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A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
Published on: February 2, 2021
A Prediction Model for Acute Kidney Injury After Pericardiectomy: An Observational Study
Jin Wang1, Chunhua Yu1, Yuelun Zhang2
1Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Insights
Acute kidney injury (AKI) is common after pericardiectomy. A new prediction model identifies risk factors like age and BMI, aiding early detection and management of AKI post-surgery.
Area of Science:
- Cardiology
- Nephrology
- Surgical Outcomes
Background:
- Acute kidney injury (AKI) is a frequent and serious complication following pericardiectomy for constrictive pericarditis.
- AKI is associated with adverse patient outcomes and increased healthcare costs.
Purpose of the Study:
- To identify risk factors for postoperative AKI after pericardiectomy.
- To develop a predictive model for early recognition and management of AKI.
Main Methods:
- A cohort of 211 patients undergoing isolated pericardiectomy (2013-2021) was analyzed.
- AKI was diagnosed using KDIGO criteria; risk factors were assessed via multivariable regression.
- A predictive nomogram was constructed and validated.
Main Results:
- 45% of patients developed AKI, with higher rates in older patients with higher BMI, hypertension, and preoperative renal dysfunction.
- Independent predictors of AKI included advanced age, high BMI, atrial arrhythmia, renal dysfunction, high central venous pressure, and low cardiac index.
- The prediction model demonstrated good performance (AUC 0.78).
Conclusions:
- A validated prediction model can aid in the early identification of patients at high risk for AKI post-pericardiectomy.
- Early recognition facilitates timely management and may reduce the incidence and severity of AKI.
Objectives:
Acute kidney injury is a common complication after pericardiectomy for constrictive pericarditis, which predisposes patients to worse outcomes and high medical costs. We aimed to investigate potential risk factors and consequences and establish a prediction model.
Methods:
We selected patients with constrictive pericarditis receiving isolated pericardiectomy from January 2013 to January 2021. Patients receiving concomittant surgery or repeat percardiectomy, as well as end-stage of renal disease were excluded. Acute kidney injury was diagnosed and classified according to the KDIGO criteria. Clinical features were compared between patients with and without postoperative acute kidney injury. A prediction model was established based on multivariable regression analysis.
Results:
Among two hundred and eleven patients, ninety-five (45.0%) developed postoperative acute kidney injury, with fourty-three (45.3%), twenty-eight (29.5%), and twenty-four (25.3%) in mild, moderate and severe stages, respectively. Twenty-nine (13.7%) patients received hemofiltration. Nine (4.3%) patients died perioperatively and were all in the acute kidney injury (9.5%) group. Eleven (5.2%) patients were considered to have chronic renal dysfunction states at the 6-month postoperative follow-up, and eight (72.7%) of them experienced moderate to severe stages of postoperative acute kidney injury. Univariable analysis showed that patients with acute kidney injury were older (difference 8 years, P < 0.001); had higher body mass index (difference 1.68 kg·m-2, P = 0.002); rates of smoking (OR = 2, P = 0.020), hypertension (OR = 2.83, P = 0.004), and renal dysfunction (OR = 3.58, P = 0.002); higher central venous pressure (difference 3 cm H2O, P < 0.001); and lower cardiac index (difference -0.23 L·min-1·m-2, P < 0.001) than patients without acute kidney injury. Multivariable regression analysis showed that advanced age (OR 1.03, P = 0.003), high body mass index (OR 1.10, P = 0.024), preoperative atrial arrhythmia (OR 3.12, P = 0.041), renal dysfunction (OR 2.70 P = 0.043), high central venous pressure (OR 1.12, P = 0.002), and low cardiac index (OR 0.36, P = 0.009) were associated with a high risk of postoperative acute kidney injury. A nomogram was established based on the regression results. The model showed good model fitness (Hosmer-Lemeshow test P = 0.881), with an area under the curve value of 0.78 (95% CI: 0.71, 0.84, P < 0.001).
Conclusion:
The prediction model may help with early recognition, management, and reduction of acute kidney injury after pericardiectomy.
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