Impact of mean perfusion pressure and vasoactive drugs on occurrence and reversal of cardiac surgery-associate acute kidney injury: A cohort study

Acute kidney injury after cardiac surgery

PURPOSE OF REVIEW

Patients undergoing cardiac surgery are at high risk to develop cardiac surgery-associated acute kidney injury (CS-AKI) postoperatively. CS-AKI is associated with an increased risk for persistent renal dysfunction, morbidity and mortality. This review summarizes the epidemiology and pathophysiology of CS-AKI, as well as current treatment and prevention strategies.

RECENT FINDINGS

As AKI is a syndrome with complex pathophysiology, no causative treatment strategies exist. Recent advances in the field of AKI biomarkers offer new perspectives on the issue and the implementation of biomarker-guided preventive strategies may reduce rates of CS-AKI. Finally, nephroprotective treatments and angiotensin II as a novel vasopressor may offer new opportunities for high-risk patients undergoing cardiac surgery.

SUMMARY

Based on the described novel approaches for early detection, prevention and management of CS-AKI, a precision-medicine approach should be implemented in order to prevent the development of AKI in patients undergoing cardiac surgery.

Gender differences and risk factors for acute kidney injury following cardiac surgery: A single center retrospective cohort study

Background

We studied AKI incidence and prognosis in cardiac surgery patients under and over 60 years old.

Methods

We studied AKI in patients who underwent cardiac surgery at the First Affiliated Hospital of Wenzhou Medical University between Jan 2020 and Dec 2021, using improved global prognostic criteria for diagnosis.

Results

After analyzing 781 patients (402 males, 379 females), AKI incidence after surgery was 30.22 %. Adjusting for propensity scores revealed no significant difference in AKI incidence between young males (24.1 %) and females (19.3 %). However, young females had higher AKI stages. Among older patients, AKI incidence was comparable between males (43.4 %) and females (42.2 %), but females had longer intubation times. Independent risk factors for AKI included age, male gender, and BMI, while intraoperative hemoglobin level was protective.

Conclusions

No gender gap in AKI frequency for <60 years old and ≥60 years old post-cardiac surgery, yet women display increased AKI severity and extended intubation duration.

Incorporating intraoperative blood pressure time-series variables to assist in prediction of acute kidney injury after type a acute aortic dissection repair: an interpretable machine learning model

BACKGROUND

Acute kidney injury (AKI) is a common and serious complication after the repair of Type A acute aortic dissection (TA-AAD). However, previous models have failed to account for the impact of blood pressure fluctuations on predictive performance. This study aims to develop machine learning (ML) models combined with intraoperative medicine and blood pressure time-series data to improve the accuracy of early prediction for postoperative AKI risk.

METHODS

Indicators reflecting the duration and depth of hypotension were obtained by analyzing continuous mean arterial pressure (MAP) monitored intraoperatively with multiple thresholds (<65, 60, 55, 50) set in the study. The predictive features were selected by logistic regression and the least absolute shrinkage and selection operator (LASSO), and 4 ML models were built based on the above features. The performance of the models was evaluated by area under receiver operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA). Shapley additive interpretation (SHAP) was used to explain the prediction models.

RESULTS

Among the indicators reflecting intraoperative hypotension, 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI (p < .001). Among 4 models, the extreme gradient boosting (XGBoost) model demonstrated the highest AUROC: 0.800 (95% 0.683-0.917) and sensitivity: 0.717 in the testing set and was verified the best-performing model. The SHAP summary plot indicated that intraoperative urine output, cumulative time of mean arterial pressure lower than 65 mmHg outside cardiopulmonary bypass (OUT_CPB_MAP_65 time), autologous blood transfusion, and smoking were the top 4 features that contributed to the prediction model.

CONCLUSION

With the introduction of intraoperative blood pressure time-series variables, we have developed an interpretable XGBoost model that successfully achieve high accuracy in predicting the risk of AKI after TA-AAD repair, which might aid in the perioperative management of high-risk patients, particularly for intraoperative hemodynamic regulation.