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Development and Internal Validation of Machine Learning Algorithms for Predicting Hyponatremia After TJA.

Kyle N Kunze1, Peter K Sculco1, Haoyan Zhong2,3

  • 1Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY.

The Journal of Bone and Joint Surgery. American Volume
|December 13, 2021
PubMed
Summary

Machine learning accurately predicts hyponatremia after total joint arthroplasty (TJA). Key predictors include preoperative sodium, age, blood loss, procedure time, BMI, and ASA score, enabling risk assessment.

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Area of Science:

  • Orthopedics
  • Medical Informatics
  • Nephrology

Background:

  • Hyponatremia post-total joint arthroplasty (TJA) is linked to adverse events, longer hospital stays, and increased costs.
  • Developing predictive models for hyponatremia is crucial for patient management and resource optimization.

Purpose of the Study:

  • To develop and internally validate machine learning (ML) algorithms for predicting hyponatremia after TJA.
  • To identify key clinical variables associated with postoperative hyponatremia risk.

Main Methods:

  • A cohort of 30,703 TJA patients was analyzed.
  • Five ML algorithms were trained and validated using a dataset of 19 potential predictor variables.
  • Performance was assessed using discrimination, calibration, decision-curve analysis, and Brier score.

Main Results:

  • 17.8% of TJA patients developed hyponatremia.
  • The stochastic gradient boosting (SGB) algorithm achieved the best performance (c-statistic: 0.75).
  • Key predictors identified were preoperative serum sodium, age, intraoperative blood loss, procedure time, BMI, and ASA score.

Conclusions:

  • The SGB algorithm effectively predicts hyponatremia risk following TJA.
  • A real-time risk calculator was developed, but external validation is necessary before clinical implementation.