Validation of Machine Learning Model Performance in Predicting Blood Transfusion After Primary and Revision Total Hip Arthroplasty

Anirudh Buddhiraju; Michelle Riyo Shimizu; Murad A Subih; Tony Lin-Wei Chen; Henry Hojoon Seo; Young-Min Kwon

doi:10.1016/j.arth.2023.06.002

Validation of Machine Learning Model Performance in Predicting Blood Transfusion After Primary and Revision Total Hip Arthroplasty

J Arthroplasty. 2023 Oct;38(10):1959-1966. doi: 10.1016/j.arth.2023.06.002. Epub 2023 Jun 12.

Authors

Anirudh Buddhiraju¹, Michelle Riyo Shimizu¹, Murad A Subih¹, Tony Lin-Wei Chen¹, Henry Hojoon Seo¹, Young-Min Kwon¹

Affiliation

¹ Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.

PMID: 37315632
DOI: 10.1016/j.arth.2023.06.002

Abstract

Background: The rates of blood transfusion following primary and revision total hip arthroplasty (THA) remain as high as 9% and 18%, respectively, contributing to patient morbidity and healthcare costs. Existing predictive tools are limited to specific populations, thereby diminishing their clinical applicability. This study aimed to externally validate our previous institutionally developed machine learning (ML) algorithms to predict the risk of postoperative blood transfusion following primary and revision THA using national inpatient data.

Methods: Five ML algorithms were trained and validated using data from 101,266 primary THA and 8,594 revision THA patients from a large national database to predict postoperative transfusion risk after primary and revision THA. Models were assessed and compared based on discrimination, calibration, and decision curve analysis.

Results: The most important predictors of transfusion following primary and revision THA were preoperative hematocrit (<39.4%) and operation time (>157 minutes), respectively. All ML models demonstrated excellent discrimination (area under the curve (AUC) >0.8) in primary and revision THA patients, with artificial neural network (AUC = 0.84, slope = 1.11, intercept = -0.04, Brier score = 0.04), and elastic-net-penalized logistic regression (AUC = 0.85, slope = 1.08, intercept = -0.01, and Brier score = 0.12) performing best, respectively. On decision curve analysis, all 5 models demonstrated a higher net benefit than the conventional strategy of intervening for all or no patients in both patient cohorts.

Conclusions: This study successfully validated our previous institutionally developed ML algorithms for the prediction of blood transfusion following primary and revision THA. Our findings highlight the potential generalizability of predictive ML tools developed using nationally representative data in THA patients.

Keywords: clinical prediction; machine learning; total hip arthroplasties; transfusion; validation.

MeSH terms

Algorithms
Arthroplasty, Replacement, Hip* / adverse effects
Blood Transfusion
Humans
Machine Learning
Neural Networks, Computer
Retrospective Studies