Personalized application of machine learning algorithms to identify pediatric patients at risk for recurrent ureteropelvic junction obstruction after dismembered pyeloplasty.
Machine learning
Pediatrics
Personalized medicine
Pyeloplasty
Journal
World journal of urology
ISSN: 1433-8726
Titre abrégé: World J Urol
Pays: Germany
ID NLM: 8307716
Informations de publication
Date de publication:
Feb 2022
Feb 2022
Historique:
received:
22
07
2021
accepted:
02
11
2021
pubmed:
14
11
2021
medline:
17
3
2022
entrez:
13
11
2021
Statut:
ppublish
Résumé
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). 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. 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/ . 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.
Identifiants
pubmed: 34773476
doi: 10.1007/s00345-021-03879-z
pii: 10.1007/s00345-021-03879-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
593-599Subventions
Organisme : american urological association foundation
ID : 839859
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Salem YH, Majd M, Rushton HG et al (1995) Outcome analysis of pediatric pyeloplasty as a function of patient age, presentation and differential renal function. J Urol 154:1889–1893
doi: 10.1016/S0022-5347(01)66819-8
Seixas-Mikelus SA, Jenkins LC, Williot P et al (2009) Pediatric pyeloplasty: comparison of literature meta-analysis of laparoscopic and open techniques with open surgery at a single institution. J Urol 182:2428–2434
doi: 10.1016/j.juro.2009.07.051
Braga LHP, Lorenzo AJ, Skeldon S et al (2007) Failed pyeloplasty in children: comparative analysis of retrograde endopyelotomy versus redo pyeloplasty. J Urol 178:2571–2575
doi: 10.1016/j.juro.2007.08.050
Dy GW, Hsi RS, Holt SK et al (2016) National trends in secondary procedures following pediatric pyeloplasty. J Urol 195:1209–1214
doi: 10.1016/j.juro.2015.11.010
Romao RLP, Koyle MA, Salle JLP et al (2013) Failed pyeloplasty in children: revisiting the unknown. Urology 82:1145–1149
doi: 10.1016/j.urology.2013.06.049
Chan YY, Durbin-Johnson B, Sturm RM et al (2017) Outcomes after pediatric open, laparoscopic, and robotic pyeloplasty at academic institutions. J Pediatr Urol 13:49-e1
doi: 10.1016/j.jpurol.2016.08.029
Tan H-J, Ye Z, Roberts WW et al (2011) Failure after laparoscopic pyeloplasty: prevention and management. J Endourol 25:1457–1462
doi: 10.1089/end.2010.0647
Tonekaboni S, Mazwi M, Laussen P, Eytan D, Greer R, Goodfellow SD, Goodwin A, Brudno M, Goldenberg A (2018) Prediction of cardiac arrest from physiological signals in the pediatric ICU. In: Machine learning for healthcare conference, Nov 29. PMLR 2018, pp 534–550
Nemati S, Holder A, Razmi F et al (2018) An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med 46:547
doi: 10.1097/CCM.0000000000002936
Bertsimas D, Li M, Estrada C et al (2021) Selecting children with vesicoureteral reflux who are most likely to benefit from antibiotic prophylaxis: application of machine learning to RIVUR. J Urol 205:1170–1179
doi: 10.1097/JU.0000000000001445
He Y, Song H, Liu P et al (2020) Primary laparoscopic pyeloplasty in children: a single-center experience of 279 patients and analysis of possible factors affecting complications. J Pediatr Urol 16:331-e1
doi: 10.1016/j.jpurol.2019.10.029
Lorenzo AJ, Rickard M, Braga LH et al (2019) Predictive analytics and modeling employing machine learning technology: the next step in data sharing, analysis, and individualized counseling explored with a large. Prospect Prenat Hydronephrosis Database Urol 123:204–209
Adam A, Smith GHH (2016) Anderson–Hynes pyeloplasty: are we all really on the same page? ANZ J Surg 86:143–147
doi: 10.1111/ans.13114
Peng Y (2003) Fitting semiparametric cure models. Comput Stat Data Anal 41:481–490
doi: 10.1016/S0167-9473(02)00184-6
Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1
doi: 10.18637/jss.v033.i01
Simon N, Friedman J, Hastie T et al (2011) Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw 39:1
doi: 10.18637/jss.v039.i05
Lee JD, Sun DL, Sun Y et al (2016) Exact post-selection inference, with application to the lasso. Ann Stat 44:907–927
doi: 10.1214/15-AOS1371
Taylor J, Tibshirani R (2018) Post-selection inference for-penalized likelihood models. Can J Stat 46:41–61
doi: 10.1002/cjs.11313
Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak 26:565–574
doi: 10.1177/0272989X06295361
Moscardi PRM, Barbosa JABA, Andrade HS et al (2017) Reoperative laparoscopic ureteropelvic junction obstruction repair in children: safety and efficacy of the technique. J Urol 197:798–804
doi: 10.1016/j.juro.2016.10.062
Lim DJ, Walker RDIII (1996) Management of the failed pyeloplasty. J Urol 156:738–740
doi: 10.1016/S0022-5347(01)65801-4
Yin S, Peng Q, Li H, Zhang Z, You X, Liu H, Fischer K, Furth SL, Tasian GE, Fan Y (2019) Multi-instance deep learning with graph convolutional neural networks for diagnosis of kidney diseases using ultrasound imaging. In: Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. Springer, Cham, pp 146–154
doi: 10.1007/978-3-030-32689-0_15
Zheng Q, Furth SL, Tasian GE et al (2019) Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features. J Pediatr Urol 15:75-e1
doi: 10.1016/j.jpurol.2018.10.020
Rickard M, Lorenzo AJ, Braga LH (2017) Renal parenchyma to hydronephrosis area ratio (PHAR) as a predictor of future surgical intervention for infants with high-grade prenatal hydronephrosis. Urology 101:85–89
doi: 10.1016/j.urology.2016.09.029
Blum ES, Porras AR, Biggs E et al (2018) Early detection of ureteropelvic junction obstruction using signal analysis and machine learning: a dynamic solution to a dynamic problem. J Urol 199:847–852
doi: 10.1016/j.juro.2017.09.147
Watkinson P, Clifton D, Collins G, McCulloch P, Morgan L (2021) DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence. Nat Med 27:186–187
doi: 10.1038/s41591-021-01229-5
Beam AL, Kohane IS (2018) Big data and machine learning in health care. JAMA 319:1317–1318
doi: 10.1001/jama.2017.18391
Dalton WB, Forde PM, Kang H et al (2017) Personalized medicine in the oncology clinic: implementation and outcomes of the Johns Hopkins molecular tumor board. JCO Precis Oncol 1:1–19
Ankerst DP, Straubinger J, Selig K et al (2018) A contemporary prostate biopsy risk calculator based on multiple heterogeneous cohorts. Eur Urol 74:197–203
doi: 10.1016/j.eururo.2018.05.003
Corbett HJ, Mullassery D (2015) Outcomes of endopyelotomy for pelviureteric junction obstruction in the paediatric population: a systematic review. J Pediatr Urol 11:328–336
doi: 10.1016/j.jpurol.2015.08.014
Ramsden A, Rainsbury P, Sells H (2011) Defining success in laparoscopic pyeloplasty. Br J Med Surg Urol 4:108–112
doi: 10.1016/j.bjmsu.2010.08.002