ArticleJournal of translational medicine2024
AKI-Pro score for predicting progression to severe acute kidney injury or death in patients with early acute kidney injury after cardiac surgery.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Early risk stratification of acute kidney injury severity in patients with acute myocardial infarction and diabetes using machine learning.Scientific reports · 2026Article
- Association Between the Modified Furosemide Response Index and Prognosis in Patients with Chronic Kidney Disease: A Retrospective Cohort Study Based on the MIMIC-IV Database.International journal of nephrology and renovascular disease · 2026Article
- Prediction of intraoperative acquired pressure injury in elderly cardiac surgery patients via machine learning: model development with the MIMIC-IV and external validation.BMC medical informatics and decision making · 2025Article
- Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study.Journal of medical Internet research · 2025Article
- Acute kidney injury: pathogenesis and therapeutic interventions.Molecular biomedicine · 2025Review
- Relationship between intra-bladder pressure and acute kidney injury in patients with acute pancreatitis: interpretable machine learning approach.BMC nephrology · 2025Article
- Predictability of kinetic estimated glomerular filtration rate for acute kidney injury in patients undergoing coronary artery bypass surgery.BMC nephrology · 2025Article
- Development and validation of a prediction model for acute kidney injury following cardiac valve surgery.Frontiers in medicine · 2025Article
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Authors and funding
13 authors.
Funding
Abstract
backgroundNo reliable clinical tools exist to predict acute kidney injury (AKI) progression. We aim to explore a scoring system for predicting the composite outcome of progression to severe AKI or death within seven days among early AKI patients after cardiac surgery.
methodsIn this study, we used two independent cohorts, and patients who experienced mild/moderate AKI within 48 h after cardiac surgery were enrolled. Eventually, 3188 patients from the MIMIC-IV database were used as the derivation cohort, while 499 patients from the Zhongshan cohort were used as external validation. The primary outcome was defined by the composite outcome of progression to severe AKI or death within seven days after enrollment. The variables identified by LASSO regression analysis were entered into logistic regression models and were used to construct the risk score.
resultsThe composite outcome accounted for 3.7% (n = 119) and 7.6% (n = 38) of the derivation and validation cohorts, respectively. Six predictors were assembled into a risk score (AKI-Pro score), including female, baseline eGFR, aortic surgery, modified furosemide responsiveness index (mFRI), SOFA, and AKI stage. And we stratified the risk score into four groups: low, moderate, high, and very high risk. The risk score displayed satisfied predictive discrimination and calibration in the derivation and validation cohort. The AKI-Pro score discriminated the composite outcome better than CRATE score, Cleveland score, AKICS score, Simplified renal index, and SRI risk score (all P < 0.05).
conclusionsThe AKI-Pro score is a new clinical tool that could assist clinicians to identify early AKI patients at high risk for AKI progression or death.
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