Sehoon Park1,2, Hyunjeong Cho1,3, Seokwoo Park1,2, Soojin Lee2, Kwangsoo Kim4, Hyung Jin Yoon5, Jiwon Park6, Yunhee Choi6, Suehyun Lee7, Ju Han Kim8, Sejoong Kim9, Ho Jun Chin9,10,11, Dong Ki Kim2,10,11, Kwon Wook Joo2,10,11, Yon Su Kim1,2,10,11, Hajeong Lee12,10,11. 1. Department of Biomedical Sciences. 2. Department of Internal Medicine. 3. Department of Internal Medicine, Chungbuk National University Hospital, Chungcheongbuk-do, Korea. 4. Division of Clinical Bioinformatics, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea. 5. Department of Biomedical Engineering. 6. Medical Research Collaborating Center, and. 7. Department of biomedical informatics, College of Medicine, Konyang University, Daejeon, Korea; and. 8. Division of Biomedical Informatics. 9. Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea. 10. Department of Internal Medicine, and. 11. Kidney Research Institute, Seoul National University College of Medicine, Seoul, Korea. 12. Department of Internal Medicine, mdhjlee@gmail.com.
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. METHODS: We 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. RESULTS: The 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.
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. METHODS: We 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. RESULTS: The 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.
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