CHADS-VASc Score predicts stroke and death in congestive heart failure patients

The CHA₂DS₂-VASc score (congestive heart failure, hypertension, age ≥75 years [doubled], diabetes, stroke/transient ischemic attack/thromboembolism [doubled], vascular disease [prior myocardial infarction, peripheral artery disease, or aortic plaque], age 65-75 years, sex category [female]) is used clinically for stroke risk stratification in atrial fibrillation (AF). However, whether it can predict these outcomes in patients with heart failure remained unknown.

Melgaard et al, examined the nationwide prospective cohort study using Danish registries, including 42,987 patients (21.9% with concomitant AF) not receiving anticoagulation who were diagnosed as having incident HF during 2000-2012.

In patients without AF, the risks of ischemic stroke, thromboembolism, and death were 3.1% (n = 977), 9.9% (n = 3187), and 21.8% (n = 6956), respectively.  The risks were greater with increasing CHA₂DS₂-VASc scores. Interestingly, the absolute risk of thromboembolic complications was higher among patients without AF compared with patients with concomitant AF at high CHA₂DS₂-VASc scores. However, predictive accuracy was modest, and the clinical utility of the CHA₂DS₂-VASc score in patients with HF remains to be determined.

Treatment of Abdominal Aortic Aneurysm

What is a better treatment option for patients with abdominal aortic aneurysms (AA)? Currently, there are two main options; endovascular repair (below left) or open surgical repair (below right).

Chang and his colleagues analyzed the longitudinally linked California Office of Statewide Health Planning and Development inpatient database from 2001 to 2009 with a median follow-up of 3.3 years. They studied 23,670 patients, with 52% receiving endovascular repair. Endovascular repair was associated with improved 30-day outcomes (all-cause mortality, readmission, surgical site infection, pneumonia, and sepsis), as well as significantly improved survival until 3 years postoperatively. After 3 years, mortality was higher for patients who underwent an endovascular repair. No significant difference in long-term mortality was observed for the entire cohort on adjusted analysis (hazard ratio, 0.99; 95% CI, 0.94-1.04; P = .64). Endovascular repair was found to be associated with a significantly higher rate of re-interventions and AAA late ruptures.

Some Stuff from ESC

 

Warfarin Discontinuation Increases Stroke Risk

Non-steroidal Mineralcorticoid Receptor Antagonist Could Cut Mortality

One study says:
- Stimulation of either the right or left vagus nerve appeared to improve cardiac function in patients with heart failure
And other one says:
- Stimulating the vagus nerve didn't improve cardiac function in heart failure patients
Go figure!

Antidote for Dabigatran

Until recently, warfarin (also known as Coumadin), a vitamin K antagonist (VKA), had been the only available oral anticoagulant. The use of warfarin has been always complicated by many issues including its narrow therapeutic index and multiple drug and diet interactions affected its safety, compliance, and efficacy. Patients needed regular and close monitoring of the its anticoagulant effect (how thin is blood?). Despite regular monitoring, patients suffered bleeding complications when blood was too thin (supra-therapeutic range) or blood was thin within the desired range but other patient factors (such as trauma/injury) resulted in bleeding.

A very common use of warfarin is for anticoagulation in patients who suffer from atrial fibrillation. With increasing age, the risk of atrial fibrillation increases and atrial fibrillation is common older individuals. Patients with atrial fibrillation can develop a clot in the left atrium of the heart and this clot can dislodge and go to other parts of the body. If this dislodged clot goes to arteries that supply blood to the brain, it usually results in large stroke. The risk of stroke with atrial fibrillation varies from person to person but can be calculated using a CHADS2 score and may vary from 1.8% per year to 18% per year without anticoagulation.

The above noted problems with warfarin prompted the development of new oral anticoagulants that target key coagulation proteins. Within past few years, FDA has approved several new oral anticoagulants that don’t require regular monitoring with blood tests and have very few drug interactions. However, one limitation with these new anticoagulants is lack of an antidote that can quickly reverse the effect of these drugs in cases of emergency, such as when a patient is bleeding. On the other hand, vitamin K can be used to reverse the effect of warfarin. Several pharmaceutical companies and other research groups are trying to develop agents that can effectively reverse the effects of these new anticoagulants.

Pollack and colleagues have published in this issue of the New England Journal of Medicine a trial of such an antidote of dabigatran, an oral thrombin inhibitor that is approved for the prevention of stroke in patients with non-valvular atrial fibrillation and for the prevention and treatment of venous thromboembolism. Investigators used idarucizumab, a monoclonal antibody fragment that binds dabigatran with an affinity that is 350 times as high as that of dabigatran’s affinity with thrombin (a coagulant protein through which dabigatran acts). In blood, idarucizumab binds free and thrombin-bound dabigatran and neutralizes its activity. In this prospective cohort study, investigators examined the safety of 5 g of intravenous idarucizumab and its inhibitory effect on dabigatran in patients who had serious bleeding (group A) or who required an urgent procedure (group B). Investigators determined the maximum percentage reversal of the anticoagulant effect of dabigatran within 4 hours after the administration of idarucizumab (primary endpoint).

Of the 90 patients who received idarucizumab (51 patients in group A and 39 in group B), idarucizumab normalized the blood coagulation in 88 to 98% of the patients often within minutes. Concentrations of unbound dabigatran was below 20 ng per milliliter at 24 hours in 79% of the patients. Among 35 patients in group A who could be assessed, bleeding was controlled at a median of 11.4 hours. Among 36 patients in group B who underwent a procedure, normal intraoperative hemostasis was reported in 33, and mildly or moderately abnormal hemostasis was reported in 2 patients and 1 patient, respectively. One thrombotic event occurred within 72 hours after idarucizumab administration in a patient in whom anticoagulants had not been reinitiated.

As noted above idarucizumab is specific for dabigatran and is unlikely to be effective with other new oral antocagulants. However, various reversal agents and/or strategies, nonspecific to dabigatran, are available to physicians, including prothrombin complex concentrates, activated prothrombin complex concentrates, or recombinant factor VIIa.

Southern Dietary Pattern and Heart Disease

We are what we eat – and the diseases we get are the often (at least partly) a result of dietary choices we make.

A study published in the American Heart Association’s journal, Circulation, finds that people who have Southern dietary pattern – characterized by added fats, fried food, eggs, organ and processed meats, and sugar-sweetened beverages – are at a higher risk of heart attacks and sudden cardiac death. Investigators analyzed data from 17,418 participants in Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a national, population-based, longitudinal study of white and black adults aged ≥45 years, enrolled from 2003-2007. They found 56% higher risk of heart attacks and sudden cardiac death among individuals who eat ‘Southern’ food than those who rarely eat such food.

So next time when you are thinking about what to eat, chose something healthy!

Updated Guidelines - Diabetics and Cardiovascular Disease

American Heart Association and American Diabetes Association has recently published an update to the guidelines for the prevention of cardiovascular disease in adults with type 2 diabetes mellitus. There are certain interesting aspects to it.

1. Use of Hemoglobin A_1C for the diagnosis of diabetes mellitus
 Pre-diabetes = A_1C between 5.7% and 6.4%
Diabetes Mellitus = A_1C >/= 6.5%

2. Lifestyle Management of Diabetes
Physical Activity
Nutrition
Weight Reduction (through physical activity, nutrition, weight-loss drugs, and/or bariatric surgery)

3. Cardiovascular risk reduction
Control of blood glucose (A_1C <7%)
Control of blood pressure (< 140/90)
Control of Cholesterol (statins)
Aspirin for moderate 10-year CVD risk (5-10%)

4. Screening for cardiovascular diseases in asymptomatic patients
Paucity of data suggesting any specific benefits of invasive interventions over medical therapy alone makes any CAD screening in the asymptomatic patient with diabetes mellitus highly controversial.

Statins After Stroke: What is the effect on mortality and morbidity?

Statins (cholesterol lowering drugs such as Lipitor or Crestor and others) have been shown to reduce major cardiovascular events after stroke and are considered the standard of therapy in patients with stroke. Most patients with new stroke are prescribed one of the statins when discharged from hospital. However, whether the beneficial effects of statins seen in strict clinical trials settings are also present in non-clinical trial settings remains to be seen. In other words, while it is established that statins have efficacy (work in clinical trials) it remains to be seen whether statin have effectiveness (work in usual delivery of healthcare).

The distinction between efficacy and effectiveness is an important one. A clinical trial has inclusion (and exclusion) criteria which limit enrollment to only a certain group of individuals. Patients who volunteer for clinical trials are also known to be more compliant and receptive to medical advice. Furthermore, patients in clinical trial are closely followed (and observed) and, therefore, perhaps get better care. Thus, results seen in a clinical trial setting may not hold true in the usual healthcare delivery environment where all sort of patients get drugs (or interventions), compliance may be an issue, and patients are not as closely followed. This necessitates effectiveness studies, which are not commonly performed, although thought to be quite important. Perhaps the biggest reason is that such studies are not required for a drug approval by FDA and perhaps drug companies fear that effectiveness studies may show that a particular drug (or intervention) is not as effective as shown in the clinical trial (or not effective at all).

