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Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study

BACKGROUND: Acute kidney injury is common in the surgical intensive care unit (ICU). It is associated with poor patient outcomes and high healthcare resource usage. This study’s primary objective is to help identify which ICU patients are at high risk for acute kidney injury. Its secondary objective...

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Autores principales: Wong, Wen En Joseph, Chan, Siew Pang, Yong, Juin Keith, Tham, Yen Yu Sherlyn, Lim, Jie Rui Gerald, Sim, Ming Ann, Soh, Chai Rick, Ti, Lian Kah, Chew, Tsong Huey Sophia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898752/
https://www.ncbi.nlm.nih.gov/pubmed/33618695
http://dx.doi.org/10.1186/s12882-021-02238-9
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author Wong, Wen En Joseph
Chan, Siew Pang
Yong, Juin Keith
Tham, Yen Yu Sherlyn
Lim, Jie Rui Gerald
Sim, Ming Ann
Soh, Chai Rick
Ti, Lian Kah
Chew, Tsong Huey Sophia
author_facet Wong, Wen En Joseph
Chan, Siew Pang
Yong, Juin Keith
Tham, Yen Yu Sherlyn
Lim, Jie Rui Gerald
Sim, Ming Ann
Soh, Chai Rick
Ti, Lian Kah
Chew, Tsong Huey Sophia
author_sort Wong, Wen En Joseph
collection PubMed
description BACKGROUND: Acute kidney injury is common in the surgical intensive care unit (ICU). It is associated with poor patient outcomes and high healthcare resource usage. This study’s primary objective is to help identify which ICU patients are at high risk for acute kidney injury. Its secondary objective is to examine the effect of acute kidney injury on a patient’s prognosis during and after the ICU admission. METHODS: A retrospective cohort of patients admitted to a Singaporean surgical ICU between 2015 to 2017 was collated. Patients undergoing chronic dialysis were excluded. The outcomes were occurrence of ICU acute kidney injury, hospital mortality and one-year mortality. Predictors were identified using decision tree algorithms. Confirmatory analysis was performed using a generalized structural equation model. RESULTS: A total of 201/940 (21.4%) patients suffered acute kidney injury in the ICU. Low ICU haemoglobin levels, low ICU bicarbonate levels, ICU sepsis, low pre-ICU estimated glomerular filtration rate (eGFR) and congestive heart failure was associated with the occurrence of ICU acute kidney injury. Acute kidney injury, together with old age (> 70 years), and low pre-ICU eGFR, was associated with hospital mortality, and one-year mortality. ICU haemoglobin level was discretized into 3 risk categories for acute kidney injury: high risk (haemoglobin ≤9.7 g/dL), moderate risk (haemoglobin between 9.8–12 g/dL), and low risk (haemoglobin > 12 g/dL). CONCLUSION: The occurrence of acute kidney injury is common in the surgical ICU. It is associated with a higher risk for hospital and one-year mortality. These results, in particular the identified haemoglobin thresholds, are relevant for stratifying a patient’s acute kidney injury risk. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-021-02238-9.
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spelling pubmed-78987522021-02-23 Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study Wong, Wen En Joseph Chan, Siew Pang Yong, Juin Keith Tham, Yen Yu Sherlyn Lim, Jie Rui Gerald Sim, Ming Ann Soh, Chai Rick Ti, Lian Kah Chew, Tsong Huey Sophia BMC Nephrol Research Article BACKGROUND: Acute kidney injury is common in the surgical intensive care unit (ICU). It is associated with poor patient outcomes and high healthcare resource usage. This study’s primary objective is to help identify which ICU patients are at high risk for acute kidney injury. Its secondary objective is to examine the effect of acute kidney injury on a patient’s prognosis during and after the ICU admission. METHODS: A retrospective cohort of patients admitted to a Singaporean surgical ICU between 2015 to 2017 was collated. Patients undergoing chronic dialysis were excluded. The outcomes were occurrence of ICU acute kidney injury, hospital mortality and one-year mortality. Predictors were identified using decision tree algorithms. Confirmatory analysis was performed using a generalized structural equation model. RESULTS: A total of 201/940 (21.4%) patients suffered acute kidney injury in the ICU. Low ICU haemoglobin levels, low ICU bicarbonate levels, ICU sepsis, low pre-ICU estimated glomerular filtration rate (eGFR) and congestive heart failure was associated with the occurrence of ICU acute kidney injury. Acute kidney injury, together with old age (> 70 years), and low pre-ICU eGFR, was associated with hospital mortality, and one-year mortality. ICU haemoglobin level was discretized into 3 risk categories for acute kidney injury: high risk (haemoglobin ≤9.7 g/dL), moderate risk (haemoglobin between 9.8–12 g/dL), and low risk (haemoglobin > 12 g/dL). CONCLUSION: The occurrence of acute kidney injury is common in the surgical ICU. It is associated with a higher risk for hospital and one-year mortality. These results, in particular the identified haemoglobin thresholds, are relevant for stratifying a patient’s acute kidney injury risk. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-021-02238-9. BioMed Central 2021-02-22 /pmc/articles/PMC7898752/ /pubmed/33618695 http://dx.doi.org/10.1186/s12882-021-02238-9 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research Article
Wong, Wen En Joseph
Chan, Siew Pang
Yong, Juin Keith
Tham, Yen Yu Sherlyn
Lim, Jie Rui Gerald
Sim, Ming Ann
Soh, Chai Rick
Ti, Lian Kah
Chew, Tsong Huey Sophia
Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study
title Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study
title_full Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study
title_fullStr Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study
title_full_unstemmed Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study
title_short Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study
title_sort assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898752/
https://www.ncbi.nlm.nih.gov/pubmed/33618695
http://dx.doi.org/10.1186/s12882-021-02238-9
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