Advanced decision-making using patient-reported outcome measures in total joint replacement

Stepping Up Recovery: Integrating Patient-reported Outcome Measures and Wearable Technology for Rehabilitation Following Knee Arthroplasty

Improvement after knee arthroplasty (KA) is often measured using patient-reported outcome measures (PROMs). However, PROMs are limited due to their subjectivity. Therefore, wearable technology is becoming commonly utilized to objectively assess physical activity and function. We assessed the correlation between PROMs and step/stair flight counts in total (TKA) and partial knee arthroplasty (PKA) patients.Analysis of a multicenter, prospective, longitudinal cohort study investigating the collection of average daily step and stair flight counts, was performed. Subjects (N = 1,844 TKA patients and N = 489 PKA patients) completed the Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) and provided numerical rating scale pain scores pre- and postoperatively. Only patients who reported living in a multilevel home environment (N = 896 TKA patients and N = 258 PKA patients) were included in analysis of stair flight counts. Pearson correlation coefficients were calculated to determine correlations between variables.Among TKA patients, pain scores demonstrated a negative correlation to mean step counts at preoperative (r = -0.14, p < 0.0001) and 1-month follow-up (r = -0.14, p < 0.0001). Similar negative correlations were true for pain and stair flight counts at preoperative (r = -0.16, p < 0.0001) and 1-month follow-up (r = -0.11, p = 0.006). KOOS JR scores demonstrated weak positive correlations with mean step counts at preoperative (r = 0.19, p < 0.0001) and 1-month postoperative (r = 0.17, p < 0.0001). Similar positive correlations were true for KOOS JR scores and stair flight counts preoperatively (r = 0.13, p = 0.0002) and at 1-month postoperatively (r = 0.10, p = 0.0048). For PKA patients, correlations between pain and KOOS JR with step/stair counts demonstrated similar directionality.Given the correlation between wearable-generated data and PROMs, wearable technology may be beneficial in evaluating patient outcomes following KA. By combining subjective feedback with the objective data, health care providers can gain a holistic view of patients' progress and tailor treatment plans accordingly.

Socioeconomic Status Affects Patient-Reported Outcome Measures in Total Hip and Knee Arthroplasty: A Retrospective Dutch Registry Study

BACKGROUND

To determine the association between socioeconomic status (SES) and patient-reported outcome measures in a Dutch cohort who have undergone total hip arthroplasty (THA) or total knee arthroplasty (TKA).

METHODS

A retrospective national registry study of all patients who underwent primary THA or TKA between 2014 and 2020 in the Netherlands was performed. Linear mixed effects regression models were used to assess the association between SES and patient-reported outcome measures for THA and TKA patients separately. The following measures were collected: numeric rating scale for pain, Oxford Hip/Knee Score, Hip/Knee disability and Osteoarthritis Outcome Score, and the EuroQol 5-Dimensions questionnaire. Sex, age, body mass index, American Society of Anesthesiologists classification, Charnley classification, and smoking status were considered as covariates in the models.

RESULTS

THA patients (n = 97,443) were on average 70 years old with a body mass index of 27.4 kg/m2, and TKA patients (n = 78,811) were on average 69 years old with a body mass index of 29.7 kg/m2. Preoperatively, patients with a lower SES undergoing THA or TKA reported more severe symptoms and lower health-related quality of life. At 1-year follow-up, they also reported lower scores and less improvement over time compared to patients with a higher SES.

CONCLUSIONS

Patients with lower SES report worse symptoms when admitted for surgery and less improvement after surgery. Future research must address potentially mediating factors of the association between SES and symptom reporting such as access to surgery and rehabilitation, subjectivity in reporting, and patient expectation for THA and TKA outcomes.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Signficant Femoral Version Abnormalities and Patient-Reported Quality of Life

INTRODUCTION

The purpose of this study was to determine how the Limb Deformity-Modified Scoliosis Research Society (LD-SRS) and Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaire scores differ between patients with femoral version abnormalities and healthy control subjects.

METHODS

A retrospective database review identified patients with femoral version abnormalities between December 2018 and September 2022. A total of 21 adult patients scheduled for femoral derotational osteotomy and 33 control subjects were included. All individuals completed the LD-SRS and PROMIS questionnaires.

RESULTS

Patients with femoral version abnormalities reported significantly worse scores than control subjects on all LD-SRS and PROMIS domains: LD-SRS (Total [3.46 ± 0.66 vs. 4.58 ± 0.37, P < 0.001]; Function/Activity [3.48 ± 0.83 vs. 4.44 ± 0.4, P < 0.001]; Mental Health [3.41 ± 0.96 vs. 4.3 ± 0.73, P < 0.001]; Pain [3.55 ± 0.9 vs. 4.81 ± 0.31, P < 0.001]; and Self-Image/Appearance [3.37 ± 0.79 vs. 4.75 ± 0.43, P < 0.001]) and PROMIS (Function [41.6 ± 7.58 vs. 60.0 ± 7.28, P < 0.001]; Pain Intensity [45.85 ± 8.04 vs. 33.7 ± 4.89, P < 0.001]; Pain Interference [56.78 ± 9.63 vs. 42.8 ± 6.6, P < 0.001]; Global Mental Health [47.97 ± 9.68 vs. 55.3 ± 7.81, P = 0.004]; and Global Physical Health [45.23 ± 7.49 vs. 58.2 ± 7.07, P < 0.001]).

