Impact of a national guideline on use of knee arthroscopy: An interrupted time-series analysis

Reduction in use of MRI and arthroscopy among patients with degenerative knee disease in independent treatment centers versus general hospitals: a time series analysis

The use of MRI and arthroscopy are considered low-value care in most patients with degenerative knee disease. To reduce these modalities, there have been multiple efforts to increase awareness. Reductions have been shown for general hospitals (GH), but it is unclear whether this may be partly explained by a shift of patients receiving these modalities in independent treatment centers (ITCs). The aims of this study were to assess (i) whether the trend in use of MRI and arthroscopy in patients with degenerative knee disease differs between ITCs and GH, and (ii) whether the Dutch efforts to raise awareness on these recommendations were associated with a change in the trend for both types of providers. All patients insured by a Dutch healthcare insurer aged ≥50 years with a degenerative knee disease who were treated in a GH or ITC between July 2014 and December 2019 were included. Linear regression was used with the quarterly percentage of patients receiving an MRI or knee arthroscopy weighted by center volume, as the primary outcome. Interrupted time-series analysis was used to evaluate the effect of the Dutch efforts to raise awareness. A total of 14 702 patients included were treated in 90 GHs (n = 13 303, 90.5%) and 29 ITCs (n = 1399, 9.5%). Across the study period, ITCs on an average had a 16% higher MRI use (P < .001) and 9% higher arthroscopy use (P = .003). MRI use did not change in both provider types, but arthroscopy use significantly decreased and became stronger in ITCs (P = .01). The Dutch efforts to increase awareness did not significantly influence either MRI or arthroscopy use in ITCs (P = .55 and P = .84) and GHs (P = .13 and P = .70). MRI and arthroscopy uses were higher in ITCs than GHs. MRI use did not change significantly among patients ≥ 50 years with degenerative knee disease in both provider types between 2014 and 2019. MRI- and arthroscopy use decreased with ITCs on average having higher rates for both modalities, but also showing a stronger decrease in arthroscopy use. The Dutch efforts to increase awareness did not accelerate the already declining trend in the Netherlands.

Clinical and Functional Outcome of Meniscal Injuries Treated with Platelet-Rich Plasma: A Single-Center Case Series

Meniscal injuries are among the most frequently encountered conditions in the knee joint. Therapeutic approaches are diverse and are largely dependent on the extent and location of the injury. The purpose of this study was to describe the clinical and functional outcomes of an intraarticular and percutaneous platelet-rich plasma (PRP) injection regime in patients with stable meniscal injuries. Demographics, the type of tear, affected knee, surgical procedure, type of intervention, follow-up period, and outcomes were recorded in all cases. Patient-reported outcome measures included the Knee Injury and Osteoarthritis Outcome Score (KOOS) and Tegner activity level scale. Overall patient satisfaction, quality of life, and pain intensity were also assessed. A total of 38 cases (8 females) had sustained a stable meniscal lesion (32 medial, 6 lateral) and met the inclusion criteria. All of them received three intraarticular and percutaneous PRP injections. Patients receiving the PRP injection regime reported clinically (p = 0.000) and functionally (p = 0.000 and p = 0.001) significant improvement in all outcome measures during this interval. All patients reported they were very satisfied or satisfied with the outcome. The results of this study suggest that the treatment of stable meniscal injuries with percutaneous-intraarticular PRP injections can achieve a significant clinical and functional improvement.

Evaluation of Trends in Knee Arthroscopy from 2004 to 2019 in Ontario, Canada

BACKGROUND: How changes in recommendations for the use of knee arthroscopy have influenced real-world practice remains unclear. We assessed temporal trends in knee arthroscopy volume, costs, and rates of progression to knee arthroplasty following arthroscopy in Ontario, Canada. METHODS: We used diagnostic codes from population-based administrative databases from Ontario, Canada, to identify patients who underwent knee arthroscopy from April 1, 2004 to March 31, 2019. We calculated arthroscopy volume, costs, and rates of progression to knee arthroplasty within 1, 2, and 5 years following arthroscopy. RESULTS: A total of 408,040 arthroscopy procedures were included. The number of procedures declined 8.9% from 24,070 in 2004/2005 to 21,930 in 2018/2019. The volume of arthroscopy for osteoarthritis declined by 77.9% between 2007/2008 and 2018/2019. For degenerative meniscus disorders, the volume increased by 57.6% between 2004/2005 and 2013/2014, and then declined by 34.6% between 2013/2014 and 2018/2019. Among patients with osteoarthritis, rates of progression to knee arthroplasty were 3.8%, 9.6%, and 16.0%, at 1, 2, and 5 years, respectively, compared with rates among patients with degenerative meniscal disorders, which were 1.6%, 4.1%, and 7.3% at 1, 2, and 5 years, respectively. Over this period, progression to knee arthroplasty rates declined across diagnosis groups. These trends remained after adjusting for patient, surgeon, and hospital characteristics. CONCLUSIONS: In Ontario, Canada, utilization of knee arthroscopy declined between 2004/2005 and 2018/2019, with a concomitant decline in the rates of progression to knee arthroplasty within 1 to 5 years. Among the possible interpretations, our data are consistent with the hypothesis that clinical practice evolved as evidence-based recommendations against the use of knee arthroscopy for degenerative diagnoses were promulgated. (Funded by the Toronto General and Western Hospital Foundation through the University Health Network–Schroeder Arthritis Institute.)

