Effect of different surgical type of coronary artery bypass grafting on kidney injury: A propensity score analysis

Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury

Background

The cardiac surgery-associated acute kidney injury (CSA-AKI) occurs in up to 1 out of 3 patients. Off-pump coronary artery bypass grafting (OPCABG) is one of the major cardiac surgeries leading to CSA-AKI. Early identification and timely intervention are of clinical significance for CSA-AKI. In this study, we aimed to establish a prediction model of off-pump coronary artery bypass grafting-associated acute kidney injury (OPCABG-AKI) after surgery based on machine learning methods.

Methods

The preoperative and intraoperative data of 1,041 patients who underwent OPCABG in Chest Hospital, Tianjin University from June 1, 2021 to April 30, 2023 were retrospectively collected. The definition of OPCABG-AKI was based on the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria. The baseline data and intraoperative time series data were included in the dataset, which were preprocessed separately. A total of eight machine learning models were constructed based on the baseline data: logistic regression (LR), gradient-boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT). The intraoperative time series data were extracted using a long short-term memory (LSTM) deep learning model. The baseline data and intraoperative features were then integrated through transfer learning and fused into each of the eight machine learning models for training. Based on the calculation of accuracy and area under the curve (AUC) of the prediction model, the best model was selected to establish the final OPCABG-AKI risk prediction model. The importance of features was calculated and ranked by DT model, to identify the main risk factors.

Results

Among 701 patients included in the study, 73 patients (10.4%) developed OPCABG-AKI. The GBDT model was shown to have the best predictions, both based on baseline data only (AUC =0.739, accuracy: 0.943) as well as based on baseline and intraoperative datasets (AUC =0.861, accuracy: 0.936). The ranking of importance of features of the GBDT model showed that use of insulin aspart was the most important predictor of OPCABG-AKI, followed by use of acarbose, spironolactone, alfentanil, dezocine, levosimendan, clindamycin, history of myocardial infarction, and gender.

Conclusions

A GBDT-based model showed excellent performance for the prediction of OPCABG-AKI. The fusion of preoperative and intraoperative data can improve the accuracy of predicting OPCABG-AKI.

Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study

BACKGROUND

Acute kidney injury (AKI) after coronary artery bypass grafting (CABG) surgery is associated with poor outcomes. The objective of this study was to apply a new machine learning (ML) method to establish prediction models of AKI after CABG.

METHODS

A total of 2,780 patients from two medical centers in East China who underwent primary isolated CABG were enrolled. The dataset was randomly divided for model training (80%) and model testing (20%). Four ML models based on LightGBM, Support vector machine (SVM), Softmax and random forest (RF) algorithms respectively were established in Python. A total of 2,051 patients from two other medical centers were assigned to an external validation group to verify the performances of the ML prediction models. The models were evaluated using the area under the receiver operating characteristics curve (AUC), Hosmer-Lemeshow goodness-of-fit statistic, Bland-Altman plots, and decision curve analysis. The outcome of the LightGBM model was interpreted using SHapley Additive exPlanations (SHAP).

RESULTS

The incidence of postoperative AKI in the modeling group was 13.4%. Similarly, the incidence of postoperative AKI of the two medical centers in the external validation group was 8.2% and 13.6% respectively. LightGBM performed the best in predicting, with an AUC of 0.8027 in internal validation group and 0.8798 and 0.7801 in the external validation group. The SHAP revealed the top 20 predictors of postoperative AKI ranked according to the importance, and the top three features on prediction were the serum creatinine in the first 24 h after operation, the last preoperative Scr level, and body surface area.

CONCLUSION

This study provides a LightGBM predictive model that can make accurate predictions for AKI after CABG surgery. The LightGBM model shows good predictive ability in both internal and external validation. It can help cardiac surgeons identify high-risk patients who may experience AKI after CABG surgery.

The outcomes of three different techniques of coronary artery bypass grafting: On-pump arrested heart, on-pump beating heart, and off-pump

OBJECTIVE

Conventional coronary artery bypass grafting (CABG) or on-pump arrested heart CABG (ONCAB) is a standard and simple technique. However, adverse effects can occur due to the use of aortic cross-clamp and cardiopulmonary bypass. Performing off-pump CABG (OPCAB) aims to avoid these adverse effects but may result in incomplete revascularization. On-pump beating heart CABG (ONBHCAB) combines the benefits of both ONCAB and OPCAB. This study focuses on comparing the short- and long-term outcomes of different CABG techniques.

METHOD

Retrospective observational cohort included 2,028 patients who underwent ONCAB, OPCAB, and ONBHCAB. The short-term outcomes including postoperative ischemic injury, hemodynamic functions, and adverse events were compared. The long-term outcomes were overall survival and the occurrence of major adverse cardiovascular events (MACE). Propensity score matching ensured comparability among the three patient groups.

RESULTS

After matching, there were no differences in baseline characteristics. Regarding ischemic injury, OPCAB showed the lowest peak cardiac enzyme levels (all p≤0.001). There were no statistically significant differences in the change of hemodynamic function (cardiac index) between the three groups (p = 0.158). Ten-year survival for OPCAB, ONBHCAB, and ONCAB were 80.5%, 75.9%, and 73.7%, respectively. OPCAB was associated with a significant reduction in mortality risk and MACE when compared to others (Mortality HR = 0.33, p = 0.001, MACE HR = 0.52, p = 0.004).

CONCLUSION

OPCAB implementation resulted in a lower occurrence of postoperative ischemic injury than ONCAB and ONBHCAB. No differences in postoperative hemodynamic function in all three techniques were observed. OPCAB respectively were preferable techniques beneficial for long-term outcomes.

Use of matching methods in observational studies with critical patients and renal outcomes. Scoping review

Introduction: The use of matching techniques in observational studies has been increasing and is not always used appropriately. Clinical experiments are not always feasible in critical patients with renal outcomes, and observational studies are an important alternative. Objective: Through a scoping review, determine the available evidence on the use of matching methods in studies involving critically ill patients and assessing renal outcomes. Methods: Medline, Embase, and Cochrane databases were used to identify articles published between 1992 and 2020 up to week 10, which studied different exposures in the critically ill patient with renal outcomes and used propensity matching methods. Results: Most publications are cohort studies 94 (94. 9 %), five studies (5. 1 %) were cross-sectional. The main pharmacological intervention was the use of antibiotics in seven studies (7. 1%) and the main risk factor studied was renal injury prior to ICU admission in 10 studies (10. 1%). The balance between the baseline characteristics assessed by standardized means, in only 28 studies (28. 2%). Most studies 95 (96 %) used logistic regression to calculate the propensity index. Conclusion: Major inconsistencies were observed in the use of methods and in the reporting of findings. A summary is made of the aspects to be considered in the use of the methods and reporting of the findings with the matching by propensity index.