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Abstract Coronary artery disease (CAD) and its complications remain the main cause of morbidity and mortality worldwide. Patients with extensive CAD and multiple comorbidities who require complex, high‐risk percutaneous coronary intervention (HR‐PCI) are at risk of haemodynamic instability and may require short‐term mechanical circulatory support (MCS) during the procedure to maintain sufficient perfusion and prevent ischaemia. Impella is a microaxial continuous blood flow pump used for percutaneous support of the left ventricle in patients undergoing HR‐PCI. Data from randomized controlled trials and registries suggested an advantage for Impella devices in patients undergoing HR‐PCI, compared with other types of MCS. As a thorough understanding of the benefits and drawbacks of the Impella technology is crucial for patient outcomes, we provide a technological overview of Impella and share our experiences gathered during the implementation of institutional Impella programmes in Poland as a roadmap of selection and periprocedural care for patients treated with Impella in the setting of HR‐PCI. We propose 10 steps for implementation of an institutional Impella programme for HR‐PCI, including (i) dedicated staff training; (ii) standard operating procedure and troubleshooting algorithms prior to the first intervention; (iii) patient selection by the multidisciplinary Heart Team; (iv) patient preparation using multimodality imaging; (v) procedure planning in terms of large‐bore access, equipment, and complete revascularization; (vi) starting with HR‐PCI support; (vii) starting with femoral artery access in a patient without extensive peripheral artery disease; (viii) multidisciplinary care after the procedure; (ix) haemodynamic and laboratory monitoring to ensure immediate diagnosis of access‐site complications, bleeding, haemolysis, acute kidney injury, and infections; and (x) careful revision of every HR‐PCI case with the team.

MEMS (micro-electro-mechanical system) gyroscopes and accelerometers are used in several applications. They are very popular due to their small size, low price, and accessibility. The design of MEMS accelerometers enables the measurement of vibrations, with frequencies from tenths of hertz to even 1 kHz. MEMS gyroscopes can be applied to measure angular rates, and indirectly also angular oscillations with frequencies similar to accelerometers. Despite significant stochastic errors, MEMS sensors are used not only in popular domestic appliances (e.g., smartphones) but also in safety-critical units, such as aeronautical attitude and heading reference systems (AHRSs). In engineering, methods of stochastic properties analysis are important tools for sensor selection, verification, and the design of measurement algorithms. In this article, three methods used for the analysis of stochastic properties of sensors are presented and comparative analyses are shown. The applied measurement frequencies (1 kHz) were much higher than those typically found in MEMS sensor applications. Additionally, an exemplary analysis of temperature drift frequency, as well as the possibility for the synthesis of complementary filter parameters with the use of the described methods, is shown. Assessment of the stochastic properties of MEMS accelerometers and gyroscopes was performed under both constant and variable temperature conditions (during warm-up after switching on) with the use of classic methods, such as power spectral density (PSD) and Allan variance (AV), as well as the less known but very promising generalized method of wavelet moments (GMWM).

Acute heart failure (AHF) is a sudden, life-threatening condition, defined as a gradual or rapid onset of symptoms and/or signs of HF. AHF requires urgent medical attention, being the most frequent cause of unplanned hospital admission in patients above 65 years of age. AHF is associated with a 4–12% in-hospital mortality rate and a 21–35% 1-year mortality rate post-discharge. Considering the serious prognosis in AHF patients, it is very important to understand the mechanisms and haemodynamic status in an individual AHF patient, thus preventing end-organ failure and death. Haemodynamic monitoring is a serial assessment of cardiovascular function, intended to detect physiologic abnormalities at the earliest stages, determine which interventions could be most effective, and provide the basis for initiating the most appropriate therapy and evaluate its effects. Over the past decades, haemodynamic monitoring techniques have evolved greatly. Nowadays, they range from very invasive to non-invasive, from intermittent to continuous, and in terms of the provided parameters. Invasive techniques contain pulmonary artery catheterization and transpulmonary thermodilution. Minimally invasive techniques include oesophageal Doppler and noncalibrated pulse wave analysis. Non-invasive techniques contain echocardiography, bioimpedance, and bioreactance techniques as well as non-invasive pulse contour methods. Each of these techniques has specific indications and limitations. In this article, we aimed to provide a pathophysiological explanation of the physical terms and parameters used for haemodynamic monitoring in AHF and to summarize the working principles, advantages, and disadvantages of the currently used methods of haemodynamic monitoring.

