Forecasting Postoperative Pain Following Knee Arthroplasty: Anticipating Adverse Outcomes and Managing Expectations

4 pages•Published: December 17, 2024

Julien Lebleu, Andries Pauwels, Eduardo Vannini, Ward Servaes, Wanne Wiersinga and Pierre-Antoine Absil

Abstract

Early identification and prediction of chronic pain in patients after total knee arthroplasty can significantly impact treatment strategies and improve patient satisfaction. This study introduces an innovative artificial intelligence model that predicts pain levels and pain evolution after TKA, empowering surgeons with insights for personalized patient care.
The pain intensity was measured with a visual analog scale on a mobile application, from 1650 knee arthroplasty patients from one week before surgery and up to 12 weeks after surgery. A training set was first used to identify patterns in the data that could best approximate pain trajectories. Out-of-sample pain trajectories were predicted by estimating pattern weights and reconstructing the remaining timepoints. Confidence intervals were calculated to determine prediction accuracy.
The model's accuracy was evaluated based on the percentage of predictions falling within 10 % of the true pain values. With an observation time of up to week 2, the model achieved 67% accuracy in forecasting pain levels for the next 4 weeks, and 61% accuracy for the next 10 weeks. By extending the observation time to week 4, the accuracy improved to 84% and 69% respectively.
The artificial intelligence model showed promising results in predicting pain evolution. By utilizing this model, surgeon teams can manage patient expectations and tailor pain management strategies. The model's predictions facilitate efficient tele- monitoring, enabling remote patient monitoring of patient with less good evolution prediction, reducing the need for frequent clinic visits. Incorporating this technology into surgical practice can enhance surgical outcomes and patient satisfaction.

Keyphrases: artificial intelligence, chronic pain, personalisation, satisfaction, telemonitoring, total knee arthroplasty

In: Joshua W Giles and Aziliz Guezou-Philippe (editors). Proceedings of The 24th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 7, pages 141-144.

Links:	https://easychair.org/publications/paper/RX92
	https://doi.org/10.29007/873v

BibTeX entry

@inproceedings{CAOS2024:Forecasting_Postoperative_Pain_Following,
  author    = {Julien Lebleu and Andries Pauwels and Eduardo Vannini and Ward Servaes and Wanne Wiersinga and Pierre-Antoine Absil},
  title     = {Forecasting Postoperative Pain Following Knee Arthroplasty: Anticipating Adverse Outcomes and Managing Expectations},
  booktitle = {Proceedings of The 24th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Joshua W Giles and Aziliz Guezou-Philippe},
  series    = {EPiC Series in Health Sciences},
  volume    = {7},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {/publications/paper/RX92},
  doi       = {10.29007/873v},
  pages     = {141-144},
  year      = {2024}}

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