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Neural network models accurately predict discharge disposition after revision total knee arthroplasty?

  • KNEE
  • Published:
Knee Surgery, Sports Traumatology, Arthroscopy Aims and scope

Abstract

Purpose

Based on the rising incidence of revision total knee arthroplasty (TKA), bundled payment models may be applied to revision TKA in the near future. Facility discharge represents a significant cost factor for those bundled payment models; however, accurately predicting discharge disposition remains a clinical challenge. The purpose of this study was to develop and validate artificial intelligence algorithms to predict discharge disposition following revision total knee arthroplasty.

Methods

A retrospective review of electronic patient records was conducted to identify patients who underwent revision total knee arthroplasty. Discharge disposition was defined as either home discharge or non-home discharge, which included rehabilitation and skilled nursing facilities. Four artificial intelligence algorithms were developed to predict this outcome and were assessed by discrimination, calibration and decision curve analysis.

Results

A total of 2228 patients underwent revision TKA, of which 1405 patients (63.1%) were discharged home, whereas 823 patients (36.9%) were discharged to a non-home facility. The strongest predictors for non-home discharge following revision TKA were American Society of Anesthesiologist (ASA) score, Medicare insurance type and revision surgery for peri-prosthetic joint infection, non-white ethnicity and social status (living alone). The best performing artificial intelligence algorithm was the neural network model which achieved excellent performance across discrimination (AUC = 0.87), calibration and decision curve analysis.

Conclusion

This study developed four artificial intelligence algorithms for the prediction of non-home discharge disposition for patients following revision total knee arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four candidate algorithms. Therefore, these models have the potential to guide preoperative patient counselling and improve the value (clinical and functional outcomes divided by costs) of revision total knee arthroplasty patients.

Level of evidence

IV.

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Funding

The study did not receive any funding. Data are available upon request. Only standard software was used for analysis. CK: data collection, analysis, write-up. ACU: data collection, analysis, write-up. MJH: write-up. SL: data collection. YH: data collection. Y-MK: analysis, write-up.

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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

    Christian Klemt, Akachimere Cosmas Uzosike, Michael Joseph Harvey, Samuel Laurencin, Yasamin Habibi & Young-Min Kwon

Authors
  1. Christian Klemt
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  2. Akachimere Cosmas Uzosike
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  3. Michael Joseph Harvey
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  4. Samuel Laurencin
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  5. Yasamin Habibi
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  6. Young-Min Kwon
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Correspondence to Young-Min Kwon.

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All authors report no conflict of interest or financial disclosures.

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This study was approved by the Institutional Review Board (IRB).

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Klemt, C., Uzosike, A.C., Harvey, M.J. et al. Neural network models accurately predict discharge disposition after revision total knee arthroplasty?. Knee Surg Sports Traumatol Arthrosc 30, 2591–2599 (2022). https://doi.org/10.1007/s00167-021-06778-3

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  • DOI: https://doi.org/10.1007/s00167-021-06778-3

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