DISCUSSION

Patients with femoral version abnormalities reported markedly worse quality of life as measured on the LD-SRS and PROMIS scores compared with healthy control subjects. The combination of these two surveys effectively captures the multifaceted quality-of-life-deficit individuals with excessive femoral version may experience.

Patient-Reported Outcome Measures: State of the Art in Patient-Reported Outcome Measure Application in Lower Extremity Orthopaedics

With an increasing shift toward a value-based and outcome-driven healthcare system, patient-reported outcome measures (PROMs) will continue to play a prominent role in assessing performance, making clinical decisions, shared decision making, and determining the comparative effectiveness of procedures such as total joint arthroplasty for lower extremity conditions, such as ankle, hip, and knee osteoarthritis. As the application of PROMs in evaluating surgical outcomes has evolved from that of a research setting to that of a clinical setting, their use in the decision-making process has become more prevalent. As a result, preoperative optimization, surgical indications, and improved outcomes after surgery have been greatly enhanced. To enable benchmarking, quality reporting, and performance measurement at an aggregate level, it is crucial to have a comprehensive PROM collection system. However, achieving this goal is contingent upon addressing the variability in reported PROMs and the patient-centered benchmarks used to analyze clinical significance.

Longitudinal Invariance Testing Of The Knee Injury Osteoarthritis Outcome Score For Joint Replacement Scale (KOOS-JR)

Background

The Knee Osteoarthritis Outcome Score for Joint Replacement (KOOS-JR) is a seven-item patient reported outcome measure used to assess perceived knee health. Though commonly used, the longitudinal psychometric properties of the KOOS-JR have not been established and further characterization of its structural validity and multi-group invariance properties is warranted.

Purpose

The purpose of this study was to evaluate psychometric properties of the KOOS-JR in a large sample of patients who received care for knee pathology.

Study Design

Original research.

Methods

Longitudinal data extracted from the Surgical Outcome System (SOS) database of 13,470 knee pathology patients who completed the KOOS-JR at baseline, three-months, six- months, and one-year. Scale structure was assessed with confirmatory factor analysis (CFA), while multi-group and longitudinal invariance properties were assessed with CFA-based procedures. Latent group means were compared with statistical significance set at α ≤ .05 and Cohen's d effect size as d = 0.2 (small), d = 0.5 (medium), and d = 0.8 (large).

Results

CFA results exceeded goodness-of-fit indices at all timepoints. Multi-group invariance properties passed test requirements. Longitudinal analysis identified a biased item resulting in removal of item #1; the retained six-item model (KOOS-JR-6) passed longitudinal invariance requirements. KOOS-JR-6 scores significantly changed over time (p ≤ .001, Mdiff = 1.08, Cohen's d = 0.57): the highest scores were at baseline examination and the lowest at 12-month assessment.

Conclusions

The KOOS-JR can be used to assess baseline differences between males and females, middle and older aged adults, and patients receiving total knee arthroplasty or non-operative care. Caution is warranted if the KOOS-JR is used longitudinally due to potential measurement error associated with item #1. The KOOS-JR-6 may be a more viable option to assess change over time; however, more research is warranted.

Level of Evidence

3© The Author(s).

Exploring treatment effect heterogeneity of a PROMs alert intervention in knee and hip arthroplasty patients: A causal forest application

Patient reported outcome measures (PROMs) experience an uptake in use for hip (HA) and knee arthroplasty (KA) patients. As they may be used for patient monitoring interventions, it remains unclear whether their use in HA/KA patients is effective, and which patient groups benefit the most. Nonetheless, knowledge about treatment effect heterogeneity is crucial for decision makers to target interventions towards specific subgroups that benefit to a greater extend. Therefore, we evaluate the treatment effect heterogeneity of a remote PROM monitoring intervention that includes ∼8000 HA/KA patients from a randomized controlled trial conducted in nine German hospitals. The study setting gave us the unique opportunity to apply a causal forest, a recently developed machine learning method, to explore treatment effect heterogeneity of the intervention. We found that among both HA and KA patients, the intervention was especially effective for patients that were female, >65 years of age, had a blood pressure disease, were not working, reported no backpain and were adherent. When transferring the study design into standard care, policy makers should make use of the knowledge obtained in this study and allocate the treatment towards subgroups for which the treatment is especially effective.

