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Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery

BACKGROUND: Acute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after...

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Autores principales: Yan, Yun, Gong, Hairong, Hu, Jie, Wu, Di, Zheng, Ziyu, Wang, Lini, Lei, Chong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10028074/
https://www.ncbi.nlm.nih.gov/pubmed/36960471
http://dx.doi.org/10.3389/fcvm.2023.1094997
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author Yan, Yun
Gong, Hairong
Hu, Jie
Wu, Di
Zheng, Ziyu
Wang, Lini
Lei, Chong
author_facet Yan, Yun
Gong, Hairong
Hu, Jie
Wu, Di
Zheng, Ziyu
Wang, Lini
Lei, Chong
author_sort Yan, Yun
collection PubMed
description BACKGROUND: Acute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after valvular cardiac surgery in the Chinese population. METHODS: Models were developed from a retrospective cohort of patients undergoing valve surgery from December 2013 to November 2018. Three models were developed to predict all-stage, or moderate to severe AKI, as diagnosed according to Kidney Disease: Improving Global Outcomes (KDIGO) based on patient characteristics and perioperative variables. Models were developed based on lasso logistics regression (LLR), random forest (RF), and extreme gradient boosting (XGboost). The accuracy was compared among three models and against the previously published reference AKICS score. RESULTS: A total of 3,392 patients (mean [SD] age, 50.1 [11.3] years; 1787 [52.7%] male) were identified during the study period. The development of AKI was recorded in 50.5% of patients undergoing valve surgery. In the internal validation testing set, the LLR model marginally improved discrimination (C statistic, 0.7; 95% CI, 0.66–0.73) compared with two machine learning models, RF (C statistic, 0.69; 95% CI, 0.65–0.72) and XGBoost (C statistic, 0.66; 95% CI, 0.63–0.70). A better calibration was also found in the LLR, with a greater net benefit, especially for the higher probabilities as indicated in the decision curve analysis. All three newly developed models outperformed the reference AKICS score. CONCLUSION: Among the Chinese population undergoing CPB-assisted valvular cardiac surgery, prediction models based on perioperative variables were developed. The LLR model demonstrated the best predictive performance was selected for predicting all-stage AKI after surgery. CLINICAL TRIAL REGISTRATION: Trial registration: Clinicaltrials.gov, NCT04237636.
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spelling pubmed-100280742023-03-22 Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery Yan, Yun Gong, Hairong Hu, Jie Wu, Di Zheng, Ziyu Wang, Lini Lei, Chong Front Cardiovasc Med Cardiovascular Medicine BACKGROUND: Acute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after valvular cardiac surgery in the Chinese population. METHODS: Models were developed from a retrospective cohort of patients undergoing valve surgery from December 2013 to November 2018. Three models were developed to predict all-stage, or moderate to severe AKI, as diagnosed according to Kidney Disease: Improving Global Outcomes (KDIGO) based on patient characteristics and perioperative variables. Models were developed based on lasso logistics regression (LLR), random forest (RF), and extreme gradient boosting (XGboost). The accuracy was compared among three models and against the previously published reference AKICS score. RESULTS: A total of 3,392 patients (mean [SD] age, 50.1 [11.3] years; 1787 [52.7%] male) were identified during the study period. The development of AKI was recorded in 50.5% of patients undergoing valve surgery. In the internal validation testing set, the LLR model marginally improved discrimination (C statistic, 0.7; 95% CI, 0.66–0.73) compared with two machine learning models, RF (C statistic, 0.69; 95% CI, 0.65–0.72) and XGBoost (C statistic, 0.66; 95% CI, 0.63–0.70). A better calibration was also found in the LLR, with a greater net benefit, especially for the higher probabilities as indicated in the decision curve analysis. All three newly developed models outperformed the reference AKICS score. CONCLUSION: Among the Chinese population undergoing CPB-assisted valvular cardiac surgery, prediction models based on perioperative variables were developed. The LLR model demonstrated the best predictive performance was selected for predicting all-stage AKI after surgery. CLINICAL TRIAL REGISTRATION: Trial registration: Clinicaltrials.gov, NCT04237636. Frontiers Media S.A. 2023-03-07 /pmc/articles/PMC10028074/ /pubmed/36960471 http://dx.doi.org/10.3389/fcvm.2023.1094997 Text en © 2023 Yan, Gong, Hu, Wu, Zheng, Wang and Lei. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Yan, Yun
Gong, Hairong
Hu, Jie
Wu, Di
Zheng, Ziyu
Wang, Lini
Lei, Chong
Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery
title Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery
title_full Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery
title_fullStr Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery
title_full_unstemmed Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery
title_short Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery
title_sort perioperative parameters-based prediction model for acute kidney injury in chinese population following valvular surgery
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10028074/
https://www.ncbi.nlm.nih.gov/pubmed/36960471
http://dx.doi.org/10.3389/fcvm.2023.1094997
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