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Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis

OBJECTIVE: To explore a machine learning model for the early prediction of acute kidney injury (AKI) and to screen the related factors affecting new-onset AKI in the ICU. METHODS: A retrospective analysis was performed used the MIMIC-III data source. New onset of AKI defined based on the serum creat...

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Autores principales: Pan, Pan, Liu, Yuhong, Xie, Fei, Duan, Zhimei, Li, Lina, Gu, Hongjun, Xie, Lixin, Lu, Xiangyun, Su, Longxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10075490/
https://www.ncbi.nlm.nih.gov/pubmed/37013397
http://dx.doi.org/10.1080/0886022X.2023.2194433
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author Pan, Pan
Liu, Yuhong
Xie, Fei
Duan, Zhimei
Li, Lina
Gu, Hongjun
Xie, Lixin
Lu, Xiangyun
Su, Longxiang
author_facet Pan, Pan
Liu, Yuhong
Xie, Fei
Duan, Zhimei
Li, Lina
Gu, Hongjun
Xie, Lixin
Lu, Xiangyun
Su, Longxiang
author_sort Pan, Pan
collection PubMed
description OBJECTIVE: To explore a machine learning model for the early prediction of acute kidney injury (AKI) and to screen the related factors affecting new-onset AKI in the ICU. METHODS: A retrospective analysis was performed used the MIMIC-III data source. New onset of AKI defined based on the serum creatinine changed. We included 19 variables for AKI assessment using four machine learning models: support vector machines, logistic regression, and random forest. and XGBoost, using accuracy, specificity, precision, recall, F1 score, and AUROC (area under the ROC curve) to evaluate model performance. The four models predicted new-onset AKI 3–6–9–12 h ahead. The SHapley Additive exPlanation (SHAP) value is used to evaluate the feature importance of the model. RESULTS: We finally respectively extracted 1130 AKI patients and non-AKI patients from the MIMIC-III database. With the extension of the early warning time, the prediction performance of each model showed a downward trend, but the relative performance was consistent. The prediction performance comparison of the four models showed that the XGBoost model performed the best in all evaluation indicators in all the time point at new-onset AKI 3–6–9–12 h ahead (accuracy 0.809 vs 0.78 vs 0.744 vs 0.741, specificity 0.856 vs 0.826 vs 0.797 vs 0.787, precision 0.842 vs 0.81 vs 0.775 vs 0.766, recall 0.759 vs 0.734 vs 0.692 vs 0.694, Fl score 0.799 vs 0.769 vs 0.731 vs 0.729, AUROC 0.892 vs 0.857 vs 0.827 vs 0.818). In the prediction of AKI 6, 9 and 12 h ahead, the importance of creatinine, platelets, and height was the most important based on SHapley. CONCLUSIONS: The machine learning model described in this study can predict AKI 3–6–9–12 h before the new-onset of AKI in ICU. In particular, platelet plays an important role.
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spelling pubmed-100754902023-04-06 Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis Pan, Pan Liu, Yuhong Xie, Fei Duan, Zhimei Li, Lina Gu, Hongjun Xie, Lixin Lu, Xiangyun Su, Longxiang Ren Fail Clinical Study OBJECTIVE: To explore a machine learning model for the early prediction of acute kidney injury (AKI) and to screen the related factors affecting new-onset AKI in the ICU. METHODS: A retrospective analysis was performed used the MIMIC-III data source. New onset of AKI defined based on the serum creatinine changed. We included 19 variables for AKI assessment using four machine learning models: support vector machines, logistic regression, and random forest. and XGBoost, using accuracy, specificity, precision, recall, F1 score, and AUROC (area under the ROC curve) to evaluate model performance. The four models predicted new-onset AKI 3–6–9–12 h ahead. The SHapley Additive exPlanation (SHAP) value is used to evaluate the feature importance of the model. RESULTS: We finally respectively extracted 1130 AKI patients and non-AKI patients from the MIMIC-III database. With the extension of the early warning time, the prediction performance of each model showed a downward trend, but the relative performance was consistent. The prediction performance comparison of the four models showed that the XGBoost model performed the best in all evaluation indicators in all the time point at new-onset AKI 3–6–9–12 h ahead (accuracy 0.809 vs 0.78 vs 0.744 vs 0.741, specificity 0.856 vs 0.826 vs 0.797 vs 0.787, precision 0.842 vs 0.81 vs 0.775 vs 0.766, recall 0.759 vs 0.734 vs 0.692 vs 0.694, Fl score 0.799 vs 0.769 vs 0.731 vs 0.729, AUROC 0.892 vs 0.857 vs 0.827 vs 0.818). In the prediction of AKI 6, 9 and 12 h ahead, the importance of creatinine, platelets, and height was the most important based on SHapley. CONCLUSIONS: The machine learning model described in this study can predict AKI 3–6–9–12 h before the new-onset of AKI in ICU. In particular, platelet plays an important role. Taylor & Francis 2023-04-04 /pmc/articles/PMC10075490/ /pubmed/37013397 http://dx.doi.org/10.1080/0886022X.2023.2194433 Text en © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
spellingShingle Clinical Study
Pan, Pan
Liu, Yuhong
Xie, Fei
Duan, Zhimei
Li, Lina
Gu, Hongjun
Xie, Lixin
Lu, Xiangyun
Su, Longxiang
Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis
title Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis
title_full Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis
title_fullStr Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis
title_full_unstemmed Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis
title_short Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis
title_sort significance of platelets in the early warning of new-onset aki in the icu by using supervise learning: a retrospective analysis
topic Clinical Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10075490/
https://www.ncbi.nlm.nih.gov/pubmed/37013397
http://dx.doi.org/10.1080/0886022X.2023.2194433
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