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Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models

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

Abstract

Background

Prolonged surgical operative time is associated with postoperative adverse outcomes following total knee arthroplasty (TKA). Increasing operating room efficiency necessitates the accurate prediction of surgical operative time for each patient. One potential way to increase the accuracy of predictions is to use advanced predictive analytics, such as machine learning. The aim of this study is to use machine learning to develop an accurate predictive model for surgical operative time for patients undergoing primary total knee arthroplasty.

Methods

A retrospective chart review of electronic medical records was conducted to identify patients who underwent primary total knee arthroplasty at a tertiary referral center. Three machine learning algorithms were developed to predict surgical operative time and were assessed by discrimination, calibration and decision curve analysis. Specifically, we used: (1) Artificial Neural Networks (ANNs), (2) Random Forest (RF), and (3) K-Nearest Neighbor (KNN).

Results

We analyzed the surgical operative time for 10,021 consecutive patients who underwent primary total knee arthroplasty. The neural network model achieved the best performance across discrimination (AUC = 0.82), calibration and decision curve analysis for predicting surgical operative time. Based on this algorithm, younger age (< 45 years), tranexamic acid non-usage, and a high BMI (> 40 kg/m2) were the strongest predictors associated with surgical operative time.

Conclusions

This study shows excellent performance of machine learning models for predicting surgical operative time in primary total knee arthroplasty. The accurate estimation of surgical duration is important in enhancing OR efficiency and identifying patients at risk for prolonged surgical operative time.

Level of evidence

Level III, case control retrospective analysis.

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Funding

This study did not receive any funding. All authors report no conflict of interest or financial disclosures. There is no funding source.

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Authors and Affiliations

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

    Ingwon Yeo, Christian Klemt, Christopher M. Melnic, Meghan H. Pattavina, Bruna M. Castro De Oliveira & Young-Min Kwon

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  2. Christian Klemt
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Correspondence to Young-Min Kwon.

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Yeo, I., Klemt, C., Melnic, C.M. et al. Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models. Arch Orthop Trauma Surg 143, 3299–3307 (2023). https://doi.org/10.1007/s00402-022-04588-x

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