The effect of population-based blood pressure screening on long-term cardiometabolic morbidity and mortality in Germany: A regression discontinuity analysis.

BACKGROUND: Hypertension represents one of the major risk factors for cardiovascular morbidity and mortality globally. Early detection and treatment of this condition is vital to prevent complications. However, hypertension often goes undetected, and even if detected, not every patient receives adequate treatment. Identifying simple and effective interventions is therefore crucial to fight this problem and allow more patients to receive the treatment they need. Therefore, we aim at investigating the impact of a population-based blood pressure (BP) screening and the subsequent "low-threshold" information treatment on long-term cardiovascular disease (CVD) morbidity and mortality. METHODS AND FINDINGS: We examined the impact of a BP screening embedded in a population-based cohort study in Germany and subsequent personalized "light touch" information treatment, including a hypertension diagnosis and a recommendation to seek medical attention. We pooled four waves of the KORA study, carried out between 1984 and 1996 (N = 14,592). Using a sharp multivariate regression discontinuity (RD) design, we estimated the impact of the information treatment on CVD mortality and morbidity over 16.9 years. Additionally, we investigated potential intermediate outcomes, such as hypertension awareness, BP, and behavior after 7 years. No evidence of effect of BP screening was observed on CVD mortality (hazard ratio (HR) = 1.172 [95% confidence interval (CI): 0.725, 1.896]) or on any (fatal or nonfatal) long-term CVD event (HR = 1.022 [0.636, 1.641]) for individuals just above (versus below) the threshold for hypertension. Stratification for previous self-reported diagnosis of hypertension at baseline did not reveal any differential effect. The intermediate outcomes, including awareness of hypertension, were also unaffected by the information treatment. However, these results should be interpreted with caution since the analysis might not be sufficiently powered to detect a potential intervention effect. CONCLUSIONS: The study does not provide evidence of an effect of the assessed BP screening and subsequent information treatment on BP, health behavior, or long-term CVD mortality and morbidity. Future studies should consider larger datasets to detect possible effects and a shorter follow-up for the intermediate outcomes (i.e., BP and behavior) to detect short-, medium-, and long-term effects of the intervention along the causal pathway.

Adipositas Care and Health Therapy (ACHT) after Bariatric-Metabolic Surgery: A Prospective, Non-Randomized Intervention Study.

INTRODUCTION: Almost 25% of German adults have obesity and numbers are rising, making it an important health issue. Bariatric-metabolic surgery reduces body weight and complications for persons with obesity, but therapeutic success requires long-term postoperative care. Since no German standards for follow-up by family physicians exist, follow-up is provided by surgical obesity centers, but they are reaching their limits. The ACHT study, funded by the German Innovation Fund, is designed to establish and evaluate the follow-up program, with local physicians following patients supported remotely by obesity centers. METHODS: ACHT is a multicenter, prospective, non-randomized control group study. The 18-month ACHT follow-up program is a digitally supported, structured, cross-sectoral, and close-to-home program to improve success after bariatric-metabolic surgery. Four groups are compared: intervention group 1 starts the program immediately (3 weeks) after Roux-en-Y gastric bypass or sleeve gastrectomy (months 1-18 postoperatively), intervention group 2 begins the program 18 months after surgery (months 19-36 postoperatively). Intervention groups are compared to respective control groups that had surgery 18 and 36 months previously. In total, 250 patients, enrolled in the intervention groups, are compared with 360 patients in the control groups, who only receive standard care. RESULTS: The primary endpoint to compare intervention and control groups is the adapted King's score, a composite tool evaluating physical, psychological, socioeconomic, and functional health status. Secondary endpoints include changes in care structures and care processes for the intervention groups. Multivariate regression analyses adjusting for confounders (including the type of surgery) are used to compare intervention and control groups and evaluate determinants in longitudinal analyses. The effect of the intervention on healthcare costs will be evaluated based on health insurance billing data of patients who had bariatric-metabolic surgery in the 3 years prior to the start of the study and of patients who undergo bariatric-metabolic surgery during the study period. CONCLUSIONS: ACHT will be the one of the first evaluated structured, close-to-home follow-up programs for bariatric surgery in Germany. It will evaluate the effectiveness of the implemented program regarding improvements in health status, mental health, quality of life, and the feasibility of such a program outside of specialized obesity centers.

Active learning for convenient annotation and classification of secondary ion mass spectrometry images.

Digital staining for the automated annotation of mass spectrometry imaging (MSI) data has previously been achieved using state-of-the-art classifiers such as random forests or support vector machines (SVMs). However, the training of such classifiers requires an expert to label exemplary data in advance. This process is time-consuming and hence costly, especially if the tissue is heterogeneous. In theory, it may be sufficient to only label a few highly representative pixels of an MS image, but it is not known a priori which pixels to select. This motivates active learning strategies in which the algorithm itself queries the expert by automatically suggesting promising candidate pixels of an MS image for labeling. Given a suitable querying strategy, the number of required training labels can be significantly reduced while maintaining classification accuracy. In this work, we propose active learning for convenient annotation of MSI data. We generalize a recently proposed active learning method to the multiclass case and combine it with the random forest classifier. Its superior performance over random sampling is demonstrated on secondary ion mass spectrometry data, making it an interesting approach for the classification of MS images.

SIMA: simultaneous multiple alignment of LC/MS peak lists.

