Assessing machine learning for diagnostic classification of hypertension types identified by ambulatory blood pressure monitoring

Background: Inaccurate blood pressure classification results in inappropriate treatment. We tested if machine learning (ML), using routine clinical data, can serve as a reliable alternative to Ambulatory Blood Pressure Monitoring (ABPM) in classifying blood pressure status. Methods: This study employed a multi-centre approach involving three derivation cohorts from Glasgow, Gdańsk, and Birmingham, and a fourth independent evaluation cohort. ML models were trained using office BP, ABPM, and clinical, laboratory, and demographic data, collected from patients referred for hypertension assessment. Seven ML algorithms were trained to classify patients into five groups: Normal/Target, Hypertension-Masked, Normal/Target-White-Coat, Hypertension-White-Coat, and Hypertension. The 10-year cardiovascular outcomes and 27-year all-cause mortality risks were calculated for the ML-derived groups using the Cox proportional hazards model. Results: Overall XGBoost showed the highest AUROC of 0.85-0.88 across derivation cohorts, Glasgow (n=923; 43% females; age 50.7±16.3 years), Gdańsk (n=709; 46% females; age 54.4±13 years), and Birmingham (n=1,222; 56% females; age 55.7±14 years). But accuracy (0·57-0·72) and F1 scores (0·57-0·69) were low across the three patient cohorts. The evaluation cohort (n=6213, 51% females; age 51.2±10.8 years) indicated elevated 10-year risks of composite cardiovascular events in the Normal/Target-White-Coat and Hypertension-White-Coat groups, with heightened 27-year all-cause mortality observed in all groups except Hypertension-Masked, compared to the Normal/Target group. Conclusions: Machine learning has limited potential in accurate blood pressure classification when ABPM is unavailable. Larger studies including diverse patient groups and different resource settings are warranted

Wytyczne ESC/ESH dotyczące postępowania w nadciśnieniu tętniczym (2018)

SŁOWA KLUCZOWE: wytyczne, nadciśnienie tętnicze, ciśnienie tętnicze, pomiar ciśnienia tętniczego, progi rozpoznania i cele terapeutyczne leczenia nadciśnienia tętniczego, zależne od nadciśnienia powikłania narządowe, modyfikacje stylu życia, farmakoterapia, terapia złożona, leczenie inwazyjne, wtórne nadciśnienie tętnicz

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Development of last stage blade of 13K215 turbine intermediate pressure module

Paper is considering the purpose and the process of development of last stage blade for intermediate pressure module of 13K215 steam turbine. In the last 20–30 years most of the steam turbine manufacturers were focused on improving such a turbine mainly by upgrading low pressure module. In a result of such a modernization technology were changed from impulse to reaction. The best results of upgrading were given by developing low pressure last stage blade. With some uncertainty and based on state of art knowledge, it can be stand that improving of this part of steam turbine is close to the end. These above indicators show an element on which future research should be focused on – in the next step it should be intermediate pressure module. In the primary design the height of intermediate pressure last stage blade was 500 mm but because of change of technology this value was decreased to 400 mm. When to focus on reaction technology, the height of the last stage blade is related to output power and efficiency. Considered here is the checking the possibility of implementing blades, in a reaction technology, higher than 400 mm and potentially highest. Article shows a whole chosen methodology of topic described above. It leads through the reasons of research, limitations of 13K215 steam turbine, creation of three-dimensional models, fluid flow calculations, mechanical integrity calculations and proposed solutions of design

Reverse engineering methodology as a way of steam turbine blades designing for Loviisa Nuclear Power

This article concerns a reverse engineering-based design process of last stage blade (LSB) for other original equipment manufacturer (oOEM). For Loviisa Power Plant (Finland) GE designed and delivered a set of oOEM LSBs to be fit into existing low pressure (LP) turbine module steam path. Although cost competitiveness is a one of major selection criterion for steam turbine spare parts components supplier, diversification of suppliers is also a strategic for power plant owner. Considered here is a process of reengineering of oOEM LSB and all relevant challenges related to this process especially management of geometry deviations between reverse-engineered and oOEM blade. In this article, there are a design steps described taken to qualify reverse-engineered design. Moreover, a manufacturing process of the LSB is shown

Steam turbine stress control using NARX neural network

Considered here is concept of steam turbine stress control, which is based on Nonlinear AutoRegressive neural networks with eXogenous inputs. Using NARX neural networks,whichwere trained based on experimentally validated FE model allows to control stresses in protected thickwalled steam turbine element with FE model quality. Additionally NARX neural network, which were trained base on FE model, includes: nonlinearity of steam expansion in turbine steam path during transients, nonlinearity of heat exchange inside the turbine during transients and nonlinearity of material properties during transients. In this article NARX neural networks stress controls is shown as an example of HP rotor of 18K390 turbine. HP part thermodynamic model as well as heat exchange model in vicinity of HP rotor,whichwere used in FE model of the HP rotor and the HP rotor FE model itself were validated based on experimental data for real turbine transient events. In such a way it is ensured that NARX neural network behave as real HP rotor during steam turbine transient events