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Electronic health records accurately predict renal replacement therapy in acute kidney injury
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 predictio...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6357378/ https://www.ncbi.nlm.nih.gov/pubmed/30704418 http://dx.doi.org/10.1186/s12882-019-1206-4 |
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author | 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 |
author_facet | 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 |
author_sort | Low, Sanmay |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12882-019-1206-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6357378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63573782019-02-07 Electronic health records accurately predict renal replacement therapy in acute kidney injury 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 BMC Nephrol Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12882-019-1206-4) contains supplementary material, which is available to authorized users. BioMed Central 2019-01-31 /pmc/articles/PMC6357378/ /pubmed/30704418 http://dx.doi.org/10.1186/s12882-019-1206-4 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article 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 Electronic health records accurately predict renal replacement therapy in acute kidney injury |
title | Electronic health records accurately predict renal replacement therapy in acute kidney injury |
title_full | Electronic health records accurately predict renal replacement therapy in acute kidney injury |
title_fullStr | Electronic health records accurately predict renal replacement therapy in acute kidney injury |
title_full_unstemmed | Electronic health records accurately predict renal replacement therapy in acute kidney injury |
title_short | Electronic health records accurately predict renal replacement therapy in acute kidney injury |
title_sort | electronic health records accurately predict renal replacement therapy in acute kidney injury |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6357378/ https://www.ncbi.nlm.nih.gov/pubmed/30704418 http://dx.doi.org/10.1186/s12882-019-1206-4 |
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