ArticleRenal failure2021
Derivation and validation of a model to predict acute kidney injury following cardiac surgery in patients with normal renal function.
Article in Renal failure, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 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
- Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis.Frontiers in cardiovascular medicine · 2022Pooled it
- Development and validation of a prediction model for acute kidney injury following cardiac valve surgery.Frontiers in medicine · 2025Article
- Development and Internal Validation of a Nomogram for Predicting Acute Kidney Injury After Cardiac Valve Surgery Using the Serum Uric Acid-to-Albumin Ratio.Journal of inflammation research · 2025Article
- Risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal function.Frontiers in cardiovascular medicine · 2025Article
- Artificial intelligence: a new field of knowledge for nephrologists?Clinical kidney journal · 2023Article
- Article
- Combined serum free light chain predicts prognosis in acute kidney injury following cardiovascular surgery.Renal failure · 2022Article
- A new model to predict acute kidney injury after cardiac surgery in patients with renal insufficiency.Renal failure · 2022Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe study aimed to construct a clinical model based on preoperative data for predicting acute kidney injury (AKI) following cardiac surgery in patients with normal renal function.
methodsA total of 22,348 consecutive patients with normal renal function undergoing cardiac surgery were enrolled. Among them, 15,701 were randomly selected for the training group and the remaining for the validation group. To develop a model visualized as a nomogram for predicting AKI, logistic regression was performed with variables selected using least absolute shrinkage and selection operator regression. The discrimination, calibration, and clinical value of the model were evaluated.
resultsThe incidence of AKI was 25.2% in the training group. The new model consisted of nine preoperative variables, including age, male gender, left ventricular ejection fraction, hypertension, hemoglobin, uric acid, hypomagnesemia, and oral renin-angiotensin system inhibitor and non-steroidal anti-inflammatory drug within 1 week before surgery. The model had a good performance in the validation group. The discrimination was good with an area under the receiver operating characteristic curve of 0.740 (95% confidence interval, 0.726-0.753). The calibration plot indicated excellent agreement between the model prediction and actual observations. Decision curve analysis also showed that the model was clinically useful.
conclusionsThe new model was constructed based on nine easily available preoperative clinical data characteristics for predicting AKI following cardiac surgery in patients with normal kidney function, which may help treatment decision-making, and rational utilization of medical resources.
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