Predictive Tool Use and Willingness for Surgery in Patients With Knee Osteoarthritis: A Randomized Clinical Trial

Overview

Journal JAMA Netw Open

Publisher American Medical Association

Specialty General Medicine

Date 2024 Mar 8

PMID 38457182

Authors

Yushy Zhou

Lauren Patten

Tim Spelman

Samantha Bunzli

Peter F M Choong

Michelle M Dowsey

Chris Schilling

Affiliations

Soon will be listed here.

Abstract

Importance: Despite the increasing number of tools available to predict the outcomes of total knee arthroplasty (TKA), the effect of these predictive tools on patient decision-making remains uncertain.

Objective: To assess the effect of an online predictive tool on patient-reported willingness to undergo TKA.

Design, Setting, And Participants: This parallel, double-masked, 2-arm randomized clinical trial compared predictive tool use with treatment as usual (TAU). The study was conducted between June 30, 2022, and July 31, 2023. Participants were followed up for 6 months after enrollment. Participants were recruited from a major Australian private health insurance company and from the surgical waiting list for publicly funded TKA at a tertiary hospital. Eligible participants had unilateral knee osteoarthritis, were contemplating TKA, and had previously tried nonsurgical interventions, such as lifestyle modifications, physiotherapy, and pain medications.

Intervention: The intervention group was provided access to an online predictive tool at the beginning of the study. This tool offered information regarding the likelihood of improvement in quality of life if patients chose to undergo TKA. The predictions were based on the patient's age, sex, and baseline symptoms. Conversely, the control group received TAU without access to the predictive tool.

Main Outcomes And Measures: The primary outcome measure was the reduction in participants' willingness to undergo surgery at 6 months after tool use as measured by binomial logistic regression. Secondary outcome measures included participant treatment preference and the quality of their decision-making process as measured by the Knee Decision Quality Instrument.

Results: Of 211 randomized participants (mean [SD] age, 65.8 [8.3] years; 118 female [55.9%]), 105 were allocated to the predictive tool group and 106 to the TAU group. After adjusting for baseline differences in willingness for surgery, the predictive tool did not significantly reduce the primary outcome of willingness for surgery at 6 months (adjusted odds ratio, 0.85; 95% CI, 0.42-1.71; P = .64).

Conclusions And Relevance: Despite the absence of treatment effect on willingness for TKA, predictive tools might still enhance health outcomes of patients with knee osteoarthritis. Additional research is needed to optimize the design and implementation of predictive tools, address limitations, and fully understand their effect on the decision-making process in TKA.

Trial Registration: ANZCTR.org.au Identifier: ACTRN12622000072718.

References

King L, Marshall D, Faris P, Woodhouse L, Jones C, Noseworthy T . Use of Recommended Non-surgical Knee Osteoarthritis Management in Patients prior to Total Knee Arthroplasty: A Cross-sectional Study. J Rheumatol. 2019; 47(8):1253-1260. DOI: 10.3899/jrheum.190467. View

Harris I, Madan N, Naylor J, Chong S, Mittal R, Jalaludin B . Trends in knee arthroscopy and subsequent arthroplasty in an Australian population: a retrospective cohort study. BMC Musculoskelet Disord. 2013; 14:143. PMC: 3648388. DOI: 10.1186/1471-2474-14-143. View

Schilling C, Dowsey M, Petrie D, Clarke P, Choong P . Predicting the Long-Term Gains in Health-Related Quality of Life After Total Knee Arthroplasty. J Arthroplasty. 2016; 32(2):395-401.e2. DOI: 10.1016/j.arth.2016.07.036. View

Zhou Y, Weeden C, Patten L, Dowsey M, Bunzli S, Choong P . Evaluating willingness for surgery using the SMART Choice (Knee) patient prognostic tool for total knee arthroplasty: study protocol for a pragmatic randomised controlled trial. BMC Musculoskelet Disord. 2022; 23(1):179. PMC: 8876449. DOI: 10.1186/s12891-022-05123-0. View

Zhou Y, Dowsey M, Spelman T, Choong P, Schilling C . SMART choice (knee) tool: a patient-focused predictive model to predict improvement in health-related quality of life after total knee arthroplasty. ANZ J Surg. 2023; 93(1-2):316-327. DOI: 10.1111/ans.18250. View

