Study on Qi Deficiency Syndrome Identification Modes of Coronary Heart Disease Based on Metabolomic Biomarkers

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and mortality. With the era of systems biology bursting into reality, the analysis of the whole biological systems whether they are cells, tissues, organs, or the whole organisms has now become the norm of biological researches. Metabolomics is the branch of science concerned with the quantitative understandings of the metabolite complement of integrated living systems and their dynamic responses to the changes of both endogenous and exogenous factors. The aim of this study is to discuss the characteristics of plasma metabolites in CHD patients and CHD Qi deficiency syndrome patients and explore the composition and concentration changes of the plasma metabolomic biomarkers. The results show that 25 characteristic metabolites related to the CHD patients comparing with the healthy people, and 4 identifiable variables had significant differences between Qi deficiency and non-Qi deficiency patients. On the basis of identifying the different plasma endogenous metabolites between CHD patients and healthy people, we further prompted the metabolic rules, pathogenesis, and biological essence in Qi deficiency syndrome patients

Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning

<p>Abstract</p> <p>Background</p> <p>Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem.</p> <p>Methods</p> <p>Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets.</p> <p>Results</p> <p>A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.</p> <p>Conclusions</p> <p>The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.</p

Study on TCM Syndrome Identification Modes of Coronary Heart Disease Based on Data Mining

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and high mortality. TCM has played an important role in the treatment of CHD. Syndrome differentiation based on information from traditional four diagnostic methods has met challenges and questions with the rapid development and wide application of system biology. In this paper, methods of complex network and CHAID decision tree were applied to identify the TCM core syndromes of patients with CHD, and to establish TCM syndrome identification modes of CHD based on biological parameters. At the same time, external validation modes were also constructed to confirm the identification modes

Gender, Smoking and Blood Pressure and the Initial Presentation of a Wide Range of Cardiovascular Diseases: Prospective Cohort Study in 1.5 Million Patients Using Linked Electronic Health Records

Background: Myocardial infarction (MI) and stroke are the predominant endpoints of large-scale epidemiological research on cardiovascular disease (CVD) in healthy populations. This thesis capitalised on opportunities presented by linked electronic health records (EHR) to investigate the association of risk factors with the initial presentation of a wide range of pathologically diverse CVDs, with a focus on gender differences. Objective: To determine whether gender, smoking and blood pressure have homogeneous associations with the initial presentation of a range of CVDs. Design: Cohort studies using data from the CALIBER research platform linking four data sources (primary care, disease registry, hospitalisation and mortality records) for 1,758,584 patients free from symptomatic CVD registered with 225 UK general practices between 2001 and 2010. Main outcome measures: Initial presentation of CVD with stable angina, unstable angina, MI, heart failure, ventricular arrhythmias, coronary death, stroke, abdominal aortic aneurysm (AAA) and peripheral arterial disease (PAD). Results: 69% of initial presentations of CVD (N=95,267) were neither MI nor stroke. Men had higher rates of all presentations, with age-adjusted gender rate ratios from 1.23 (95% confidence interval 1.19-1.27)) for stroke to 4.27 (3.92-4.65) for AAA. The association of current smoking (compared to non-smoking) varied from an age-adjusted hazard ratio (HR) of 1.01 (0.90-1.13) for arrhythmia to 4.71 (4.15-5.35) for AAA. Gender differences were found only for MI (women: 2.59 (2.37-2.83); men: 2.20 (2.05-2.37)) and AAA (women: 7.00 (5.64-8.70); men: 3.88 (3.33-4.53)). The association of systolic blood pressure was similar across all CVD presentations, excepting AAA, ranging from age-adjusted HR of 1.19 (1.16-1.22) per standard deviation (SD) for heart failure to 1.28 (1.24-1.31) for PAD, with minimal gender differences. Conclusions: Gender, smoking and blood pressure had heterogeneous associations across a wide range of initial CVD presentations. The implications of these findings for public health, clinical practice and research are discussed

High-sensitivity troponin assays for the early rule-out or diagnosis of acute myocardial infarction in people with acute chest pain: a systematic review and cost-effectiveness analysis.

