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Abstract
Get a quick, expert overview of the many key facets of heart failure research with this concise, practical resource by Dr. Longjian Liu. This easy-to-read reference focuses on the incidence, distribution, and possible control of this significant clinical and public health problem which is often associated with higher mortality and morbidity, as well as increased healthcare expenditures. This practical resource brings you up to date with what’s new in the field and how it can benefit your patients.
- Features a wealth of information on epidemiology and research methods related to heart failure.
- Discusses pathophysiology and risk profile of heart failure, research and design, biostatistical basis of inference in heart failure study, advanced biostatistics and epidemiology applied in heart failure study, and precision medicine and areas of future research.
- Consolidates today’s available information and guidance in this timely area into one convenient resource.
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| Front Cover | Cover | ||
| Heart Failure: Epidemiology and\rResearch Methods | i | ||
| Heart Failure: Epidemiology and Research Methods | iii | ||
| Copyright | iv | ||
| Preface | v | ||
| Acknowledgments | vii | ||
| Contents | ix | ||
| 1 - Introduction\r | 1 | ||
| CARDIOLOGY, PREVENTIVE CARDIOLOGY, AND CARDIOVASCULAR DISEASE EPIDEMIOLOGY | 1 | ||
| Basic Concepts | 1 | ||
| Cardiology | 1 | ||
| Preventive cardiology | 1 | ||
| Epidemiology | 1 | ||
| The theory of “miasma” | 1 | ||
| Theory of bacteriology | 2 | ||
| Black box theory | 2 | ||
| System theory | 2 | ||
| Principles of epidemiology | 2 | ||
| Who are attacked by the disease?. Fig. 1.1 depicts the trend of age-specific mortality rates (per 100,000) from heart failure in... | 2 | ||
| When did/does the disease change its pattern or when did/does the disease occur?. Fig. 1.2 depicts the trend of age-adjusted mor... | 3 | ||
| Where did/does the disease exist and/or have?. Fig. 1.3 depicts the trend of age-adjusted mortality rates in patients with heart... | 3 | ||
| Cardiovascular Disease Epidemiology | 4 | ||
| Examples of cardiovascular disease epidemiology | 4 | ||
| Framingham Heart Study. The US Framingham Heart Study (FHS), initiated in 1947, is one of the earliest cardiovascular epidemiolo... | 4 | ||
| WHO MONICA Study. Since the early 1980s, several national and international studies in CVD epidemiology and prevention have been... | 4 | ||
| The Atherosclerosis Risk in Communities Study. The Atherosclerosis Risk in Communities (ARIC) Study is a prospective study to in... | 4 | ||
| The Cardiovascular Health Study. The Cardiovascular Health Study (CHS) is a population-based, longitudinal study of CHD and stro... | 4 | ||
| The WHO-CARDIAC Study. The WHO-coordinated Cardiovascular Disease Alimentary Comparison Study (WHO-CARDIAC Study) was designed t... | 5 | ||
| The Jackson Heart Study. The purposes of the Jackson Heart Study are to (1) establish a single-site cohort study to identify the... | 5 | ||
| The Multi-Ethnic Study of Atherosclerosis. The Multi-Ethnic Study of Atherosclerosis (MESA) is a prospective cohort study of men... | 5 | ||
| Significance | 5 | ||
| EPIDEMIOLOGY OF HEART FAILURE: NEW INSIGHTS INTO RESEARCH AND PREVENTION | 5 | ||
| Basic Concepts | 5 | ||
| Heart failure mortality rates increase in recent years | 6 | ||
| Heart failure mortality rates vary by states in the United States | 6 | ||
| Trends of heart failure | 6 | ||
| Increased mortality rate in young adults | 6 | ||
| Increased trend in multiple comorbidities | 6 | ||
