Machine learning and wearable sensors at preoperative assessments: Functional recovery prediction to set realistic expectations for knee replacements

Staying Ahead of the Curve: The Case for Recovery Curves in Total Knee Arthroplasty

BACKGROUND

Sensor technology embedded within the total knee arthroplasty (TKA) implant has the potential to record data that can track recovery and provide diagnostic information. In this study, we introduce the concept of physical function recovery curve analytics, which are created from daily spatial-temporal gait metrics and step counts from a large cohort of TKA patients.

METHODS

In our study population, 258 patients underwent a primary TKA with a smart implanted tibial extension between October 4, 2021, and July 15, 2022, by 33 surgeons. The average age was 63 years, with 138 (54%) women. All kinematic data were collected on a Health Insurance Portability and Accountability Act-compliant cloud data management platform.

RESULTS

Summaries of the gait parameters at 6 weeks are suggestive of differences between people over and under 65 years, with the older patients walking more slowly and having shorter stride lengths. The 6-week percentiles demonstrated a strong linear correlation to the 12-week percentiles for each gait parameter, with correlation coefficients ranging from 0.87 to 0.92.

CONCLUSIONS

A novel screening gait test at 6 weeks shows promising results for predicting patients who will likely have poor recovery based on at least one gait parameter recovery curve at 12 weeks with high sensitivity and specificity. A future study is needed to validate the screening tool with an independent set of patients.

A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

BACKGROUND

Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of modern medicine have found ways to incorporate such technologies into mainstream practice. Despite the enthusiasm, robust evidence to support the utility of AI in contemporary total knee arthroplasty (TKA) remains limited. The purpose of this review was to provide an up-to-date summary of the use of AI in TKA and to explore its current and future value.

METHODS

Initially, a structured systematic review of the literature was carried out, following PRISMA search principles, with the aim of summarising the understanding of the field and identifying clinical and knowledge gaps.

RESULTS

A limited body of published work exists in this area. Much of the available literature is of poor methodological quality and many published studies could be best described as "demonstration of concepts" rather than "proof of concepts". There exists almost no independent validation of reported findings away from designer/host sites, and the extrapolation of key results to general orthopaedic sites is limited.

CONCLUSION

While AI has certainly shown value in a small number of specific TKA-associated applications, the majority to date have focused on risk, cost and outcome prediction, rather than surgical care, per se. Extensive future work is needed to demonstrate external validity and reliability in non-designer settings. Well-performed studies are warranted to ensure that the scientific evidence base supporting the use of AI in knee arthroplasty matches the global hype.

Predicting Length of Stay using machine learning for total joint replacements performed at a rural community hospital

BACKGROUND

Predicting patient's Length of Stay (LOS) before total joint replacement (TJR) surgery is vital for hospitals to optimally manage costs and resources. Many hospitals including in rural areas use publicly available models such as National Surgical Quality Improvement Program (NSQIP) calculator which, unfortunately, performs suboptimally when predicting LOS for TJR procedures.

OBJECTIVE

The objective of this research was to develop a Machine Learning (ML) model to predict LOS for TJR procedures performed at a Perioperative Surgical Home implemented rural community hospital for better accuracy and interpretation than the NSQIP calculator.

METHODS

A total of 158 TJR patients were collected and analyzed from a rural community hospital located in Montana. A random forest (RF) model was used to predict patient's LOS. For interpretation, permuted feature importance and partial dependence plot methods were used to identify the important variables and their relationship with the LOS.

RESULTS

The root mean square error for the RF model (0.7) was lower than the NSQIP calculator (1.21). The five most important variables for predicting LOS were BMI, Duke Activity Status-Index, diabetes, patient's household income, and patient's age.

CONCLUSION

This pilot study is the first of its kind to develop an ML model to predict LOS for TJR procedures that were performed at a small-scale rural community hospital. This pilot study contributes an approach for rural hospitals, making them more independent by developing their own predictions instead of relying on public models.

Patient and Implant Performance of Satisfied and Dissatisfied Total Knee Arthroplasty Patients

BACKGROUND

Implant migration and altered kinematics have been thought to impact patient-reported outcome measures (PROMs) and postoperative patient satisfaction. In this study comparing satisfied and dissatisfied total knee arthroplasty (TKA) patients, we hypothesized that dissatisfied patients will have greater continuous implant migration and that there will be differences in joint kinematics, objective functional measurements, and PROMs between satisfied and dissatisfied patients.

METHODS

The Knee Society Score Satisfaction Subsection questions regarding satisfaction with function were used at least 6 months postoperation to split 50 patients into satisfied and dissatisfied groups. Patients underwent radiostereometric analysis to evaluate migration and kinematics. A wearable sensor system obtained objective measurements of patient function during timed up and go tests. PROMs were recorded preoperation and postoperation.

