Download PDFOpen PDF in browser

Preliminary Validation of a Patient Outcome Prediction Tool Relative to Surgeon Predictions of Patient Outcome

5 pagesPublished: September 26, 2020

Abstract

Currently, patient selection for Total Knee Arthroplasties (TKA) is surgeon specific. A combination of patient reported symptoms, clinical examination findings, and radiological criteria are combined in an idiosyncratic fashion based on the surgeon’s individual clinical experiences. Predictive models offer an alternative by providing more detailed information on expected patient outcome. This study presents validation relative to surgeon predictions of such a predictive model, the Patient Expectation Management (PEM) tool.
A cohort of patients undertook a survey covering the KOOS questionnaire and a number of other questions pertaining to comorbidities prior to their consultation. From this survey, a prediction of final state and assessment of current state was generated. Prior to seeing the prediction but after consulting the patient, surgeons were asked to score out of 100 a) their understanding of the patient’s current pain state and b) their prediction of the patient’s pain level following surgery. 35 of the patients were selected for TKA surgery and have gone on to have 12 month Knee Osteoarthritis and Outcome Scores (KOOS) captured.
The predicted change in the PEM predicted score (preop to postop difference) had a relatively high correlation with the actual KOOS pain improvement achieved (r=0.71, p<0.001), compared to no significant correlation for the surgeon prediction (r=0.24, p=0.20). Significant correlations also existed for changes in KOOS symptoms score (r=0.70, p<0.001) and KOOS Activities of Daily Living (ADL) score (r=0.42, p=0.02).
This study showed that, compared to a set of surgeon predictions of outcome following a consultation with patients, a predictive analytics tool was able to outperform in terms of predicting the improvement patients are likely to report following TKA.

Keyphrases: Consultation, outcome, Total knee arthroplasty

In: Ferdinando Rodriguez Y Baena and Fabio Tatti (editors). CAOS 2020. The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 4, pages 264--268

Links:
BibTeX entry
@inproceedings{CAOS2020:Preliminary_Validation_of_Patient,
  author    = {Joshua Twiggs and David Liu and Justin Roe and David Parker and Brad Miles},
  title     = {Preliminary Validation of a Patient Outcome Prediction Tool Relative to Surgeon Predictions of Patient Outcome},
  booktitle = {CAOS 2020. The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Ferdinando Rodriguez Y Baena and Fabio Tatti},
  series    = {EPiC Series in Health Sciences},
  volume    = {4},
  pages     = {264--268},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {https://easychair.org/publications/paper/TfrS},
  doi       = {10.29007/6t3s}}
Download PDFOpen PDF in browser