Prediction and detection models for acute kidney injury in hospitalized older adults.

Journal: BMC medical informatics and decision making
Published Date:

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.

Authors

  • Rohit J Kate
    Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA. Electronic address: katerj@uwm.edu.
  • Ruth M Perez
    Patient Centered Research, Aurora Research Institute, Aurora Health Care, Milwaukee, WI, 53233, USA.
  • Debesh Mazumdar
    Milwaukee Kidney Associates, Milwaukee, WI, USA.
  • Kalyan S Pasupathy
    Mayo Clinic Rochester, MN.
  • Vani Nilakantan
    Allina Health System, Minneapolis, MN, USA.