Patients Who Have Kellgren-Lawrence Grade 3 and 4 Osteoarthritis Benefit Equally From Total Knee Arthroplasty

BACKGROUND

Recently, some payers have limited access to total knee arthroplasty (TKA) to patients who have Kellgren-Lawrence (KL) grade 4 osteoarthritis only. This study compared the outcomes of patients who have KL grade 3 and 4 osteoarthritis after TKA to determine if this new policy is justified.

METHODS

This was a secondary analysis of a series originally established to collect outcomes for a single, cemented implant design. A total of 152 patients underwent primary, unilateral TKA at two centers from 2014 to 2016. Only patients who had KL grade 3 (n = 69) or 4 (n = 83) osteoarthritis were included. There was no difference in age, sex, American Society of Anesthesiologists score, or preoperative Knee Society Score (KSS) between the groups. Patients who had KL grade 4 disease had a higher body mass index. KSS and Forgotten Joint Score (FJS) were collected preoperatively and at 6 weeks, 6 months, 1 year, and 2 years postoperatively. Generalized linear models were used to compare outcomes.

RESULTS

Controlling for demographics, improvements in KSS were comparable between the groups at all time points. There was no difference in KSS, FJS, and the proportion that achieved the patient acceptable symptom state for FJS at 2 years.

CONCLUSION

Patients who had KL grade 3 and 4 osteoarthritis experienced similar improvement at all time points up to 2 years after primary TKA. There is no justification for payers to deny access to surgical treatment for patients who have KL grade 3 osteoarthritis and have otherwise failed nonoperative treatment.

Evaluating a web-based personalized decision report for total knee or hip replacement: Lessons learned from patients

RATIONALE

Patient-reported outcomes (PROs) are increasingly used in the context of clinical care, but evaluation of patients' perspectives of PRO-based applications in routine care remains limited.

AIMS AND OBJECTIVES

This paper investigates patients' acceptability of a personalized web-based decision report for total knee or hip replacement and identifies opportunities to refine the report.

METHOD

This qualitative evaluation was embedded in a pragmatic cluster randomized trial of the report. We interviewed 25 patients with knee and hip osteoarthritis about their experiences using the personalized decision report in the context of a surgical consultation. The web-based report contained current descriptive PRO scores of pain, function and general physical health; tailored predicted postoperative PRO scores (i.e., personalized likely outcomes based on actual knee or hip replacement outcomes of similar patients in a national registry); and information about alternative nonoperative treatments. Two trained researchers analysed the interview data qualitatively using a combination of inductive and deductive coding.

RESULTS

We identified three major categories for evaluation: content of report, presentation of data in report and engagement with report. Patients generally liked the report overall but specifically valued different pages of the report based on where they were in the surgical decision-making process. Patients identified areas of confusion in data presentation related to graph orientation, terminology and interpretation of T-scores. Patients also highlighted support needs to meaningfully engage with the information in the report.

CONCLUSION

Our findings highlight areas of opportunity to further refine this personalized web-based decision report and similar patient-facing PRO applications for routine clinical care. Specific examples include additional tailoring of reports via filterable web-based dashboards and scalable educational supports to facilitate more independent patient understanding and use.

Patient preferences as human factors for health data recommender systems and shared decision making in orthopaedic practice

Background

A core set of requirements for designing AI-based Health Recommender Systems (HRS) is a thorough understanding of human factors in a decision-making process. Patient preferences regarding treatment outcomes can be one important human factor. For orthopaedic medicine, limited communication may occur between a patient and a provider during the short duration of a clinical visit, limiting the opportunity for the patient to express treatment outcome preferences (TOP). This may occur despite patient preferences having a significant impact on achieving patient satisfaction, shared decision making and treatment success. Inclusion of patient preferences during patient intake and/or during the early phases of patient contact and information gathering can lead to better treatment recommendations.

Aim

We aim to explore patient treatment outcome preferences as significant human factors in treatment decision making in orthopedics. The goal of this research is to design, build, and test an app that collects baseline TOPs across orthopaedic outcomes and reports this information to providers during a clinical visit. This data may also be used to inform the design of HRSs for orthopaedic treatment decision making.

Methods

We created a mobile app to collect TOPs using a direct weighting (DW) technique. We used a mixed methods approach to pilot test the app with 23 first-time orthopaedic visit patients presenting with joint pain and/or function deficiency by presenting the app for utilization and conducting qualitative interviews and quantitative surveys post utilization.

Results

The study validated five core TOP domains, with most users dividing their 100-point DW allocation across 1-3 domains. The tool received moderate to high usability scores. Thematic analysis of patient interviews provides insights into TOPs that are important to patients, how they can be communicated effectively, and incorporated into a clinical visit with meaningful patient-provider communication that leads to shared decision making.

Conclusion

Patient TOPs may be important human factors to consider in determining treatment options that may be helpful for automating patient treatment recommendations. We conclude that inclusion of patient TOPs to inform the design of HRSs results in creating more robust patient treatment profiles in the EHR thus enhancing opportunities for treatment recommendations and future AI applications.