MOTIVATION: Alignment of multiple liquid chromatography/mass spectrometry (LC/MS) experiments is a necessity today, which arises from the need for biological and technical repeats. Due to limits in sampling frequency and poor reproducibility of retention times, current LC systems suffer from missing observations and non-linear distortions of the retention times across runs. Existing approaches for peak correspondence estimation focus almost exclusively on solving the pairwise alignment problem, yielding straightforward but suboptimal results for multiple alignment problems. RESULTS: We propose SIMA, a novel automated procedure for alignment of peak lists from multiple LC/MS runs. SIMA combines hierarchical pairwise correspondence estimation with simultaneous alignment and global retention time correction. It employs a tailored multidimensional kernel function and a procedure based on maximum likelihood estimation to find the retention time distortion function that best fits the observed data. SIMA does not require a dedicated reference spectrum, is robust with regard to outliers, needs only two intuitive parameters and naturally incorporates incomplete correspondence information. In a comparison with seven alternative methods on four different datasets, we show that SIMA yields competitive and superior performance on real-world data. AVAILABILITY: A C++ implementation of the SIMA algorithm is available from http://hci.iwr.uni-heidelberg.de/MIP/Software.

Visualizing a homogeneous blend in bulk heterojunction polymer solar cells by analytical electron microscopy.

To increase efficiency of bulk heterojunctions for photovoltaic devices, the functional morphology of active layers has to be understood, requiring visualization and discrimination of materials with very similar characteristics. Here we combine high-resolution spectroscopic imaging using an analytical transmission electron microscope with nonlinear multivariate statistical analysis for classification of multispectral image data. We obtain a visual representation showing homogeneous phases of donor and acceptor, connected by a third composite phase, depending in its extent on the way the heterojunction is fabricated. For the first time we can correlate variations in nanoscale morphology determined by material contrast with measured solar cell efficiency. In particular we visualize a homogeneously blended phase, previously discussed to diminish charge separation in solar cell devices.

A Telemedicine-Guided Self-Collection Approach for PCR-Based SARS-CoV-2 Testing: Comparative Study.

BACKGROUND: Large-scale, polymerase chain reaction (PCR)-based SARS-CoV-2 testing is expensive, resource intensive, and time consuming. A self-collection approach is a probable alternative; however, its feasibility, cost, and ability to prevent infections need to be evaluated. OBJECTIVE: This study aims to compare an innovative self-collection approach with a regular SARS-CoV-2 testing strategy in a large European industrial manufacturing site. METHODS: The feasibility of a telemedicine-guided PCR-based self-collection approach was assessed for 150 employees (intervention group) and compared with a regular SARS-CoV-2 testing approach used for 143 employees (control group). Acceptance, ergonomics, and efficacy were evaluated using a software application. A simulation model was implemented to evaluate the effectiveness. An interactive R shiny app was created to enable customized simulations. RESULTS: The test results were successfully communicated to and interpreted without uncertainty by 76% (114/150) and 76.9% (110/143) of the participants in the intervention and control groups, respectively (P=.96). The ratings for acceptability, ergonomics, and efficacy among intervention group participants were noninferior when compared to those among control group participants (acceptability: 71.6% vs 37.6%; ergonomics: 88.1% vs 74.5%; efficacy: 86.4% vs 77.5%). The self-collection approach was found to be less time consuming (23 min vs 38 min; P<.001). The simulation model indicated that both testing approaches reduce the risk of infection, and the self-collection approach tends to be slightly less effective owing to its lower sensitivity. CONCLUSIONS: The self-collection approach for SARS-CoV-2 diagnosis was found to be technically feasible and well rated in terms of acceptance, ergonomics, and efficacy. The simulation model facilitates the evaluation of test effectiveness; nonetheless, considering context specificity, appropriate adaptation by companies is required.

Concise representation of mass spectrometry images by probabilistic latent semantic analysis.

Imaging mass spectrometry (IMS) is a promising technology which allows for detailed analysis of spatial distributions of (bio)molecules in organic samples. In many current applications, IMS relies heavily on (semi)automated exploratory data analysis procedures to decompose the data into characteristic component spectra and corresponding abundance maps, visualizing spectral and spatial structure. The most commonly used techniques are principal component analysis (PCA) and independent component analysis (ICA). Both methods operate in an unsupervised manner. However, their decomposition estimates usually feature negative counts and are not amenable to direct physical interpretation. We propose probabilistic latent semantic analysis (pLSA) for non-negative decomposition and the elucidation of interpretable component spectra and abundance maps. We compare this algorithm to PCA, ICA, and non-negative PARAFAC (parallel factors analysis) and show on simulated and real-world data that pLSA and non-negative PARAFAC are superior to PCA or ICA in terms of complementarity of the resulting components and reconstruction accuracy. We further combine pLSA decomposition with a statistical complexity estimation scheme based on the Akaike information criterion (AIC) to automatically estimate the number of components present in a tissue sample data set and show that this results in sensible complexity estimates.

One plus one makes three (for social networks).

Members of social network platforms often choose to reveal private information, and thus sacrifice some of their privacy, in exchange for the manifold opportunities and amenities offered by such platforms. In this article, we show that the seemingly innocuous combination of knowledge of confirmed contacts between members on the one hand and their email contacts to non-members on the other hand provides enough information to deduce a substantial proportion of relationships between non-members. Using machine learning we achieve an area under the (receiver operating characteristic) curve (AUC) of at least 0.85 for predicting whether two non-members known by the same member are connected or not, even for conservative estimates of the overall proportion of members, and the proportion of members disclosing their contacts.

Toward digital staining using imaging mass spectrometry and random forests.

We show on imaging mass spectrometry (IMS) data that the Random Forest classifier can be used for automated tissue classification and that it results in predictions with high sensitivities and positive predictive values, even when intersample variability is present in the data. We further demonstrate how Markov Random Fields and vector-valued median filtering can be applied to reduce noise effects to further improve the classification results in a posthoc smoothing step. Our study gives clear evidence that digital staining by means of IMS constitutes a promising complement to chemical staining techniques.