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A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
Published on: February 2, 2021
Development and validation of a triglyceride-glucose integrated nomogram for acute kidney injury prediction in acute
Xinling Zhang1, Ranran Ding2, Guangming Yang2
1Department of Healthcare Ward, Jining No. 1 People's Hospital, Jining, Shandong, China.
Insights
A new model accurately predicts acute kidney injury (AKI) in acute myocardial infarction (AMI) patients using the triglyceride-glucose (TyG) index and other clinical factors. This tool aids early risk stratification and preventive strategies in intensive care units.
Area of Science:
- Critical Care Medicine
- Nephrology
- Cardiology
Background:
- Acute kidney injury (AKI) is a significant complication in acute myocardial infarction (AMI) patients, increasing mortality.
- Early identification of high-risk individuals for AKI post-AMI is a persistent clinical challenge.
Purpose of the Study:
- To develop and validate a predictive model for AKI in AMI patients.
- To incorporate the triglyceride-glucose (TyG) index into a nomogram for risk assessment.
Main Methods:
- Utilized data from MIMIC-IV and eICU critical care databases.
- Applied LASSO and multivariable logistic regression to identify predictors.
- Constructed a nomogram integrating the TyG index and clinical variables.
Main Results:
- The final model included TyG index, BUN, SOFA score, age, serum sodium, serum albumin, and SBP.
- Achieved excellent discrimination with AUCs of 0.85 (training), 0.83 (internal), and 0.81 (external).
- The TyG index was independently associated with increased AKI risk (OR 1.31).
Conclusions:
- A TyG index-based nomogram offers a practical tool for early AKI prediction in AMI patients.
- The model enables multidimensional risk stratification by integrating metabolic, hemodynamic, and organ dysfunction markers.
- This tool may facilitate timely preventive interventions in the ICU.
Background:
Acute kidney injury (AKI) is a life-threatening complication in patients with acute myocardial infarction (AMI), leading to increased morbidity and mortality. Early prediction of high-risk patients remains a clinical challenge.
Methods:
We developed and validated a predictive model for AKI using data from two large critical care databases: MIMIC-IV (n = 1,227) and eICU (n = 1,954). Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression were applied to identify independent predictors. A nomogram was constructed incorporating the triglyceride-glucose (TyG) index and clinical variables.
Results:
Seven predictors were included in the final model: TyG index, blood urea nitrogen (BUN), SOFA score, age, serum sodium, serum albumin and systolic blood pressure (SBP). The model demonstrated excellent discrimination with area under the curve (AUC) values of 0.85 in the training cohort, 0.83 in the internal validation cohort and 0.81 in the external validation cohort. Decision curve analysis showed clinical usefulness across a wide range of risk thresholds (22%-45%). The TyG index was independently associated with increased AKI risk (odds ratio 1.31; 95% CI: 1.07-1.60). The model also showed improved risk reclassification (net reclassification index: 0.22; p < 0.001).
Conclusion:
The TyG-based nomogram provides a practical and accurate tool for early prediction of AKI in AMI patients. By integrating metabolic, hemodynamic, and organ dysfunction markers, this model enables multidimensional risk stratification and may support timely preventive strategies in the ICU setting.
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