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Abstract: TH-PO025

Risk Factors for AKI in High-Risk Populations: Systematic Review and Meta-Analyses

Session Information

Category: Acute Kidney Injury

  • 101 AKI: Epidemiology, Risk Factors, and Prevention


  • Zafar, Waleed, Geisinger, Dnville, Pennsylvania, United States
  • Garcia-Arce, Andres, Geisinger, Dnville, Pennsylvania, United States
  • Hu, Yirui, Geisinger, Dnville, Pennsylvania, United States
  • Kwiecien, Sara J., Geisinger, Dnville, Pennsylvania, United States
  • Singh, Gurmukteshwar, Geisinger Medical Center- Nephrology, Danville, Pennsylvania, United States
  • Saunders, Sara, Geisinger, Dnville, Pennsylvania, United States
  • Bucaloiu, Ion D., Geisinger Medical Center, Danville, Pennsylvania, United States
  • Kirchner, H. Lester, Geisinger Clinic, Danville, Pennsylvania, United States
  • Ho, Kevin, Geisinger Medical Center, Danville, Pennsylvania, United States

Predictive models help identify patients at increased risk for Acute kidney injury (AKI) for preventive care. It is unclear how various risk factors perform in different clinical settings. We systematically reviewed predictive models for AKI in four high risk settings and performed meta-analyses of common AKI risk factors


We searched MedLine, EMBASE, CINAHL and Cochrane Library, and performed hand searches of the retrieved reference lists, from 2010 through 2017 for English language studies of prediction models for hospital-acquired AKI (KDIGO, AKIN or RIFLE criteria) in adult hospital populations, patients undergoing cardiac surgery, patients in intensive care unit (ICU) and those undergoing contrast procedures. PRISMA-P 2015 statement was used for data extraction and study appraisal. For the meta-analyses of risk factors, studies with insufficient or non-standardized data and unadjusted risk factors were excluded. Random effects meta-analyses were used to assess pooled adjusted odds ratio (OR) for diabetes (DM), congestive heart failure (CHF) and chronic kidney disease (CKD) for developing AKI


74 studies testing AKI prediction models in the four clinical settings were included. Of these, 10 were in the general hospital setting, 8 were in the ICU, 27 in percutaneous coronary interventions, and 29 in cardiac surgery. There was considerable heterogeneity among studies in the choice and definition of predictors. Meta-analyses included 14 studies reporting diabetes (135955 patients), 16 studies reporting CKD (137846 patients) and 22 studies reporting CHF (180224 patients). Stratified analyses based on high-risk clinical settings were consistent with the overall results (Table 1)


In the combined as well as sub-populations, DM, CKD, and CHF are associated with increased risk for AKI. Analyses of differential risk levels in sub-populations was limited by heterogeneity. Using uniform definitions for AKI and its risk factors in the development of predictive models for AKI is essential for their clinical application

Meta-analyses results
 Overall estimate
OR (95% CI)
Percutaneous coronary intervention
OR (95% CI)
Cardiac surgery
OR (95% CI)
Diabetes1.72 (1.38-2.14)1.86 (1.47-2.34)1.26 (1.01-1.56)
CKD1.47 (1.32-1.64)2.53 (1.10-5.83)1.31 (1.15-1.51)
CHF1.78 (1.54-2.06)2.07 (1.62-2.65)1.55 (1.31-1.83)


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