Background: Oxygen uptake (VO2) is one of the most important measures of fitness and critical vital sign. Cardiopulmonary exercise testing (CPET) is a valuable method of assessing fitness in sport and clinical settings. There is a lack of large studies on athletic populations to predict VO2max using somatic or submaximal CPET variables. Thus, this study aimed to: (1) derive prediction models for maximal VO2 (VO2max) based on submaximal exercise variables at anaerobic threshold (AT) or respiratory compensation point (RCP) or only somatic and (2) internally validate provided equations. Methods: Four thousand four hundred twenty-four male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (nrunners = 1998; ncyclist = 656), model building (nrunners = 666; ncyclist = 219), and validation (nrunners = 666; ncyclist = 219). Random forest was used to select the most significant variables. Models were derived and internally validated with multiple linear regression. Results: Runners were 36.24±8.45 years; BMI = 23.94 ± 2.43 kg·m−2; VO2max=53.81±6.67 mL·min−1·kg−1. Cyclists were 37.33±9.13 years; BMI = 24.34 ± 2.63 kg·m−2; VO2max=51.74±7.99 mL·min−1·kg−1. VO2 at AT and RCP were the most contributing variables to exercise equations. Body mass and body fat had the highest impact on the somatic equation. Model performance for VO2max based on variables at AT was R2=0.81, at RCP was R2=0.91, at AT and RCP was R2=0.91 and for somatic-only was R2=0.43. Conclusions: Derived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO2max based on submaximal exercise performance or somatic variables. Presented models are applicable for sport and clinical settling. They are a valuable supplementary method for fitness practitioners to adjust individualised training recommendations. Funding: No external funding was received for this work.

ObjectivesPatients with bicuspid aortic valve (BAV) stenosis were excluded from the pivotal trials of transcatheter aortic valve implantation (TAVI). We compared the in-hospital and long-term outcomes between patients undergoing TAVI for bicuspid and tricuspid aortic valve (TAV) stenosis.MethodsWe performed a retrospective registry-based analysis on patients who underwent TAVI for BAV and TAV at five different centers between January 2009 and August 2017. The primary outcome was long-term all-cause mortality. Secondary outcomes were in-hospital mortality, procedural complications, and valve performance.ResultsOf 1,451 consecutive patients who underwent TAVI, two propensity-matched cohorts consisting of 130 patients with BAV and 390 patients with TAV were analyzed. All-cause mortality was comparable in both groups up to 10 years following TAVI (HR 1.09, 95% CI: 0.77–1.51). Device success and in-hospital mortality were comparable between the groups (96 vs. 95%, p = 0.554 and 2.3 vs. 2.1%, p = 0.863, respectively). Incidence of procedural complications was similar in both groups, with a trend toward a higher rate of stroke in patients with BAV (5 vs. 2%, p = 0.078). Incidence of moderate or severe paravalvular leak (PVL) at discharge was comparable in both groups (2 vs. 2%, p = 0.846). Among patients with BAV, all-cause mortality was similar in self-expanding and balloon-expandable prostheses (HR 1.02, 95% CI: 0.52–1.99) and lower in new-generation devices compared to old-generation valves (HR 0.27, 95% CI 0.12–0.62).ConclusionPatients who had undergone TAVI for BAV had comparable mortality to patients with TAV up to 10 years after the procedure. The device success, in-hospital mortality, procedural complications, and PVL rate were comparable between the groups. The high rate of neurological complications (5%) in patients with BAV warrants further investigation.

Currently, quite accurate measurements of atmospheric gusts are carried out by airport systems only in the vicinity of the runways. There is a still open issue of availability of information about real wind gusts at cruising altitudes and during approach at a considerable distance from the airfield. Standard on-board systems of a jet transport airplane provide some information which is desirable to have knowledge of how flight parameters reflect real gust parameters and their impact on the aircraft dynamics. The paper proposes an algorithm for headwind gust magnitude estimation in relation to aircraft response. The analysed estimation algorithms assume the use of data available from the existing on-board systems only without the employment of any extra sensors or ground and satellite systems. In this way, many problems caused by different structures, configurations, and ways of installation of additional sensors and structural changes are rejected. The algorithms use the classical method for estimation of wind parameters as well as a linear longitudinal model of aircraft dynamics, taking into account the influence of wind gusts. Data fusion was realised with the use of three filtration methods. Results were evaluated to select the most accurate method of the estimation. Test data were obtained from advanced flight simulation. The experimental scenario considered a flight of a passenger twin-engine jet airplane through a layer of programmed gusts. The results of the flight simulations allowed us to determine the accuracy of the proposed gust estimation algorithms in reference to the ideal wind-speed data analysis obtained directly from the simulation environment (with the accuracy of the simulation process). The use of the proposed gust estimation algorithms may provide more accurate signal for integrated on-board systems, especially for wind shear detection and sped-up response time of flight control systems, protecting aircrafts against the adverse impact of encountered wind shear or gusts, e.g., auto-thrust or auto-throttle systems. The dedicated algorithm presented in the paper may increase the safety level of take-off and approach phases in gusty conditions and also during significant changes in wind speed at cruising altitudes in the case of crossing the area of jet stream occurrence.

Use case diagrams play an important role in modeling with UML. Careful modeling is crucialin obtaining a correct and efficient system architecture. The paper refers to the formalanalysis of the use case diagrams. A formal model of use cases is proposed and its constructionfor typical relationships between use cases is described. Two methods of formal analysis andverification are presented. The first one based on a states’ exploration represents a modelchecking approach. The second one refers to the symbolic reasoning using formal methodsof temporal logic. Simple but representative example of the use case scenario verification isdiscussed.