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Using machine and deep learning to predict short-term complications following trigger digit release surgery.

Rohan M Shah1, Rushmin Khazanchi1, Anitesh Bajaj1

  • 1Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Journal of Hand and Microsurgery
|January 29, 2025
PubMed
Summary

Machine learning models can predict complications after trigger finger surgery. Random Forest and XGBoost showed promise in identifying patients at higher risk for adverse outcomes, aiding surgical decision-making.

Keywords:
Deep learningMachine learningTrigger finger release

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Area of Science:

  • Orthopedic Surgery
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Trigger finger is a common hand condition causing pain and locking.
  • Surgical release of the A1 pulley is an option for refractory cases.
  • Predicting short-term complications after surgery is crucial for patient management.

Purpose of the Study:

  • To evaluate machine learning (ML) techniques for predicting short-term complications after trigger digit release surgery.
  • To identify key predictors of complications using feature importance analysis.
  • To assess the performance of different ML algorithms in risk stratification.

Main Methods:

  • Retrospective analysis of 1209 trigger digit release cases (2005-2020) from the ACS-NSQIP database.
  • Evaluation of Random Forest (RF), Elastic-Net Regression (ENet), Extreme Gradient Boosted Tree (XGBoost), and Neural Network (NN) algorithms.
  • Analysis of 30-day complications and reoperations, with feature importance assessment.

Main Results:

  • XGBoost performed best for medical complications (AUC: 0.70) and reoperations (AUC: 0.60).
  • Random Forest was optimal for predicting wound complications (AUC: 0.64).
  • All models significantly outperformed the benchmark AUC of 0.50; age was a key predictor for wound complications.

Conclusions:

  • Machine learning is effective for risk stratification in surgical patients undergoing trigger digit release.
  • Hand surgeons should explore further applications of ML in surgical practice.
  • ML aids in identifying patients at risk for post-operative complications, improving surgical outcomes.