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Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy
DC Field | Value | Language |
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dc.contributor.author | 한현호 | - |
dc.date.accessioned | 2024-03-22T06:08:27Z | - |
dc.date.available | 2024-03-22T06:08:27Z | - |
dc.date.issued | 2023-08 | - |
dc.identifier.issn | 1010-660X | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/198419 | - |
dc.description.abstract | 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 (R2 = 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. © 2023 by the authors. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | MEDICINA-LITHUANIA | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Acute Kidney Injury* / diagnosis | - |
dc.subject.MESH | Acute Kidney Injury* / etiology | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Artificial Intelligence* | - |
dc.subject.MESH | Creatinine* | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Machine Learning | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Models, Theoretical | - |
dc.subject.MESH | Nephrectomy* / adverse effects | - |
dc.subject.MESH | Postoperative Period | - |
dc.subject.MESH | Retrospective Studies | - |
dc.title | Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Urology (비뇨의학교실) | - |
dc.contributor.googleauthor | Tae Young Shin | - |
dc.contributor.googleauthor | Hyunho Han | - |
dc.contributor.googleauthor | Hyun-Seok Min | - |
dc.contributor.googleauthor | Hyungjoo Cho | - |
dc.contributor.googleauthor | Seonggyun Kim | - |
dc.contributor.googleauthor | Sung Yul Park | - |
dc.contributor.googleauthor | Hyung Joon Kim | - |
dc.contributor.googleauthor | Jung Hoon Kim | - |
dc.contributor.googleauthor | Yong Seong Lee | - |
dc.identifier.doi | 10.3390/medicina59081402 | - |
dc.contributor.localId | A04333 | - |
dc.relation.journalcode | J03886 | - |
dc.identifier.eissn | 1648-9144 | - |
dc.identifier.pmid | 37629692 | - |
dc.subject.keyword | acute kidney injury | - |
dc.subject.keyword | artificial intelligence | - |
dc.subject.keyword | partial nephrectomy | - |
dc.subject.keyword | postoperative renal function | - |
dc.contributor.alternativeName | Han, Hyun Ho | - |
dc.contributor.affiliatedAuthor | 한현호 | - |
dc.citation.volume | 59 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 1402 | - |
dc.identifier.bibliographicCitation | MEDICINA-LITHUANIA, Vol.59(8) : 1402, 2023-08 | - |
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