Decision Aids and Elective Joint Replacement - How Knowledge Affects Utilization

Total Joint Arthroplasty Utilization After Orthopaedic Surgery Referral: Identifying Disparities Along the Care Pathway

BACKGROUND

Although racial and ethnic disparities in total joint arthroplasty (TJA) have been thoroughly described, only a few studies have sought to determine exactly where along the care pathway these disparities are perpetuated. The purpose of this study was to investigate disparities in TJA utilization occurring after patients who had diagnosed hip or knee osteoarthritis were referred to a group of orthopaedic providers within an integrated academic institution.

METHODS

A retrospective, multi-institutional study evaluating patients with diagnosed hip or knee osteoarthritis was conducted between 2015 and 2019. Information pertaining to patient demographics, timing of clinic visits, and subsequent surgical intervention was collected. Utilization rates and time to surgery from the initial clinic visit were calculated by race, and logistic regressions were performed to control for various demographic as well as health related variables.

RESULTS

White patients diagnosed with knee osteoarthritis were significantly more likely to receive total knee arthroplasty (TKA) than Black and Hispanic patients, even after adjusting for various demographic variables (Black patients: odds ratio [OR] = 0.63, 95% CI = 0.55-0.72, P = .002; Hispanic patients: OR = 0.69, 95% CI = 0.57-0.83, P = .039). Similar disparities were found among patients diagnosed with hip osteoarthritis who underwent total hip arthroplasty (THA; Black patients: OR = 0.73, 95% CI = 0.60-0.89, P = <.001; Hispanic patients: OR = 0.72, 95% CI = 0.53-0.98, P <.001). There were no differences in time to surgery between races (P > .05 for all).

CONCLUSION

In this study, racial and ethnic disparities in TJA utilization were found to exist even after referral to an orthopaedic surgeon, highlighting a critical point along the care pathway during which inequalities in TJA care can emerge. Similar time to surgery between White, Black, and Hispanic patients suggest that these disparities in TJA utilization may largely be perpetuated before surgical planning while patients are deciding whether to undergo surgery. Further studies are needed to better elucidate which patient and provider-specific factors may be preventing these patients from pursuing surgery during this part of the care pathway.

LEVEL OF EVIDENCE

Level IV.

An individualized decision aid for physicians and patients for total knee replacement in osteoarthritis (Value-based TKR study): study protocol for a multi-center, stepped wedge, cluster randomized controlled trial

Background

Total knee replacement (TKR) is one of the most commonly performed routine procedures in the world. Prognostic studies indicate that the number of TKR will further increase constituting growing burden on healthcare systems. There is also substantial regional heterogeneity in TKR rates within and between countries. Despite the known therapeutic effects, a subset of patients undergoing TKR does not benefit from the procedure as intended. To improve the appropriateness of TKR indication, the EKIT initiative (“evidence and consensus based indication critera for total arthroplasty”) developed a clinical guideline for Germany on the indication of TKR. This guideline is the basis for a digital medical decision aid (EKIT tool) to facilitate shared decision making (SDM) in order to improve decision quality for elective surgery. The aim of this cluster randomized trial is to investigate the effectiveness of the EKIT tool on decision quality.

Methods

The Value-based TKR study is a prospective pragmatic multi-center, stepped wedge, cluster randomized controlled trial (SW-RCT). The EKIT tool provides (1) a systematic presentation of individual patient and disease-specific information (symptoms, expectations), (2) the fulfillment of the indication criteria and (3) health information about safety and effectiveness of TKR. All study sites will follow routine care as control clusters until the start of the intervention. In total, there will be 10 clusters (study sites) and 6 sequential steps over 16 month, with clusters receiving the intervention with a minimum 2 months of standard routine care. The primary outcome is patients’ decision quality measured with the Decision Quality Instrument (DQI)-Knee Osteoarthritis questionnaire. Furthermore, we will collect information on global patient satisfaction, patient reported outcome measures and the fulfilment of the individual expectations 12 months after SDM. The power calculation yielded an estimated power of 89% using robust Poisson regression under the following assumptions: 10 study sites with a total of N=1,080 patients (including a dropout rate of 11%), a 10% increase in decision quality due to the use of the EKIT tool, and a significance level of 5%.

Discussion

There is a high potential for transferring the intervention into routine practice if the evaluation is positive.

Trial registration

ClinicalTrials.gov: NCT04837053. Registered on 08/04/2021.

Effect of a Patient-Centered Decision Support Tool on Rates of Trial of Labor After Previous Cesarean Delivery: The PROCEED Randomized Clinical Trial

Importance

Reducing cesarean delivery rates in the US is an important public health goal; despite evidence of the safety of vaginal birth after cesarean delivery, most women have scheduled repeat cesarean deliveries. A decision support tool could help increase trial-of-labor rates.

Objective

To analyze the effect of a patient-centered decision support tool on rates of trial of labor and vaginal birth after cesarean delivery and decision quality.

Design, Setting, and Participants

Multicenter, randomized, parallel-group clinical trial conducted in Boston, Chicago, and the San Francisco Bay area. A total of 1485 English- or Spanish-speaking women with 1 prior cesarean delivery and no contraindication to trial of labor were enrolled between January 2016 and January 2019; follow-up was completed in June 2019.

Interventions

Participants were randomized to use a tablet-based decision support tool prior to 25 weeks' gestation (n=742) or to receive usual care (without the tool) (n=743).

Main Outcomes and Measures

The primary outcome was trial of labor; vaginal birth was the main secondary outcome. Other secondary outcomes focused on maternal and neonatal outcomes and decision quality.

Results

Among 1485 patients (mean age, 34.0 [SD, 4.5] years), 1470 (99.0%) completed the trial (n = 735 in both randomization groups) and were included in the analysis. Trial-of-labor rates did not differ significantly between intervention and control groups (43.3% vs 46.2%, respectively; adjusted absolute risk difference, -2.78% [95% CI, -7.80% to 2.25%]; adjusted relative risk, 0.94 [95% CI, 0.84-1.05]). There were no statistically significant differences in vaginal birth rates (31.8% in both groups; adjusted absolute risk difference, -0.04% [95% CI, -4.80% to 4.71%]; adjusted relative risk, 1.00 [95% CI, 0.86-1.16]) or in any of the other 6 clinical maternal and neonatal secondary outcomes. There also were no significant differences between the intervention and control groups in the 5 decision quality measures (eg, mean decisional conflict scores were 17.2 and 17.5, respectively; adjusted mean difference, -0.38 [95% CI, -1.81 to 1.05]; scores >25 are considered clinically important).

Conclusions and Relevance

Among women with 1 previous cesarean delivery, use of a decision support tool compared with usual care did not significantly change the rate of trial of labor. Further research may be needed to assess the efficacy of this tool in other clinical settings or when implemented at other times in pregnancy.

ANCHOR surgeon views of patient selection and expectations for periacetabular osteotomy

Preoperative expectations impact shared decision making and patient satisfaction. Surgeon views of patient selection, expected outcomes and patient expectations after periacetabular osteotomy (PAO) for treatment of acetabular dysplasia have not been defined. We assessed surgeon views of patient selection and expected outcomes after PAO. A sample of experienced PAO surgeons participated in semi-structured phone interviews assessing: (i) factors that determine patient candidacy for PAO; (ii) surgeon expectations for PAO outcomes; (iii) surgeon perceptions of patient expectations for PAO outcomes and (iv) surgeon perceptions of discrepancies in surgeon and patient expectations and approaches for reconciling these discrepancies. Twelve surgeons (77% of PAO-performing ANCHOR surgeons) participated. The factors most commonly mentioned in determining patient candidacy for PAO were: symptoms, radiographic findings, absence of arthritis and age. Only one-quarter of the sample mentioned patient expectations as a factor in determining patient candidacy for PAO. The most common surgeon expectations were: pain reduction, joint preservation, function with activities of daily living and return to desired activities. 58% of surgeons felt that surgeon and patient expectations align most of the time. Common expectation discrepancies included return to unrestricted activities and complete pain relief. Detailed discussion was the most commonly employed strategy to resolve expectation discrepancies. PAO surgeons felt that patient expectations of complete pain relief and return to unrestricted activities were misaligned with their own expectations. Development of an expectations survey may facilitate shared decision making.

Connected orthopedics and trauma surgery: New perspectives

Information is everywhere in the surgeon's life. It can improve medical practice and allow for personalized care. To answer the question, "How should the surgeon be connected?" we must assess the role and limitations of digital information in daily practice, particularly through mobile applications or mHealth. These tools and their scope must be defined in order to measure their impact on our clinical practice. New regulations on medical data have been introduced imposing that privacy be maintained. Connected applications can assist the surgeon in making the diagnosis and deciding on the treatment. These tools are already being used widely. Decision algorithms based on machine learning are also a promising way to optimize patient care. Connected applications make the clinical follow-up easier by allowing more reliable, relevant and frequent data transmission. They also provide access to information and training, either early academic learning or continuing medical education. We must adapt to these new modes of learning. Thus, smartphones, tablets and digital applications now have a central role in modern orthopedic surgery. Surgeons have information, technical resources and storage for research data at their disposal, while patients can establish a link with their doctor (current or future) and find lay information about their condition.

