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Updated: Aug 5, 2025
A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
Published on: February 2, 2021
Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular
Yun Yan1, Hairong Gong1, Jie Hu2
1Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.
Insights
New models predict acute kidney injury (AKI) after valvular cardiac surgery in Chinese patients. The lasso logistics regression (LLR) model showed the best performance for predicting AKI risk.
Area of Science:
- Nephrology
- Cardiology
- Data Science
Background:
- Acute kidney injury (AKI) is a significant complication following cardiac surgery, linked to increased morbidity and mortality.
- Existing risk prediction tools demonstrate limitations, particularly within the Chinese population.
- Valvular cardiac surgery poses a specific risk for AKI development.
Purpose of the Study:
- To develop and validate novel prediction models for AKI after valvular cardiac surgery.
- To specifically address the performance limitations of current tools in the Chinese demographic.
- To identify key perioperative variables for accurate AKI risk assessment.
Main Methods:
- Development of three predictive models (LLR, RF, XGBoost) using a retrospective cohort of 3,392 patients.
- Models utilized patient characteristics and perioperative variables for predicting all-stage and moderate-to-severe AKI (KDIGO criteria).
- Internal validation compared model accuracy against each other and the established AKICS score.
Main Results:
- AKI developed in 50.5% of patients undergoing valve surgery.
- The LLR model achieved the highest discrimination (C-statistic: 0.7) and superior calibration compared to RF and XGBoost models.
- All developed models outperformed the reference AKICS score in predictive accuracy.
Conclusions:
- Novel prediction models for AKI in Chinese patients undergoing CPB-assisted valvular cardiac surgery were successfully developed.
- The LLR model demonstrated the best predictive performance and is recommended for clinical use.
- These models offer improved risk stratification for AKI in this specific patient population.
Background:
Acute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after valvular cardiac surgery in the Chinese population.
Methods:
Models were developed from a retrospective cohort of patients undergoing valve surgery from December 2013 to November 2018. Three models were developed to predict all-stage, or moderate to severe AKI, as diagnosed according to Kidney Disease: Improving Global Outcomes (KDIGO) based on patient characteristics and perioperative variables. Models were developed based on lasso logistics regression (LLR), random forest (RF), and extreme gradient boosting (XGboost). The accuracy was compared among three models and against the previously published reference AKICS score.
Results:
A total of 3,392 patients (mean [SD] age, 50.1 [11.3] years; 1787 [52.7%] male) were identified during the study period. The development of AKI was recorded in 50.5% of patients undergoing valve surgery. In the internal validation testing set, the LLR model marginally improved discrimination (C statistic, 0.7; 95% CI, 0.66-0.73) compared with two machine learning models, RF (C statistic, 0.69; 95% CI, 0.65-0.72) and XGBoost (C statistic, 0.66; 95% CI, 0.63-0.70). A better calibration was also found in the LLR, with a greater net benefit, especially for the higher probabilities as indicated in the decision curve analysis. All three newly developed models outperformed the reference AKICS score.
Conclusion:
Among the Chinese population undergoing CPB-assisted valvular cardiac surgery, prediction models based on perioperative variables were developed. The LLR model demonstrated the best predictive performance was selected for predicting all-stage AKI after surgery.
Clinical Trial Registration:
Trial registration: Clinicaltrials.gov, NCT04237636.
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