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Preoperative predictive model for acute kidney injury after elective cardiac surgery: a prospective multicenter
Raquel Callejas1, Alfredo Panadero2, Marc Vives3
1Department of Anesthesia and Critical Care, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain - rcallejas@unav.es.
Insights
Existing models for predicting cardiac surgery-associated acute kidney injury (CS-AKI) perform poorly in elective cases. A new, simpler predictive model was developed, offering improved risk assessment for patients undergoing elective cardiac surgery.
Area of Science:
- Nephrology
- Cardiology
- Perioperative Medicine
Background:
- Predictive models for cardiac surgery-associated acute kidney injury (CS-AKI) often focus on emergency settings.
- Validated models like Thakar's and Demirjian's have shown suboptimal performance in elective cardiac surgery.
- There is a need for improved risk stratification tools in elective cardiac surgery patients.
Purpose of the Study:
- To evaluate the performance of existing predictive models (Thakar and Demirjian) for CS-AKI in elective cardiac surgery.
- To develop and validate a new, more accurate predictive model for CS-AKI in this specific patient population.
Main Methods:
- A prospective, multicenter, observational study included 942 patients undergoing elective cardiac surgery.
- Patients requiring emergency surgery or intra-aortic balloon pump support were excluded.
- CS-AKI was defined as requiring dialysis or a doubling of baseline creatinine levels.
Main Results:
- Both Thakar's (AUC=0.57) and Demirjian's (AUC=0.64) models demonstrated poor discrimination in elective surgery.
- A new model incorporating anemia, age, hypertension, obesity, heart failure, prior surgery, and surgery type was developed.
- The new model achieved acceptable discrimination (AUC=0.72) and significantly outperformed existing models (P<0.01).
Conclusions:
- A novel, simple predictive model for CS-AKI in elective cardiac surgery was developed using preoperative data.
- This new model offers improved discriminatory ability compared to previous tools.
- The model can aid in risk communication and perioperative decision-making, pending external validation.
Background:
Predictive models of acute kidney injury after cardiac surgery (CS-AKI) include emergency surgery and patients with hemodynamic instability. Our objective was to evaluate the performance of validated predictive models (Thakar and Demirjian) in elective cardiac surgery and to propose a better score in the case of poor performance.
Methods:
A prospective, multicenter, observational study was designed. Data were collected from 942 patients undergoing cardiac surgery, after excluding emergency surgery and patients with an intra-aortic balloon pump. The main outcome measure was CS-AKI defined by the composite of requiring dialysis or doubling baseline creatinine values.
Results:
Both models showed poor discrimination in elective surgery (Thakar's model, AUC=0.57, 95% CI: 0.50-0.64 and Demirjian's model, AUC=0.64, 95% CI: 0.58-0.71). We generated a new model whose significant independent predictors were: anemia, age, hypertension, obesity, congestive heart failure, previous cardiac surgery and type of surgery. It classifies patients with scores 0-3 as at low risk (<5%), patients with scores 4-7 as at medium risk (up to 15%), and patients with scores >8 as at high risk (>30%) of developing CS-AKI with a statistically significant correlation (P<0.001). Our model reflects acceptable discriminatory ability (AUC=0.72, 95% CI: 0.66-0.78) which is significantly better than Thakar and Demirjian's models (P<0.01).
Conclusions:
We developed a new simple predictive model of CS-AKI in elective surgery based on available preoperative information. Our new model is easy to calculate and can be an effective tool for communicating risk to patients and guiding decision-making in the perioperative period. The study requires external validation.
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