Health Services Research in Anesthesia: A Brief Overview of Common Methodologies

The use of large data sources such as registries and claims-based data sets to perform health services research in anesthesia has increased considerably, ultimately informing clinical decisions, supporting evaluation of policy or intervention changes, and guiding further research. These observational data sources come with limitations that must be addressed to effectively examine all aspects of health care services and generate new individual- and population-level knowledge. Several statistical methods are growing in popularity to address these limitations, with the goal of mitigating confounding and other biases. In this article, we provide a brief overview of common statistical methods used in health services research when using observational data sources, guidance on their interpretation, and examples of how they have been applied to anesthesia-related health services research. Methods described involve regression, propensity scoring, instrumental variables, difference-in-differences, interrupted time series, and machine learning.

A tailored intervention does not reduce low value MRI's and arthroscopies in degenerative knee disease when the secular time trend is taken into account: a difference-in-difference analysis

PURPOSE

To evaluate the effectiveness of a tailored intervention to reduce low value MRIs and arthroscopies among patients ≥ 50 years with degenerative knee disease in 13 Dutch orthopaedic centers (intervention group) compared with all other Dutch orthopaedic centers (control group).

METHODS

All patients with degenerative knee disease ≥ 50 years admitted to Dutch orthopaedic centers from January 2016 to December 2018 were included. The tailored intervention included participation of clinical champions, education on the Dutch Choosing Wisely recommendation for MRI's and arthroscopies in degenerative knee disease, training of orthopaedic surgeons to manage patient expectations, performance feedback, and provision of a patient brochure. A difference-in-difference analysis was used to compare the time trend before (admitted January 2016-June 2017) and after introduction of the intervention (July 2017-December 2018) between intervention and control hospitals. Primary outcome was the monthly percentage of patients receiving a MRI or knee arthroscopy, weighted by type of hospital.

RESULTS

136,446 patients were included, of whom 32,163 were treated in the intervention hospitals. The weighted percentage of patients receiving a MRI on average declined by 0.15% per month (β =  - 0.15, P < 0.001) and by 0.19% per month for arthroscopy (β =  - 0.19, P < 0.001). However, these changes over time did not differ between intervention and control hospitals, neither for MRI (β =  - 0.74, P = 0.228) nor arthroscopy (β = 0.13, P = 0.688).

CONCLUSIONS

The extent to which patients ≥ 50 years with degenerative knee disease received a MRI or arthroscopy declined significantly over time, but could not be attributed to the tailored intervention. This secular downward time trend may reflect anoverall focus of reducing low value care in The Netherlands.

LEVEL OF EVIDENCE

III.

Previous History of Knee Arthroscopy in Patients Undergoing Total Knee Arthroplasty: An Examination of the Effect of the Literature and American Academy of Orthopaedic Surgeons 2013 Arthroscopy Guidelines on Clinical Practice

Purpose

To compare the rate of previous knee arthroscopy in patients undergoing total knee arthroplasty (TKA) before (2005-2006) and after (2018) publication of landmark studies that examined the effectiveness of knee arthroscopy as well as the American Academy of Orthopaedic Surgeons 2013 knee arthroscopy guidelines.

Methods

We performed a retrospective chart review of 214 patients who underwent a TKA between 2005 and 2006 (Group 1) and 213 patients who underwent a TKA in 2018 (Group 2). We reviewed medical records to determine whether previous knee arthroscopy was performed. Group comparisons were made using the t test for continuous data and the χ² test for categorical data, and means ± standard deviation are presented.

Results

Patients in Group 2 were older than those in Group 1 (67.0 ± 8.3 vs 64.3 ± 11.5 years, P = .01); however, there were no differences between groups with respect to sex (P = .40), body mass index (P = .36), and laterality of TKA (P = .05). There was no difference in the history of previous knee arthroscopy between Groups 1 and 2 overall (27% vs 32%, P = .27) and within 10 years of TKA (17% vs 15%, P = .44).

Conclusions

We did not find a difference in the number of previous knee arthroscopies for patients undergoing TKA in 2018 versus 2005-2006. This suggests that the recommendations set forth by landmark clinical trials that examined the effectiveness of knee arthroscopy and the AAOS 2013 knee arthroscopy guidelines did not have a sudden impact within our geographic region, but there is some evidence to suggest a gradual shift in treatment, where knee arthroscopy is withheld near imminent knee arthroplasty.

Level of Evidence

III, retrospective comparative study.

Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes

PURPOSE OF REVIEW

The propose of this viewpoint is to improve or facilitate the clinical decision-making in the management/treatment strategies of arthritis patients through knowing, understanding, and having access to an interactive process allowing assessment of the patient disease outcome in the future.

RECENT FINDINGS

In recent years, the time series (TS) concept has become the center of attention as a predictive model for making forecast of unseen data values. TS and one of its technologies, the interrupted TS (ITS) analysis (TS with one or more interventions), predict the next period(s) value(s) of a given patient based on their past and current information. Traditional TS/ITS methods involve segmented regression-based technologies (linear and nonlinear), while stochastic (linear modeling) and artificial intelligence approaches, including machine learning (complex nonlinear relationships between variables), are also used; however, each have limitations. We will briefly describe TS/ITS, provide examples of their application in arthritic diseases; describe their methods, challenges, and limitations; and propose a combined (stochastic and artificial intelligence) procedure in post-intervention that will optimize ITS modeling. This combined method will increase the accuracy of ITS modeling by profiting from the advantages of both stochastic and nonlinear models to capture all ITS deterministic and stochastic components. In addition, this combined method will allow ITS outcomes to be predicted as continuous variables without having to consider the time lag produced between the pre- and post-intervention periods, thus minimizing the prediction error not only for the given data but also for all possible future patterns in ITS. The use of reliable prediction methodologies for arthritis patients will permit treatment of not only the disease, but also the patient with the disease, ensuring the best outcome prediction for the patient.