{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zamirpour S"],"funding":["Foundation for Anesthesia Education and Research"],"pagination":["932"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10451203"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["10(8)"],"pubmed_abstract":["Acute kidney injury (AKI) is a major postoperative complication that lacks established intraoperative predictors. Our objective was to develop a prediction model using preoperative and high-frequency intraoperative data for postoperative AKI. In this retrospective cohort study, we evaluated 77,428 operative cases at a single academic center between 2016 and 2022. A total of 11,212 cases with serum creatinine (sCr) data were included in the analysis. Then, 8519 cases were randomly assigned to the training set and the remainder to the validation set. Fourteen preoperative and twenty intraoperative variables were evaluated using elastic net followed by hierarchical group least absolute shrinkage and selection operator (LASSO) regression. The training set was 56% male and had a median [IQR] ag"],"journal":["Bioengineering (Basel, Switzerland)"],"pubmed_title":["Development of a Machine Learning Model of Postoperative Acute Kidney Injury Using Non-Invasive Time-Sensitive Intraoperative Predictors."],"pmcid":["PMC10451203"],"funding_grant_id":["Mentored Research Training Grant"],"pubmed_authors":["Hubbard AE","Feng J","Bishara A","Pirracchio R","Butte AJ","Zamirpour S"],"additional_accession":[]},"is_claimable":false,"name":"Development of a Machine Learning Model of Postoperative Acute Kidney Injury Using Non-Invasive Time-Sensitive Intraoperative Predictors.","description":"Acute kidney injury (AKI) is a major postoperative complication that lacks established intraoperative predictors. Our objective was to develop a prediction model using preoperative and high-frequency intraoperative data for postoperative AKI. In this retrospective cohort study, we evaluated 77,428 operative cases at a single academic center between 2016 and 2022. A total of 11,212 cases with serum creatinine (sCr) data were included in the analysis. Then, 8519 cases were randomly assigned to the training set and the remainder to the validation set. Fourteen preoperative and twenty intraoperative variables were evaluated using elastic net followed by hierarchical group least absolute shrinkage and selection operator (LASSO) regression. The training set was 56% male and had a median [IQR] ag","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Aug","modification":"2025-04-03T22:36:41.167Z","creation":"2024-11-13T07:49:20.962Z"},"accession":"S-EPMC10451203","cross_references":{"pubmed":["37627817"],"doi":["10.3390/bioengineering10080932"]}}