Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma.
BMC Med Inform Decis Mak
; 24(1): 85, 2024 Mar 22.
Article
de En
| MEDLINE
| ID: mdl-38519947
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.Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Néphrocarcinome
/
Insuffisance rénale chronique
/
Tumeurs du rein
Limites:
Humans
Langue:
En
Journal:
BMC Med Inform Decis Mak
Sujet du journal:
INFORMATICA MEDICA
Année:
2024
Type de document:
Article