Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12882-019-1206-4
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dc.titleElectronic health records accurately predict renal replacement therapy in acute kidney injury
dc.contributor.authorLow, Sanmay
dc.contributor.authorVathsala, Anantharaman
dc.contributor.authorMurali, Tanusya Murali
dc.contributor.authorPang, Long
dc.contributor.authorMacLaren, Graeme
dc.contributor.authorNg, Wan-Ying
dc.contributor.authorHaroon, Sabrina
dc.contributor.authorMukhopadhyay, Amartya
dc.contributor.authorLim, Shir-Lynn
dc.contributor.authorTan, Bee-Hong
dc.contributor.authorLau, Titus
dc.contributor.authorChua, Horng-Ruey
dc.date.accessioned2019-06-07T01:48:39Z
dc.date.available2019-06-07T01:48:39Z
dc.date.issued2019-01-31
dc.identifier.citationLow, 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
dc.identifier.issn1471-2369
dc.identifier.issn1471-2369
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/155324
dc.description.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.
dc.language.isoen
dc.publisherBMC
dc.sourceElements
dc.subjectScience & Technology
dc.subjectLife Sciences & Biomedicine
dc.subjectUrology & Nephrology
dc.subjectAcute kidney injury
dc.subjectDecision support techniques
dc.subjectElectronic health records
dc.subjectEpidemiology
dc.subjectMortality
dc.subjectOutcomes and process assessment
dc.subjectRenal replacement therapy
dc.subjectCRITICALLY-ILL PATIENTS
dc.subjectDIALYSIS
dc.subjectAKI
dc.subjectMULTICENTER
dc.subjectINTENSITY
dc.subjectPROGNOSIS
dc.subjectFAILURE
dc.subjectDEATH
dc.subjectSCORE
dc.subjectMODEL
dc.typeArticle
dc.date.updated2019-06-03T15:42:23Z
dc.contributor.departmentMEDICINE
dc.contributor.departmentSAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
dc.contributor.departmentSURGERY
dc.contributor.departmentANAESTHESIA
dc.description.doi10.1186/s12882-019-1206-4
dc.description.sourcetitleBMC NEPHROLOGY
dc.description.volume20
dc.description.issue1
dc.published.statePublished
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