3D Bioprinting of Hyaline Articular Cartilage: Biopolymers, Hydrogels, and Bioinks

The musculoskeletal system, consisting of bones and cartilage of various types, muscles, ligaments, and tendons, is the basis of the human body. However, many pathological conditions caused by aging, lifestyle, disease, or trauma can damage its elements and lead to severe disfunction and significant worsening in the quality of life. Due to its structure and function, articular (hyaline) cartilage is the most susceptible to damage. Articular cartilage is a non-vascular tissue with constrained self-regeneration capabilities. Additionally, treatment methods, which have proven efficacy in stopping its degradation and promoting regeneration, still do not exist. Conservative treatment and physical therapy only relieve the symptoms associated with cartilage destruction, and traditional surgical interventions to repair defects or endoprosthetics are not without serious drawbacks. Thus, articular cartilage damage remains an urgent and actual problem requiring the development of new treatment approaches. The emergence of biofabrication technologies, including three-dimensional (3D) bioprinting, at the end of the 20th century, allowed reconstructive interventions to get a second wind. Three-dimensional bioprinting creates volume constraints that mimic the structure and function of natural tissue due to the combinations of biomaterials, living cells, and signal molecules to create. In our case-hyaline cartilage. Several approaches to articular cartilage biofabrication have been developed to date, including the promising technology of 3D bioprinting. This review represents the main achievements of such research direction and describes the technological processes and the necessary biomaterials, cell cultures, and signal molecules. Special attention is given to the basic materials for 3D bioprinting-hydrogels and bioinks, as well as the biopolymers underlying the indicated products.

The relation between socioeconomic status and patient symptoms before and one year after lower extremity arthroplasty

Aims

To determine whether there was a relation between socioeconomic status (SES) and patient symptoms before and one year after total knee arthroplasty (TKA), and/or total hip arthroplasty (THA) and whether a change in symptoms was clinically relevant.

Patients and methods

A secondary analysis of a prospective cohort study was conducted on SES and osteoarthritis symptoms of patients (≥45 years old) who received a primary TKA or THA between 2016 and 2018. The relation between SES and respectively pre- and postoperative and change in patient-reported outcome measures including the Oxford Knee Score (OKS), Oxford Hip Score (OHS), Western Ontario and McMaster Universities Arthritis Index (WOMAC), the visual analog scale (VAS) for pain and the EuroQol 5-Dimensions (EQ-5D) were assessed using linear mixed-effects regression models adjusted for age and sex. The following potential confounding variables were considered in the regression models: body mass index (BMI), American Society of Anesthesiologists (ASA)- classification, Charnley-classification, smoking status, and alcohol consumption.

Results

Patients with lower SES were mostly female, had a higher BMI and ASA-classification compared to patients with a higher SES. Patients with lower SES reported lower OKS (β = 3.78, P = 0.001). Patients undergoing THA reported lower scores for the OHS (β = 4.78, P = 0.001), WOMAC (β = 11.7, P = 0.001), and less pain (VAS, β = -0.91, P = 0.001). No statistically significant differences between SES groups were seen in the quality of life and health status as measured with the EQ-5D.

Conclusion

Patients with a lower socioeconomic status reported worse symptoms and showed less clinically relevant improvement at one-year follow-up.

Patient-reported outcome measures in cardiovascular disease

In today's medical ecosystem, it is vital to measure the outcomes that are most important to the patients. As such, patient-reported outcome measures (PROMS) can be an essential metric to deliver high-quality cardiovascular care, particularly in the subset of patients who remain disappointed with their outcomes. PROMS should be a reproducible and reflective report of what is fundamental to a patient over time and across treatments with proper standards in the analysis, interpretation, and reporting of the collected data. These reports can also be sensitive to changes, whether improvements or deteriorations in the quality of care and medical attitude, but a lack of standardization makes it difficult to draw robust conclusions and compare findings across treatments. As a research tool, PROMS can have a significant prognostic prominence, offering a powerful instrument of comparison between different treatment modalities. With the information technology (IT) abilities of today, we can leverage mobile tools and powerful computer systems to perform sophisticated data analysis using patient-derived data and randomization. This may eliminate guesswork and generate impactful metrics to better inform the decision-making process. PROMS analysed by proper standardized algorithms can avoid physician bias and be integrated into the hospital teamwork. Therefore, there is a strong need for integration of PROMS into the evaluation of cardiovascular interventions and procedures, and establishment of international standards in the analyses of patient-reported outcomes and quality of life data to address this need and develop therapeutic recommendations.

Longitudinal patient-reported outcomes 1 year after thoracoscopic segmentectomy versus lobectomy for early-stage lung cancer: a multicentre, prospective cohort study protocol

INTRODUCTION

Segmentectomy and lobectomy are the main surgical procedures for early-stage lung cancer. However, few studies have analysed patient-reported outcomes after segmentectomy versus lobectomy. This study aims to compare patient-reported outcomes-such as symptoms, daily functioning and quality of life-between thoracoscopic segmentectomy and lobectomy for early-stage lung cancer during the 1 year after surgery.

