Timing and risk factors for clinical fractures among postmenopausal women: a 5-year prospective study

BACKGROUND: Many risk factors for fractures have been documented, including low bone-mineral density (BMD) and a history of fractures. However, little is known about the short-term absolute risk (AR) of fractures and the timing of clinical fractures. Therefore, we assessed the risk and timing of incident clinical fractures, expressed as 5-year AR, in postmenopausal women. METHODS: In total, 10 general practice centres participated in this population-based prospective study. Five years after a baseline assessment, which included clinical risk factor evaluation and BMD measurement, 759 postmenopausal women aged between 50 and 80 years, were re-examined, including undergoing an evaluation of clinical fractures after menopause. Risk factors for incident fractures at baseline that were significant in univariate analyses were included in a multivariate Cox survival regression analysis. The significant determinants were used to construct algorithms. RESULTS: In the total group, 12.5% (95% confidence interval (CI) 10.1–14.9) of the women experienced a new clinical fracture. A previous clinical fracture after menopause and a low BMD (T-score <-1.0) were retained as significant predictors with significant interaction. Women with a recent previous fracture (during the past 5 years) had an AR of 50.1% (95% CI 42.0–58.1) versus 21.2% (95% CI 20.7–21.6) if the previous fracture had occurred earlier. In women without a fracture history, the AR was 13.8% (95% CI 10.9–16.6) if BMD was low and 7.0% (95% CI 5.5–8.5) if BMD was normal. CONCLUSION: In postmenopausal women, clinical fractures cluster in time. One in two women with a recent clinical fracture had a new clinical fracture within 5 years, regardless of BMD. The 5-year AR for a first clinical fracture was much lower and depended on BMD

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P &lt; 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Design and Evaluation of Robotic Steering of a Flexible Endoscope

In current flexible endoscopy the physician faces problems in manipulating the endoscope. A lot of experience is required to master the procedure. This paper describes the design of an add-on robotic module that improves the user interface of traditional endoscopes and allows a single physician to operate it easily. We identified critical user aspects of traditional endoscopes that need to be copied in a robotic setup. In our design the physician uses a remote control that is connected to a light drive system. It allows manipulation of the robotic endoscope in space. We conducted an experiment to judge the usability of our system. Results indicate that robotic steering, using a position-controlled touchpad or a rate-controlled joystick increases efficiency and satisfactio

The effect of macronutrients on gastric volume responses and gastric emptying in humans: A magnetic resonance imaging study

The effects of macronutrients on gastric volume changes, emptying, and gastrointestinal symptoms are incompletely understood. Three liquid meals of 500 ml (fat emulsion, 375 kcal; protein solution, 375 kcal; glucose solution, 400 kcal) were infused into the stomach of 12 healthy volunteers on three occasions. Studies were performed in seated body position using an open-configuration magnetic resonance imaging (MRI) system. MRI imaging sequences, assessing stomach and meal volumes, were performed prior to and at times t = 0, 3, 6, 9, 12, 15, 25, 35, 45, 60, 75, and 90 min after meal administration. Areas under the curve for the early emptying phase (0-15 and 0-45 min) were calculated, and characteristics of the volume curves were analyzed by a gastric emptying model. Gastrointestinal symptoms were assessed by a self-report scale. Initial (t = 0 min) and early postprandial gastric volumes were highest for glucose because of lower initial emptying. However, in the early emptying phase the characteristics of the volume curves for stomach and meal were uniform for all macronutrients. Perceptions of fullness and satiety were linearly associated with postprandial gastric volumes, but not with macronutrient composition. Isovolumic macronutrient meals modulate gastric volume response by initial meal emptying patterns. Macronutrient specific accommodation responses, as shown in barostat studies, are not reflected as gastric volume responses under noninvasive conditions