes de Oca A","Abdallah AB","Budelier TP","Gregory SH","Kannampallil T","Sontha P","Chen Y","Helsten D","Fritz BA","Kerby P"],"additional_accession":[]},"is_claimable":false,"name":"Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial.","description":"Background
Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown.Methods
This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operati","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 May","modification":"2026-04-15T03:32:15.311Z","creation":"2026-04-15T03:15:37.381Z"},"accession":"S-EPMC11142290","cross_references":{"pubmed":["38826471"],"doi":["10.1101/2024.05.22.24307754"]}}