Zoekresultaten

Lower limb orthoses

Background: Osteoarthritis (OA) is the most common rheumatic disease of the musculoskeletal system, with the knee as the most affected joint. The number of people with OA of the knee is likely to increase due to the ageing society and the obesity epidemic. The predominant clinical symptom of knee OA is pain, which is described as worsening by activity and relieving by rest. Knee instability has been recognized as an important clinical feature in persons with knee OA. Pain and knee instability are associated with limitations in performing daily activities. Non-pharmacological options in the management of knee OA consist of education, weight loss, exercise, braces and physical therapy. Knee bracing has been recommended by the Osteoarthritis Research Society International (OARSI). Valgus knee braces designed to decrease loads on the medial compartment of the knee for patients with varus alignment are the most common. It has been shown however, that valgus bracing may have little or no effect on pain and physical functioning, and adherence to this treatment in patients with knee OA is low.Because of ease of use and access, lack of complications and low cost, soft knee braces are commonly used in persons with knee OA. However, the evidence for efficacy of soft knee bracing on pain and activity limitations in knee OA is limited. Therefore, it is important to strengthen the evidence of using a soft brace to reduce pain and activity limitations as well as to evaluate the efficacy of soft knee bracing on knee instability in persons with knee OA. There is also debate about the effectiveness of soft braces in other affected joints of the lower extremity and in conditions other than OA such as rheumatoid arthritis.Objectives: The aim of the study will be to evaluate the effect of wearing a soft brace on dynamic knee instability in patients with OA of the knee.Methods: Persons with knee OA and self-reported knee instability from the Amsterdam Osteoarthritis cohort participated in a single-session lab-experimental study. A within-subject design was used, comparing no brace versus brace, and comparing a non-tight versus a tight brace (standard fit). The primary outcome measure was dynamic knee instability, expressed by the Perturbation Response (PR), i.e., a biomechanics based measure reflecting deviation in the mean knee varus-valgus angle after a controlled mechanical perturbation, standardized to the mean (SD) varus-valgus angle during level walking. Linear mixed-effect model analysis was used to evaluate the effect of a brace on dynamic knee instability.Results: The wearing of a soft brace reduced the knee instability significantly during perturbed walking. Results will also be presented from the literature search and from the lab-experimental study.Conclusion: Wearing a soft brace reduces dynamic knee instability in patients with knee OA. However, longitudinal studies are needed to evaluate the clinical implications of wearing a soft brace.

Script concordance test; a tool to assess cognitive integration?

The perception of shared decision-making in hematology by patients and physicians seems satisfactory, but important steps are still ahead of us

Patients with a hematologic malignancy increasingly prefer to be actively involved in treatment decision-making. Shared decision-making (SDM), a process that supports decision-making in preference-sensitive decisions, fits well with this need. A decision is preference sensitive when well-informed patients considerably differ in their trade-offs between the pros and cons of one option, or if more equal treatment options are available, including no treatment. SDM involves several steps: the first is choice talk, where the professional informs the patient that a decision needs to be made between the various relevant options and that the patient's opinion is important. The second is option talk, where the professional explains the options and their pros and cons. In the third step, preference talk, the professional and the patient discuss the patient's preferences. The professional supports the patient in deliberation. The final step is decision talk, where the professional and patient discuss the patient's decisional role preference, make or defer the decision and discuss possible follow-up.

The perception of shared decision-making in hematology by patients and physicians seems satisfactory, but important steps are still ahead of us

Patients with a hematologic malignancy increasingly prefer to be actively involved in treatment decision-making.1,2 Shared decision-making (SDM), a process that supports decision-making in preference-sensitive decisions, fits well with this need. A decision is preference sensitive when well-informed patients considerably differ in their trade-offs between the pros and cons of one option, or if more equal treatment options are available, including no treatment. SDM involves several steps: the first is choice talk, where the professional informs the patient that a decision needs to be made between the various relevant options and that the patient's opinion is important. The second is option talk, where the professional explains the options and their pros and cons. In the third step, preference talk, the professional and the patient discuss the patient's preferences. The professional supports the patient in deliberation. The final step is decision talk, where the professional and patient discuss the patient's decisional role preference, make or defer the decision and discuss possible follow-up.3,4

The correlation of muscle quantity and quality between all vertebra levels and level L3, measured with CT

INTRODUCTION: In patients with cancer, low muscle mass has been associated with a higher risk of fatigue, poorer treatment outcomes, and mortality. To determine body composition with computed tomography (CT), measuring the muscle quantity at the level of lumbar 3 (L3) is suggested. However, in patients with cancer, CT imaging of the L3 level is not always available. Thus far, little is known about the extent to which other vertebra levels could be useful for measuring muscle status. In this study, we aimed to assess the correlation of the muscle quantity and quality between any vertebra level and L3 level in patients with various tumor localizations.METHODS: Two hundred-twenty Positron Emission Tomography (PET)-CT images of patients with four different tumor localizations were included: 1. head and neck ( n = 34), 2. esophagus ( n = 45), 3. lung ( n = 54), and 4. melanoma ( n = 87). From the whole body scan, 24 slices were used, i.e., one for each vertebra level. Two examiners contoured the muscles independently. After contouring, muscle quantity was estimated by calculating skeletal muscle area (SMA) and skeletal muscle index (SMI). Muscle quality was assessed by calculating muscle radiation attenuation (MRA). Pearson correlation coefficient was used to determine whether the other vertebra levels correlate with L3 level. RESULTS: For SMA, strong correlations were found between C1-C3 and L3, and C7-L5 and L3 ( r = 0.72-0.95). For SMI, strong correlations were found between the levels C1-C2, C7-T5, T7-L5, and L3 ( r = 0.70-0.93), respectively. For MRA, strong correlations were found between T1-L5 and L3 ( r = 0.71-0.95). DISCUSSION: For muscle quantity, the correlations between the cervical, thoracic, and lumbar levels are good, except for the cervical levels in patients with esophageal cancer. For muscle quality, the correlations between the other levels and L3 are good, except for the cervical levels in patients with melanoma. If visualization of L3 on the CT scan is absent, the other thoracic and lumbar vertebra levels could serve as a proxy to measure muscle quantity and quality in patients with head and neck, esophageal, lung cancer, and melanoma, whereas the cervical levels may be less reliable as a proxy in some patient groups.

Does muscle mass measured with Computed Tomography analysis differ in patients with different types of cancer?

Rationale: Patients with cancer of the upper gastrointestinal tract or lung are more likely to present with malnutrition at diagnosis than, for instance, patients with melanoma. Low muscle mass is an indicator of malnutrition and can be determined by computed tomography (CT) analysis of the skeletal muscle index (SMI) at the 3rd lumbar vertebra (L3) level. However, CT images at L3 are not always available. At each vertebra level, we determined if type of cancer, i.e., head and neck cancer (HNC), oesophageal cancer (OC) or lung cancer (LC) vs. melanoma (ME) was associated with lower SMI. Methods: CT images from adult patients with HNC, OC, LC or ME were included and analyzed. Scans were performed in the patient’s initial staging after diagnosis. MIM software version 7.0.1 was used to contour the muscle areas for all vertebra levels. Skeletal muscle area was corrected for stature to calculate SMI (cm2/m2). We tested for the association of HNC, OC, or LC diagnosis vs ME with SMI by univariate and multivariate linear regression analyses. In the multivariate analyses, age (years), sex, and body mass index (BMI; kg/m2) were included. Betas (B;95%CI) were calculated and statistical significance was set at p