| HFSA AND AHA GUIDELINES FOR HEART FAILURE PREVENTION AND TREATMENT | 6 | ||
| Basic Concepts | 6 | ||
| Significance | 7 | ||
| RESEARCH METHODS APPLIED IN HEART FAILURE EPIDEMIOLOGY | 8 | ||
| Basic Concepts | 8 | ||
| Descriptive epidemiology | 8 | ||
| Analytical epidemiology | 8 | ||
| New Research and Analysis Approaches Applied in Heart Failure Study | 9 | ||
| Life course epidemiology | 9 | ||
| Propensity score | 9 | ||
| Mediation analysis | 9 | ||
| Multilevel analysis | 9 | ||
| Reduced rank regression (RRR) models | 10 | ||
| Quantile regression (QR) techniques | 10 | ||
| Mapping and visualization | 10 | ||
| Significance | 10 | ||
| REFERENCES | 11 | ||
| 2 - Pathophysiology and Risk Profiles of Heart Failure\r | 13 | ||
| THE PATHOPHYSIOLOGY OF HEART FAILURE | 13 | ||
| Basic Concepts\r | 13 | ||
| Heart failure versus congestive heart failure\r | 13 | ||
| Pathophysiologic models of heart failure | 13 | ||
| ACC and AHA heart failure staging | 15 | ||
| The importance of the ACC and AHA staging heart failure | 16 | ||
| Primordial prevention | 16 | ||
| Primary prevention | 16 | ||
| Secondary prevention | 16 | ||
| Tertiary prevention | 16 | ||
| RISK FACTORS FOR HEART FAILURE | 16 | ||
| Basic Concepts | 16 | ||
| Risk factors for heart failure | 17 | ||
| The Complex Risk Factors and Outcomes Models of Heart Failure | 17 | ||
| Impacts of Selected Risk Factors on Heart Failure | 17 | ||
| Significance | 18 | ||
| REFERENCES | 20 | ||
| 3 - Research and Design\r | 21 | ||
| CLINICAL EPIDEMIOLOGY AND TRANSLATIONAL EPIDEMIOLOGY | 21 | ||
| Basic Concepts | 21 | ||
| Clinical epidemiology\r | 21 | ||
| Translational epidemiology | 21 | ||
| Significance | 22 | ||
| THE CREDIBILITY OF STUDY | 22 | ||
| Bias | 22 | ||
| Selection bias | 22 | ||
| Information bias | 22 | ||
| Confounding | 22 | ||
| Definition of Confounder | 23 | ||
| ASSOCIATION, CAUSALITY, AND THE INTERPRETATION OF EPIDEMIOLOGIC EVIDENCE | 23 | ||
| EPIDEMIOLOGIC STUDY DESIGNS | 24 | ||
| Ecologic Study | 25 | ||
| Cross-Sectional Study | 26 | ||
| Example | 26 | ||
| Prospective Study | 28 | ||
| Retrospective cohort study | 29 | ||
| Steps to conduct a cohort study | 29 | ||
| Example 1 | 29 | ||
| Example 2 | 29 | ||
| Case-Control Study | 30 | ||
| Steps to conduct a case-control study | 30 | ||
| Selection of cases. Incident or prevalent cases are the cases selected from a hospital (or several hospitals), physicians’ offic... | 30 | ||
| Selection of controls. Nonhospitalized persons who do not have the disease | 30 | ||
| Data collection of past exposures to the risk factor(s) under study | 30 | ||
| Data analyses and interpretation | 30 | ||
| Univariate analysis. Step 1: Calculate and describe the proportions, rates, means of the exposure factors, and key covariates, a... | 30 | ||
| Multivariate analysis. In a case-control study, a logistic regression model is commonly used to analyze the dataset. Outcomes (b... | 30 | ||
| Strengths of a case-control study | 30 | ||
| Limitations of a case-control study | 30 | ||
| Methods to improve the quality of a case-control study | 31 | ||
| Matching. Matching: For each case, find a control that looks just like him/her in all other possible ways except for the disease... | 31 | ||
| Case-control study versus cross-sectional study | 31 | ||
| Example | 31 | ||
| Nested case-control study | 31 | ||
| Advantages of a nested case-control study | 32 | ||
| Example | 32 | ||
| Experimental Studies | 32 | ||
| Designing an experimental study | 32 | ||
| Some basic concepts in randomization trials | 33 | ||
| Study design | 33 | ||
| The inclusion and exclusion criteria of participants | 33 | ||
