Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12882-019-1206-4
Title: Electronic health records accurately predict renal replacement therapy in acute kidney injury
Authors: Low, Sanmay
Vathsala, Anantharaman 
Murali, Tanusya Murali 
Pang, Long 
MacLaren, Graeme 
Ng, Wan-Ying
Haroon, Sabrina 
Mukhopadhyay, Amartya 
Lim, Shir-Lynn 
Tan, Bee-Hong 
Lau, Titus
Chua, Horng-Ruey 
Keywords: Science & Technology
Life Sciences & Biomedicine
Urology & Nephrology
Acute kidney injury
Decision support techniques
Electronic health records
Epidemiology
Mortality
Outcomes and process assessment
Renal replacement therapy
CRITICALLY-ILL PATIENTS
DIALYSIS
AKI
MULTICENTER
INTENSITY
PROGNOSIS
FAILURE
DEATH
SCORE
MODEL
Issue Date: 31-Jan-2019
Publisher: BMC
Citation: Low, Sanmay, Vathsala, Anantharaman, Murali, Tanusya Murali, Pang, Long, MacLaren, Graeme, Ng, Wan-Ying, Haroon, Sabrina, Mukhopadhyay, Amartya, Lim, Shir-Lynn, Tan, Bee-Hong, Lau, Titus, Chua, Horng-Ruey (2019-01-31). Electronic health records accurately predict renal replacement therapy in acute kidney injury. BMC NEPHROLOGY 20 (1). ScholarBank@NUS Repository. https://doi.org/10.1186/s12882-019-1206-4
Abstract: © 2019 The Author(s). Background: Electronic health records (EHR) detect the onset of acute kidney injury (AKI) in hospitalized patients, and may identify those at highest risk of mortality and renal replacement therapy (RRT), for earlier targeted intervention. Methods: Prospective observational study to derive prediction models for hospital mortality and RRT, in inpatients aged ≥18 years with AKI detected by EHR over 1 year in a tertiary institution, fulfilling modified KDIGO criterion based on serial serum creatinine (sCr) measures. Results: We studied 3333 patients with AKI, of 77,873 unique patient admissions, giving an AKI incidence of 4%. KDIGO AKI stages at detection were 1(74%), 2(15%), 3(10%); corresponding peak AKI staging in hospital were 61, 20, 19%. 392 patients (12%) died, and 174 (5%) received RRT. Multivariate logistic regression identified AKI onset in ICU, haematological malignancy, higher delta sCr (sCr rise from AKI detection till peak), higher serum potassium and baseline eGFR, as independent predictors of both mortality and RRT. Additionally, older age, higher serum urea, pneumonia and intraabdominal infections, acute cardiac diseases, solid organ malignancy, cerebrovascular disease, current need for RRT and admission under a medical specialty predicted mortality. The AUROC for RRT prediction was 0.94, averaging 0.93 after 10-fold cross-validation. Corresponding AUROC for mortality prediction was 0.9 and 0.9 after validation. Decision tree analysis for RRT prediction achieved a balanced accuracy of 70.4%, and identified delta-sCr ≥ 148 μmol/L as the key factor that predicted RRT. Conclusion: Case fatality was high with significant renal deterioration following hospital-wide AKI. EHR clinical model was highly accurate for both RRT prediction and for mortality; allowing excellent risk-stratification with potential for real-time deployment.
Source Title: BMC NEPHROLOGY
URI: https://scholarbank.nus.edu.sg/handle/10635/155324
ISSN: 1471-2369
1471-2369
DOI: 10.1186/s12882-019-1206-4
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