{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:51:43Z","timestamp":1785408703309,"version":"3.56.0"},"reference-count":27,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Capital Institute of Pediatrics","award":["XZYY-2025-04"],"award-info":[{"award-number":["XZYY-2025-04"]}]},{"award":["XZYY-2025-04"],"award-info":[{"award-number":["XZYY-2025-04"]}],"id":[{"id":"https:\/\/ror.org\/00zw6et16","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82570794"],"award-info":[{"award-number":["82570794"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["82570794"],"award-info":[{"award-number":["82570794"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Children"],"abstract":"<jats:p>Objectives: Hypospadias is one of the most common congenital malformations of the male genitourinary system, and postoperative complications remain a major concern affecting surgical outcomes and patients\u2018 quality of life. Whether machine learning models can effectively predict complication risk using routinely available clinical variables remains unclear. Methods: A retrospective analysis was performed on 671 hypospadias patients who underwent urethroplasty at the Department of Urology, Capital Children\u2019s Medical Center, between December 2015 and September 2024. The final dataset included 671 patients (training set: 536; validation set: 135). The median follow-up duration was 48 months (range: 19 to 72 months). Least absolute shrinkage and selection operator (LASSO) regression with nested cross-validation within the training set was used for feature selection, followed by the development of five machine learning models (Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine). Model performance was evaluated using AUC, calibration curves, Brier score, and decision curve analysis. Feature importance was assessed using SHapley Additive exPlanations (SHAP). Results: LASSO retained four features for model development: hypospadias type, surgical technique, surgeon experience, and patient age. The overall complication rate was 22.9% (154\/671). Among the models evaluated, the Support Vector Machine (SVM) showed the most balanced performance in the validation set, achieving an AUC of 0.810 and a Brier score of 0.157. LightGBM demonstrated comparable performance (AUC: 0.802). SHAP analysis identified surgical technique as the most influential predictor, followed by surgeon volume and hypospadias type, though these findings should be interpreted with caution given the confounding between surgical complexity and disease severity. Conclusions: An interpretable SVM-based prediction model was developed and internally validated to stratify risk for postoperative complications after hypospadias repair using routinely available clinical variables. SHAP provided clinicians with visual insights into key risk-associated factors. However, given the single-center retrospective design and lack of external validation, further multicenter prospective studies are warranted to confirm the generalizability of these findings before clinical implementation.<\/jats:p>","DOI":"10.3390\/children13070962","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T16:16:39Z","timestamp":1784650599000},"page":"962","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning for Predicting Postoperative Complications After Hypospadias Surgery: A 10-Year Single-Center Retrospective Cohort Study"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4221-2793","authenticated-orcid":false,"given":"Ling","family":"Li","sequence":"first","affiliation":[{"name":"Capital Institute of Pediatrics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haosen","family":"Shen","sequence":"additional","affiliation":[{"name":"Capital Institute of Pediatrics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Urology, Capital Center for Children\u2019s Health, Capital Medical University, Capital Institute of Pediatrics, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9505-5749","authenticated-orcid":false,"given":"Baoling","family":"Bai","sequence":"additional","affiliation":[{"name":"Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Center for Children\u2019s Health, Capital Medical University, Capital Institute of Pediatrics, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Center for Children\u2019s Health, Capital Medical University, Capital Institute of Pediatrics, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangshuang","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Center for Children\u2019s Health, Capital Medical University, Capital Institute of Pediatrics, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1337-9113","authenticated-orcid":false,"given":"Chen","family":"Shen","sequence":"additional","affiliation":[{"name":"Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Center for Children\u2019s Health, Capital Medical University, Capital Institute of Pediatrics, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3146-0648","authenticated-orcid":false,"given":"Jiaxin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Center for Children\u2019s Health, Capital Medical University, Capital Institute of Pediatrics, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4597-7697","authenticated-orcid":false,"given":"Qin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Center for Children\u2019s Health, Capital Medical University, Capital Institute of Pediatrics, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianghui","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.eururo.2019.06.027","article-title":"Hypospadias Prevalence and Trends in International Birth Defect Surveillance Systems, 1980\u20132010","volume":"76","author":"Yu","year":"2019","journal-title":"Eur. Urol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101432","DOI":"10.1016\/j.gendis.2024.101432","article-title":"MAFB-Mediated CEBPA Regulated Human Urothelium Growth through Wnt\/\u03b2-Catenin Signaling Pathway","volume":"12","author":"Liu","year":"2025","journal-title":"Genes Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41598-023-27763-5","article-title":"Role of Epigenetics in the Etiology of Hypospadias through Penile Foreskin DNA Methylation Alterations","volume":"13","author":"Kaefer","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.jpurol.2021.03.022","article-title":"Comment on: Urethral Plate Quality Assessment and Its Impact on Hypospadias Repair Outcomes: A Systematic Review and Quality Assessment","volume":"17","author":"Atan","year":"2021","journal-title":"J. Pediatr. Urol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1016\/j.jpurol.2025.08.031","article-title":"Urethrocutaneous Fistula Recurrence after Hypospadias Repair: Risk Factors and Recurrence Rates in a 20-Year Single-Center Experience","volume":"21","author":"Weis","year":"2025","journal-title":"J. Pediatr. Urol."},{"key":"ref_6","first-page":"e27544","article-title":"Hypospadias: A Comprehensive Review Including Its Embryology, Etiology and Surgical Techniques","volume":"14","author":"Halaseh","year":"2022","journal-title":"Cureus"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"70.e1","DOI":"10.1016\/j.jpurol.2014.11.014","article-title":"Grade of Hypospadias Is the Only Factor Predicting for Re-Intervention after Primary Hypospadias Repair: A Multivariate Analysis from a Cohort of 474 Patients","volume":"11","author":"Spinoit","year":"2015","journal-title":"J. Pediatr. Urol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mao, K., Wang, L., Peng, Z., Mao, Y., Zhao, P., Wang, C., Zhou, G., Luo, Z., and Tan, H. (2025). Analysis of Risk Factors for Postoperative Complications in Hypospadias and Construction and Validation of a Nomogram Predictive Model. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0339188"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1007\/s00383-025-06009-7","article-title":"Development of a Clinical Prediction Model for Postoperative Complications Following Tubularized Incised Plate Urethroplasty: A Multicenter Study","volume":"41","author":"Fang","year":"2025","journal-title":"Pediatr. Surg. Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"619","DOI":"10.21037\/tp-24-368","article-title":"Nomogram-Based Prediction of Postoperative Complications in Patients with Hypospadias after Perididymis Covering","volume":"14","author":"Dong","year":"2025","journal-title":"Transl. Pediatr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2487636","DOI":"10.1080\/07853890.2025.2487636","article-title":"Predicting Postoperative Complications after Pneumonectomy Using Machine Learning: A 10-Year Study","volume":"57","author":"Wang","year":"2025","journal-title":"Ann. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106203","DOI":"10.1016\/j.ijmedinf.2025.106203","article-title":"Development and Validation of a Machine Learning Model to Predict 30-Day Mortality in Ischemic Stroke Patients with Consciousness Impairment: Insights from MIMIC-IV Database and Multicenter ICU Data in China","volume":"207","author":"Cheng","year":"2026","journal-title":"Int. J. Med. Inf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Saarela, M., and Podgorelec, V. (2024). Recent Applications of Explainable AI (XAI): A Systematic Literature Review. Appl. Sci., 14.","DOI":"10.3390\/app14198884"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1200\/JCO.2015.65.5654","article-title":"Assessing the Clinical Impact of Risk Prediction Models with Decision Curves: Guidance for Correct Interpretation and Appropriate Use","volume":"34","author":"Kerr","year":"2016","journal-title":"J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Allyn, J., Allou, N., Augustin, P., Philip, I., Martinet, O., Belghiti, M., Provenchere, S., Montravers, P., and Ferdynus, C. (2017). A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169772"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Baray, S.B., Abdelmoniem, M., Mahmud, S., Kabir, S., Faisal, M.A.A., Chowdhury, M.E.H., and Abbas, T.O. (2023). Automated Measurement of Penile Curvature Using Deep Learning-Based Novel Quantification Method. Front. Pediatr., 11.","DOI":"10.3389\/fped.2023.1149318"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Abbas, T.O., AbdelMoniem, M., and Chowdhury, M.E.H. (2022). Automated Quantification of Penile Curvature Using Artificial Intelligence. Front. Artif. Intell., 5.","DOI":"10.3389\/frai.2022.954497"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"373.e1","DOI":"10.1016\/j.jpurol.2023.03.033","article-title":"Deep Learning Based Automated Quantification of Urethral Plate Characteristics Using the Plate Objective Scoring Tool (POST)","volume":"19","author":"Abbas","year":"2023","journal-title":"J. Pediatr. Urol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.urology.2020.09.019","article-title":"Digital Pattern Recognition for the Identification and Classification of Hypospadias Using Artificial Intelligence vs Experienced Pediatric Urologist","volume":"147","author":"Fernandez","year":"2021","journal-title":"Urology"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, Z., Yang, B., Tang, Y., and Wang, X. (2024). Development and Verification of Machine Learning Model Based on Anogenital Distance, Penoscrotal Distance, and 2D:4D Finger Ratio before Puberty to Predict Hypospadias Classification. Front. Pediatr., 12.","DOI":"10.3389\/fped.2024.1297642"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"288.e1","DOI":"10.1016\/j.jpurol.2023.01.005","article-title":"Neural Network Non-Linear Modeling to Predict Hypospadias Genotype-Phenotype Correlation","volume":"19","author":"Fernandez","year":"2023","journal-title":"J. Pediatr. Urol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.jpurol.2022.08.005","article-title":"Complication Rates of Proximal Hypospadias: Meta-Analyses of Four Surgical Repairs","volume":"18","author":"Cousin","year":"2022","journal-title":"J. Pediatr. Urol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1097\/JU.0000000000004715","article-title":"Long-Term Risk of Reoperation after Hypospadias Surgery in Childhood: A Data-Linkage Study","volume":"214","author":"Vavilov","year":"2025","journal-title":"J. Urol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Section on Urology (1996). Timing of Elective Surgery on the Genitalia of Male Children with Particular Reference to the Risks, Benefits, and Psychological Effects of Surgery and Anesthesia. Pediatrics, 97, 590\u2013594.","DOI":"10.1542\/peds.97.4.590"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.eururo.2025.02.022","article-title":"European Association of Urology-European Society of Paediatric Urology Guidelines on Paediatric Urology: Summary of 2024 Updates. Part II","volume":"88","author":"Skott","year":"2025","journal-title":"Eur. Urol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abbas, T. (2025). PREDICT-H Protocol: A Multicenter Prospective Cohort Study on Preoperative Anatomical Determinants and Postoperative Complications in Primary Hypospadias Repair. Diagnostics, 15.","DOI":"10.3390\/diagnostics15162087"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI Statement: Updated Guidance for Reporting Clinical Prediction Models That Use Regression or Machine Learning Methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"}],"container-title":["Children"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9067\/13\/7\/962\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T04:11:13Z","timestamp":1784779873000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9067\/13\/7\/962"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,21]]},"references-count":27,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["children13070962"],"URL":"https:\/\/doi.org\/10.3390\/children13070962","relation":{},"ISSN":["2227-9067"],"issn-type":[{"value":"2227-9067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,21]]}}}