Intraoperative venous congestion and acute kidney injury in cardiac surgery: an observational cohort study

Lopez, Marcos G.; Shotwell, Matthew S.; Morse, Jennifer; Liang, Yafen; Wanderer, Jonathan P.; Absi, Tarek; Balsara, Keki R.; Levack, Melissa M.; Shah, Ashish S.; Hernandez, Antonio; Billings, Frederic T.

doi:10.1016/j.bja.2020.12.028

Cited by 40 publications

(31 citation statements)

References 39 publications

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“…This concept has been challenged by accumulating evidence that elevated CVP is a more powerful hemodynamic determinant than mean perfusion pressure for the development of postoperative AKI [ 24 ]. More recently, Lopez et al [ 25 ] uncovered that higher levels of CVP during cardiac surgery were independently associated with higher odds of AKI. They also demonstrated that venous congestion is more accurate than hypotension in predicting AKI.…”

Section: Discussionmentioning

confidence: 99%

Prediction of acute kidney injury after cardiac surgery: model development using a Chinese electronic health record dataset

et al. 2022

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Background Acute kidney injury (AKI) is a major complication following cardiac surgery that substantially increases morbidity and mortality. Current diagnostic guidelines based on elevated serum creatinine and/or the presence of oliguria potentially delay its diagnosis. We presented a series of models for predicting AKI after cardiac surgery based on electronic health record data. Methods We enrolled 1457 adult patients who underwent cardiac surgery at Nanjing First Hospital from January 2017 to June 2019. 193 clinical features, including demographic characteristics, comorbidities and hospital evaluation, laboratory test, medication, and surgical information, were available for each patient. The number of important variables was determined using the sliding windows sequential forward feature selection technique (SWSFS). The following model development methods were introduced: extreme gradient boosting (XGBoost), random forest (RF), deep forest (DF), and logistic regression. Model performance was accessed using the area under the receiver operating characteristic curve (AUROC). We additionally applied SHapley Additive exPlanation (SHAP) values to explain the RF model. AKI was defined according to Kidney Disease Improving Global Outcomes guidelines. Results In the discovery set, SWSFS identified 16 important variables. The top 5 variables in the RF importance matrix plot were central venous pressure, intraoperative urine output, hemoglobin, serum potassium, and lactic dehydrogenase. In the validation set, the DF model exhibited the highest AUROC (0.881, 95% confidence interval [CI] 0.831–0.930), followed by RF (0.872, 95% CI 0.820–0.923) and XGBoost (0.857, 95% CI 0.802–0.912). A nomogram model was constructed based on intraoperative longitudinal features, achieving an AUROC of 0.824 (95% CI 0.763–0.885) in the validation set. The SHAP values successfully illustrated the positive or negative contribution of the 16 variables attributed to the output of the RF model and the individual variable’s effect on model prediction. Conclusions Our study identified 16 important predictors and provided a series of prediction models to enhance risk stratification of AKI after cardiac surgery. These novel predictors might aid in choosing proper preventive and therapeutic strategies in the perioperative management of AKI patients.

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Section: Discussionmentioning

confidence: 99%

Prediction of acute kidney injury after cardiac surgery: model development using a Chinese electronic health record dataset

et al. 2022

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“…A study on patients with a broad spectrum of cardiovascular diseases, including coronary artery, valves and heart rhythm dysfunctions, suggested that central venous pressure and renal function interact with each other, and increased central venous pressure was independently associated with all-cause mortality (23). The study by Lopez et al on patients receiving on-pump cardiac surgery found that a 60 mmHg min increase above the median venous congestion AUC during the surgery was associated with increased rates of postoperative acute kidney injury (24). Potential mechanisms involved include increased intravascular volume, vascular resistance, and intrathoracic pressure (25).…”

Section: Discussionmentioning

confidence: 99%

A Prediction Model for Acute Kidney Injury After Pericardiectomy: An Observational Study

Wang

Zhang

et al. 2022

Front. Cardiovasc. Med.

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ObjectivesAcute 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.MethodsWe 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.ResultsAmong 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).ConclusionThe prediction model may help with early recognition, management, and reduction of acute kidney injury after pericardiectomy.

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“…It has been shown that volume overload in cardiac surgery patients can lead to a more severe postoperative course, in terms of congestion and associated organ dysfunction, such as acute kidney injury, and thus, increased morbidity and mortality [29][30][31]. Moreover, PhA is a parameter which is dependent on the fluid balance; it is possible that it may also describe congestion in patients with heart failure or reduced left ventricular function [17,28].…”

Section: Discussionmentioning

confidence: 99%

Preoperative Phase Angle as a Risk Indicator in Cardiac Surgery—A Prospective Observational Study

et al. 2022

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Background: The phase angle (PhA) can be used for prognostic assessments in critically ill patients. This study describes the perioperative course of PhA and associated risk indicators in a cohort of elective cardiac surgical patients. Methods: The PhA was measured in 168 patients once daily until postoperative day (POD) seven. Patients were split into two groups depending on their median preoperative PhA and analyzed for several clinical outcomes; logistic regression models were used. Results: The PhA decreased from preoperative (6.1° ± 1.9°) to a nadir on POD 2 (3.5° ± 2.5°, mean difference −2.6° (95% CI, −3.0°; −2.1°; p < 0.0001)). Patients with lower preoperative PhA were older (71.0 ± 9.1 vs. 60.9 ± 12.0 years; p < 0.0001) and frailer (3.1 ± 1.3 vs. 2.3 ± 1.1; p < 0.0001), needed more fluids (8388 ± 3168 vs. 7417 ± 2459 mL, p = 0.0287), and stayed longer in the ICU (3.7 ± 4.5 vs. 2.6 ± 3.8 days, p = 0.0182). Preoperative PhA was independently influenced by frailty (OR 0.77; 95% CI 0.61; 0.98; p = 0.0344) and cardiac function (OR 1.85; 95%CI 1.07; 3.19; p = 0.028), whereas the postoperative PhA decline was independently influenced by higher fluid balances (OR 0.86; 95% CI 0.75; 0.99; p = 0.0371) and longer cardiopulmonary bypass times (OR 0.99; 95% CI 0.98; 0.99; p = 0.0344). Conclusion: Perioperative PhA measurement is an easy-to-use bedside method that may critically influence risk evaluation for the outcome of cardiac surgery patients.

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Intraoperative venous congestion and acute kidney injury in cardiac surgery: an observational cohort study

Cited by 40 publications

References 39 publications

Prediction of acute kidney injury after cardiac surgery: model development using a Chinese electronic health record dataset

Prediction of acute kidney injury after cardiac surgery: model development using a Chinese electronic health record dataset

A Prediction Model for Acute Kidney Injury After Pericardiectomy: An Observational Study

Preoperative Phase Angle as a Risk Indicator in Cardiac Surgery—A Prospective Observational Study

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