Development and validation of electronic surveillance tool for acute kidney injury: A retrospective analysis

被引:50
|
作者
Ahmed, Adil [1 ,2 ]
Vairavan, Srinivasan [3 ]
Akhoundi, Abbasali [4 ]
Wilson, Gregory [1 ]
Chiofolo, Caitlyn [3 ]
Chbat, Nicolas [3 ]
Cartin-Ceba, Rodrigo [1 ]
Li, Guangxi [1 ]
Kashani, Kianoush [1 ,5 ]
机构
[1] Mayo Clin, Multidisciplinary Epidemiol & Translat Res Intens, Div Pulm & Crit Care Med, Dept Med, Rochester, MN 55905 USA
[2] North Cent Texas Med Fdn, Wichita Falls Family Practice Residency Program W, Wichita Falls, TX USA
[3] Philips Res North Amer, Briarcliff Manor, NY USA
[4] Shahid Beheshti Univ, Dept Anesthesiol, Tehran, Iran
[5] Mayo Clin, Dept Med, Div Nephrol & Hypertens, Rochester, MN 55905 USA
关键词
Acute kidney injury; Electronic surveillance; Electronic medical records; ACUTE-RENAL-FAILURE; BASE-LINE CREATININE; INFORMATION-TECHNOLOGY; OUTCOMES; THERAPY; ICU;
D O I
10.1016/j.jcrc.2015.05.007
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
Introduction: Timely detection of acute kidney injury (AKI) facilitates prevention of its progress and potentially therapeutic interventions. The study objective is to develop and validate an electronic surveillance tool (AKI sniffer) to detect AKI in 2 independent retrospective cohorts of intensive care unit (ICU) patients. The primary aim is to compare the sensitivity, specificity, and positive and negative predictive values of AKI sniffer performance against a reference standard. Methods: This study is conducted in the ICUs of a tertiary care center. The derivation cohort study subjects were Olmsted County, MN, residents admitted to all Mayo Clinic ICUs from July 1, 2010, through December 31, 2010, and the validation cohort study subjects were all patients admitted to a Mayo Clinic, Rochester, campus medical/surgical ICU on January 12, 2010, through March 23, 2010. All included records were reviewed by 2 independent investigators who adjudicated AKI using the Acute Kidney Injury Network criteria; disagreements were resolved by a third reviewer. This constituted the reference standard. An electronic algorithm was developed; its precision and reliability were assessed in comparison with the reference standard in 2 separate cohorts, derivation and validation. Results: Of 1466 screened patients, a total of 944 patients were included in the study: 482 for derivation and 462 for validation. Compared with the reference standard in the validation cohort, the sensitivity and specificity of the AKI sniffer were 88% and 96%, respectively. The Cohen kappa (95% confidence interval) agreement between the electronic and the reference standard was 0.84 (0.78-0.89) and 0.85 (0.80-0.90) in the derivation and validation cohorts. Conclusion: Acute kidney injury can reliably and accurately be detected electronically in ICU patients. The presented method is applicable for both clinical (decision support) and research (enrollment for clinical trials) settings. Prospective validation is required. (C) 2015 Elsevier Inc. All rights reserved.
引用
收藏
页码:988 / 993
页数:6
相关论文
共 50 条
  • [1] Electronic Data Systems and Acute Kidney Injury
    Cheungpasitporn, Wisit
    Kashani, Kianoush
    ACUTE KIDNEY INJURY - FROM DIAGNOSIS TO CARE, 2016, 187 : 73 - 83
  • [2] Validation of an electronic surveillance system for acute lung injury
    Herasevich, Vitaly
    Yilmaz, Murat
    Khan, Hasrat
    Hubmayr, Rolf D.
    Gajic, Ognjen
    INTENSIVE CARE MEDICINE, 2009, 35 (06) : 1018 - 1023
  • [3] Electronic alerts for acute kidney injury
    Selby, Nicholas M.
    CURRENT OPINION IN NEPHROLOGY AND HYPERTENSION, 2013, 22 (06): : 637 - 642
  • [4] Risk factors for the prognosis of acute kidney injury under the Acute Kidney Injury Network definition: A retrospective, multicenter study in critically ill patients
    Zhou, Jiaojiao
    Yang, Lichuan
    Zhang, Kangyi
    Liu, Yun
    Fu, Ping
    NEPHROLOGY, 2012, 17 (04) : 330 - 337
  • [5] Development and external validation of multimodal postoperative acute kidney injury risk machine learning models
    Karway, George K.
    Koyner, Jay L.
    Caskey, John
    Spicer, Alexandra B.
    Carey, Kyle A.
    Gilbert, Emily R.
    Dligach, Dmitriy
    Mayampurath, Anoop
    Afshar, Majid
    Churpek, Matthew M.
    JAMIA OPEN, 2023, 6 (04)
  • [6] Development and Validation of a Predictive Model for Acute Kidney Injury in Sepsis Patients Based on Recursive Partition Analysis
    Lai, Kunmei
    Lin, Guo
    Chen, Caiming
    Xu, Yanfang
    JOURNAL OF INTENSIVE CARE MEDICINE, 2024, 39 (05) : 465 - 476
  • [7] Acute kidney injury following total joint arthroplasty: retrospective analysis
    Weingarten, Toby N.
    Gurrieri, Carmelina
    Jarett, Paul D.
    Brown, Deforest R.
    Berntson, Novette J.
    Calaro, Reynaldo D., Jr.
    Kor, Daryl J.
    Berry, Daniel J.
    Garovic, Vesna D.
    Nicholson, Wayne T.
    Schroeder, Darrell R.
    Sprung, Juraj
    CANADIAN JOURNAL OF ANESTHESIA-JOURNAL CANADIEN D ANESTHESIE, 2012, 59 (12): : 1111 - 1118
  • [8] External validation of the Madrid Acute Kidney Injury Prediction Score
    Del Carpio, Jacqueline
    Paz Marco, Maria
    Luisa Martin, Maria
    Craver, Lourdes
    Jatem, Elias
    Gonzalez, Jorge
    Chang, Pamela
    Ibarz, Mercedes
    Pico, Silvia
    Falcon, Gloria
    Canales, Marina
    Huertas, Elisard
    Romero, Inaki
    Nieto, Nacho
    Segarra, Alfons
    CLINICAL KIDNEY JOURNAL, 2021, 14 (11) : 2377 - 2382
  • [9] Duration of acute kidney injury and mortality in critically ill patients: a retrospective observational study
    Han, Seung Seok
    Kim, Sejoong
    Ahn, Shin Young
    Lee, Jeonghwan
    Kim, Dong Ki
    Chin, Ho Jun
    Chae, Dong-Wan
    Na, Ki Young
    BMC NEPHROLOGY, 2013, 14
  • [10] The classification of acute kidney injury: A tool for critical care nurses
    Santana-Padilla, Y. G.
    Fernandez-Castillo, J. A.
    Mateos-Davila, A.
    ENFERMERIA INTENSIVA, 2022, 33 : S35 - S41