It is then no surprise that effectiveness studies for statins in stroke (one of the commonly prescribed drug class) have not been done, that is until now. O’Brien et al report in this issue of circulation such an effectiveness study. Investigators linked data from Get With The Guidelines (GWTG)-Stroke register with the Medicare data and followed patients 2-year post-discharge for major cardiovascular events, time spent at home (out of hospital or nursing home), all cause mortality, readmissions to hospitals, and hemorrhagic stroke. Investigators report that from 2007–2011, 77,468 patients who were not taking statins at the time of admission were hospitalized with ischemic stroke. Of these 77K patients, 71% were discharged on some form of statin therapy; 31% on high-intensity statin therapy.

What they found was that the rates were lower for major adverse cardiovascular events (9% lower), mortality (16% lower), and readmission (7% lower) within two years of hospital discharge were lower for patients who were taking statins as compared with those not taking a statin. On average, patients also spent 28 more days at home. Of note, these results were adjusted for risk factors. There were no differences in rates of hemorrhagic stroke, ischemic stroke, or cardiovascular readmission by statin therapy.

Now contrast this data with the results from clinical trials of statins that showed a 20% reduction in major adverse cardiovascular events, 16% reduction in risk of ischemic stroke, and as high as 32% reduced risk of mortality. Obviously, as expected, the benefits are much larger in a clinical trial setting. This example, among others, highlights the need for effectiveness trials. Of note, this trial was funded through PCORI (a tax-payer funded program) and not by a drug company.

Life Expectancy After Myocardial Infarction

This study examines sex and race differences in long-term survival after AMI using life expectancy and YPLL to account for differences in population-based life expectancy. Investigators used the Cooperative Cardiovascular Project, a prospective cohort study of Medicare beneficiaries hospitalized for AMI between 1994 and 1995 (N = 146,743).

Investigators found that the life expectancy estimates after myocardial infarction were similar for men and women of the same race but lower for black patients than white patients.

Below is a figure from the manuscript summarizing the findings

3D Printing in Medicine

Very interesting!

With 3D printing physicians can make exact replica of a particular patient’s left atrial appendage to obtain a better fit during the appendage closure procedure. Left atrial appendage is the most common site of thrombus (or clot) formation in patients with atrial fibrillation. These clots can dislodge and go into circulation, blocking blood flow, and causing damage to the affected organs. The biggest concern (and the biggest risk) is of strokes.

Lung Function Trajectories Leading to COPD

It is commonly believed that the decline in lung function may be greater in people with already poor lung function than those with normal lung function. Now a study with a relatively large sample size shows that the decline in lung function varies among people and perhaps doesn’t depend on the baseline lung function.

Peter Lange and colleagues used three independent cohorts (Framingham, Copenhagen Heart, Lovelace Smokers) and showed that low FEV₁ in early adulthood is important in the genesis of COPD and that accelerated decline in FEV₁ is not an obligate feature of COPD.

Changing Microbiology of Community Acquired Pneumonia

Since the start of pneumococcal conjugate vaccine use for routine childhood immunization, the overall rate of invasive disease and pneumonia among adults has decreased, likely due to herd immunity. We also now have have more sensitive laboratory tests to detect pathogens responsible for pneumonia in adults. This requires an updated assessment of the incidence of pneumonia and causative pathogens.

Seema Jain and her colleagues have recently explored this question in a prospective, multicenter, population-based, active surveillance study, the Centers for Disease Control and Prevention (CDC) Etiology of Pneumonia in the Community (EPIC) study, and published results in the NEJM.

The study enrolled adults 18 years of age or older were enrolled at three hospitals in Chicago (John H. Stroger, Jr., Hospital of Cook County, Northwestern Memorial Hospital, and Rush University Medical Center) and at two in Nashville (University of Tennessee Health Science Center–Saint Thomas Health and Vanderbilt University Medical Center) from January 1, 2010, to June 30, 2012.

