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Circumferential Labral Reconstruction With Knotless All-Suture Anchors Restores Hip Distractive Stability: A Cadaveric Biomechanical Analysis.

The American journal of sports medicine·2024
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Ten-Year Outcomes in Patients Aged 40 Years and Older After Primary Arthroscopic Treatment of Femoroacetabular Impingement With Labral Repair.

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Personalized Medicine Using Predictive Analytics: A Machine Learning-Based Prognostic Model for Patients Undergoing

Benjamin G Domb1,2, Vivian W Ouyang1, Cammille C Go1

  • 1American Hip Institute Research Foundation, Chicago, Illinois, USA.

The American Journal of Sports Medicine
|May 10, 2022
PubMed
Summary

This study developed predictive models for hip arthroscopy outcomes using preoperative patient data. These models aid in personalized prognostication and shared decision-making for better surgical planning.

Keywords:
hip arthroscopymachine learningorthopaedicspersonalized medicine

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Area of Science:

  • Orthopaedic Surgery
  • Machine Learning
  • Predictive Analytics

Background:

  • Personalized medicine models for predicting orthopaedic surgery outcomes are limited.
  • Existing models often require postoperative data, hindering preoperative decision-making.

Purpose of the Study:

  • To develop a predictive modeling method for individualized prognostication.
  • To enable shared decision-making using preoperative patient factors.
  • To utilize data from a prospective hip preservation registry.

Main Methods:

  • Retrospective analysis of 2415 patients undergoing hip arthroscopy for femoroacetabular impingement syndrome.
  • Evaluation of Tree-structured survival analysis (TSSA) and Cox proportional hazards modeling.
  • Development of a web-based calculator based on validated models.

Main Results:

  • Cox and Fine-Gray models successfully predicted survivorship (C-statistic=0.848) and repeat hip arthroscopy (C-statistic=0.662).
  • 13 preoperative variables predicted survivorship; 6 predicted repeat surgery.
  • TSSA model performed poorly (<0.6 C-statistic), deemed inaccurate and uninterpretable.

Conclusions:

  • An institution-specific, machine learning-based prognostic model for hip arthroscopy was created.
  • Web-based tools were developed to aid physicians in shared decision-making.
  • The methodology may be applicable to other orthopaedic surgery predictions, advancing personalized medicine.