Prediction of the development of acute kidney injury following cardiac surgery by machine learning

Abstract

Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence-based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI.

Methods: A total of 671 patients undergoing cardiac surgery from August 2016 to August 2018 were enrolled. AKI following cardiac surgery was defined according to criteria from Kidney Disease: Improving Global Outcomes (KDIGO). The variables used for analysis included demographic characteristics, clinical condition, preoperative biochemistry data, preoperative medication, and intraoperative variables such as time-series hemodynamic changes. The machine learning methods used included logistic regression, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and ensemble (RF + XGboost). The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). We also utilized SHapley Additive exPlanation (SHAP) values to explain the prediction model.

Results: Development of CSA-AKI was noted in 163 patients (24.3%) during the first postoperative week. Regarding the efficacy of the single model that most accurately predicted the outcome, RF exhibited the greatest AUC (0.839, 95% confidence interval [CI] 0.772-0.898), whereas the AUC (0.843, 95% CI 0.778-0.899) of ensemble model (RF + XGboost) was even greater than that of the RF model alone. The top 3 most influential features in the RF importance matrix plot were intraoperative urine output, units of packed red blood cells (pRBCs) transfused during surgery, and preoperative hemoglobin level. The SHAP summary plot was used to illustrate the positive or negative effects of the top 20 features attributed to the RF. We also used the SHAP dependence plot to explain how a single feature affects the output of the RF prediction model.

Conclusions: In this study, machine learning methods were successfully established to predict CSA-AKI, which determines risks following cardiac surgery, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.

Keywords: Acute kidney injury; Cardiac surgery; Machine learning; Prediction.

Fig. 1

Time period obtained during operation for ARV calculation. We obtained data for the 240 min after operation except the initial 10 min (due to noise signals) and the period between 50 and 100 min after operation due to extracorporeal circulation

Fig. 2

Comparison of AUCs among machine learning models. RF yielded the greatest AUC for single-model prediction. The AUC for RF + XGboost was even greater than for the RF model alone

Fig. 3

Simple decision tree model illustrating the classification of patients with (class = yes) and without (class = no) acute kidney injury. Each box has the following components: selected variables for classification, Gini index, number of samples classified to the box according to the previous variable, the average number of patients for each classification with 5-cross validation, and the majority of classes at the split node. Blue and orange represent the yes class and the no class, respectively, and the color densities increase when the Gini indexes decrease. Abbreviations: pRBC, packed red blood cell; BMI, body mass index; CCS, Canadian Cardiovascular Society; LV, left ventricular; HGB, hemoglobin

Fig. 4

Importance matrix plot of the RF model. This importance matrix plot depicts the importance of each covariate in the development of the final predictive model. Abbreviations: HGB, hemoglobin; eGFR, estimated glomerular filtration rate; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; LV, left ventricular; MAP, mean arterial pressure; PCA, principal component analysis; BMI, body mass index; RV, real variability; HR, heart rate; SBP, systolic blood pressure; LVEDD, left ventricular end-diastolic diameter; HTN, hypertension; FFP, fresh frozen plasma; PLT, platelet; ASA, American Society of Anesthesiologists; CHF, congestive heart failure; DM, diabetes mellitus; CCS, Canadian Cardiovascular Society; CABG, coronary artery bypass grafting; ARB, angiotensin II receptor blocker, COPD, chronic obstructive pulmonary disease; OHA, oral hypoglycemic agent; ER, emergency room; ACEi, angiotensin-converting-enzyme inhibitor; CAD, coronary artery disease; PAOD, peripheral artery occlusive disease

Fig. 5

SHAP summary plot of the top 20 features of the RF model. The higher the SHAP value of a feature, the higher the probability of postoperative acute kidney injury development. A dot is created for each feature attribution value for the model of each patient, and thus one patient is allocated one dot on the line for each feature. Dots are colored according to the values of features for the respective patient and accumulate vertically to depict density. Red represents higher feature values, and blue represents lower feature values. Abbreviations: pRBC, packed red blood cell; HGB, hemoglobin; eGFR, estimated glomerular filtration rate; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; LV, left ventricular; MAP, mean arterial pressure; PCA, principal component analysis; SBP, systolic blood pressure; HR, heart rate; RV, real variability; HTN, hypertension; PLT, platelet; BMI, body mass index

Fig. 6

SHAP dependence plot of the RF model. The SHAP dependence plot shows how a single feature affects the output of the RF prediction model. SHAP values for specific features exceed zero, representing an increased risk of acute kidney injury development. Abbreviations: pRBC, packed red blood cell; HGB, hemoglobin; eGFR, estimated glomerular filtration rate; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration