ORIGINAL RESEARCH article
Front. Surg.
Sec. Pediatric Surgery
Volume 12 - 2025 | doi: 10.3389/fsurg.2025.1688702
This article is part of the Research TopicFractures and Deformities of the Extremities in Children and Adolescents: Etiology, Diagnosis, and Treatment: 2025View all 10 articles
Explainable machine learning-based prediction of early and mid-term postoperative complications in adolescent tibial fractures
Provisionally accepted
- Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: Adolescent tibial fractures commonly lead to postoperative complications. Conventional coagulation markers (PT/APTT/FIB) lack combinatorial risk assessment. We developed an explainable ML model integrating coagulation and clinical features to predict adverse events. Methods: A retrospective cohort of 624 surgical patients (13-18 years) was analyzed. AutoML with Improved Harmony Search Optimization (IHSO) processed features: age, fracture classification, surgery duration, blood loss, and 24h-postoperative labs (coagulation triad/D-dimer/CRP). Primary outcome: 90-day composite adverse events (DVT/infection/early callus formation disorder/reoperation). SHAP explained predictions. Results: Baseline characteristics were balanced between training and test sets (P>0.05). The IHSO-optimized algorithm outperformed controls in 91.67% of CEC2022 benchmark functions. AutoML model performance significantly surpassed conventional methods: training set ROC-AUC 0.9667, test set ROC-AUC 0.9247 (PR-AUC 0.8350). Decision curves demonstrated clinical net benefit across 6-99% risk thresholds. Key feature importance ranked as: age > operative duration > fibrinogen > fracture classification > APTT > CRP > BMI > D-dimer. SHAP analysis revealed: 1) Increasing age significantly attenuates the risk contribution of surgery duration; 2) FIB >4.0 g/L + elevated CRP indicated coagulation-inflammation cascade; 3) AO-C type fractures carried highest risk. Conclusion: This AutoML model, validated through explainability techniques, confirms the core predictive value of age, operative duration, and coagulation-inflammation networks for adolescent tibial fracture risk management. Though requiring prospective validation, the three-tier warning system establishes a stepped framework for individualized intervention. Future studies should advance multicenter collaborations integrating dynamic monitoring indicators to optimize clinical applicability.
Keywords: Adolescent tibial fracture, Postoperative Complications, Explainable Machine Learning, Automated machine learning, Clotting function, risk prediction, Swarm intelligence optimization, Clinical decision system
Received: 19 Aug 2025; Accepted: 29 Sep 2025.
Copyright: © 2025 Wang, Li and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yang Liu, lymys@163.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.