Evidence map›Paper›PMID 34686914›Full record

ArticlePediatric nephrology (Berlin, Germany)2022

Posterior Urethral Valves Outcomes Prediction (PUVOP): a machine learning tool to predict clinically relevant outcomes in boys with posterior urethral valves.

Jethro Cc Kwong, Adree Khondker, Jin Kyu Kim, Michael Chua, Daniel T Keefe, Joana Dos Santos, Marta Skreta, Lauren Erdman, Neeta D'Souza, Antoine Fermin Selman and 7 more

Abstract read
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In one paragraph

Article in Pediatric nephrology (Berlin, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
10.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.

  1. Pooled it
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  9. Use of prenatal ultrasound findings to predict postnatal outcome in fetuses with lower urinary tract obstruction.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 3 institutions in 2 countries.

Jethro Cc Kwong *Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
Adree Khondker *Division of Urology, Department of Surgery, Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
Jin Kyu KimDivision of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
Michael ChuaDivision of Urology, Department of Surgery, Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
Daniel T KeefeDivision of Urology, Department of Surgery, Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
Joana Dos SantosDivision of Urology, Department of Surgery, Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
Marta SkretaCentre for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
Lauren ErdmanCentre for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
Neeta D'SouzaDivision of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Antoine Fermin SelmanDivision of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
John WeaverDivision of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Dana A WeissDivision of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Christopher LongDivision of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Gregory TasianDivision of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Chia Wei TeohDivision of Nephrology, Hospital for Sick Children, Toronto, ON, Canada.
Mandy RickardDivision of Urology, Department of Surgery, Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
Armando J LorenzoDivision of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada. armando.lorenzo@sickkids.ca.
Hospital for Sick Children · CAChildren's Hospital of Philadelphia · USUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly 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.

methodsPatients 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.

resultsA 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

conclusionML-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.

Indexed as

Renal Insufficiency, ChronicUrethral ObstructionFemaleHumansMachine LearningMaleRetrospective StudiesUrethraCatheterizationChronic kidney diseaseDialysisMachine learningPosterior urethral valveTransplant

Identifiers

PMID34686914
OpenAlexW3205076583

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.