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• W3202875346 abstract "No AccessJournal of UrologyPediatric Urology1 Jul 2019Targeted Workup after Initial Febrile Urinary Tract Infection: Using a Novel Machine Learning Model to Identify Children Most Likely to Benefit from Voiding CystourethrogramThis article is commented on by the following:Editorial CommentEditorial Comment Advanced Analytics Group of Pediatric Urology and ORC Personalized Medicine Group Advanced Analytics Group of Pediatric Urology and ORC Personalized Medicine Group *Correspondence: Department of Urology, Boston Children's Hospital, 300 Longwood Ave., HU390, Boston, Massachusetts 02115 telephone: 617-355-7652; e-mail: E-mail Address: [email protected] Department of Urology, Boston Children's Hospital, Boston, Massachusetts Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000186AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract Purpose: Significant debate persists regarding the appropriate workup in children with an initial urinary tract infection. Greatly preferable to all or none approaches in the current guideline would be a model to identify children at highest risk for a recurrent urinary tract infection plus vesicoureteral reflux to allow for targeted voiding cystourethrogram while children at low risk could be observed. We sought to develop a model to predict the probability of recurrent urinary tract infection associated vesicoureteral reflux in children after an initial urinary tract infection. Materials and Methods: We included subjects from the RIVUR (Randomized Intervention for Children with Vesico-Ureteral Reflux) and CUTIE (Careful Urinary Tract Infection Evaluation) trials in our study, excluding the prophylaxis treatment arm of the RIVUR. The main outcome was defined as recurrent urinary tract infection associated vesicoureteral reflux. Missing data were imputed using optimal tree imputation. Data were split into training, validation and testing sets. Machine learning algorithm hyperparameters were tuned by the validation set with fivefold cross-validation. Results: A total of 500 subjects, including 305 from the RIVUR and 195 from the CUTIE trials, were included in study. Of the subjects 90% were female and mean ± SD age was 21 ± 19 months. A recurrent urinary tract infection developed in 72 patients, of whom 53 also had vesicoureteral reflux (10.6% of the total). The final model included age, sex, race, weight, the systolic blood pressure percentile, dysuria, the urine albumin-to-creatinine ratio, prior antibiotic exposure and current medication. The model predicted recurrent urinary tract infection associated vesicoureteral reflux with an AUC of 0.761 (95% CI 0.714-0.808) in the testing set. Conclusions: Our predictive model using a novel machine learning algorithm provided promising performance to facilitate individualized treatment of children with an initial urinary tract infection and identify those most likely to benefit from voiding cystourethrogram after the initial urinary tract infection. This would allow for more selective application of this test, increasing the yield while also minimizing overuse. References 1. : Association between urinary symptoms at 7 years old and previous urinary tract infection. Arch Dis Child 1991; 66: 232. Crossref, Medline, Google Scholar 2. : Children with urinary infection: a comparison of those with and those without vesicoureteric reflux. Kidney Int 1981; 20: 717. Google Scholar 3. : Technical report: urinary tract infections in febrile infants and young children: the Urinary Tract Subcommittee of the American Academy of Pediatrics Committee on Quality Improvement. Pediatrics 1999; 103: e54. Google Scholar 4. Committee on Quality Improvement, Subcommittee on Urinary Tract Infection: Practice parameter: the diagnosis, treatment, and evaluation of the initial urinary tract infection in febrile infants and young children. Pediatrics 1999; 103: 843. Google Scholar 5. : Nomograms for predicting annual resolution rate of primary vesicoureteral reflux: results from 2,462 children. J Urol 2009; 182: 1535. Link, Google Scholar 6. : Dysfunctional elimination syndrome: is it related to urinary tract infection or vesicoureteral reflux diagnosed early in life?Pediatrics 2003; 112: 1134. Google Scholar 7. : Investigating febrile UTI in infants: is a cystogram necessary?J Pediatr Urol 2010; 6: 148. Google Scholar 8. : Urinary tract infection: clinical practice guideline for the diagnosis and management of the initial UTI in febrile infants and children 2 to 24 months. Subcommittee on Urinary Tract Infection, Steering Committee on Quality Improvement and Management. Pediatrics 2011; 128: 595. Google Scholar 9. : Predictive value of specific ultrasound findings when used as a screening test for abnormalities on VCUG. J Pediatr Urol 2015; 11: 176.e1. Google Scholar 10. : Incidence of abnormal imaging and recurrent pyelonephritis after first febrile urinary tract infection in children 2 to 24 months old. J Urol 2013; 190: 1505. Link, Google Scholar 11. : Accept or decline? An analytics-based decision tool for kidney offer evaluation. Transplantation 2017; 101: 2898. Google Scholar 12. : Antimicrobial prophylaxis for children with vesicoureteral reflux. RIVUR Trial Investigators. N Engl J Med 2014; 371: 1072. Google Scholar 13. : The RIVUR trial: profile and baseline clinical associations of children with vesicoureteral reflux. Pediatrics 2013; 132: e34. Google Scholar 14. : From predictive methods to missing data imputation: an optimization approach. J Mach Learn Res 2018; 18: 1. Google Scholar 15. : Optimal classification trees. Mach Learn 2017; 106: 1039. Google Scholar 16. RIVUR Data Dictionary and Analysis Manual. RIVUR Data Coordinating Center, C. S. C. C., Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 2010. Google Scholar 17. : The dysfunctional voiding scoring system: quantitative standardization of dysfunctional voiding symptoms in children. J Urol 2000; 164: 1011. Link, Google Scholar 18. : Prevalence of urinary tract infection in childhood: a meta-analysis. Pediatr Infect Dis J 2008; 27: 302. Google Scholar 19. : Incidence and severity of vesicoureteral reflux in children related to age, gender, race