Key result
The Xiang-ya predictive model, utilizing 13 preoperative risk factors, successfully predicted surgery-related acute kidney injury in hypertensive patients with an AUC of 0.87 in the validation cohort.
Cohort (n=24,451)
No
Effect estimate: AUC 0.87 (95% CI 0.86-0.89)
A novel 13-variable preoperative risk model (Xiang-ya Model) accurately predicts surgery-related acute kidney injury in hypertensive patients undergoing general surgery.
May aid preoperative AKI risk stratification in hypertensives; hypothesis-generating pending prospective validation.
BACKGROUND: Acute kidney injury (AKI) is a serious post-surgery complication; however, few preoperative risk models for AKI have been developed for hypertensive patients undergoing general surgery. Thus, in this study involving a large Chinese cohort, we developed and validated a risk model for surgery-related AKI using preoperative risk factors. METHODS AND FINDINGS: This retrospective cohort study included 24,451 hypertensive patients aged ≥18 years who underwent general surgery between 2007 and 2015. The endpoints for AKI classification utilized by the KDIGO (Kidney Disease: Improving Global Outcomes) system were assessed. The most discriminative predictor was selected using Fisher scores and was subsequently used to construct a stepwise multivariate logistic regression model, whose performance was evaluated via comparisons with models used in other published works using the net reclassification index (NRI) and integrated discrimination improvement (IDI) index. RESULTS: Surgery-related AKI developed in 1994 hospitalized patients (8.2%). The predictors identified by our Xiang-ya Model were age, gender, eGFR, NLR, pulmonary infection, prothrombin time, thrombin time, hemoglobin, uric acid, serum potassium, serum albumin, total cholesterol, and aspartate amino transferase. The area under the receiver-operating characteristic curve (AUC) for the validation set and cross validation set were 0.87 (95% CI 0.86-0.89) and (0.89; 95% CI 0.88-0.90), respectively, and was therefore similar to the AUC for the training set (0.89; 95% CI 0.88-0.90). The optimal cutoff value was 0.09. Our model outperformed that developed by Kate et al., which exhibited an NRI of 31.38% (95% CI 25.7%-37.1%) and an IDI of 8% (95% CI 5.52%-10.50%) for patients who underwent cardiac surgery (n = 2101). CONCLUSIONS/SIGNIFICANCE: We developed an AKI risk model based on preoperative risk factors and biomarkers that demonstrated good performance when predicting events in a large cohort of hypertensive patients who underwent general surgery.
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Liu et al. (2016) conducted a cohort in Hypertension undergoing general surgery (n=24,451). Preoperative risk factors (Xiang-ya Model) was evaluated on Prediction of surgery-related acute kidney injury (AUC in validation set) (AUC 0.87, 95% CI 0.86-0.89). The Xiang-ya predictive model, utilizing 13 preoperative risk factors, successfully predicted surgery-related acute kidney injury in hypertensive patients with an AUC of 0.87 in the validation cohort.
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