RESULTS

No statistically significant differences were found in migration between satisfied and dissatisfied groups. Statistical kinematic differences existed in lateral anteroposterior contact location at 20° and 40° of flexion at 1 year, where the dissatisfied group had more anteriorly located lateral contact. No statistically significant differences were present in objective functional measurements. Satisfied and dissatisfied groups had differing PROMs at 4 timepoints or greater for each questionnaire.

CONCLUSIONS

No differences were found in tibial component migration or objectively measured function between satisfied and dissatisfied patients. Functionally dissatisfied patients had more anteriorly positioned contact on the lateral condyle in early flexion and reported more pain and unmet expectations. These findings suggest that improving the functional satisfaction of TKA requires restoration of kinematics in early flexion and management of patient's pain and expectations.

Perioperative Risk Stratification and Modification

This article discusses the important topic of perioperative risk stratification and the interventions that can be used in the perioperative period for risk modification. It begins with a brief overview of the commonly used scoring systems, risk-prediction models, and assessments of functional capacity and discusses some of the evidence behind each. It then moves on to examine how perioperative risk can be modified through the use of shared decision making, management of multimorbidity, and prehabilitation programs, before considering what the future of risk stratification and modification may hold.

Can minimal clinically important differences in patient reported outcome measures be predicted by machine learning in patients with total knee or hip arthroplasty? A systematic review

OBJECTIVES

To systematically review studies using machine learning (ML) algorithms to predict whether patients undergoing total knee or total hip arthroplasty achieve an improvement as high or higher than the minimal clinically important differences (MCID) in patient reported outcome measures (PROMs) (classification problem).

METHODS

Studies were eligible to be included in the review if they collected PROMs both pre- and postintervention, reported the method of MCID calculation and applied ML. ML was defined as a family of models which automatically learn from data when selecting features, identifying nonlinear relations or interactions. Predictive performance must have been assessed using common metrics. Studies were searched on MEDLINE, PubMed Central, Web of Science Core Collection, Google Scholar and Cochrane Library. Study selection and risk of bias assessment (ROB) was conducted by two independent researchers.

RESULTS

517 studies were eligible for title and abstract screening. After screening title and abstract, 18 studies qualified for full-text screening. Finally, six studies were included. The most commonly applied ML algorithms were random forest and gradient boosting. Overall, eleven different ML algorithms have been applied in all papers. All studies reported at least fair predictive performance, with two reporting excellent performance. Sample size varied widely across studies, with 587 to 34,110 individuals observed. PROMs also varied widely across studies, with sixteen applied to TKA and six applied to THA. There was no single PROM utilized commonly in all studies. All studies calculated MCIDs for PROMs based on anchor-based or distribution-based methods or referred to literature which did so. Five studies reported variable importance for their models. Two studies were at high risk of bias.

DISCUSSION

No ML model was identified to perform best at the problem stated, nor can any PROM said to be best predictable. Reporting standards must be improved to reduce risk of bias and improve comparability to other studies.

Inertial measurement units and application for remote healthcare in hip and knee osteoarthritis: a narrative review (Preprint)

Background

Measuring and modifying movement-related joint loading is integral to the management of lower extremity osteoarthritis (OA). Although traditional approaches rely on measurements made within the laboratory or clinical environments, inertial sensors provide an opportunity to quantify these outcomes in patients’ natural environments, providing greater ecological validity and opportunities to develop large data sets of movement data for the development of OA interventions.

Objective

This narrative review aimed to discuss and summarize recent developments in the use of inertial sensors for assessing movement during daily activities in individuals with hip and knee OA and to identify how this may translate to improved remote health care for this population.

Methods

A literature search was performed in November 2018 and repeated in July 2019 and March 2021 using the PubMed and Embase databases for publications on inertial sensors in hip and knee OA published in English within the previous 5 years. The search terms encompassed both OA and wearable sensors. Duplicate studies, systematic reviews, conference abstracts, and study protocols were also excluded. One reviewer screened the search result titles by removing irrelevant studies, and 2 reviewers screened study abstracts to identify studies using inertial sensors as the main sensing technology and a primary outcome related to movement quality. In addition, after the March 2021 search, 2 reviewers rescreened all previously included studies to confirm their relevance to this review.

Results

From the search process, 43 studies were determined to be relevant and subsequently included in this review. Inertial sensors have been successfully implemented for assessing the presence and severity of OA (n=11), assessing disease progression risk and providing feedback for gait retraining (n=7), and remotely monitoring intervention outcomes and identifying potential responders and nonresponders to interventions (n=14). In addition, studies have validated the use of inertial sensors for these applications (n=8) and analyzed the optimal sensor placement combinations and data input analysis for measuring different metrics of interest (n=3). These studies show promise for remote health care monitoring and intervention delivery in hip and knee OA, but many studies have focused on walking rather than a range of activities of daily living and have been performed in small samples (<100 participants) and in a laboratory rather than in a real-world environment.

Conclusions

Inertial sensors show promise for remote monitoring, risk assessment, and intervention delivery in individuals with hip and knee OA. Future opportunities remain to validate these sensors in real-world settings across a range of activities of daily living and to optimize sensor placement and data analysis approaches.