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.

Renal Risk Prediction in Cardiac Surgery using traditional Methods of Acute Kidney Injury prediction: A Systematic Review

“…35 studies introduced bias into outcome determination. 28 resulted from prior knowledge of predictor information (11-14, 16-18, 22, 25, 27, 28, 30, 31, 33-36, 39, 41, 42, 44, 46-52, 54) , 12 inappropriately determined/insu ciently described the outcome de nition (11,17,19,22,25,28,34,36,45,49,51,52) , whilst 12 did not determine the outcome equally for all (18,19,22,23,25,33,35,36,45,47,49,50) . 41 studies introduced signi cant bias through statistical analysis.…”

“…Calibration was not performed. Of the externally validated papers, 15 were validated for dialysis (11,16,18,21,22,24,28,31,33,35,41,43,46,49,50) . The remainder were validated for Any-Stage AKI, composite outcome AKI or speci c severity AKI.…”

Diagnosis, pathophysiology and preventive strategies for cardiac surgery-associated acute kidney injury: a narrative review

“…Several risk scores based on these factors have been developed (Table 3 ). After the introduction of traditional clinical scores [ 40 – 43 ], new scores have applied various methods to improve predictive performance, such as adding biomarkers [ 44 , 45 ], laboratory test-based [ 46 , 47 ], and using machine learning technology [ 48 ] to build predictive models. These scores have been reported with diagnostic accuracy in the range of 70–85%.…”

Meaning and Management of Perioperative Oliguria

“…Preoperatory scores and models that include patients’ characteristics have been developed to estimate the risk for postoperative severe AKI but overall have low accuracy. 26,27 Some populations, such as patients with chronic kidney disease, diabetes, or end-stage liver disease, have higher baseline risk for postoperative AKI and should undergo closer monitoring. 14 Liver disease patients undergoing liver transplant have a 20 to 50% incidence of AKI.…”