Key result
Both the SPARK Classification and GS AKI Index demonstrated poor discriminative power to predict post-operative AKI, with c-statistics ranging from 0.46 to 0.61 and 0.41 to 0.54, respectively.
Why the study?
Postoperative AKI is a significant complication and clinical risk prediction models are lacking for non-cardiac surgery, with no prior studies comparing the diagnostic accuracy of the SPARK Classification and GS AKI Index.
Do the SPARK Classification and GS AKI Index accurately predict post-operative AKI in adult patients undergoing non-cardiac surgery?
Cross-Sectional (n=340)
No
Do the SPARK Classification and GS AKI Index accurately predict post-operative AKI in adult patients undergoing non-cardiac surgery?
Effect estimate: c-statistic 0.46 to 0.61 (SPARK) and 0.41 to 0.54 (GS AKI)
Both the SPARK Classification and GS AKI Index demonstrated poor discriminative power for predicting post-operative AKI in a Filipino cohort undergoing non-cardiac surgery.
Neither tool showed reliable discrimination here; leaves open need for improved AKI prediction models in non-cardiac surgery.
Background Postoperative AKI is a significant postoperative complication. Clinical risk prediction models are lacking for patients undergoing non-cardiac surgery. SPARK Classification and GS AKI Index are tools that have shown fair discriminative ability to predict post-operative AKI in non-cardiac surgery and have external validation in their original cohorts. There is no study that compares the diagnostic accuracy of both tools. Objectives This study aims to compare the diagnostic accuracy of SPARK Classification vs GS-AKI Risk Index in predicting post-operative AKI among patients who will undergo non-cardiac surgery at a tertiary hospital in the Philippines. Methods This is a cross-sectional study, including adult patients who underwent non-cardiac surgeries from January 2019 to July 2021. The individual risk of post-operative AKI for both models were determined. Descriptive data was described using t-test and logistic regression. Measures of accuracy were described using sensitivity, specificity, positive and negative predictive value, positive and negative likelihood ratio, and discriminative ability using concordance (c) statistic. Results Of the 340 patients in this study, 77 (22.65%) developed post-operative AKI and 24 (7.06%) developed critical AKI. Based on demographic data, older age, pre-existing renal disease, longer duration of surgery, anemia, hypoalbuminemia, and hyponatremia were associated with higher incidence of post-operative AKI. SPARK had a sensitivity ranging from 17-43% and specificity ranging from 58-93% for Class B to C. GS AKI had a sensitivity ranging from 10-26% and specificity ranging from 61-97% for Class I to V. SPARK had a discriminative power (c statistic) ranging from 0.46 to 0.61 while GS AKI had a discriminative power ranging from 0.41 to 0.54. Conclusion Based on this study, there is an association between higher risk classification in both SPARK and GS AKI and postoperative AKI. However, both clinical prediction models demonstrate poor discriminative power to predict post-operative AKI.
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Te et al. (2022) conducted a cross-sectional in Postoperative Acute Kidney Injury (AKI) (n=340). SPARK Classification and GS AKI Index was evaluated on Discriminative power (c-statistic) for predicting post-operative AKI (c-statistic 0.46 to 0.61 (SPARK) and 0.41 to 0.54 (GS AKI)). Both the SPARK Classification and GS AKI Index demonstrated poor discriminative power to predict post-operative AKI, with c-statistics ranging from 0.46 to 0.61 and 0.41 to 0.54, respectively.
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