Jethro Cc Kwong1,2, Adree Khondker2,3, Jin Kyu Kim1,2, Michael Chua2, Daniel T Keefe2, Joana Dos Santos2, Marta Skreta4, Lauren Erdman4, Neeta D'Souza5, Antoine Fermin Selman5, John Weaver5, Dana A Weiss5, Christopher Long5, Gregory Tasian5, Chia Wei Teoh6,7, Mandy Rickard2, Armando J Lorenzo8,9. 1. Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada. 2. Division of Urology, Department of Surgery, Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada. 3. Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada. 4. Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada. 5. Division of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA. 6. Division of Nephrology, Hospital for Sick Children, Toronto, ON, Canada. 7. Department of Paediatrics, University of Toronto, Toronto, ON, Canada. 8. Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada. armando.lorenzo@sickkids.ca. 9. Division of Urology, Department of Surgery, Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada. armando.lorenzo@sickkids.ca.
Abstract
BACKGROUND: Early kidney and anatomic features may be predictive of future progression and need for additional procedures in patients with posterior urethral valve (PUV). The objective of this study was to use machine learning (ML) to predict clinically relevant outcomes in these patients. METHODS: Patients diagnosed with PUV with kidney function measurements at our institution between 2000 and 2020 were included. Pertinent clinical measures were abstracted, including estimated glomerular filtration rate (eGFR) at each visit, initial vesicoureteral reflux grade, and renal dysplasia at presentation. ML models were developed to predict clinically relevant outcomes: progression in CKD stage, initiation of kidney replacement therapy (KRT), and need for clean-intermittent catheterization (CIC). Model performance was assessed by concordance index (c-index) and the model was externally validated. RESULTS: A total of 103 patients were included with a median follow-up of 5.7 years. Of these patients, 26 (25%) had CKD progression, 18 (17%) required KRT, and 32 (31%) were prescribed CIC. Additionally, 22 patients were included for external validation. The ML model predicted CKD progression (c-index = 0.77; external C-index = 0.78), KRT (c-index = 0.95; external C-index = 0.89) and indicated CIC (c-index = 0.70; external C-index = 0.64), and all performed better than Cox proportional-hazards regression. The models have been packaged into a simple easy-to-use tool, available at https://share.streamlit.io/jcckwong/puvop/main/app.py CONCLUSION: ML-based approaches for predicting clinically relevant outcomes in PUV are feasible. Further validation is warranted, but this implementable model can act as a decision-making aid. A higher resolution version of the Graphical abstract is available as Supplementary information.
BACKGROUND: Early kidney and anatomic features may be predictive of future progression and need for additional procedures in patients with posterior urethral valve (PUV). The objective of this study was to use machine learning (ML) to predict clinically relevant outcomes in these patients. METHODS: Patients diagnosed with PUV with kidney function measurements at our institution between 2000 and 2020 were included. Pertinent clinical measures were abstracted, including estimated glomerular filtration rate (eGFR) at each visit, initial vesicoureteral reflux grade, and renal dysplasia at presentation. ML models were developed to predict clinically relevant outcomes: progression in CKD stage, initiation of kidney replacement therapy (KRT), and need for clean-intermittent catheterization (CIC). Model performance was assessed by concordance index (c-index) and the model was externally validated. RESULTS: A total of 103 patients were included with a median follow-up of 5.7 years. Of these patients, 26 (25%) had CKD progression, 18 (17%) required KRT, and 32 (31%) were prescribed CIC. Additionally, 22 patients were included for external validation. The ML model predicted CKD progression (c-index = 0.77; external C-index = 0.78), KRT (c-index = 0.95; external C-index = 0.89) and indicated CIC (c-index = 0.70; external C-index = 0.64), and all performed better than Cox proportional-hazards regression. The models have been packaged into a simple easy-to-use tool, available at https://share.streamlit.io/jcckwong/puvop/main/app.py CONCLUSION: ML-based approaches for predicting clinically relevant outcomes in PUV are feasible. Further validation is warranted, but this implementable model can act as a decision-making aid. A higher resolution version of the Graphical abstract is available as Supplementary information.
Authors: George J Schwartz; Alvaro Muñoz; Michael F Schneider; Robert H Mak; Frederick Kaskel; Bradley A Warady; Susan L Furth Journal: J Am Soc Nephrol Date: 2009-01-21 Impact factor: 10.121