{"doi":"10.3390/bioengineering10080932","title":"Development of a Machine Learning Model of Postoperative Acute Kidney Injury Using Non-Invasive Time-Sensitive Intraoperative Predictors","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] age of 62 (51-72) and a 6% AKI rate. Retained model variables were preoperative sCr values, the number of minutes meeting cutoffs for urine output, heart rate, perfusion index intraoperatively, and the total estimated blood loss. The area under the receiver operator characteristic curve was 0.81 (95% CI, 0.77-0.85). At a score threshold of 0.767, specificity was 77% and sensitivity was 74%. A web application that calculates the model score is available online. Our findings demonstrate the utility of intraoperative time series data for prediction problems, including a new potential use of the perfusion index. Further research is needed to evaluate the model in clinical settings.","journal":"Bioengineering","year":2023,"id":5109,"datarank":0.38281354701134,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.07089731575936459,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.07089731575936459,"corpus_percentile":49.38974776240846,"corpus_rank":623,"citation_count":8,"citer_count":7,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5196,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-08-05","fair_score":36.25,"fair_percentile":17.963940193491645,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":51907,"name":"Alan E. Hubbard","orcid":null,"position":1,"is_corresponding":false},{"id":51908,"name":"Jean Feng","orcid":"0000-0003-2041-3104","position":2,"is_corresponding":false},{"id":51,"name":"Atul Janardhan Butte","orcid":"0000-0002-7433-2740","position":3,"is_corresponding":false},{"id":51909,"name":"Romain Pirracchio","orcid":"0000-0002-7007-4908","position":4,"is_corresponding":false},{"id":19283,"name":"Andrew Bishara","orcid":"0000-0002-5427-482X","position":5,"is_corresponding":false},{"id":51910,"name":"Alan Hubbard","orcid":"0000-0002-3769-0127","position":6,"is_corresponding":false},{"id":51906,"name":"Siavash Zamirpour","orcid":"0000-0002-3742-4320","position":0,"is_corresponding":true}],"reference_count":41,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":"37627817","pmcid":"PMC10451203","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":"gold","license":"cc-by","views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":52.5,"fair_a":55.0,"fair_i":12.5,"fair_r":25.0,"fair_zscore":-0.8106,"fair_rationale":{"fair_score":36.25,"has_llm":true,"dimensions":{"F":{"name":"Findable","score":52.5,"criteria":[{"key":"f_has_doi","label":"Has a persistent DOI","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"DOI present","rationale":null},{"key":"f_repository_presence","label":"Indexed in repositories / literature DBs","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"datacite=0, pmcid=True, pmid=True","rationale":null},{"key":"f_persistent_ids","label":"Resolvable scholarly identifiers 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repository (e.g., Zenodo, Figshare) with a persistent DOI.","Add structured machine-readable metadata (e.g., DataCite or schema.org) to the paper's landing page and supplementary files.","Use standardized biomedical ontologies (e.g., LOINC for lab tests, SNOMED CT for diagnoses) for all variables to improve interoperability.","Provide a detailed data dictionary and explicit instructions (e.g., README) for reproducing the model and figures."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v2","fulltext_source":"epmc_xml"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v2","fair_fulltext_source":"epmc_xml","fair_has_llm":true,"fair_computed_at":"2026-06-18T00:51:01.445246Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}