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Objective: We tested the effects of structured health care for the diabetic foot in one region in Germany aiming to reduce the number of major amputations. Research design and methods: In a prospective study we investigated patients with diabetic foot in a structured system of outpatient, in-patient and rehabilitative treatment. Subjects were recruited between January 1st, 2000 and December 31, 2007. All participants underwent a two-year follow-up. The modified University of Texas Wound Classification System (UT) was the basis for documentation and data analysis. We evaluated numbers of major amputations, rates of ulcer healing and mortality. In order to compare the effect of the structured health care program with usual care in patients with diabetic foot we evaluated the same parameters at another regional hospital without interdisciplinary care of diabetic foot (controls). Results: 684 patients with diabetic foot and 508 controls were investigated. At discharge from hospital 28.3% (structured health care program, SHC) vs. 23.0% (controls) of all ulcers had healed completely. 51.5% (SHC) vs. 49.8% (controls) were in UT grade 1. Major amputations were performed in 32 subjects of the structured health care program group (4.7%) vs. 110 (21.7%) in controls (p<0.0001). Mortality during hospitalization was 2.5% (SHC) vs. 9.4% in controls (p<0.001). Conclusions: With the structured health care program we achieved a significant reduction of major amputation rates by more than 75% as compared to standard care. [ABSTRACT FROM AUTHOR]

Background: Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field., Methods: In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets., Findings: Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p<0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%., Interpretation: Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up., Funding: Deutsche Forschungsgemeinschaft., Competing Interests: Declaration of interests CP, AF, and GE declare a filed German patent A5914/TUD 017, with the title “Verfahren zur Vorhersage eines Nebennierentumours sowie eines Metastaserisikos mithilfe klinisch relevanter Parameter”, relevant to this manuscript. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

The proprotein convertase subtilisin / kexin type 9 (PCSK9) plays an important role in LDL cholesterol (LDL-C) metabolism. Subjects harboring loss-of-function mutations in the gene encoding for PCSK9 display markedly reduced LDL-C plasma levels. PCSK9 is secreted by the liver, binds to the LDL receptor and, following endocytosis, induces lysosomal degradation of the receptor together with the bound LDL-C. Current PCSK9 inhibitors are monoclonal antibodies that specifically absorb PCSK9. Subsequently, instead of being degraded the receptor can dissociate from LDL-C and recycle, consecutively resulting in an increased hepatocyte LDL receptor density and higher LDL-C clearance. In clinical trials, the PCSK9 inhibitors alirocumab and evolocumab induced reductions in LDL-C of up to 70 % in statin-treated as well as statin-naïve patients. So far, serious side effects (requiring cessation of drug treatment) occurred only in rare cases. Since this new class of lipid lowering drugs promises a high potential benefit, they have been approved by the EMA even before completion of the studies addressing clinically relevant endpoints like cardiovascular events and mortality. Therefore, the expected publication of these study results in 2017 may allow a better assessment of the efficacy and safety of PCSK9 inhibitors., (© Georg Thieme Verlag KG Stuttgart · New York.)