Numeracy and Understanding of Quantitative Aspects of Predictive Models: A Pilot Study

BACKGROUND

The assessment of user preferences for performance characteristics of patient-oriented clinical prediction models is lacking. It is unknown if complex statistical aspects of prediction models are readily understandable by a general audience.

OBJECTIVE

A pilot study was conducted among nonclinical audiences to determine the feasibility of interpreting statistical concepts that describe the performance of prediction models.

METHODS

We conducted a cross-sectional electronic survey using the Amazon Mechanical Turk platform. The survey instrument included educational modules about predictive models, sensitivity, specificity, and confidence intervals (CIs). Follow-up questions tested participants' abilities to interpret these characteristics with both verbatim and gist knowledge. Objective and subjective numeracy were assessed using previously validated instruments. We also tested understanding of these concepts when embedded in a sample discrete choice experiment task to establish feasibility for future elicitation of preferences using a discrete choice experiment design. Multivariable linear regression was used to identify factors associated with correct interpretation of statistical concepts.

RESULTS

Among 534 respondents who answered all nine questions, the mean correct responses was 95.9% (95% CI, 93.8-97.4) for sensitivity, 93.1% (95% CI, 90.5-95.0) for specificity, and 86.6% (95% CI, 83.3-89.3) for CIs. Verbatim interpretation was high for all concepts, but significantly higher than gist only for CIs (p < 0.001). Scores on each discrete choice experiment tasks were slightly lower in each category. Both objective and subjective numeracy were positively associated with an increased proportion of correct responses (p < 0.001).

CONCLUSION

These results suggest that a nonclinical audience can interpret quantitative performance measures of predictive models with very high accuracy. Future development of patient-facing clinical prediction models can feasibly incorporate patient preferences for model features into their development.

Patients' preferences for treatment after neoadjuvant chemoradiotherapy for oesophageal cancer

Background

After neoadjuvant chemoradiotherapy (nCRT) plus surgery for oesophageal cancer, 29 per cent of patients have a pathologically complete response in the resection specimen. Active surveillance after nCRT (instead of standard oesophagectomy) may improve health-related quality of life (HRQoL), but patients need to undergo frequent diagnostic tests and it is unknown whether survival is worse than that after standard oesophagectomy. Factors that influence patients' preferences, and trade-offs that patients are willing to make in their choice between surgery and active surveillance were investigated here.

Methods

A prospective discrete-choice experiment was conducted. Patients with oesophageal cancer completed questionnaires 4–6 weeks after nCRT, before surgery. Patients' preferences were quantified using scenarios based on five aspects: 5-year overall survival, short-term HRQoL, long-term HRQoL, the risk that oesophagectomy is still necessary, and the frequency of clinical examinations using endoscopy and PET–CT. Panel latent class analysis was used.

Results

Some 100 of 104 patients (96·2 per cent) responded. All aspects, except the frequency of clinical examinations, influenced patients' preferences. Five-year overall survival, the chance that oesophagectomy is still necessary and long-term HRQoL were the most important attributes. On average, based on calculation of the indifference point between standard surgery and active surveillance, patients were willing to trade off 16 per cent 5-year overall survival to reduce the risk that oesophagectomy is necessary from 100 per cent (standard surgery) to 35 per cent (active surveillance).

Conclusion

Patients are willing to trade off substantial 5-year survival to achieve a reduction in the risk that oesophagectomy is necessary.

A Fresh Perspective on a Familiar Problem: Examining Disparities in Knee Osteoarthritis Using a Markov Model

Supplemental Digital Content is available in the text.

Background:

Disparities in the presentation of knee osteoarthritis (OA) and in the utilization of treatment across sex, racial, and ethnic groups in the United States are well documented.

Objectives:

We used a Markov model to calculate lifetime costs of knee OA treatment. We then used the model results to compute costs of disparities in treatment by race, ethnicity, sex, and socioeconomic status.

Research Design:

We used the literature to construct a Markov Model of knee OA and publicly available data to create the model parameters and patient populations of interest. An expert panel of physicians, who treated a large number of patients with knee OA, constructed treatment pathways. Direct costs were based on the literature and indirect costs were derived from the Medical Expenditure Panel Survey.

Results:

We found that failing to obtain effective treatment increased costs and limited benefits for all groups. Delaying treatment imposed a greater cost across all groups and decreased benefits. Lost income because of lower labor market productivity comprised a substantial proportion of the lifetime costs of knee OA. Population simulations demonstrated that as the diversity of the US population increases, the societal costs of racial and ethnic disparities in treatment utilization for knee OA will increase.

Conclusions:

Our results show that disparities in treatment of knee OA are costly. All stakeholders involved in treatment decisions for knee OA patients should consider costs associated with delaying and forgoing treatment, especially for disadvantaged populations. Such decisions may lead to higher costs and worse health outcomes.