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Abstract: PO0047

Profiling AKI Trajectories: Early Results from the Million AKI Project

Session Information

Category: Acute Kidney Injury

  • 101 AKI: Epidemiology, Risk Factors, and Prevention

Authors

  • Bowe, Benjamin Charles, VA St. Louis Health Care System, St. Louis, Missouri, United States
  • Xie, Yan, VA St. Louis Health Care System, St. Louis, Missouri, United States
  • Gibson, Andrew K., VA St. Louis Health Care System, St. Louis, Missouri, United States
  • Cai, Miao, VA St. Louis Health Care System, St. Louis, Missouri, United States
  • Xian, Hong, Saint Louis University, Saint Louis, Missouri, United States
  • Maddukuri, Geetha S., VA St. Louis Health Care System, St. Louis, Missouri, United States
  • Al-Aly, Ziyad, VA St. Louis Health Care System, St. Louis, Missouri, United States
Background

Clinical guidelines for risk stratification of acute kidney injury (AKI) patients are based on peak increases in serum creatinine (SC). These definitions do not consider other characteristics of change in SC that may provide information on risk of adverse outcomes. Identification of patient groups that display distinct patterns in trajectory of SC and outcome profiles may lead to more nuanced risk-based definitions of AKI.

Methods

Data from the United States Veterans Health Administration were obtained. A latent class growth model identified patient groups based on similar patterns of trajectory in serum creatinine during a hospitalization with an AKI. Regression models examined associations of trajectory groups with risk factors and in-hospital mortality.

Results

We constructed a cohort of 480,575 veterans with an AKI during an inpatient stay (a subset of the Million AKI Project cohort) . Of these, 343,471 (71.5%), 63,665 (13.3%), and 73,439 (15.3%) met KDIGO AKI stages 1, 2, and 3 criteria. 9.4% died during their hospitalization. We identified 9 latent trajectories summarized by 4 phenotypes: a mild increase in SC from low baseline (66%), and varying degrees of increase in SC with no (9%), moderate (17%), and near-full recovery (8%). Higher systolic blood pressure (OR=1.02; 95% CI=1.01-1.02 per 1 mmHg), sepsis (2.24; 2.10-2.39), non-use of ACE/ARB (1.54;1.47-1.61), diuretic use (1.16; 1.12-1.20), albuminuria (1.36; 1.31-1.41), and prior history of AKI (1.27, 1.22-1.32) were associated with trajectories with larger increases in SC, while major surgeries (2.48; 2.37-2.60) were associated with trajectories that recovered. Compared to the mild increase group, those with partial or no recovery had a higher odd of in-hospital mortality (1.64; 1.57-1.71) that increased in magnitude with higher baseline SC and a greater increase in SC (2.73; 2.66-2.80). Groups that experienced near-full recovery showed no evidence of a difference in mortality profile (0.97; 0.90-1.05) despite differences in other aspects of the trajectory.

Conclusion

Leveraging the depth and breadth of a high-quality longitudinal electronic health record system, we characterized nearly half a million cases of AKI; our results suggest that profiling of AKI trajectories informs risk stratification and may guide deployment of post AKI care.

Funding

  • Veterans Affairs Support