A loss of physical functioning (i.e., a low physical capacity and/or a low physical activity) is a common feature in patients with chronic obstructive pulmonary disease (COPD). To date, the primary care physiotherapy and specialized pulmonary rehabilitation are clearly underused, and limited to patients with a moderate to very severe degree of airflow limitation (GOLD stage 2 or higher). However, improved referral rates are a necessity to lower the burden for patients with COPD and for society. Therefore, a multidisciplinary group of healthcare professionals and scientists proposes a new model for referral of patients with COPD to the right type of exercise-based care, irrespective of the degree of airflow limitation. Indeed, disease instability (recent hospitalization, yes/no), the burden of disease (no/low, mild/moderate or high), physical capacity (low or preserved) and physical activity (low or preserved) need to be used to allocate patients to one of the six distinct patient profiles. Patients with profile 1 or 2 will not be referred for physiotherapy; patients with profiles 3–5 will be referred for primary care physiotherapy; and patients with profile 6 will be referred for screening for specialized pulmonary rehabilitation. The proposed Dutch model has the intention to get the right patient with COPD allocated to the right type of exercise-based care and at the right moment.
DOCUMENT
Background: Limited information is available on the experiences of patients during rehabilitation after anterior cruciate ligament reconstruction (ACLR). Aim: The current study aimed to identify factors that differentiated positive and negative patient experiences during rehabilitation after ACLR. Method and Design: A survey-based study with an online platform was used to identify factors that differentiated positive and negative patient experiences during rehabilitation after ACLR. Seventy-two patients (age 27.8 [8.8] y) after ACLR participated. Data were analyzed and themes were identified by comparing categories and subcategories on similarity. Main Findings: Positive patient experiences were room for own input, supervision, attention, knowledge, honesty, and professionalism of the physiotherapist. Additionally, a varied and structured rehabilitation program, adequate facilities, and contact with other patients were identified as positive patient experiences. Negative experiences were a lack of attention, lack of professionalism of the physiotherapists, a lack of sport-specific field training, a lack of goal setting, a lack of adequate facilities, and health insurance costs. Conclusions: The current study identified factors that differentiated positive and negative patient experiences during rehabilitation after ACLR. These findings can help physiotherapists in understanding the patient experiences during rehabilitation after ACLR.
DOCUMENT
Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions ofdifferentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RACD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso.
MULTIFILE
