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Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy

Background and Objectives: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it difficult to predict postoper...

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Autores principales: Shin, Tae Young, Han, Hyunho, Min, Hyun-Seok, Cho, Hyungjoo, Kim, Seonggyun, Park, Sung Yul, Kim, Hyung Joon, Kim, Jung Hoon, Lee, Yong Seong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10456500/
https://www.ncbi.nlm.nih.gov/pubmed/37629692
http://dx.doi.org/10.3390/medicina59081402
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author Shin, Tae Young
Han, Hyunho
Min, Hyun-Seok
Cho, Hyungjoo
Kim, Seonggyun
Park, Sung Yul
Kim, Hyung Joon
Kim, Jung Hoon
Lee, Yong Seong
author_facet Shin, Tae Young
Han, Hyunho
Min, Hyun-Seok
Cho, Hyungjoo
Kim, Seonggyun
Park, Sung Yul
Kim, Hyung Joon
Kim, Jung Hoon
Lee, Yong Seong
author_sort Shin, Tae Young
collection PubMed
description Background and Objectives: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it difficult to predict postoperative renal function. Therefore, we aimed to build an artificial intelligence (AI) model that utilizes perioperative factors to predict residual renal function and incidence of AKI following PN. Methods and Materials: This retrospective study included 785 patients (training set 706, test set 79) from six tertiary referral centers who underwent open or robotic PN. Forty-four perioperative features were used as inputs to train the AI prediction model. XG-Boost and genetic algorithms were used for the final model selection and to determine feature importance. The primary outcome measure was immediate postoperative serum creatinine (Cr) level. The secondary outcome was the incidence of AKI (estimated glomerular filtration rate (eGFR) < 60 mL/h). The average difference between the true and predicted serum Cr levels was considered the mean absolute error (MAE) and was used as a model evaluation parameter. Results: An AI model for predicting immediate postoperative serum Cr levels was selected from 2000 candidates by providing the lowest MAE (0.03 mg/dL). The model-predicted immediate postoperative serum Cr levels correlated closely with the measured values (R(2) = 0.9669). The sensitivity and specificity of the model for predicting AKI were 85.5% and 99.7% in the training set, and 100.0% and 100.0% in the test set, respectively. The limitations of this study included its retrospective design. Conclusions: Our AI model successfully predicted accurate serum Cr levels and the likelihood of AKI. The accuracy of our model suggests that personalized guidelines to optimize multidisciplinary plans involving pre- and postoperative care need to be developed.
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spelling pubmed-104565002023-08-26 Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy Shin, Tae Young Han, Hyunho Min, Hyun-Seok Cho, Hyungjoo Kim, Seonggyun Park, Sung Yul Kim, Hyung Joon Kim, Jung Hoon Lee, Yong Seong Medicina (Kaunas) Article Background and Objectives: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it difficult to predict postoperative renal function. Therefore, we aimed to build an artificial intelligence (AI) model that utilizes perioperative factors to predict residual renal function and incidence of AKI following PN. Methods and Materials: This retrospective study included 785 patients (training set 706, test set 79) from six tertiary referral centers who underwent open or robotic PN. Forty-four perioperative features were used as inputs to train the AI prediction model. XG-Boost and genetic algorithms were used for the final model selection and to determine feature importance. The primary outcome measure was immediate postoperative serum creatinine (Cr) level. The secondary outcome was the incidence of AKI (estimated glomerular filtration rate (eGFR) < 60 mL/h). The average difference between the true and predicted serum Cr levels was considered the mean absolute error (MAE) and was used as a model evaluation parameter. Results: An AI model for predicting immediate postoperative serum Cr levels was selected from 2000 candidates by providing the lowest MAE (0.03 mg/dL). The model-predicted immediate postoperative serum Cr levels correlated closely with the measured values (R(2) = 0.9669). The sensitivity and specificity of the model for predicting AKI were 85.5% and 99.7% in the training set, and 100.0% and 100.0% in the test set, respectively. The limitations of this study included its retrospective design. Conclusions: Our AI model successfully predicted accurate serum Cr levels and the likelihood of AKI. The accuracy of our model suggests that personalized guidelines to optimize multidisciplinary plans involving pre- and postoperative care need to be developed. MDPI 2023-07-31 /pmc/articles/PMC10456500/ /pubmed/37629692 http://dx.doi.org/10.3390/medicina59081402 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shin, Tae Young
Han, Hyunho
Min, Hyun-Seok
Cho, Hyungjoo
Kim, Seonggyun
Park, Sung Yul
Kim, Hyung Joon
Kim, Jung Hoon
Lee, Yong Seong
Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy
title Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy
title_full Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy
title_fullStr Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy
title_full_unstemmed Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy
title_short Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy
title_sort prediction of postoperative creatinine levels by artificial intelligence after partial nephrectomy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10456500/
https://www.ncbi.nlm.nih.gov/pubmed/37629692
http://dx.doi.org/10.3390/medicina59081402
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