METHODS AND ANALYSIS

Overall, 788 newly diagnosed patients with early-stage lung cancer (tumour size ≤2 cm), who are scheduled to undergo thoracoscopic segmentectomy or lobectomy, will be recruited in this multicentre, prospective cohort study. The patients will receive standardised care after surgery. The Perioperative Symptom Assessment for Lung Surgery-a validated lung cancer surgery-specific scale-will be used to assess the symptoms and functions at baseline, at discharge and monthly after discharge for 1 year. The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 and Lung Cancer module 29 will be used to assess the patients' quality of life at the same time points. The primary outcome will be the shortness of breath scores during the first year after thoracoscopic segmentectomy and lobectomy and will be compared using mixed-effects models. The secondary outcomes will include other symptoms, indicators of daily functioning, quality of life scores and traditional clinical outcomes. These will be compared using mixed-effects models and the Student's t-test, non-parametric test or Χ² test. Propensity score matching will be used to ensure an even distribution of known confounders between the groups.

ETHICS AND DISSEMINATION

The Ethics Committee for Medical Research and New Medical Technology of Sichuan Cancer Hospital approved this study (approval number: SCCHEC-02-2022-002). All participants will be instructed to provide informed consent. The manuscript is based on protocol version 3.0. The study results will be presented at medical conferences and published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

ChiCTR2200060753.

Equity-Driven Implementation of Patient-Reported Outcome Measures in Musculoskeletal Care: Advancing Value for All

ABSTRACT

The clinical use of patient-reported outcome measures (PROMs) in musculoskeletal care is expanding, encompassing both individual patient management and population-level applications. However, without thoughtful implementation, we risk introducing or exacerbating disparities in care processes or outcomes. We outline examples of opportunities, challenges, and priorities throughout PROM implementation to equitably advance value-based care at both the patient and population level. Balancing standardization with tailored strategies can enable the large-scale implementation of PROMs while optimizing care processes and outcomes for all patients.

A predictive score of high tibial osteotomy survivorship to help in surgical decision-making: the SKOOP score

INTRODUCTION

The high tibial osteotomy (HTO) survival rate is strongly correlated with surgical indications and predictive factors. This study aims to assess HTO survival in the long term, to determine the main predictive factors of this survival, to propose a predictive score for HTO based on those factors.

METHODS

This multicentric study included 481 HTO between 2004 and 2015. The inclusion criteria were all primary HTO in patients 70 years old and younger, without previous anterior cruciate ligament injury, and without the limitation of body mass index (BMI). The assessed data were preoperative clinical and radiological parameters, the surgical technique, the complications, the HKA (hip knee ankle angle) correction postoperatively, and the surgical revision at the last follow-up.

RESULTS

The mean follow-up was 7.8 ± 2.9 years. The HTO survival was 93.1% at 5 years and 74.1% at 10 years. Age < 55, female sex, BMI < 25 kg/m² and incomplete narrowing were preoperative factors that positively impacted HTO survival. A postoperative HKA angle greater than 180° was a positive factor for HTO survival. The SKOOP (Sfa Knee OsteOtomy Predictive) score, including age (threshold value of 55 years), BMI (threshold values of 25 and 35 kg/m²), and the presence or absence of complete joint line narrowing, have been described. If the scale was greater than 3, the survival probability was significantly lower (p < 0.001) than if the scale was less than 3.

CONCLUSION

A predictive score including age, BMI, and the presence or absence of joint line narrowing can be a helpful in making decisions about HTO, particularly in borderline cases.

LEVEL OF EVIDENCE

Retrospective cohort study.

Does Value-Based Care Threaten Joint Arthroplasty Access for Vulnerable Patient Populations?: AOA Critical Issues

Health-care expenses have been projected to increase from 17.7% of the U.S. gross domestic product (GDP) in 2014 to 19.6% in 2024. The unsustainable increase in health-care costs has contributed toward support for value-based health care (VBHC) reform. Contemporary VBHC reform programs relevant to orthopaedic surgery include the voluntary Bundled Payments for Care Improvement initiatives (BPCI and BPCI-Advanced) and the Comprehensive Care for Joint Replacement (CJR) program, a mandatory bundled payment program.The purported benefits of transitioning from volume-based reimbursement to value-based reimbursement include moving from a fragmented provider-centered care model to a patient-centered model, with greater care coordination and alignment among providers focused on improving value. VBHC models allow innovative strategies to proactively invest resources to promote value (e.g., the use of nurse navigators) while eliminating unnecessary resources that do not promote value. However, major concerns regarding VBHC include the absence of medical and socioeconomic risk stratification as well as decreased access for higher-risk patients.This article identifies the benefits and potential unintended consequences of VBHC reform, with a focus on joint arthroplasty. We also discuss some potential strategies to promote innovation and improve value without compromising access for vulnerable patients.

