A new machine-learning model to predict long-term renal function impairment after minimally invasive partial nephrectomy: the Fundació Puigvert predictive model.
AKI
CKD
Nephron-sparing surgery
Partial nephrectomy
Renal function
Journal
World journal of urology
ISSN: 1433-8726
Titre abrégé: World J Urol
Pays: Germany
ID NLM: 8307716
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
04
05
2023
accepted:
22
08
2023
medline:
9
11
2023
pubmed:
16
9
2023
entrez:
15
9
2023
Statut:
ppublish
Résumé
To provide a new model to predict long-term renal function impairment after partial nephrectomy (PN). Data of consecutive patients who underwent minimally invasive PN from 2005 to 2022 were analyzed. A minimum of 12 months of follow-up was required. We relied on a machine-learning algorithm, namely classification and regression tree (CART), to identify the predictors and associated clusters of chronic kidney disease (CKD) stage migration during follow-up. 568 patients underwent minimally invasive PN at our center. A total of 381 patients met our inclusion criteria. The median follow-up was 69 (IQR 38-99) months. A total of 103 (27%) patients experienced CKD stage migration at last follow-up. Progression of CKD stage after surgery, ACCI and baseline CKD stage were selected as the most informative risk factors to predict CKD progression, leading to the creation of four clusters. The progression of CKD stage rates for cluster #1 (no progression of CKD stage after surgery, baseline CKD stage 1-2, ACCI 1-4), #2 (no progression of CKD stage after surgery, baseline CKD stage 1-2, ACCI ≥ 5), #3 (no progression of CKD stage after surgery and baseline CKD stage 3-4-5) and #4 (progression of CKD stage after surgery) were 6.9%, 28.2%, 37.1%, and 69.6%, respectively. The c-index of the model was 0.75. We developed a new model to predict long-term renal function impairment after PN where the perioperative loss of renal function plays a pivotal role to predict lack of functional recovery. This model could help identify patients in whom functional follow-up should be intensified to minimize possible worsening factors of renal function.
Identifiants
pubmed: 37714966
doi: 10.1007/s00345-023-04593-8
pii: 10.1007/s00345-023-04593-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2985-2990Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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