title: Prediction and detection models for acute kidney injury in hospitalized older adults.
abstract: BACKGROUND
  Acute Kidney Injury (AKI) occurs in at least 5 % of hospitalized patients and can result in 40-70 % morbidity and mortality. Even following recovery, many subjects may experience progressive deterioration of renal function. The heterogeneous etiology and pathophysiology of AKI complicates its diagnosis and medical management and can add to poor patient outcomes and incur substantial hospital costs. AKI is predictable and may be avoidable if early risk factors are identified and utilized in the clinical setting. Timely detection of undiagnosed AKI in hospitalized patients can also lead to better disease management.
  
  
  METHODS
  Data from 25,521 hospital stays in one calendar year of patients 60 years and older was collected from a large health care system. Four machine learning models (logistic regression, support vector machines, decision trees and naïve Bayes) along with their ensemble were tested for AKI prediction and detection tasks. Patient demographics, laboratory tests, medications and comorbid conditions were used as the predictor variables. The models were compared using the area under ROC curve (AUC) evaluation metric.
  
  
  RESULTS
  Logistic regression performed the best for AKI detection (AUC 0.743) and was a close second to the ensemble for AKI prediction (AUC ensemble: 0.664, AUC logistic regression: 0.660). History of prior AKI, use of combination drugs such as ACE inhibitors, NSAIDS and diuretics, and presence of comorbid conditions such as respiratory failure were found significant for both AKI detection and risk prediction.
  
  
  CONCLUSIONS
  The machine learning models performed fairly well on both predicting AKI and detecting undiagnosed AKI. To the best of our knowledge, this is the first study examining the difference between prediction and detection of AKI. The distinction has clinical relevance, and can help providers either identify at risk subjects and implement preventative strategies or manage their treatment depending on whether AKI is predicted or detected.
authors.affiliation: Department of Health Informatics and Administration, University of Wisconsin-Milwaukee, Milwaukee, WI, 53211, USA. katerj@uwm.edu.
authors.forename: Rohit J
authors.identifier: http://orcid.org/0000-0001-5522-0674
authors.initials: RJ
authors.lastname: Kate
authors.affiliation: Patient Centered Research, Aurora Research Institute, Aurora Health Care, Milwaukee, WI, 53233, USA.
authors.forename: Ruth M
authors.initials: RM
authors.lastname: Perez
authors.affiliation: Milwaukee Kidney Associates, Milwaukee, WI, 53212, USA.
authors.forename: Debesh
authors.initials: D
authors.lastname: Mazumdar
authors.affiliation: Division of Health Care Policy & Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, 55902, USA.
authors.forename: Kalyan S
authors.initials: KS
authors.lastname: Pasupathy
authors.affiliation: Patient Centered Research, Aurora Research Institute, Aurora Health Care, Milwaukee, WI, 53233, USA.
authors.forename: Vani
authors.initials: V
authors.lastname: Nilakantan
mesh_terms: D058186:Acute Kidney Injury / Q000175:diagnosis*; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D008962:Models, Theoretical; D011379:Prognosis
keywords: Acute kidney injury (AKI); Detection; Elderly; Machine learning; Modeling; Prediction