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Artificial neural networks for the prediction of transfusion rates in primary total hip arthroplasty

  • Hip Arthroplasty
  • Published:
Archives of Orthopaedic and Trauma Surgery Aims and scope Submit manuscript

Abstract

Background

Despite advancements in total hip arthroplasty (THA) and the increased utilization of tranexamic acid, acute blood loss anemia necessitating allogeneic blood transfusion persists as a post-operative complication. The prevalence of allogeneic blood transfusion in primary THA has been reported to be as high as 9%. Therefore, this study aimed to develop and validate novel machine learning models for the prediction of transfusion rates following primary total hip arthroplasty.

Methods

A total of 7265 consecutive patients who underwent primary total hip arthroplasty were evaluated using a single tertiary referral institution database. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with transfusion rates. Four state-of-the-art machine learning algorithms were developed to predict transfusion rates following primary THA, and these models were assessed by discrimination, calibration, and decision curve analysis.

Results

The factors most significantly associated with transfusion rates include tranexamic acid usage, bleeding disorders, and pre-operative hematocrit (< 33%). The four machine learning models all achieved excellent performance across discrimination (AUC > 0.78), calibration, and decision curve analysis.

Conclusion

This study developed machine learning models for the prediction of patient-specific transfusion rates following primary total hip arthroplasty. The results represent a novel application of machine learning, and has the potential to improve outcomes and pre-operative planning.

Level of evidence

III, case–control retrospective analysis.

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Authors and Affiliations

  1. Bioengineering Department, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA

    Wayne Brian Cohen-Levy, Christian Klemt, Venkatsaiakhil Tirumala, Jillian C. Burns, Ameen Barghi, Yasamin Habibi & Young-Min Kwon

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  1. Wayne Brian Cohen-Levy
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Correspondence to Young-Min Kwon.

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The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

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Cohen-Levy, W.B., Klemt, C., Tirumala, V. et al. Artificial neural networks for the prediction of transfusion rates in primary total hip arthroplasty. Arch Orthop Trauma Surg 143, 1643–1650 (2023). https://doi.org/10.1007/s00402-022-04391-8

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  • DOI: https://doi.org/10.1007/s00402-022-04391-8

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