Development of a clinical risk score for pain and function following total knee arthroplasty

results from the TRIO study

Joanna Shim, David J Mclernon, David Hamilton, Hamish A Simpson, Marcus Beasley, Gary J Macfarlane

Research output: Contribution to journalArticle

4 Citations (Scopus)
7 Downloads (Pure)

Abstract

ObjectivesTo develop and validate a simple clinical prediction model, based on easily collected preoperative information, to identify patients at high risk of pain and functional disability 6 months after total knee arthroplasty (TKA).MethodsThis was a multi-centre cohort study of patients from 9 centres across the UK, who were undergoing a primary TKA for osteoarthritis. Information on socio-demographic, psychosocial, clinical, and quality of life measures were collected at recruitment. The primary outcome measure for this analysis was Oxford Knee Score, measured 6 months postoperatively by postal questionnaire. Multivariable logistic regression was used to develop the model. Model performance (discrimination and calibration) and internal validity was assessed, and a simple clinical risk score developed.Results721 participants (mean age 68.3 years; 53% female) provided data for the current analysis and 14% had a poor outcome at 6 months. Key predictors were poor clinical status, widespread body pain, high expectation of postoperative pain, and lack of active coping. The developed model based on these variables demonstrated good discrimination. At the optimal cut-off, the final model had a sensitivity of 83%, specificity of 61%, and positive likelihood ratio of 2.11. Excellent agreement was found between observed and predicted outcomes, and there was no evidence of overfitting in the model.ConclusionWe have developed and validated a clinical prediction model that can be used to identify patients at high risk of a poor outcome after TKA. This clinical risk score may be an aid to shared decision-making between patient and clinician.
Original languageEnglish
Article numberrky021
JournalRheumatology Advances in Practice
Volume2
Issue number2
Early online date29 May 2018
DOIs
Publication statusPublished - 1 Jul 2018

Fingerprint

Knee Replacement Arthroplasties
Pain
Knee Osteoarthritis
Postoperative Pain
Calibration
Knee
Decision Making
Cohort Studies
Logistic Models
Quality of Life
Demography
Outcome Assessment (Health Care)
Sensitivity and Specificity
Discrimination (Psychology)

Keywords

  • knee pain
  • osteoarthritis
  • total knee arthroplasty
  • prediction modelling
  • clinical risk score
  • model calibration
  • model discrimination

Cite this

@article{95903dbc64c24f52915668cdac01896a,
title = "Development of a clinical risk score for pain and function following total knee arthroplasty: results from the TRIO study",
abstract = "ObjectivesTo develop and validate a simple clinical prediction model, based on easily collected preoperative information, to identify patients at high risk of pain and functional disability 6 months after total knee arthroplasty (TKA).MethodsThis was a multi-centre cohort study of patients from 9 centres across the UK, who were undergoing a primary TKA for osteoarthritis. Information on socio-demographic, psychosocial, clinical, and quality of life measures were collected at recruitment. The primary outcome measure for this analysis was Oxford Knee Score, measured 6 months postoperatively by postal questionnaire. Multivariable logistic regression was used to develop the model. Model performance (discrimination and calibration) and internal validity was assessed, and a simple clinical risk score developed.Results721 participants (mean age 68.3 years; 53{\%} female) provided data for the current analysis and 14{\%} had a poor outcome at 6 months. Key predictors were poor clinical status, widespread body pain, high expectation of postoperative pain, and lack of active coping. The developed model based on these variables demonstrated good discrimination. At the optimal cut-off, the final model had a sensitivity of 83{\%}, specificity of 61{\%}, and positive likelihood ratio of 2.11. Excellent agreement was found between observed and predicted outcomes, and there was no evidence of overfitting in the model.ConclusionWe have developed and validated a clinical prediction model that can be used to identify patients at high risk of a poor outcome after TKA. This clinical risk score may be an aid to shared decision-making between patient and clinician.",
keywords = "knee pain, osteoarthritis, total knee arthroplasty, prediction modelling, clinical risk score, model calibration, model discrimination",
author = "Joanna Shim and Mclernon, {David J} and David Hamilton and Simpson, {Hamish A} and Marcus Beasley and Macfarlane, {Gary J}",
note = "Funding Targeted Rehabilitation to Improve Outcome after knee replacement (TRIO) was supported by Arthritis Research UK (Grant No: 20100) (chief investigator TRIO-Physio, Prof. Hamish A Simpson; chief investigator TRIO-POPULAR, Prof. Gary J Macfarlane).",
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journal = "Rheumatology Advances in Practice",
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T1 - Development of a clinical risk score for pain and function following total knee arthroplasty

T2 - results from the TRIO study

AU - Shim, Joanna

AU - Mclernon, David J

AU - Hamilton, David

AU - Simpson, Hamish A

AU - Beasley, Marcus

AU - Macfarlane, Gary J

N1 - Funding Targeted Rehabilitation to Improve Outcome after knee replacement (TRIO) was supported by Arthritis Research UK (Grant No: 20100) (chief investigator TRIO-Physio, Prof. Hamish A Simpson; chief investigator TRIO-POPULAR, Prof. Gary J Macfarlane).

PY - 2018/7/1

Y1 - 2018/7/1

N2 - ObjectivesTo develop and validate a simple clinical prediction model, based on easily collected preoperative information, to identify patients at high risk of pain and functional disability 6 months after total knee arthroplasty (TKA).MethodsThis was a multi-centre cohort study of patients from 9 centres across the UK, who were undergoing a primary TKA for osteoarthritis. Information on socio-demographic, psychosocial, clinical, and quality of life measures were collected at recruitment. The primary outcome measure for this analysis was Oxford Knee Score, measured 6 months postoperatively by postal questionnaire. Multivariable logistic regression was used to develop the model. Model performance (discrimination and calibration) and internal validity was assessed, and a simple clinical risk score developed.Results721 participants (mean age 68.3 years; 53% female) provided data for the current analysis and 14% had a poor outcome at 6 months. Key predictors were poor clinical status, widespread body pain, high expectation of postoperative pain, and lack of active coping. The developed model based on these variables demonstrated good discrimination. At the optimal cut-off, the final model had a sensitivity of 83%, specificity of 61%, and positive likelihood ratio of 2.11. Excellent agreement was found between observed and predicted outcomes, and there was no evidence of overfitting in the model.ConclusionWe have developed and validated a clinical prediction model that can be used to identify patients at high risk of a poor outcome after TKA. This clinical risk score may be an aid to shared decision-making between patient and clinician.

AB - ObjectivesTo develop and validate a simple clinical prediction model, based on easily collected preoperative information, to identify patients at high risk of pain and functional disability 6 months after total knee arthroplasty (TKA).MethodsThis was a multi-centre cohort study of patients from 9 centres across the UK, who were undergoing a primary TKA for osteoarthritis. Information on socio-demographic, psychosocial, clinical, and quality of life measures were collected at recruitment. The primary outcome measure for this analysis was Oxford Knee Score, measured 6 months postoperatively by postal questionnaire. Multivariable logistic regression was used to develop the model. Model performance (discrimination and calibration) and internal validity was assessed, and a simple clinical risk score developed.Results721 participants (mean age 68.3 years; 53% female) provided data for the current analysis and 14% had a poor outcome at 6 months. Key predictors were poor clinical status, widespread body pain, high expectation of postoperative pain, and lack of active coping. The developed model based on these variables demonstrated good discrimination. At the optimal cut-off, the final model had a sensitivity of 83%, specificity of 61%, and positive likelihood ratio of 2.11. Excellent agreement was found between observed and predicted outcomes, and there was no evidence of overfitting in the model.ConclusionWe have developed and validated a clinical prediction model that can be used to identify patients at high risk of a poor outcome after TKA. This clinical risk score may be an aid to shared decision-making between patient and clinician.

KW - knee pain

KW - osteoarthritis

KW - total knee arthroplasty

KW - prediction modelling

KW - clinical risk score

KW - model calibration

KW - model discrimination

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DO - 10.1093/rap/rky021

M3 - Article

VL - 2

JO - Rheumatology Advances in Practice

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ER -