and diagnosis. J Urol 2003; 170: 1548. Link, Google Scholar 20. : Treatment of recurrent complicated urinary tract infections in children with vesicoureteral reflux. J Microbiol Immunol Infect 2016; 49: 717. Google Scholar 21. : Previous antimicrobial exposure is associated with drug-resistant urinary tract infections in children. Pediatrics 2010; 125: 664. Google Scholar 22. : Pediatric urological causes of hypertension. J Urol 2005; 173: 697. Link, Google Scholar 23. : Reflux nephropathy. J Pediatr Urol 2008; 4: 414. Google Scholar 24. : Should a cystography be performed on all breastfeeding infants with mild to moderate dilatation of the urinary tract? Renal function tests can help to answer this question. Nefrologia 2011; 31: 192. Google Scholar 25. : Microalbuminuria in children with vesicoureteral reflux. Ren Fail 2008; 30: 639. Google Scholar 26. : Measurement of urinary endothelin-1-like immunoreactivity and comparison with other urinary parameters in patients with primary vesicoureteral reflux. A preliminary report. Eur Urol 1994; 25: 326. Google Scholar 27. : Body composition with bioelectrical impedance analysis and body growth in late-diagnosed vesicoureteral reflux. Minerva Pediatr 2017; 69: 174. Google Scholar 28. : Height and weight growth in children with vesicoureteral reflux diagnosed before one year old. Urology 2009; 74: 1314. Google Scholar 29. : The impact of obesity on febrile urinary tract infection and renal scarring in children with vesicoureteral reflux. J Pediatr Urol 2017; 13: 67.e1. Google Scholar 30. : Does this child have a urinary tract infection?JAMA 2007; 298: 2895. Google Scholar The corresponding author certifies that, when applicable, a statement(s) has been included in the manuscript documenting institutional review board, ethics committee or ethical review board study approval; principles of Helsinki Declaration were followed in lieu of formal ethics committee approval; institutional animal care and use committee approval; all human subjects provided written informed consent with guarantees of confidentiality; IRB approved protocol number; animal approved project number. The funder had no role the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication. No direct or indirect commercial, personal, academic, political, religious or ethical incentive is associated with publishing this article. Supported by Agency for Healthcare Research and Quality Grant No. T32-HS000063-24 (HHW). © 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsCited byScott Wang H, Cahill D, Panagides J, Logvinenko T and Nelson C (2021) Top-Down versus Bottom-Up Approach in Children Presenting with Urinary Tract Infection: Comparative Effectiveness Analysis Using RIVUR and CUTIE DataJournal of Urology, VOL. 206, NO. 5, (1284-1290), Online publication date: 1-Nov-2021.Wan J (2019) This Month in Pediatric UrologyJournal of Urology, VOL. 202, NO. 1, (3-4), Online publication date: 1-Jul-2019.Related articlesJournal of Urology7 Jun 2019Editorial CommentJournal of Urology7 Jun 2019Editorial Comment Volume 202Issue 1July 2019Page: 144-152Supplementary Materials Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.Keywordsvesico-ureteral refluxmachine learningforecastingurinary bladderurinary tract infectionAcknowledgmentsHsin-Hsiao Scott Wang conceptualized and designed the study, acquired the data, carried out the analysis, drafted the initial manuscript, and reviewed and revised the manuscript. Michael Li conceptualized and designed the study, carried out the analysis, critically reviewed the manuscript, and provided technical support. Jack Dunn and Daisy Zhuo developed algorithms and provided technical support. Dimitris Bertsimas, Carlos Estrada, Jr. and Caleb Nelson conceptualized and designed the study, critically reviewed the manuscript, and supervised the study.MetricsAuthor Information Advanced Analytics Group of Pediatric Urology and ORC Personalized Medicine Group Department of Urology, Boston Children's Hospital, Boston, Massachusetts Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts *Correspondence: Department of Urology, Boston Children's Hospital, 300 Longwood Ave., HU390, Boston, Massachusetts 02115 telephone: 617-355-7652; e-mail: E-mail Address: [email protected] More articles by this author Expand All The corresponding author certifies that, when applicable, a statement(s) has been included in the manuscript documenting institutional review board, ethics committee or ethical review board study approval; principles of Helsinki Declaration were followed in lieu of formal ethics committee approval; institutional animal care and use committee approval; all human subjects provided written informed consent with guarantees of confidentiality; IRB approved protocol number; animal approved project number. The funder had no role the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication. No direct or indirect commercial, personal, academic, political, religious or ethical incentive is associated with publishing this article. Supported by Agency for Healthcare Research and Quality Grant No. T32-HS000063-24 (HHW). Advertisement PDF downloadLoading ..." @default.
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• W3202875346 cites W2017023104 @default.
• W3202875346 cites W2021841215 @default.
• W3202875346 cites W2053853213 @default.
• W3202875346 cites W2061336950 @default.
• W3202875346 cites W2068406686 @default.
• W3202875346 cites W2087594543 @default.
• W3202875346 cites W2094591155 @default.
• W3202875346 cites W2095918352 @default.
• W3202875346 cites W2111396055 @default.
• W3202875346 cites W2115133875 @default.
• W3202875346 cites W2118199972 @default.
• W3202875346 cites W2126552785 @default.
• W3202875346 cites W2128806628 @default.
• W3202875346 cites W2142429267 @default.
• W3202875346 cites W2148452842 @default.
• W3202875346 cites W2150670322 @default.
• W3202875346 cites W2463735744 @default.
• W3202875346 cites W2528485912 @default.
• W3202875346 cites W2615418449 @default.
• W3202875346 cites W3184192997 @default.
• W3202875346 cites W4236723852 @default.
• W3202875346 cites W4238738483 @default.
• W3202875346 cites W923601815 @default.
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