| Assignments and outcome measures | 34 | ||
| Results | 34 | ||
| Clinical trial in drug development | 34 | ||
| Recommended sample size in a clinical trial for drug development | 34 | ||
| Impact of clinical trials versus community trials | 34 | ||
| STRATEGIES FOR DATA COLLECTION | 35 | ||
| Predictors | 35 | ||
| Covariates | 35 | ||
| SMART Approach | 35 | ||
| DETERMINING THE SAMPLE SIZE | 36 | ||
| Basic Concepts | 36 | ||
| Effect size | 36 | ||
| Type I error and type II error | 36 | ||
| Probability α and probability β | 36 | ||
| Power of study | 36 | ||
| Calculation of Sample Size | 36 | ||
| Example | 36 | ||
| Example | 37 | ||
| GENERALIZABILITY OF RESULTS | 37 | ||
| COMMON DATA SOURCES IN HEART FAILURE STUDY | 37 | ||
| Primary Data Collection | 38 | ||
| Secondary Data Collection | 38 | ||
| NHLBI Biorepository | 38 | ||
| NIDDK Central Repository | 38 | ||
| STATISTICAL ANALYSIS STRATEGIES BY STUDY DESIGNS | 38 | ||
| SIGNIFICANCE | 39 | ||
| REFERENCES | 40 | ||
| 4 - Biostatistical Basis of Inference in Heart Failure Study\r | 43 | ||
| BASIC STATISTICS CONCEPTS | 43 | ||
| Types of Statistical Data | 43 | ||
| Numerical data | 43 | ||
| Categorical data | 43 | ||
| Ordinal data | 43 | ||
| Changes from Continuous Data to Categorical Data | 43 | ||
| DESCRIPTIVE BIOSTATISTICS | 44 | ||
| Definition | 44 | ||
| Arithmetic mean | 44 | ||
| Median | 44 | ||
| Mode | 44 | ||
| Geometric mean | 44 | ||
| Measures of spread | 44 | ||
| Standard deviation | 44 | ||
| Coefficient of variation | 44 | ||
| Example. In a study sample, we get mean SBP (mm Hg), =137.86, and SD=21.22 | 44 | ||
| Percentiles | 45 | ||
| Interquartile range | 45 | ||
| Another measure of spread: range | 45 | ||
| What SAS stands for?. SAS stands for “Statistical Analysis System,” was developed in the early 1970s at North Carolina State Uni... | 45 | ||
| SAS environment | 45 | ||
| SAS statement in general | 45 | ||
| SAS Statement by Steps | 46 | ||
| Start with SAS | 46 | ||
| Estimate means. SAS practice 1 | 46 | ||
| SAS computing in data with a large sample size | 48 | ||
| Count | 49 | ||
| Proportion | 49 | ||
| Example. A/(A+B) | 49 | ||
| Ratio | 49 | ||
| Example. Male/female ratio | 49 | ||
| Rate | 49 | ||
| Incidence | 49 | ||
| Prevalence rate | 50 | ||
| Mortality rate | 50 | ||
| Example. In a hospital, the annual heart failure specific mortality in 2016=total number of death from heart failure in 2016 div... | 50 | ||
| Case fatality rate | 50 | ||
| Definition of person-time | 50 | ||
| Specific rate and total (crude) rate | 50 | ||
| Example. Table 4.2 shows a hypothetical sample to demonstrate the calculations of total and age-specific prevalence of heart fai... | 50 | ||
| Age-Standardized (Adjusted) Rates | 50 | ||
| Compare age-specific rates | 51 | ||
| Direct standardization method | 51 | ||
| Age-standardization rates. From the hypothetical example, Table 4.3, we can see that the difference between crude and age-specif... | 51 | ||
| Steps for direct standardization method | 51 | ||
| Step 1: to select a standard population. The standard population can be selected from US census (such as 2000 or 2010 data), or ... | 51 | ||
| Step 2: to calculate the expected number of disease | 52 | ||
| Example. The expected number of disease in those aged 45–54 in urban residents | 52 | ||
| Step 3: to calculate the age-standardized rate | 53 | ||
| Example | 53 | ||
| Indirect standardization method | 53 | ||
| Step 1: to select a standardized rate. Table 4.4 shows the death rate (per 1000) among the general population in patients with h... | 54 | ||
