A new model to predict acute kidney injury after cardiac surgery in patients with renal insufficiency

Abstract

Objective: To establish a simple model for predicting postoperative acute kidney injury (AKI) requiring renal replacement therapy (RRT) in patients with renal insufficiency (CKD stages 3-4) who underwent cardiac surgery.

Methods: A total of 330 patients were enrolled. Among them, 226 were randomly selected for the development group and the remaining 104 for the validation group. The primary outcome was AKI requiring RRT. A nomogram was constructed based on the multivariate analysis with variables selected by the application of the least absolute shrinkage and selection operator. Meanwhile, the discrimination, calibration, and clinical power of the new model were assessed and compared with those of the Cleveland Clinic score and Simplified Renal Index (SRI) score in the validation group. Results: The rate of RRT in the development group was 10.6% (n = 24), while the rate in the validation group was 14.4% (n = 15). The new model included four variables such as postoperative creatinine, aortic cross-clamping time, emergency, and preoperative cystatin C, with a C-index of 0.851 (95% CI, 0.779-0.924). In the validation group, the areas under the receiver operating characteristic curves for the new model, SRI score, and Cleveland Clinic score were 0.813, 0.791, and 0.786, respectively. Furthermore, the new model demonstrated greater clinical net benefits compared with the Cleveland Clinic score or SRI score.

Conclusions: We developed and validated a powerful predictive model for predicting severe AKI after cardiac surgery in patients with renal insufficiency, which would be helpful to assess the risk for severe AKI requiring RRT.

Keywords: Acute kidney injury; cardiac surgery; renal insufficiency; renal replacement therapy; risk model.

Figure 1.

Flowchart of participant selection.

Figure 2.

Least absolute shrinkage and selection operator (LASSO) binary logistic regression analysis in prediction of RRT. (A) The optimal parameter (λ) of Lasso is selected by the minimum criterion for five times cross-validation. The dotted vertical lines were plotted at the optimal values using the minimum criteria and the one standard error of the minimum criteria (the 1 − SE criteria). Finally, the λ value of 0.0191 was selected. (B) The distribution of the lasso coefficient of fifty-five variables. A coefficient profile plot was produced against the log (λ) sequence. Predictors were selected based on the minimum criteria, where the best λ produced fourteen predictors with non-zero coefficients.

Figure 3.

Prediction of RRT in renal insufficiency patients after cardiac surgery by nomogram model. In order to get every factor’s position on the corresponding axis, lines were drawn on the point axis to represent the number of points. Added all points, find the position of the total score to determine the RRT probability of that line in the nomogram. Cys C, preoperative cystatin C (μg/L); Cr, creatinine (μmol/L); RRT, renal replacement therapy.

Figure 4.

Calibration curves in the validation group for the new model (A), SRI score (B), and Cleveland score (C), respectively. The predicted RRT was plotted on the X-axis, and the actual RRT occurrence was plotted on the Y-axis. A plot along the 45° line would indicate a perfect calibration model in which the predicted RRT is identical to the actual RRT. The dotted line has a close fit to the solid line, which indicated better predictive accuracy of the model.

Figure 5.

The AUC for models in the validation group. Comparison of AUC among models for RRT in renal inadequacy patients after cardiac surgery. New model AUC: 0.813; SRI score AUC: 0.791; Cleveland Clinic score AUC: 0.786. The new model versus SRI score, P = 0.809; new model versus Cleveland score, P = 0.746.

Figure 6.

Decision curve analyses for prediction models. The x‐axis shows the threshold probability. The y‐axis shows the net benefit. The black solid lines hypothesized that all patients were RRT positive or negative, respectively. Across the range of decision thresholds, the new model was positive and had a larger net benefit than the SRI and Cleveland scores.