External Validation of a Machine Learning Algorithm for Predicting Clinically Meaningful Functional Improvement After Arthroscopic Hip Preservation Surgery

BACKGROUND

Individualized risk prediction has become possible with machine learning (ML), which may have important implications in enhancing clinical decision making. We previously developed an ML algorithm to predict propensity for clinically meaningful outcome improvement after hip arthroscopy for femoroacetabular impingement syndrome. External validity of prognostic models is critical to determine generalizability, although it is rarely performed.

PURPOSE

To assess the external validity of an ML algorithm for predicting clinically meaningful improvement after hip arthroscopy.

STUDY DESIGN

Cohort study; Level of evidence, 3.

METHODS

An independent hip preservation registry at a tertiary academic medical center was queried for consecutive patients/athletes who underwent hip arthroscopy for femoroacetabular impingement syndrome between 2015 and 2017. By assuming a minimal clinically important difference (MCID) outcome/event proportion of 75% based on the original study, a minimum sample of 132 patients was required. In total, 154 patients were included. Age, body mass index, alpha angle on anteroposterior pelvic radiographs, Tönnis grade and angle, and preoperative Hip Outcome Score-Sports Subscale were used as model inputs to predict the MCID for the Hip Outcome Score-Sports Subscale 2 years postoperatively. Performance was assessed using identical metrics to the internal validation study and included discrimination, calibration, Brier score, and decision curve analysis.

RESULTS

The concordance statistic in the validation cohort was 0.80 (95% CI, 0.71 to 0.87), suggesting good to excellent discrimination. The calibration slope was 1.16 (95% CI, 0.74 to 1.61) and the calibration intercept 0.13 (95% CI, -0.26 to 0.53). The Brier score was 0.15 (95% CI, 0.12 to 0.18). The null model Brier score was 0.20. Decision curve analysis revealed favorable net treatment benefit for patients with use of the algorithm as compared with interventional changes made for all and no patients.

CONCLUSION

The performance of this algorithm in an independent patient population in the northeast region of the United States demonstrated superior discrimination and comparable calibration to that of the derivation cohort. The external validation of this algorithm suggests that it is a reliable method to predict propensity for clinically meaningful improvement after hip arthroscopy and is an essential step forward toward introducing initial use in clinical practice. Potential uses include integration into electronic medical records for automated prediction, enhanced shared decision making, and more informed allocation of resources to optimize patient outcomes.

Patients' perspectives on the benefits of feedback on patient-reported outcome measures in a web-based personalized decision report for hip and knee osteoarthritis

Background

Applications of patient-reported outcome measures (PROMs) for individual patient management are expanding with the support of digital tools. Providing PROM-based information to patients can potentially improve care experiences and outcomes through informing and activating patients. This study explored patients’ perspectives on the benefits of receiving feedback on PROMs in the context of a web-based personalized decision report to guide care for their hip or knee osteoarthritis.

Methods

This qualitative descriptive interview study was nested in a pragmatic clinical trial of a personalized report, which includes descriptive PROM scores and predicted postoperative PROM scores. Patients completed a semi-structured interview within 6 weeks of an office visit with an orthopaedic surgeon. Only patients who reported receiving the report and reviewing it with the surgeon and/or a health educator were included. Data were iteratively analyzed using a combination of deductive and inductive coding strategies.

Results

Twenty-five patients aged 49–82 years (60% female, 72% surgical treatment decision) participated and described three primary benefits of the PROM feedback within the report: 1. Gaining Information About My Health Status, including data teaching new information, confirming what was known, or providing a frame of reference; 2. Fostering Communication Between Patient and Surgeon, encompassing use of the data to set expectations, ask and answer questions, and facilitate shared understanding; and 3. Increasing My Confidence and Trust, relating to the treatment outcomes, treatment decision, and surgeon.

Conclusions

Patients identified actual and hypothetical benefits of receiving feedback on PROM scores in the context of a web-based decision report, including advantages for those who had already made a treatment decision before seeing the surgeon. Findings provide insight into patients’ perspectives on how digital PROM data can promote patient-centered care. Results should be considered in the context of the homogeneous sample and complex trial. While participants perceived value in this personalized report, questions remain regarding best practices in patient-facing data presentation and engagement.

Trial registration

ClinicalTrials.gov, NCT03102580. Registered on 5 April 2017.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05764-1.

Artificial intelligence in knee arthroplasty: current concept of the available clinical applications

Background

Artificial intelligence (AI) is defined as the study of algorithms that allow machines to reason and perform cognitive functions such as problem-solving, objects, images, word recognition, and decision-making. This study aimed to review the published articles and the comprehensive clinical relevance of AI-based tools used before, during, and after knee arthroplasty.

Methods

The search was conducted through PubMed, EMBASE, and MEDLINE databases from 2000 to 2021 using the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA).

