Personalized application of machine learning algorithms to identify pediatric patients at risk for recurrent ureteropelvic junction obstruction after dismembered pyeloplasty.
World J Urol
; 40(2): 593-599, 2022 Feb.
Article
em En
| MEDLINE
| ID: mdl-34773476
ABSTRACT
PURPOSE:
To develop a model that predicts whether a child will develop a recurrent obstruction after pyeloplasty, determine their survival risk score, and expected time to re-intervention using machine learning (ML).METHODS:
We reviewed patients undergoing pyeloplasty from 2008 to 2020 at our institution, including all children and adolescents younger than 18 years. We developed a two-stage machine learning model from 34 clinical fields, which included patient characteristics, ultrasound findings, and anatomical variation. We fit and trained with a logistic lasso model for binary cure model and subsequent survival model. Feature importance on the model was determined with post-selection inference. Performance metrics included area under the receiver-operating-characteristic (AUROC), concordance, and leave-one-out cross validation.RESULTS:
A total of 543 patients were identified, with a median preoperative and postoperative anteroposterior diameter of 23 and 10 mm, respectively. 39 of 232 patients included in the survival model required re-intervention. The cure and survival models performed well with a leave-one-out cross validation AUROC and concordance of 0.86 and 0.78, respectively. Post-selective inference showed that larger anteroposterior diameter at the second post-op follow-up, and anatomical variation in the form of concurrent anomalies were significant model features predicting negative outcomes. The model can be used at https//sickkidsurology.shinyapps.io/PyeloplastyReOpRisk/ .CONCLUSION:
Our ML-based model performed well in predicting the risk of and time to re-intervention after pyeloplasty. The implementation of this ML-based approach is novel in pediatric urology and will likely help achieve personalized risk stratification for patients undergoing pyeloplasty. Further real-world validation is warranted.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Procedimentos Cirúrgicos Urológicos
/
Ureter
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Obstrução Ureteral
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Aprendizado de Máquina
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Pelve Renal
Tipo de estudo:
Etiology_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adolescent
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Child
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Humans
Idioma:
En
Revista:
World J Urol
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Canadá