| Step 2: to calculate the expected number of death | 54 | ||
| Step 3: to calculate SMR | 54 | ||
| SAS Computing | 54 | ||
| To calculate sex-specific rates using SAS Proc Freq | 54 | ||
| Calculate Person-Year Rates | 54 | ||
| Risk Assessments | 55 | ||
| Absolute risk, risk difference, and relative risk | 55 | ||
| Absolute risk. Absolute risk is the incidence of a disease in a population. Incidence rates and risk statements can also be calc... | 55 | ||
| Odds ratios | 56 | ||
| Example. To examine the odds ratios of heart failure in patients with diabetes versus those without diabetes, as shown Chapter 3... | 56 | ||
| Attributable risk | 56 | ||
| Example. To calculate AR using data from Table 4.5 | 56 | ||
| Population attributable risk (PAR) | 57 | ||
| Example. To calculate PAR using data from Table 4.5 | 57 | ||
| Application of relative risk and attributable risk | 57 | ||
| Hazard ratio | 58 | ||
| ANALYTICAL BIOSTATISTICS (I) | 58 | ||
| Definition of Analytical Biostatistics | 58 | ||
| Parameter and statistic | 58 | ||
| Methods of sampling | 58 | ||
| Sampling error | 58 | ||
| Sampling distribution | 59 | ||
| Standard error of mean: the standard deviation of mean | 59 | ||
| Example. To estimate the SEM from the sample means of SBP | 59 | ||
| Standard deviation versus standard error of mean | 59 | ||
| Normal Distribution | 61 | ||
| Standard normal distribution | 61 | ||
| Confidence intervals for population mean (μ) | 61 | ||
| Example. We can calculate their 95%CI of means using the same dataset in Fig. 4.6, samples A, B, and C | 62 | ||
| Example. In the same dataset, HFBKBG 1, serum mean triglycerides (TG)=148.56mg/dL, SD=76.53, SEM=1.55. Fig. 4.9A depicts the dis... | 62 | ||
| Methods for Data Transformation | 62 | ||
| Logarithms | 62 | ||
| Reciprocal (inverse) | 62 | ||
| Square root | 62 | ||
| Arcsine | 64 | ||
| Parametric data versus nonparametric data | 64 | ||
| SAS Computing | 64 | ||
| ANALYTICAL BIOSTATISTICS (II) | 65 | ||
| Example. Null hypothesis (H0) is a statement of “no difference” in means of SBP (μ1=μ2, or μ1−μ2=0) between the two study popula... | 65 | ||
| Step 2: select significance level | 65 | ||
| Definition of z-test | 66 | ||
| Two-tailed or one-tailed test | 66 | ||
| Step 3: select an appropriate statistic | 66 | ||
| Step 4: calculate the selected statistic and conclusion | 66 | ||
| z-Test for Comparing Two Means | 66 | ||
| Example | 67 | ||
| t-Test for Comparing Two Means | 67 | ||
| t distribution | 67 | ||
| Paired t-test for mean difference from one group of samples | 67 | ||
| Example. Table 4.7 shows an intensive intervention program for 10 subjects with BMI more than 25kg/m2. After a 12-week lifestyle... | 68 | ||
| t-Test for two independent means | 68 | ||
| Example. In a study, compare mean age in two samples: one in subjects without heart failure (HF), n=2787, mean age=66.65years, S... | 68 | ||
| Analysis of Variance for Comparing Three or More than Three Means | 68 | ||
| Nonparametric Tests | 70 | ||
| Wilcoxon signed rank test | 70 | ||
| Example. To compare the difference in mean serum glucose between patients with heart failure and those without heart failure, a ... | 70 | ||
| SAS computing | 70 | ||
| The Kruskal-Wallis test mean difference among three or more than groups | 73 | ||
| Example. This example is to test means difference in serum glucose levels among nonsmokers, former smokers, and current smokers.... | 73 | ||
| ANALYTICAL BIOSTATISTICS (III) | 73 | ||
| Correlation and Regression Analysis for Two Continuous Variables | 73 | ||
| Correlation | 74 | ||
| Linear regression | 74 | ||