BACKGROUND: Early diagnosis of acute myocardial infarction (AMI) can ensure quick and effective treatment but only 20% of adults with emergency admissions for chest pain have an AMI. High-sensitivity cardiac troponin (hs-cTn) assays may allow rapid rule-out of AMI and avoidance of unnecessary hospital admissions and anxiety. OBJECTIVE: To assess the clinical effectiveness and cost-effectiveness of hs-cTn assays for the early (within 4 hours of presentation) rule-out of AMI in adults with acute chest pain. METHODS: Sixteen databases, including MEDLINE and EMBASE, research registers and conference proceedings, were searched to October 2013. Study quality was assessed using QUADAS-2. The bivariate model was used to estimate summary sensitivity and specificity for meta-analyses involving four or more studies, otherwise random-effects logistic regression was used. The health-economic analysis considered the long-term costs and quality-adjusted life-years (QALYs) associated with different troponin (Tn) testing methods. The de novo model consisted of a decision tree and Markov model. A lifetime time horizon (60 years) was used. RESULTS: Eighteen studies were included in the clinical effectiveness review. The optimum strategy, based on the Roche assay, used a limit of blank (LoB) threshold in a presentation sample to rule out AMI [negative likelihood ratio (LR-) 0.10, 95% confidence interval (CI) 0.05 to 0.18]. Patients testing positive could then have a further test at 2 hours; a result above the 99th centile on either sample and a delta (Δ) of ≥ 20% has some potential for ruling in an AMI [positive likelihood ratio (LR+) 8.42, 95% CI 6.11 to 11.60], whereas a result below the 99th centile on both samples and a Δ of < 20% can be used to rule out an AMI (LR- 0.04, 95% CI 0.02 to 0.10). The optimum strategy, based on the Abbott assay, used a limit of detection (LoD) threshold in a presentation sample to rule out AMI (LR- 0.01, 95% CI 0.00 to 0.08). Patients testing positive could then have a further test at 3 hours; a result above the 99th centile on this sample has some potential for ruling in an AMI (LR+ 10.16, 95% CI 8.38 to 12.31), whereas a result below the 99th centile can be used to rule out an AMI (LR- 0.02, 95% CI 0.01 to 0.05). In the base-case analysis, standard Tn testing was both most effective and most costly. Strategies considered cost-effective depending upon incremental cost-effectiveness ratio thresholds were Abbott 99th centile (thresholds of < £6597), Beckman 99th centile (thresholds between £6597 and £30,042), Abbott optimal strategy (LoD threshold at presentation, followed by 99th centile threshold at 3 hours) (thresholds between £30,042 and £103,194) and the standard Tn test (thresholds over £103,194). The Roche 99th centile and the Roche optimal strategy [LoB threshold at presentation followed by 99th centile threshold and/or Δ20% (compared with presentation test) at 1-3 hours] were extendedly dominated in this analysis. CONCLUSIONS: There is some evidence to suggest that hs-CTn testing may provide an effective and cost-effective approach to early rule-out of AMI. Further research is needed to clarify optimal diagnostic thresholds and testing strategies. STUDY REGISTRATION: This study is registered as PROSPERO CRD42013005939. FUNDING: The National Institute for Health Research Health Technology Assessment programme

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Evidence Synthesis and targeting further research for adherence and stratification in health economic evaluations

Cost-effectiveness analysis (CEA) models, used to make health policy decisions, are usually subject to uncertainty. This thesis aims to develop statistical methods to quantify uncertainty and target where reducing uncertainty is most beneficial in a CEA. This enables policy decisions, based on the models, to be better informed. There is a focus on two areas: adherence to interventions and heterogeneity in treatment effects, which are often not modelled due to a lack of good data. A case study of treatment for patients with sleep apnoea is used to illustrate these methods and techniques. Value of Information measures can help prioritise where to focus further research and estimate the expected benefits from a study of particular design and size. Until recently, it has been difficult to evaluate these quantities due to computational complexity. Various recently developed methods to calculate the expected value of information are summarised. Through an application to the case study, the importance of an adequate number of simulations to gain reliable results is highlighted. Adherence to interventions is often neglected in CEAs due to limited and sparse data. Data on adherence to interventions for sleep apnoea is collected. Through Bayesian model-based meta-analyses, implemented by Markov Chain Monte Carlo simulation, the impact of modelling adherence to interventions on the CEA results is explored. Additionally, the value of collecting further information on adherence to interventions is calculated, indicating value in collecting data even at few time points, and in the early period of follow-up. Another under explored area within CEAs is stratification of the optimal treatment decision. Here, the focus is on stratification based on continuous measures of disease severity, which may be associated with differential cost-effectiveness through variations in treatment effects. Aggregate and individual participant data on the impact of baseline covariates and treatment effects is summarised. Bayesian model-based meta-regression is used to explore stratification on one or two measure of treatment severity. The value of collecting further data on factors relating to stratification has been explored by using and extending recent non-parametric regression methods. By using evidence synthesis methods, to make use of all available data, this thesis has found it is possible to incorporate uncertainty due to adherence to interventions and stratifications of treatment decisions into CEA models, allowing future research priorities to be assessed through value of information methods.Claire Simons was funded by an MRC Unit PhD Studentship for the duration of this work (2014-2018) (Grant Number: MC_U105260556)

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

High-sensitivity troponin assays for the early rule-out or diagnosis of acute myocardial infarction in people eith acute chest pain: a systematic review and cost-effectiveness analysis

Background: Early diagnosis of acute myocardial infarction (AMI) can ensure quick and effective treatment but only 20% of adults with emergency admissions fo