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During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Postoperative acute kidney injury (PO-AKI) is a frequent complication of major surgery, associated with prolonged hospitalization, higher costs, and increased mortality. Accurate preoperative risk stratification is essential for prevention and early intervention. The Simple Postoperative AKI Risk (SPARK) classification, developed in South Korea, uses common clinical and laboratory variables to estimate risk in non-cardiac surgery patients and has shown good performance in its derivation cohorts. However, external validations have reported variable accuracy, raising concerns about its generalizability across different populations. Given the distinct demographic and clinical profiles of Filipino surgical patients, this study evaluated the predictive accuracy of SPARK for PO-AKI in this setting and explored alternative predictors to optimize local risk estimation.
A retrospective cohort study was conducted on 200 adults who underwent general surgery between January 2020 and December 2024. PO-AKI was defined by Kidney Disease: Improving Global Outcomes (KDIGO) criteria within 7 days postoperatively. Patients were stratified into SPARK risk classes A–D. Discriminative performance was assessed using sensitivity, specificity, predictive values, likelihood ratios, and the area under the receiver operating characteristic curve (AUC). Calibration was evaluated through Hosmer-Lemeshow testing and decile-level analyses. Multivariable logistic regression was performed on modified SPARK variables, substituting serum creatinine for estimated glomerular filtration rate (eGFR), to identify independent predictors of PO-AKI.
The cohort had a median age of 61 years, and 51% were male. PO-AKI occurred in 8.6% of patients, with incidence rising progressively across SPARK classes (3.4% in Class A, 12.3% in Class B, 19.7% in Class C, 31.8% in Class D; trend p=0.0038). Five patients (2.5%) required dialysis, limited to Classes C and D. The SPARK classification showed poor discrimination (AUC=0.689) and risk overestimation in higher strata, with sensitivities of 3–47% and specificities of 62–91%. Positive predictive values were low (3–32%), while negative predictive values remained high (82–86%). Calibration analysis showed modest agreement between predicted and observed risks. In multivariable analysis, hypoalbuminemia (adjusted OR 0.33; 95% CI 0.14–0.78; p=0.011) and anemia (adjusted OR 6.85; 95% CI 1.21–38.75; p=0.029) were independent predictors of PO-AKI. A modified model incorporating these variables achieved superior discrimination (AUC=0.83) and excellent calibration (Hosmer-Lemeshow p=0.67) (Figure 1).
The SPARK classification demonstrated limited utility in this Filipino surgical cohort due to poor discrimination and risk overestimation. Incorporating locally relevant predictors, particularly serum albumin and hemoglobin, improved predictive performance. These findings highlight the need for local recalibration of risk tools and support the integration of simple laboratory parameters for targeted perioperative interventions to mitigate PO-AKI risk.
