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Artificial intelligence algorithms accurately predict prolonged length of stay following revision total knee arthroplasty

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Knee Surgery, Sports Traumatology, Arthroscopy Aims and scope

Abstract

Purpose

Although the average length of hospital stay following revision total knee arthroplasty (TKA) has decreased over recent years due to improved perioperative and intraoperative techniques and planning, prolonged length of stay (LOS) continues to be a substantial driver of hospital costs. The purpose of this study was to develop and validate artificial intelligence algorithms for the prediction of prolonged length of stay for patients following revision TKA.

Methods

A total of 2512 consecutive patients who underwent revision TKA were evaluated. Those patients with a length of stay greater than 75th percentile for all length of stays were defined as patients with prolonged LOS. Three artificial intelligence algorithms were developed to predict prolonged LOS following revision TKA and these models were assessed by discrimination, calibration and decision curve analysis.

Results

The strongest predictors for prolonged length of stay following revision TKA were age (> 75 years; p < 0.001), Charlson Comorbidity Index (> 6; p < 0.001) and body mass index (> 35 kg/m2; p < 0.001). The three artificial intelligence algorithms all achieved excellent performance across discrimination (AUC > 0.84) and decision curve analysis (p < 0.01).

Conclusion

The study findings demonstrate excellent performance on discrimination, calibration and decision curve analysis for all three candidate algorithms. This highlights the potential of these artificial intelligence algorithms to assist in the preoperative identification of patients with an increased risk of prolonged LOS following revision TKA, which may aid in strategic discharge planning.

Level of evidence

IV.

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Data availability

Data are available upon request. Only standard software was used for analysis.

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Funding

The study did not receive any funding.

Author information

Authors and Affiliations

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

    Christian Klemt, Venkatsaiakhil Tirumala, Ameen Barghi, Wayne Brian Cohen-Levy, Matthew Gerald Robinson & Young-Min Kwon

Authors
  1. Christian Klemt
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  2. Venkatsaiakhil Tirumala
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  3. Ameen Barghi
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  4. Wayne Brian Cohen-Levy
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  5. Matthew Gerald Robinson
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  6. Young-Min Kwon
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Contributions

CK: data collection, analysis, write-up. VT: data collection. AB: analysis. WBC-L: write-up. MGR: write-up. Y-MK: study design, write-up.

Corresponding author

Correspondence to Young-Min Kwon.

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All authors report no conflict of interest or financial disclosures.

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We acknowledge that this study was approved by the institutional review board (IRB).

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Klemt, C., Tirumala, V., Barghi, A. et al. Artificial intelligence algorithms accurately predict prolonged length of stay following revision total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc 30, 2556–2564 (2022). https://doi.org/10.1007/s00167-022-06894-8

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  • DOI: https://doi.org/10.1007/s00167-022-06894-8

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