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.

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.

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.