ArticlePloS one2019
Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 4 of them syntheses that pooled it.
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Who cites it
51 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review.Hernia : the journal of hernias and abdominal wall surgery · 2024Pooled it
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.BMC nephrology · 2022Pooled it
- Early Nephrology Consultation and Acute Kidney Injury in Hospitalized Patients: A Randomized Clinical Trial.JAMA network open · 2026Trial
- Identification of acute kidney injury after non-cardiac surgery by machine learning leveraging a large electronic health record database.Scientific reports · 2026Article
- Machine learning in early prediction of acute kidney injury: model reliability, clinical applicability, and generalizability.International urology and nephrology · 2026Article
- Integration of intraoperative data in interpretable machine learning models to predict postoperative AKI in noncardiac surgery patients.JAMIA open · 2026Article
- Dynamic machine learning prediction of persistent AKI after cardiac surgery with modifiable perioperative risk factors.iScience · 2026Article
- Explainable machine learning models for early prediction of acute kidney injury after cardiac surgery.International urology and nephrology · 2026Article
- Explainable machine learning for postoperative respiratory failure prediction in open-heart surgery patients - a study based on the MIMIC-IV database.BMC medical informatics and decision making · 2026Article
- Article
- Perioperative Artificial Intelligence Driven Integrated Modeling of Surgeries using Anesthetic, Physical and Cognitive Statuses for Predicting Hospital Outcomes.Research square · 2025Article
- Prediction of acute kidney injury after noncardiac surgery in intensive care unit by machine learning.Annals of medicine and surgery (2012) · 2025Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Federated Learning for Predicting Major Postoperative Complications.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025Article
- A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study.PLoS medicine · 2025Observational
- Incidence and risk factors of acute kidney injury in redo cardiac surgery: a single center analysis.Scientific reports · 2024Article
- 10 tips on how to use dynamic risk assessment and alerts for AKI.Clinical kidney journal · 2024Review
- A time series algorithm to predict surgery in neonatal necrotizing enterocolitis.BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundAcute kidney injury (AKI) is a common complication after surgery that is associated with increased morbidity and mortality. The majority of existing perioperative AKI risk prediction models are limited in their generalizability and do not fully utilize intraoperative physiological time-series data. Thus, there is a need for intelligent, accurate, and robust systems to leverage new information as it becomes available to predict the risk of developing postoperative AKI.
methodsA retrospective single-center cohort of 2,911 adults who underwent surgery at the University of Florida Health between 2000 and 2010 was utilized for this study. Machine learning and statistical analysis techniques were used to develop perioperative models to predict the risk of developing AKI during the first three days after surgery, first seven days after surgery, and overall (after surgery during the index hospitalization). The improvement in risk prediction was examined by incorporating intraoperative physiological time-series variables. Our proposed model enriched a preoperative model that produced a probabilistic AKI risk score by integrating intraoperative statistical features through a machine learning stacking approach inside a random forest classifier. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and Net Reclassification Improvement (NRI).
resultsThe predictive performance of the proposed model is better than the preoperative data only model. The proposed model had an AUC of 0.86 (accuracy of 0.78) for the seven-day AKI outcome, while the preoperative model had an AUC of 0.84 (accuracy of 0.76). Furthermore, by integrating intraoperative features, the algorithm was able to reclassify 40% of the false negative patients from the preoperative model. The NRI for each outcome was AKI at three days (8%), seven days (7%), and overall (4%).
conclusionsPostoperative AKI prediction was improved with high sensitivity and specificity through a machine learning approach that dynamically incorporated intraoperative data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.