Current concept in the diagnosis,treatment and rehabilitation of patients with congestive heart failure

Heart failure(HF)is a major public health problem with a prevalence of 1%-2%in developed countries.The underlying pathophysiology of HF is complex and as a clinical syndrome is characterized by various symptoms and signs.HF is classified according to left ventricular ejection fraction(LVEF)and falls into three groups:LVEF≥50%-HF with preserved ejection fraction(HFpEF),LVEF<40%-HF with reduced ejection fraction(HFrEF),LVEF 40%-49%-HF with mid-range ejection fraction.Diagnosing HF is primarily a clinical approach and it is based on anamnesis,physical examination,echocardiogram,radiological findings of the heart and lungs and laboratory tests,including a specific markers of HF-brain natriuretic peptide or N-terminal pro-B-type natriuretic peptide as well as other diagnostic tests in order to elucidate possible etiologies.Updated diagnostic algorithms for HFpEF have been recommended(H2FPEF,HFA-PEFF).New therapeutic options improve clinical outcomes as well as functional status in patients with HFrEF(e.g.,sodium-glucose cotransporter-2-SGLT2 inhibitors)and such progress in treatment of HFrEF patients resulted in new working definition of the term“HF with recovered left ventricular ejection fraction”.In line with rapid development of HF treatment,cardiac rehabilitation becomes an increasingly important part of overall approach to patients with chronic HF for it has been proven that exercise training can relieve symptoms,improve exercise capacity and quality of life as well as reduce disability and hospitalization rates.We gave an overview of latest insights in HF diagnosis and treatment with special emphasize on the important role of cardiac rehabilitation in such patients.

The role of B-type natriuretic peptide in the diagnosis and treatment of decompensated heart failure

Heart failure (HF) is a common disease associated with increasing age. B-type natriuretic peptide (BNP), is a cardiac neurohormone, and is released as prepro BNP and then enzyrnatically cleaved to the Ntenninal-proBNP (NT-proBNP) and BNP upon ventricular myocyte stretch. Blood measurements of BNP have been used to identify patients with I-IF. The BNP assay is currently used as a diagnostic and prognostic aid in HF. In general, a BNP level below 100 pg/mL excludes acutely decompensated HF and levels ＞ 500 pg/ml indicate decompensation. Recombinant human BNP (hBNP, nesiritide) is an approved intravenous treatment for acute,decompensated -HF. Nesiritide given in supraphysiologic doses causes vasodilation, natriuresis, diuresis, and improved symptoms over the course of a 48-hour infusion. This paper will sort out the literature concerning the use of this peptide both as a diagnostic test and as an intravenous therapy.

A new diagnostic method for identifying working conditions of submersible reciprocating pumping systems

The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this paper we proposed a diagnostic method for identifying the working condition of the submersible pumping system. Based on analyzing the working principle of the pumping unit and the pump structure, different characteristics in loading and unloading processes of the submersible linear motor were obtained at different working conditions. The characteristic quantities were extracted from operation data of the submersible linear motor. A diagnostic model based on the support vector machine （SVM） method was proposed for identifying the working condition of the submersible pumping unit, where the inputs of the SVM classifier were the characteristic quantities. The performance and the misjudgment rate of this method were analyzed and validated by the data acquired from an experimental simulation platform. The model proposed had an excellent performance in failure diagnosis of the submersible pumping system. The SVM classifier had higher diagnostic accuracy than the learning vector quantization （LVQ） classifier.

The Role of Bedside Troponin T Test for Identification of High Risk Patients With Acute Chest Pain

Objectives Evaluation of patients with acute chest pain when they admitted is time-consuming. We prospectively investigated the role of bedside troponin T test for predicting the risk of death and acute heart failure of patients with acute chest pain.Methods and Results 502 consecutive patients with chest pain for less than 24 hours were determined by troponin T test at bedside and quantitative troponin I test in lab. For bedside troponin T tests, there were 160 patients in positive and 323 in negative. During 30 days of followed-up. Myocardial infarction evolved in 139 patients among 160 patients in positive troponin T test, only 7 patients in negative one. Acute heart failure occurred in 51 patients among the positive group, but 37 occurred it at negative group. The odds ratio of acute heart failure of positive group vs. negative group was 3.6. Patients died 39 in positive group, 15 in negative group, the all-cause death odds ratio of positive group vs. negative group was 6.7; 31 patients died with cardiac event in positive group, 5 in negative group only. Conclusions Bedside Troponin T test is a powerful and independent predictor of death and acute heart failure for patients with acute chest pain.

Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Malfunction or breakdown of certain mission critical systems(MCSs) may cause losses of life, damage the environments, and/or lead to high costs. Therefore, recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper, we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes, includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations, and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance.