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other·surgery, clinical trial, observational study, research methods, preclinical research·PMC10448293

Using Presurgical Biopsychosocial Features to Develop an Advanced Clinical Decision-Making Support Tool for Predicting Recovery Trajectories in Patients Undergoing Total Knee Arthroplasty: Protocol for a Prospective Observational Study

JMIR Research Protocols · 10 authors, 7 centres

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TL;DR

The study aims to use machine learning to identify key predictors, but final predictive model results are pending.

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This study protocol outlines a prospective observational design to develop a clinical decision support tool for predicting recovery after total knee arthroplasty (TKA). Recovery outcomes, measured 3 months post-TKA, include quality of life, knee symptoms, satisfaction, and mood. The primary analysis will use machine learning on a subset of 851 patients with complete data to build a predictive model, which will undergo internal validation. The study highlights the potential to improve patient stratification and rehabilitation planning by incorporating biopsychosocial factors. Limitations include the early 3-month follow-up and the exclusion of surgical variables like surgeon or prosthesis type, meaning the model will not explain all outcome variability. The final predictive model results are not yet reported in this protocol.

STUDY TYPE

other

SPECIALTY

surgery

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AI pipeline

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ORIGINAL PAPER

PMC10448293

Using Presurgical Biopsychosocial Features to Develop an Advanced Clinical Decision-Making Support Tool for Predicting Recovery Trajectories in Patients Undergoing Total Knee Arthroplasty: Protocol for a Prospective Observational Study

JMIR Research Protocols

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ABSTRACT

Background Following total knee arthroplasty (TKA), 10% to 20% of patients report dissatisfaction with procedural outcomes. There is growing recognition that postsurgical satisfaction is shaped not only by the quality of surgery but also by psychological and social factors. Surprisingly, information on the psychological and social determinants of surgical outcomes is rarely collected before surgery. A comprehensive collection of biopsychosocial information could assist clinicians in making recommendations in relation to rehabilitation, particularly if there is robust evidence to support the ability of presurgical constructs to predict postsurgical outcomes. Clinical decision support tools can help identify factors influencing patient outcomes and support the provision of interventions or services that can be tailored to meet individuals’ needs. However, despite their potential clinical benefit, the application of such tools remains limited. Objective This study aims to develop a clinical decision tool that will assist with patient stratification and more precisely targeted clinical decision-making regarding prehabilitation and rehabilitation for TKA, based on the identified individual biopsychosocial needs. Methods In this prospective observational study, all participants provided written or electronic consent before study commencement. Patient-completed questionnaires captured information related to a broad range of biopsychosocial parameters during the month preceding TKA. These included demographic factors (sex, age, and rurality), psychological factors (mood status, pain catastrophizing, resilience, and committed action), quality of life, social support, lifestyle factors, and knee symptoms. Physical measures assessing mobility, balance, and functional lower body strength were performed via video calls with patients in their home. Information related to preexisting health issues and concomitant medications was derived from hospital medical records. Patient recovery outcomes were assessed 3 months after the surgical procedure and included quality of life, patient-reported knee symptoms, satisfaction with the surgical procedure, and mood status. Machine learning data analysis techniques will be applied to determine which presurgery parameters have the strongest power for predicting patient recovery following total knee replacement. On the basis of these analyses, a predictive model will be developed. Predictive models will undergo internal validation, and Bayesian analysis will be applied to provide additional metrics regarding prediction accuracy. Results Patient recruitment and data collection commenced in November 2019 and was completed in June 2022. A total of 1050 patients who underwent TKA were enrolled in this study. Conclusions Our findings will facilitate the development of the first comprehensive biopsychosocial prediction tool, which has the potential to objectively predict a patient’s individual recovery outcomes following TKA once selected by an orthopedic surgeon to undergo TKA. If successful, the tool could also inform the evolution rehabilitation services, such that factors in addition to physical performance can be addressed and have the potential to further enhance patient recovery and satisfaction. International Registered Report Identifier (IRRID) DERR1-10.2196/48801

CITATION EXPORT

Karen Ribbons, Sarah Johnson, Elizabeth Ditton et al.. Using Presurgical Biopsychosocial Features to Develop an Advanced Clinical Decision-Making Support Tool for Predicting Recovery Trajectories in Patients Undergoing Total Knee Arthroplasty: Protocol for a Prospective Observational Study. JMIR Research Protocols. (2023). https://doi.org/10.2196/48801

AUTHORS

10 · 7 CENTRES

Karen Ribbons, Sarah Johnson, Elizabeth Ditton, Adrian Wills, Gillian Mason, Traci Flynn, Jodie Cochrane, Michael Pollack, Frederick Rohan Walker, Michael Nilsson

1 Centre for Rehab Innovations University of Newcastle New Lambton Heights Australia
2 Hunter Medical Research Institute Kookaburra Circuit New Lambton Heights Australia
3 College of Health, Medicine and Wellbeing University of Newcastle Callaghan Australia
4 College of Science and Engineering University of Newcastle Callaghan Australia
5 College of Human and Social Futures University of Newcastle Callaghan Australia
6 Hunter New England Local Health District Rankin Park Centre New Lambton Heights Australia
7 Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore

EDITOR / REVIEWER

Gunther Eysenbach, Jill Meirte, Joseph Walsh, Suptendra Sarbadhikari, Xin Shi