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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Oh, Seol Whan Byun, Seok-Soo Kim, Jung Kwon Jeong, Chang Wook Kwak, Cheol Hwang, Eu Chang Kang, Seok Ho Chung, Jinsoo Kim, Yong-June Ha, Yun-Sok Hong, Sung-Hoo |
| Abstract | Background Patients with renal cell carcinoma (RCC) have an elevated risk of chronic kidney disease (CKD) following nephrectomy. Therefore, continuous monitoring and subsequent interventions are necessary. It is recommended to evaluate renal function postoperatively. Therefore, a tool to predict CKD onset is essential for postoperative follow-up and management. Methods We constructed a cohort using data from eight tertiary hospitals from the Korean Renal Cell Carcinoma (KORCC) database. A dataset of 4389 patients with RCC was constructed for analysis from the collected data. Nine machine learning (ML) models were used to classify the occurrence and nonoccurrence of CKD after surgery. The final model was selected based on the area under the receiver operating characteristic (AUROC), and the importance of the variables constituting the model was confirmed using the shapley additive explanation (SHAP) value and Kaplan-Meier survival analyses. Results The gradient boost algorithm was the most effective among the various ML models tested. The gradient boost model demonstrated superior performance with an AUROC of 0.826. The SHAP value confirmed that preoperative eGFR, albumin level, and tumor size had a significant impact on the occurrence of CKD after surgery. Conclusions We developed a model to predict CKD onset after surgery in patients with RCC. This predictive model is a quantitative approach to evaluate post-surgical CKD risk in patients with RCC, facilitating improved prognosis through personalized postoperative care. |
| Related Links | https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-024-02473-8.pdf |
| Ending Page | 10 |
| Page Count | 10 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14726947 |
| DOI | 10.1186/s12911-024-02473-8 |
| Journal | BMC Medical Informatics and Decision Making |
| Issue Number | 1 |
| Volume Number | 24 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-03-22 |
| Access Restriction | Open |
| Subject Keyword | Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Renal cell carcinoma Machine learning Chronic kidney disease KOrean Renal Cell Carcinoma Gradient boost |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Informatics Computer Science Applications Health Policy |
| Journal Impact Factor | 3.3/2023 |
| 5-Year Journal Impact Factor | 3.9/2023 |
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