Results

A total of 731 potential articles were reviewed, and 132 were included based on the inclusion criteria and exclusion criteria. Some steps of the knee arthroplasty procedure were assisted and improved by using AI-based tools. Before surgery, machine learning was used to aid surgeons in optimizing decision-making. During surgery, the robotic-assisted systems improved the accuracy of knee alignment, implant positioning, and ligamentous balance. After surgery, remote patient monitoring platforms helped to capture patients’ functional data.

Conclusion

In knee arthroplasty, the AI-based tools improve the decision-making process, surgical planning, accuracy, and repeatability of surgical procedures.

Current State of Data and Analytics Research in Baseball

PURPOSE OF REVIEW

Baseball has become one of the largest data-driven sports. In this review, we highlight the historical context of how big data and sabermetrics began to transform baseball, the current methods for data collection and analysis in baseball, and a look to the future including emerging technologies.

RECENT FINDINGS

Machine learning (ML), artificial intelligence (AI), and modern motion-analysis techniques have shown promise in predicting player performance and preventing injury. With the advent of the Health Injury Tracking System (HITS), numerous studies have been published which highlight the epidemiology and performance implications for specific injuries. Wearable technologies allow for the prospective collection of kinematic data to improve pitching mechanics and prevent injury. Data and analytics research has transcended baseball over time, and the future of this field remains bright.

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.

Real-world patient data: Can they support decision making and patient engagement?

Patient-reported outcomes (PROs) capture data related to patients' perception of their health status and aspects of health care delivery. In parallel, digital innovations have advanced the administration, storage, processing, and accessibility of PROs, allowing these data to become actively incorporated in day-to-day clinical practice along the entire patient care pathway. Further, the emergence of shared decision making, where patients are engaged in informed treatment selection aligned with their preferences, values, and needs, can be realized by PROs and technology. This technology-enabled, data-driven approach provides insights which, when actioned, can enhance musculoskeletal care of patients and populations, while enriching the clinician-patient experience of decision making. In this review, we provide an overview of the opportunities enabled by PROs and technology for the cycle of orthopedic care.

Artificial intelligence in arthroplasty

Artificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.

Patient Engagement Approaches in Total Joint Arthroplasty: A Review of Two Decades

Patient engagement is a comprehensive approach to health care where the physician inspires confidence in the patient to be involved in their own care. Most research studies of patient engagement in total joint arthroplasty (TJA) have come in the past 5 years (2015-2020), with no reviews investigating the different patient engagement methods in TJA. The primary purpose of this review is to examine patient engagement methods in TJA. The search identified 31 studies aimed at patient engagement methods in TJA. Based on our review, the conclusions therein strongly suggest that patient engagement methods in TJA demonstrate benefits throughout care delivery through tools focused on promoting involvement in decision making and accessible care delivery (eg, virtual rehabilitation, remote monitoring). Future work should understand the influence of social determinants on patient involvement in care, and overall cost (or savings) of engagement methods to patients and society.

Artificial Intelligence for the Orthopaedic Surgeon: An Overview of Potential Benefits, Limitations, and Clinical Applications

Artificial intelligence (AI), along with its subset technology machine learning, has transformed numerous industries through newfound efficiencies and supportive decision-making. These technologies have similarly begun to find application within United States healthcare, particularly orthopaedics. Although these modalities have the potential to similarly transform health care, there exist limitations that must also be recognized and understood. Unfortunately, most clinicians do not have an understanding of the fundamentals of AI and therefore may have challenges in contextualizing its impact in modern healthcare. The purpose of this review was to provide an overview of the key concepts of AI and machine learning with the orthopaedic surgeon in mind. The review further highlights the potential benefits and limitations of AI, along with an overview of its applications, in orthopaedics.

Psychometric properties of the Patient-Reported Outcomes Measurement Information System (PROMIS®) pediatric item bank peer relationships in the Dutch general population

PURPOSE

This study aimed to validate the PROMIS Pediatric item bank v2.0 Peer Relationships and compare reliability of the full item bank to its short form, computerized adaptive test (CAT) and the social functioning (SF) subscale of the Pediatric Quality of Life Inventory (PedsQL™).

METHODS

Children aged 8-18 (n = 1327), representative of the Dutch population completed the Peer Relationships item bank. A graded response model (GRM) was fit to the data. Structural validity was assessed by checking item-fit statistics (S-X², p < 0.001 = misfit). For construct validity, a moderately strong correlation (> 0.50) was expected between Peer Relationships and the PedsQL SF subscale. Cross-cultural DIF between U.S. and NL was assessed using logistic regression, where an item with McFadden's pseudo R² > 0.02 was considered to have DIF. Percentage of participants reliably measured was assessed using the standard error of measurement (SEM) < 0.32 as a criterion (reliability of 0.90). Relative efficiency ((1-SEM²)/n_items) was calculated to compare how well the instruments performed relative to the amount of items administered.

