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Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection

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Knee Surgery, Sports Traumatology, Arthroscopy Aims and scope

Abstract

Purpose

This study aimed to develop and validate machine-learning models for the prediction of recurrent infection in patients following revision total knee arthroplasty for periprosthetic joint infection.

Methods

A total of 618 consecutive patients underwent revision total knee arthroplasty for periprosthetic joint infection. The patient cohort included 165 patients with confirmed recurrent periprosthetic joint infection (PJI). Potential risk factors including patient demographics and surgical characteristics served as input to three machine-learning models which were developed to predict recurrent periprosthetic joint. The machine-learning models were assessed by discrimination, calibration and decision curve analysis.

Results

The factors most significantly associated with recurrent PJI in patients following revision total knee arthroplasty for PJI included irrigation and debridement with/without modular component exchange (p < 0.001), > 4 prior open surgeries (p < 0.001), metastatic disease (p < 0.001), drug abuse (p < 0.001), HIV/AIDS (p < 0.01), presence of Enterococcus species (p < 0.01) and obesity (p < 0.01). The machine-learning models all achieved excellent performance across discrimination (AUC range 0.81–0.84).

Conclusion

This study developed three machine-learning models for the prediction of recurrent infections in patients following revision total knee arthroplasty for periprosthetic joint infection. The strongest predictors were previous irrigation and debridement with or without modular component exchange and prior open surgeries. The study findings show excellent model performance, highlighting the potential of these computational tools in quantifying increased risks of recurrent PJI to optimize patient outcomes.

Level of evidence

IV.

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Data availability

Data are available upon request.

Code availability

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 St, Boston, MA, 02114, USA

    Christian Klemt, Samuel Laurencin, Akachimere Cosmas Uzosike, Jillian C. Burns, Timothy G. Costales, Ingwon Yeo, Yasamin Habibi & Young-Min Kwon

Authors
  1. Christian Klemt
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  2. Samuel Laurencin
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  3. Akachimere Cosmas Uzosike
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  4. Jillian C. Burns
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  5. Timothy G. Costales
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  6. Ingwon Yeo
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  7. Yasamin Habibi
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  8. Young-Min Kwon
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Contributions

CK: data collection, analysis, write-up. SL: data collection, analysis, write-up. ACU: write-up. JCB: data collection. TGC: write-up. IY: data collection. YH: data collection. Y-MK: analysis, write-up.

Corresponding author

Correspondence to Young-Min Kwon.

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All authors report no conflict of interest or financial disclosures.

Ethical approval

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

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Klemt, C., Laurencin, S., Uzosike, A.C. et al. Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection. Knee Surg Sports Traumatol Arthrosc 30, 2582–2590 (2022). https://doi.org/10.1007/s00167-021-06794-3

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  • DOI: https://doi.org/10.1007/s00167-021-06794-3

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