Predictive value of adductor pollicis muscle thickness for ultrasound-based sarcopenia in older adults.

BACKGROUND: The adductor pollicis muscle thickness (APMT) is least affected by subcutaneous adipose tissue and can be measured externally. The aim of this study is to identify the predictive value of APMT for the diagnosis of ultrasonography-based sarcopenia. METHODS: A total of 245 outpatients who were aged ≥65 years (49 with sarcopenia and 196 without sarcopenia) were included in this cross-sectional study. Patients with knee or hip replacement, advanced dementia, cerebrovascular disease, neurodegenerative disease, or decompensated heart failure and those using drugs that affect muscle functions, such as steroids, were excluded. Comprehensive geriatric assessments and anthropometric measurements of the patients were performed. APMT was assessed with a skinfold caliper. The diagnosis of sarcopenia was based on a low Sonographic Thigh Adjustment Ratio (STAR) index (anterior thigh muscle thickness by ultrasonography/body mass index [BMI] ratio, cutoff points are <1.4 for men and <1.0 for women) and low handgrip strength. The predictive ability of APMT for ultrasound-based sarcopenia diagnosis was examined. RESULTS: There was no significant difference in terms of arm and calf circumference and BMI between the sarcopenic and nonsarcopenic groups. APMT, handgrip strength, anterior thigh muscle thickness, and gait speed were significantly lower in the patients with sarcopenia. The best cutoff points of APMT for sarcopenia were ≤16.5 for women and ≤21.5 for men with areas under the curve of 0.70 and 0.66, respectively, and negative predictive values of 91.2 and 94.3, respectively (P < 0.001). CONCLUSION: APMT can be a useful anthropometric marker for ultrasound-based sarcopenia diagnosis.

The effect of an artificial intelligence algorithm on chest X-ray interpretation of radiology residents.

OBJECTIVE: Chest X-rays are the most commonly performed diagnostic examinations. An artificial intelligence (AI) system that evaluates the images fast and accurately help reducing workflow and management of the patients. An automated assistant may reduce the time of interpretation in daily practice. We aim to investigate whether radiology residents consider the recommendations of an AI system for their final decisions, and to assess the diagnostic performances of the residents and the AI system. METHODS: Posteroanterior (PA) chest X-rays with confirmed diagnosis were evaluated by 10 radiology residents. After interpretation, the residents checked the evaluations of the AI Algorithm and made their final decisions. Diagnostic performances of the residents without AI and after checking the AI results were compared. RESULTS: Residents' diagnostic performance for all radiological findings had a mean sensitivity of 37.9% (vs 39.8% with AI support), a mean specificity of 93.9% (vs 93.9% with AI support). The residents obtained a mean AUC of 0.660 vs 0.669 with AI support. The AI algorithm diagnostic accuracy, measured by the overall mean AUC, was 0.789. No significant difference was detected between decisions taken with and without the support of AI. CONCLUSION: Although, the AI algorithm diagnostic accuracy were higher than the residents, the radiology residents did not change their final decisions after reviewing AI recommendations. In order to benefit from these tools, the recommendations of the AI system must be more precise to the user. ADVANCES IN KNOWLEDGE: This research provides information about the willingness or resistance of radiologists to work with AI technologies via diagnostic performance tests. It also shows the diagnostic performance of an existing AI algorithm, determined by real-life data.

The relationship of mean platelet volume with retinopathy in type 2 diabetes mellitus.

BACKGROUND/AIM: Patients with diabetes mellitus have an increased risk of developing micro- and macrovascular complications. Retinopathy is one of the most important complications of diabetes whose pathophysiological cause has not yet been determined. One candidate may be platelet volume. In this study we aimed to investigate the association between mean platelet volume and diabetic retinopathy. MATERIALS AND METHODS: Mean platelet volume levels were investigated in type 2 diabetic patients with and without retinopathy, and in healthy participants. After reclassifying our diabetics in terms of the presence or absence of hypertension and hyperlipidemia, we compared their mean platelet volumes. We then checked to see if the mean platelet volume correlated with hemoglobin A1c and body mass index. RESULTS: The mean platelet volume levels were higher in all diabetic patients, and they were highest in diabetics with retinopathy. The mean platelet volume levels were not different in diabetic patients with and without hypertension or hyperlipidemia. Moreover, the mean platelet volume levels did not correlate with hemoglobin A1c or body mass index. CONCLUSION: We think that platelets may play a role in diabetic retinopathy. We can also say that, in diabetic patients, hypertension, hyperlipemia, obesity, and control of diabetes were not related to mean platelet volume.

The relationship of mean platelet volume with microalbuminuriain type 2 diabetic patients.

BACKGROUND/AIM: Activation and size of platelets have been suggested to be involved in the pathogenesis of vascular complications in diabetes mellitus. The purpose of the present study was to investigate the association of mean platelet volume with microalbuminuria in type 2 diabetic patients. MATERIALS AND METHODS: Mean platelet volume levels were investigated in healthy participants and type 2 diabetic patients with and without microalbuminuria. After their mean platelet volume values were compared, correlation of mean platelet volume with sex, duration of diabetes, microalbuminuria, fasting blood glucose, hemoglobin A1c, creatinine clearance, and body mass index was examined. RESULTS: Mean platelet volume levels were higher in all diabetic patients than those in controls. Mean platelet volume levels were highest in diabetics with microalbuminuria. The mean platelet volume levels had a positive correlation with microalbuminuria. CONCLUSION: Mean platelet volume values of diabetic patients were higher than those of nondiabetics, the highest levels being in diabetics with microalbuminuria. Our results suggest that microalbuminuria might be related with mean platelet volume in diabetic patients.

Relation of obesity with serum 25 hydroxy vitamin D3 levels in type 2 diabetic patients.

BACKGROUND: Hypovitaminosis D is associated with diabetes mellitus (DM). Aim of our study was to determine the relation of obesity with vitamin D levels in type 2 diabetic patients. MATERIALS AND METHODS: We examined 101 type 2 diabetic patients and made a correlation analysis in all parameters. Then we classified our diabetics according to their body-mass indices and compared their 25 hdroxy vitamin D3 levels. RESULTS: We found negative correlation between 25O HD and body mass index (BMI) (P: <0.001, r: -0.23). When we classified our diabetics according to their body mass indices as normal, overweight and obese, and compared their 25 hydroxy vitamin D3 levels, we determined that in every BMI group 25 hydroxy vitamin D levels were not found to be significantly different. CONCLUSION: These results suggest that at least in a Turkish population with type 2 DM vitamin D levels are low and correlate with BMI, but when vitamin D levels are so low, as obesity worsens vitamin D levels does not lessen.