| Example. Fig. 4.14A shows the scatterplot of body mass index (BMI, kg/m2) and waist circumference (WC in cm). It indicates that ... | 74 | ||
| Types of correlation | 74 | ||
| How to quantify a correlation? | 75 | ||
| Correlation coefficient. To quantify whether a linear correlation exists between two variables, we calculate two types of correl... | 75 | ||
| Pearson correlation. Pearson correlation coefficient (symbolized r) is a parametric statistic and used for data in normal or in ... | 75 | ||
| Spearman correlation. Spearman correlation coefficient (symbolized rs) is a nonparametric statistic and used for data that is no... | 75 | ||
| Properties of Pearson correlation | 75 | ||
| Independent and dependent variable | 75 | ||
| Example. To test the correlation confident from the study of the relationship between BMI and WC, we use a t-test, the formulas ... | 75 | ||
| Steps of testing a hypothesis | 76 | ||
| Coefficient of determination (R-square) | 76 | ||
| Example. In BMI and WC, the coefficient of determination=0.852=0.7225. It indicates that 72.25% of the variation in the values o... | 76 | ||
| Spearman correlation | 76 | ||
| SAS computing | 76 | ||
| Limitation of correlation coefficient | 77 | ||
| Linear regression analysis | 78 | ||
| Example. In BMI and WC relationship study | 78 | ||
| SAS computing | 78 | ||
| Assumptions in regression | 79 | ||
| Research question. In Chapter 3, we discussed an example: a cross-sectional study (n=3000) aimed to describe the frequencies of ... | 79 | ||
| The logic of the chi-square test. The total number of observations in each column (i.e., 213 and 2787) and the total number of o... | 79 | ||
| Significance test level in chi-square test. Table 4.12 shows part of the chi-square probabilities (df≤10). In a 2×2 table, df=(r... | 80 | ||
| Example. To test the difference in heart failure rates between individuals with or without DM | 80 | ||
| SAS computing | 80 | ||
| Logistic regression analysis | 81 | ||
| Basic concept of logistic regression. The logistic regression is simply a nonlinear transformation of the linear regression. The... | 81 | ||
| Interpreting logistic coefficients | 82 | ||
| Interpreting odds ratios | 82 | ||
| Example. Let us use the example used in Chapter 3 again; in a cross-sectional study (n=3000), investigators aimed to describe th... | 82 | ||
| REFERENCES | 82 | ||
| 5 - Advanced Biostatistics and Epidemiology Applied in Heart Failure Study\r | 83 | ||
| MULTIVARIATE LINEAR REGRESSION ANALYSIS AND MODELING | 83 | ||
| 6 - Precision Medicine and Areas for Further Research\r | 103 | ||
| CHALLENGES AND OPPORTUNITIES IN HEART FAILURE RESEARCH | 103 | ||
| PRECISION MEDICINE AND PRECISION PUBLIC HEALTH | 103 | ||
| Precision Medicine | 103 | ||
| Precision Public Health | 103 | ||
| AREAS FOR FURTHER RESEARCH | 103 | ||
| Prevention for People at High Risk of Health Failure | 103 | ||
| Hospitalized Heart Failure | 103 | ||
| Multicomorbidity and Cardiorenal Failure | 103 | ||
| Cardiometabolic Syndrome and Heart Failure | 103 | ||
| Therapies and Polypharmacy in Patients With Heart Failure | 104 | ||
| Readmission in Patients With Heart Failure | 104 | ||
| Biomarkers and Prediction Models in Patients With Heart Failure | 104 | ||
| Reverse Epidemiology of Heart Failure | 104 | ||
| Impact of Big Data on Heart Failure Study | 105 | ||
| REFERENCES\r | 105 | ||
| Index | 107 | ||
| A | 107 | ||
| B | 107 | ||
| C | 107 | ||
| D | 108 | ||
| E | 108 | ||
| F | 108 | ||
| H | 108 | ||
| I | 108 | ||
| J | 108 | ||
| L | 108 | ||
| M | 108 | ||
| N | 109 | ||
| P | 109 | ||
| Q | 109 | ||
| R | 109 | ||
| S | 109 | ||
| T | 109 | ||
| W | 109 |