Gunaratne R, Pratt D, Banda J, Fick D, Khan R, Robertson B . Patient Dissatisfaction Following Total Knee Arthroplasty: A Systematic Review of the Literature. J Arthroplasty. 2017; 32(12):3854-3860. DOI: 10.1016/j.arth.2017.07.021. View

Dowsey M, Spelman T, Choong P . A Nomogram for Predicting Non-Response to Surgery One Year after Elective Total Hip Replacement. J Clin Med. 2022; 11(6). PMC: 8955143. DOI: 10.3390/jcm11061649. View

Lee L, Chan P, Wen C, Fung W, Cheung A, Chan V . Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review. Arthroplasty. 2022; 4(1):16. PMC: 8897859. DOI: 10.1186/s42836-022-00118-7. View

Bansback N, Trenaman L, MacDonald K, Durand D, Hawker G, Johnson J . An online individualised patient decision aid improves the quality of decisions in patients considering total knee arthroplasty in routine care: A randomized controlled trial. Osteoarthr Cartil Open. 2022; 4(3):100286. PMC: 9718273. DOI: 10.1016/j.ocarto.2022.100286. View

10.

Saquib N, Saquib J, Ioannidis J . Practices and impact of primary outcome adjustment in randomized controlled trials: meta-epidemiologic study. BMJ. 2013; 347:f4313. PMC: 3709831. DOI: 10.1136/bmj.f4313. View

11.

Bin Abd Razak H, Tan C, Chen Y, Pang H, Tay K, Chin P . Age and Preoperative Knee Society Score Are Significant Predictors of Outcomes Among Asians Following Total Knee Arthroplasty. J Bone Joint Surg Am. 2016; 98(9):735-41. DOI: 10.2106/JBJS.15.00280. View

12.

Batailler C, Shatrov J, Sappey-Marinier E, Servien E, Parratte S, Lustig S . Artificial intelligence in knee arthroplasty: current concept of the available clinical applications. Arthroplasty. 2022; 4(1):17. PMC: 9059406. DOI: 10.1186/s42836-022-00119-6. View

13.

Adie S, Harris I, Chuan A, Lewis P, Naylor J . Selecting and optimising patients for total knee arthroplasty. Med J Aust. 2019; 210(3):135-141. DOI: 10.5694/mja2.12109. View

14.

Jiang Y, Sanchez-Santos M, Judge A, Murray D, Arden N . Predictors of Patient-Reported Pain and Functional Outcomes Over 10 Years After Primary Total Knee Arthroplasty: A Prospective Cohort Study. J Arthroplasty. 2016; 32(1):92-100.e2. PMC: 5178876. DOI: 10.1016/j.arth.2016.06.009. View

15.

Karlin E, Lin C, Meftah M, Slover J, Schwarzkopf R . The Impact of Machine Learning on Total Joint Arthroplasty Patient Outcomes: A Systemic Review. J Arthroplasty. 2022; 38(10):2085-2095. DOI: 10.1016/j.arth.2022.10.039. View

16.

Van Manen M, Nace J, Mont M . Management of primary knee osteoarthritis and indications for total knee arthroplasty for general practitioners. J Am Osteopath Assoc. 2012; 112(11):709-15. View

17.

Groenwold R, Moons K, Vandenbroucke J . Randomized trials with missing outcome data: how to analyze and what to report. CMAJ. 2014; 186(15):1153-7. PMC: 4203602. DOI: 10.1503/cmaj.131353. View

18.

Sepucha K, Stacey D, Clay C, Chang Y, Cosenza C, Dervin G . Decision quality instrument for treatment of hip and knee osteoarthritis: a psychometric evaluation. BMC Musculoskelet Disord. 2011; 12:149. PMC: 3146909. DOI: 10.1186/1471-2474-12-149. View

19.

Goh G, Bin Abd Razak H, Tay D, Lo N, Yeo S . Early post-operative oxford knee score and knee society score predict patient satisfaction 2 years after total knee arthroplasty. Arch Orthop Trauma Surg. 2020; 141(1):129-137. DOI: 10.1007/s00402-020-03612-2. View

20.

DeFrance M, Scuderi G . Are 20% of Patients Actually Dissatisfied Following Total Knee Arthroplasty? A Systematic Review of the Literature. J Arthroplasty. 2022; 38(3):594-599. DOI: 10.1016/j.arth.2022.10.011. View