Simple Postoperative AKI Risk (SPARK) Classification before Noncardiac Surgery: A Prediction Index Development Study with External Validation

Abstract

Background Researchers have suggested models to predict the risk of postoperative AKI (PO-AKI), but an externally validated risk index that can be practically implemented before patients undergo noncardiac surgery is needed. MethodsWe performed a retrospective observational study of patients without preexisting renal failure who underwent a noncardiac operation ($1 hour) at two tertiary hospitals in Korea. We fitted a proportional odds model for an ordinal outcome consisting of three categories: critical AKI (defined as Kidney Disease Improving Global Outcomes AKI stage $2, post-AKI death, or dialysis within 90 days after surgery), low-stage AKI (defined as PO-AKI events not fulfilling the definition of critical AKI), and no PO-AKI. ResultsThe study included 51,041 patients in a discovery cohort and 39,764 patients in a validation cohort. The Simple Postoperative AKI Risk (SPARK) index included a summation of the integer scores of the following variables: age, sex, expected surgery duration, emergency operation, diabetes mellitus, use of renin-angiotensin-aldosterone inhibitors, baseline eGFR, dipstick albuminuria hypoalbuminemia, anemia, and hyponatremia. The model calibration plot showed tolerable distribution of observed and predicted probabilities in both cohorts. The discrimination power of the SPARK index was acceptable in both the discovery (c-statistic 0.80) and validation (c-statistic 0.72) cohorts. When four SPARK classes were defined on the basis of the sum of the risk scores, the SPARK index and classes fairly reflected the risks of PO-AKI and critical AKI.Conclusions Clinicians may consider implementing the SPARK index and classifications to stratify patients' PO-AKI risks before performing noncardiac surgery.

Preoperative Risk and the Association between Hypotension and Postoperative Acute Kidney Injury

“…5,6 As a result, the development of AKI has been studied through predictive modeling in both the cardiac [7][8][9] and noncardiac surgery literature. 3,4,[10][11][12][13][14][15][16] Treatment of AKI remains largely supportive, 17 making AKI prevention a critical focus of investigation. Nearly all pharmacologic attempts to prevent development of AKI have been unsuccessful.…”

Preoperative N-terminal pro-B-type natriuretic peptide for prediction of acute kidney injury after non-cardiac surgery: A retrospective cohort study

“…We used the continuous NRI version for the primary analysis because it is considered the more objective way to compare across studies and because there are no widely accepted risk thresholds for postoperative AKI. Nevertheless, we performed a sensitivity analysis with categorical NRI using cutoffs (< 2, 2-10, 10-20, and > 20%) that have been used in an AKI prediction model [13]. In additional analyses, we tested the predictive value of NT-proBNP over a model including the conventional risk factors and left ventricular ejection fraction (LVEF) in the 3827 patients who had transthoracic echocardiography before surgery.…”

“…We also determined whether preoperative NT-proBNP could improve the predictive ability of two AKI preoperative prediction models that were developed and validated in non-cardiac surgical patients, i.e. the weighted General Surgery AKI (GS-AKI) risk index [9] and the Simple Postoperative AKI Risk (SPARK) index [13], respectively.…”

“…strategies to ameliorate or prevent perioperative renal injury. In the past decade, several preoperative risk prediction models for AKI following non-cardiac surgery were developed [2,[8][9][10][11][12][13].…”

Artificial Intelligence in Acute Kidney Injury Risk Prediction

“…Risk prediction scores for AKI have been reported in several clinical settings, mostly in critical care, surgery, and contrast-induced nephropathy [71][72][73][74][75][76][77]. Still, most AKI cases are reported in general hospital wards, and risk scores in this setting are scarce [78][79][80][81].…”