Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Apr 16:2017:565-574.
eCollection 2017.

Predicting Inpatient Acute Kidney Injury over Different Time Horizons: How Early and Accurate?

Peng Cheng  1   2 , Lemuel R Waitman  1 , Yong Hu  3 , Mei Liu  1
Affiliations

Predicting Inpatient Acute Kidney Injury over Different Time Horizons: How Early and Accurate?

Peng Cheng et al. AMIA Annu Symp Proc. .

Abstract

Incidence of Acute Kidney Injury (AKI) has increased dramatically over the past two decades due to rising prevalence of comorbidities and broadening repertoire of nephrotoxic medications. Hospitalized patients with AKI are at higher risk for complications and mortality, thus early recognition of AKI is crucial. Building AKI prediction models based on electronic medical records (EMRs) can enable early recognition of high-risk patients, facilitate prevention of iatrogenically induced AKI events, and improve patient outcomes. This study builds machine learning models to predict hospital-acquired AKI over different time horizons using EMR data. The study objectives are to assess (1) whether early AKI prediction is possible; (2) whether information prior to admission improves prediction; (3) what type of risk factors affect AKI prediction the most. Evaluation results showed a good cross-validated AUC of 0.765 for predicting AKI events 1-day prior and adding data prior to admission did not improve model performance.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
An illustratio n of adjusting the lower bound of data collection window before the admission date
Figure 2.
Figure 2.
An illustration of adjusting the prediction point (i.e., upper bound of data collection window) before the AKI occurrence date
See this image and copyright information in PMC

References

    1. Fortescue EB, Bates DW, Chertow GM. Predicting acute renal failure after coronary bypass surgery: crossvalidation of two risk-stratification algorithms. Kidney international. 2000 Jun;57(6):2594–602. - PubMed
    1. Hou SH, Bushinsky DA, Wish JB, Cohen JJ, Harrington JT. Hospital-acquired renal insufficiency: a prospective study. The American journal of medicine. 1983 Feb;74(2):243–8. - PubMed
    1. Wijeysundera DN, Karkouti K, Dupuis JY, et al. Derivation and validation of a simplified predictive index for renal replacement therapy after cardiac surgery. JAMA. 2007 Apr 25;297(16):1801–9. - PubMed
    1. Chertow GM, Burdick E, Honour M, Bonventre JV, Bates DW. Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. Journal of the American Society of Nephrology: JASN. 2005 Nov;16(11):3365–70. - PubMed
    1. Liano F, Junco E, Pascual J, Madero R, Verde E. The spectrum of acute renal failure in the intensive care unit compared with that seen in other settings. The Madrid Acute Renal Failure Study Group. Kidney international Supplement. 1998 May;66:S16–24. - PubMed

Publication types

LinkOut - more resources