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Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study
OBJECTIVES: There are many studies of acute kidney injury (AKI) diagnosis models lack of external validation and prospective validation. We constructed the models using three databases to predict severe AKI within 48 hours in intensive care unit (ICU) patients. DESIGN: A retrospective and prospectiv...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8896056/ https://www.ncbi.nlm.nih.gov/pubmed/35241466 http://dx.doi.org/10.1136/bmjopen-2021-054092 |
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author | Liang, Qiqiang Xu, Yongfeng Zhou, Yu Chen, Xinyi Chen, Juan Huang, Man |
author_facet | Liang, Qiqiang Xu, Yongfeng Zhou, Yu Chen, Xinyi Chen, Juan Huang, Man |
author_sort | Liang, Qiqiang |
collection | PubMed |
description | OBJECTIVES: There are many studies of acute kidney injury (AKI) diagnosis models lack of external validation and prospective validation. We constructed the models using three databases to predict severe AKI within 48 hours in intensive care unit (ICU) patients. DESIGN: A retrospective and prospective cohort study. SETTING: We studied critically ill patients in our database (SHZJU-ICU) and two other public databases, the Medical Information Mart for Intensive Care (MIMIC) and AmsterdamUMC databases, including basic demographics, vital signs and laboratory results. We predicted the diagnosis of severe AKI in patients in the next 48 hours using machine-learning algorithms with the three databases. Then, we carried out real-time severe AKI prediction in the prospective validation study at our centre for 1 year. PARTICIPANTS: All patients included in three databases with uniform exclusion criteria. PRIMARY AND SECONDARY OUTCOME MEASURES: Effect evaluation index of prediction models. RESULTS: We included 58 492 patients, and a total of 5257 (9.0%) patients met the definition of severe AKI. In the internal validation of the SHZJU-ICU and MIMIC databases, the best area under the receiver operating characteristic curve (AUROC) of the model was 0.86. The external validation results by AmsterdamUMC database were also satisfactory, with the best AUROC of 0.86. A total of 2532 patients were admitted to the centre for prospective validation; 358 positive results were predicted and 344 patients were diagnosed with severe AKI, with the best sensitivity of 0.72, the specificity of 0.80 and the AUROC of 0.84. CONCLUSION: The prediction model of severe AKI exhibits promises as a clinical application based on dynamic vital signs and laboratory results of multicentre databases with prospective and external validation. |
format | Online Article Text |
id | pubmed-8896056 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-88960562022-03-22 Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study Liang, Qiqiang Xu, Yongfeng Zhou, Yu Chen, Xinyi Chen, Juan Huang, Man BMJ Open Intensive Care OBJECTIVES: There are many studies of acute kidney injury (AKI) diagnosis models lack of external validation and prospective validation. We constructed the models using three databases to predict severe AKI within 48 hours in intensive care unit (ICU) patients. DESIGN: A retrospective and prospective cohort study. SETTING: We studied critically ill patients in our database (SHZJU-ICU) and two other public databases, the Medical Information Mart for Intensive Care (MIMIC) and AmsterdamUMC databases, including basic demographics, vital signs and laboratory results. We predicted the diagnosis of severe AKI in patients in the next 48 hours using machine-learning algorithms with the three databases. Then, we carried out real-time severe AKI prediction in the prospective validation study at our centre for 1 year. PARTICIPANTS: All patients included in three databases with uniform exclusion criteria. PRIMARY AND SECONDARY OUTCOME MEASURES: Effect evaluation index of prediction models. RESULTS: We included 58 492 patients, and a total of 5257 (9.0%) patients met the definition of severe AKI. In the internal validation of the SHZJU-ICU and MIMIC databases, the best area under the receiver operating characteristic curve (AUROC) of the model was 0.86. The external validation results by AmsterdamUMC database were also satisfactory, with the best AUROC of 0.86. A total of 2532 patients were admitted to the centre for prospective validation; 358 positive results were predicted and 344 patients were diagnosed with severe AKI, with the best sensitivity of 0.72, the specificity of 0.80 and the AUROC of 0.84. CONCLUSION: The prediction model of severe AKI exhibits promises as a clinical application based on dynamic vital signs and laboratory results of multicentre databases with prospective and external validation. BMJ Publishing Group 2022-03-03 /pmc/articles/PMC8896056/ /pubmed/35241466 http://dx.doi.org/10.1136/bmjopen-2021-054092 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Intensive Care Liang, Qiqiang Xu, Yongfeng Zhou, Yu Chen, Xinyi Chen, Juan Huang, Man Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study |
title | Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study |
title_full | Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study |
title_fullStr | Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study |
title_full_unstemmed | Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study |
title_short | Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study |
title_sort | severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study |
topic | Intensive Care |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8896056/ https://www.ncbi.nlm.nih.gov/pubmed/35241466 http://dx.doi.org/10.1136/bmjopen-2021-054092 |
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