RESULTS

In total, 527 (response rate: 39.7%) children completed the PROMIS v2.0 Peer Relationships item bank (n_items = 15) and the PedsQL™ (n_items = 23). Structural validity of the Peer Relationships item bank was sufficient, but one item displayed misfit in the GRM model (S-X² < 0.001); 5152R1r ("I played alone and kept to myself"). The item 733R1r ("I was a good friend") was the only item that displayed cross-cultural DIF (R² = 0.0253). The item bank correlated moderately high (r = 0.61) with the PedsQL SF subscale Reliable measurements were obtained at the population mean and > 2SD in the clinically relevant direction. CAT outperformed all other measures in efficiency. Mean T-score of the Dutch general population was 46.9(SD 9.5).

CONCLUSION

The pediatric PROMIS Peer Relationships item bank was successfully validated for use within the Dutch population and reference data are now available.

Current Status and Future Prospects for Shared Decision Making Before and After Total Knee Replacement Surgery-A Scoping Review

Background. To gain insight into the current state-of-the-art of shared decision making (SDM) during decisions related to pre and postoperative care process regarding primary total knee replacement (TKR). Methods. A scoping review was performed to synthesize existing scientific research regarding (1) decisional needs and preferences of patients preparing for, undergoing and recovering from TKR surgery, (2) the relation between TKR decision-support interventions and SDM elements (i.e., team talk, option talk, and decision talk), (3) the extent to which TKR decision-support interventions address patients' decisional needs and preferences. Results. 2526 articles were identified, of which 17 articles met the inclusion criteria. Of the 17 articles, ten had a qualitative study design and seven had a quantitative study design. All included articles focused on the decision whether to undergo TKR surgery or not. Ten articles (all qualitative) examined patients' decisional needs and preferences. From these, we identified four domains that affected the patients' decision to undergo TKR: (1) personal factors, (2) external factors, (3) information sources and (4) preferences towards outcome prediction. Seven studies (5) randomized controlled trials and 2 cohort studies) used quantitative analyses to probe the effect of decision aids on SDM and/or clinical outcomes. In general, existing decision aids did not appear to be tailored to patient needs and preferences, nor were the principles of SDM well-articulated in the design of decision aids. Conclusions. SDM in TKR care is understudied; existing research appears to be narrow in scope with limited relevance to established SDM principles and the decisional needs of patients undertaking TKR surgery.

Meaningful Clinical Applications of Patient-Reported Outcome Measures in Orthopaedics

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Patient-reported outcome measures (PROMs) comprise valuable data, when combined with traditional clinical information, for patient-centered health outcome assessment.

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While PROMs form the foundation of orthopaedic clinical research, they are invaluable tools for clinical care.

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PROMs play a critical role in shared decision-making with patients, as they are quantitative measures of patient health (function, pain, and satisfaction).

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PROMs should be incorporated into routine postoperative care for effective clinical monitoring and understanding of the response to surgery.

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PROMs can be additionally utilized for meaningful clinical research, predictive analytics, and value-based care delivery pathways.

Pain severity and healthcare resource utilization in patients with osteoarthritis in the United States

OBJECTIVE

To evaluate healthcare resource utilization (HCRU) by osteoarthritis (OA) pain severity.

METHODS

Cross-sectional surveys of US physicians and their patients were conducted between February and May 2017. Using the Numeric Rating Scale, patients were classified by self-reported pain intensity in the last week into mild (0-3), moderate (4-6), and severe (7-10) cohorts. Parameters assessed included clinical characteristics, HCRU, and current caregiver support. Descriptive statistics were obtained, and analysis of variance and chi-square tests were performed.

RESULTS

Patients (n = 841) were mostly female (60.9%) and white (77.8%), with mean age of 64.6 years. Patients reported mild (45.4%), moderate (35.9%), and severe (18.7%) OA pain. Mean number of affected joints varied by pain severity (range mild: 2.7 to severe: 3.6; p < 0.0001). Pain severity was associated with an increased number of physician-reported and patient-reported overall healthcare provider visits (HCPs; both p < 0.001). As pain increased, patients reported an increased need for mobility aids, accessibility modifications to homes, and help with daily activities due to functional disability. The number of imaging tests used to diagnose OA was similar across pain severity but varied when used for monitoring (X-rays: p < 0.0001; computerized tomography scans: p < 0.0447). Hospitalization rates for OA were low but were significantly associated with pain severity (mild: 4.9%; severe: 11.5%). Emergency department visits were infrequent but increasing pain severity was associated with more prior and planned surgeries.

CONCLUSION

Greater current pain was associated with more prior HCRU including imaging for monitoring progression, HCP visits including more specialty care, hospitalizations, surgery/planned surgery, and loss of independence due to functional disability. Yet rates of hospitalizations and X-ray use were still sizable even among patients with mild pain. These cross-sectional findings warrant longitudinal assessment to further elucidate the impact of pain on HCRU.