The Attitudes of Patients Toward Orthopaedic Post-surgical Scars.

Background Post-surgical scars (PSS) are an expected consequence of surgery. Several factors have previously been associated with PSS satisfaction including patient age and time elapsed post-operative. Little data are available regarding patient attitudes toward orthopaedic PSS. Knowledge of patient attitudes and the various associated factors may allow physicians to administer peri-operative care to mitigate the potential negative effects of PSS. Our study aims to investigate the attitudes of patients toward their PSS using quantitative scar assessment scales and to identify factors associated with PSS satisfaction. Methods We conducted a retrospective study with a follow-up. We included all patients with orthopaedic PSS on their upper or lower limbs between two and 18 weeks postoperative attending Cork University Hospital, Ireland, between February and August 2022. Patients completed an initial baseline questionnaire and then a follow-up questionnaire six months post-operative. The Patient and Observer Scar Assessment Scale (POSAS) evaluated PSS satisfaction. The European Quality of Life 5 Domain (EQ-5D), alongside several Likert scales, evaluated the patient's quality of life (QoL). Results In total, 91 patients were included. The mean POSAS score was 28.41 (95% CI, 25.85-30.97). Younger patient age (p=0.045) and decreased time passed post-operatively (p=0.002) were associated with poorer PSS satisfaction. Patients reporting their PSS appearing worse than expected were more likely to agree that their QoL had been adversely affected by it (p=0.001). Conclusion Most patients were satisfied with their orthopaedic PSS. This study identified several factors associated with poor PSS satisfaction. Our finding, which associated patient scar expectations and QoL, is novel and has not been previously examined. Accordingly, peri-operative interventions, including scar expectation management, may be implemented to mitigate scar-related QoL impact.

Patient-centered clinical trials.

We apply Bayesian decision analysis (BDA) to incorporate patient preferences in the regulatory approval process for new therapies. By assigning weights to type I and type II errors based on patient preferences, the significance level (α) and power (1-ß) of a randomized clinical trial (RCT) for a new therapy can be optimized to maximize the value to current and future patients and, consequently, to public health. We find that for weight-loss devices, potentially effective low-risk treatments have optimal αs larger than the traditional one-sided significance level of 5%, whereas potentially less effective and riskier treatments have optimal αs below 5%. Moreover, the optimal RCT design, including trial size, varies with the risk aversion and time-to-access preferences and the medical need of the target population.

Incorporating patient-preference evidence into regulatory decision making.

BACKGROUND: Patients have a unique role in deciding what treatments should be available for them and regulatory agencies should take their preferences into account when making treatment approval decisions. This is the first study designed to obtain quantitative patient-preference evidence to inform regulatory approval decisions by the Food and Drug Administration Center for Devices and Radiological Health. METHODS: Five-hundred and forty United States adults with body mass index (BMI) ≥ 30 kg/m(2) evaluated tradeoffs among effectiveness, safety, and other attributes of weight-loss devices in a scientific survey. Discrete-choice experiments were used to quantify the importance of safety, effectiveness, and other attributes of weight-loss devices to obese respondents. A tool based on these measures is being used to inform benefit-risk assessments for premarket approval of medical devices. RESULTS: Respondent choices yielded preference scores indicating their relative value for attributes of weight-loss devices in this study. We developed a tool to estimate the minimum weight loss acceptable by a patient to receive a device with a given risk profile and the maximum mortality risk tolerable in exchange for a given weight loss. For example, to accept a device with 0.01 % mortality risk, a risk tolerant patient will require about 10 % total body weight loss lasting 5 years. CONCLUSIONS: Patient preference evidence was used make regulatory decision making more patient-centered. In addition, we captured the heterogeneity of patient preferences allowing market approval of effective devices for risk tolerant patients. CDRH is using the study tool to define minimum clinical effectiveness to evaluate new weight-loss devices. The methods presented can be applied to a wide variety of medical products. This study supports the ongoing development of a guidance document on incorporating patient preferences into medical-device premarket approval decisions.