ArticleClinical cardiology2020
A novel machine learning algorithm, Bayesian networks model, to predict the high-risk patients with cardiac surgery-associated acute kidney injury.
Article in Clinical cardiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 5 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Machine learning for the prediction of acute kidney injury post cardiac surgery: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled 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
- Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis.Frontiers in cardiovascular medicine · 2022Pooled it
- Remote Ischemic Preconditioning to Prevent Acute Kidney Injury After Cardiac Surgery: A Meta-Analysis of Randomized Controlled Trials.Frontiers in cardiovascular medicine · 2021Pooled it
- Predicting In-Hospital Acute Kidney Injury after Cardiac Surgery Using Machine Learning.Journal of cardiothoracic and vascular anesthesia · 2026Observational
- Development and Validation of a Machine Learning Model for Predicting Serum Creatinine-Defined Acute Kidney Injury in Older Adults After Cardiac Surgery.Clinical interventions in aging · 2026Article
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Shaping the future of cardiac interventions and cardiac surgeries: The impact of virtual reality and artificial intelligence.Global cardiology science & practice · 2025Review
- A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Central Venous Pressure as a Predictor of Acute Kidney Injury in Cardiac Surgery: A Systematic Review of Observational Studies.Diagnostics (Basel, Switzerland) · 2025Review
- Development and validation of a predictive model for acute kidney injury in patients with ureterolithiasis.Renal failure · 2024Article
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- Interpretable machine learning models for early prediction of acute kidney injury after cardiac surgery.BMC nephrology · 2023Article
- Predictors on outcomes of cardiovascular disease of male patients in Malaysia using Bayesian network analysis.BMJ open · 2023Article
- Prediction of acute kidney injury following coronary artery bypass graft surgery in elderly Chinese population.Journal of cardiothoracic surgery · 2023Article
- Development and Validation of a Machine Learning Predictive Model for Cardiac Surgery-Associated Acute Kidney Injury.Journal of clinical medicine · 2023Article
- Machine Learning-Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study.Journal of medical Internet research · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury.Advances in kidney disease and health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundCardiac surgery-associated acute kidney injury (CSA-AKI) is a well-recognized complication with an ominous outcome. HYPOTHESIS: Bayesian networks (BNs) not only can reveal the complex interrelationships between predictors and CSA-AKI, but predict the individual risk of CSA-AKI occurrence.
methodsDuring 2013 and 2015, we recruited 5533 eligible participants who underwent cardiac surgery from a tertiary hospital in eastern China. Data on demographics, clinical and laboratory information were prospectively recorded in the electronic medical system and analyzed by gLASSO-logistic regression and BNs.
resultsThe incidences of CSA-AKI and severe CSA-AKI were 37.5% and 11.1%. BNs model revealed that gender, left ventricular ejection fractions (LVEF), serum creatinine (SCr), serum uric acid (SUA), platelet, and aortic cross-clamp time (ACCT) were found as the parent nodes of CSA-AKI, while ultrafiltration volume and postoperative central venous pressure (CVP) were connected with CSA-AKI as children nodes. In the severe CSA-AKI model, age, proteinuria, and SUA were directly linked to severe AKI; the new nodes of NYHA grade and direct bilirubin created relationships with severe AKI through was related to LVEF, surgery types, and SCr level. The internal AUCs for predicting CSA-AKI and severe AKI were 0.755 and 0.845, which remained 0.736 and 0.816 in the external validation. Given the known variables, the risk for CSA-AKI can be inferred at individual levels based on the established BNs model and prior information.
conclusionBNs model has a high accuracy, good interpretability, and strong generalizability in predicting CSA-AKI. It facilitates physicians to identify high-risk patients and implement protective strategies to improve the prognosis.
Indexed as
Identifiers
What OpenQuestion holds
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.