There were 2320 cases of pneumonia confirmed with radiographs. Quite interestingly, and in contrast to what would one expect to see, pneumonia were distributed about evenly between younger (18-49) middle (50-64) and older (>64) age groups, roughly one third in each category. Most (78%) had some underlying condition predisposing to pneumonia. Surprisingly, less than half were vaccinated with influenza or pneumococcal vaccine. Only in 38% of patients, a pathogen was detected despite using an extensive battery of laboratory diagnostics. Pathogens detected were as follows: one or more viruses in 530 (23%), bacteria in 247 (11%), bacterial and viral pathogens in 59 (3%), and a fungal or mycobacterial pathogen in 17 (1%). The most common pathogens were human rhinovirus (in 9% of patients), influenza virus (in 6%), and pneumococcus (in 5%).

At a population level, the annual incidence of pneumonia was 24.8 cases (95% confidence interval, 23.5 to 26.1) per 10,000 adults, with the highest rates among adults 65 to 79 years of age (63.0 cases per 10,000 adults) and those 80 years of age or older (164.3 cases per 10,000 adults).

The results overall reaffirm the common observation that pneumonia incidence is highest in the elderly population. Results further show that despite current diagnostic tests, no pathogen was detected in the majority of patients. The overall pathogens for pneumonia are changing with respiratory viruses being detected more frequently than bacteria.

Left Atrial Appendage Occlusion Device

Oral anticoagulants such as warfarin, factor Xa inhibitors, and direct thrombin inhibitors are the current standard of care in high-risk patients with atrial fibrillation to reduce the risk of stroke in patients with risk factors, albeit at the expense of an increase in bleed. However, the benefit of oral anticoagulation needs to be weighed against an increased risk of bleeding.

Left atrial appendage occlusion devices have the potential to change the therapy for stroke prevention in atrial fibrillation patients. ACC/HRS/SCAI have just published an overview of the literature on this topic. The overview reviews several questions related to the use of these occlusion devices. The overview starts with literature review of currently available devices (WATCHMAN, Amplatzer Cardiac Plug, LARIAT, and others) and then delves into the question of the need and requirements for care team and facilities needed for the use of such devices. This is followed by training requirement for the operator, standardization of protocols, and selection of patients for occlusion device placement. The overview is an interesting read and is available here.

Individualized Care Plans for High Utilizers of Hospital Services

There are always a small number of patients frequently visit Emergency Department (ED) and are frequently admitted to the hospital. The underlying reasons are sometimes medical conditions and sometimes are complex psychological and social issues. Formulating a care plan that is individualized for a patient with appropriate support from healthcare professionals may help to decrease utilization of healthcare services and resources by such patients.

A study published in this month’s Journal of Hospital Medicine examined the same question. Investigators formed a multidisciplinary team that developed individualized care plans integrated into electronic medical record (EMR) that summarized patient histories, utilization patterns, and management strategies. They enrolled twenty-four medically and psychosocially complex patients with the highest rates of inpatient admissions and ED visits from August 1, 2012 to August 31, 2013.

Investigators found that hospital admissions decreased by 56% (P < 0.001) and 50.5% (P = 0.003), 6 and 12 months after care-plan implementation. Thirty-day readmissions decreased by 66% (P < 0.001) and 51.5% (P = 0.002), 6 and 12 months after care-plan implementation. ED visits, ED costs, and inpatient LOS did not significantly change. Inpatient variable direct costs were reduced by 47.7% (P = 0.001) and 35.8% (P = 0.052), 6 and 12 months after care-plan implementation.

At least this one study found that individualized care plans developed by a multidisciplinary team and integrated with the existing healthcare workforce and EMR reduce hospital admissions, 30-day readmissions, and hospital costs for complex, high-utilizing patients.

Plotting Histograms in R

Histogram is probably one of the first things that we plot to look at a continuous variable. In R you can draw a histogram using its built-in ‘hist’ command. Other packages, such as ggplot2 has much more developed functions to plot histograms although one does need to learn how to use functions within those packages.

First, lets simulate data (generate fake data)
DAT = rnorm(1000, 100, 10)
Above line will generate 1000 draws from a normal distribution with a mean of 100 and standard deviation of 10

Now lets take a look at first few rows we generated
head(DAT)

Lets look at the summary of the data
summary(DAT)

Note: You may get different data every time as we have not set a seed but that is not important at this time.

Now draw first histogram
hist(DAT)

Add color to the histogram
hist(DAT, col="blue")

Now lets take control on the number of histogram bars
hist(DAT, col="blue", breaks=25)

Change Y-axis from frequency (which is default) to density
hist(DAT, col="blue", breaks=25, probability=TRUE)

Add labels to the histogram
hist(DAT, col="blue", breaks=25, probability=TRUE,
     main="My Pretty Histogram",    ### For title of the figure
     xlab="My Fake Data")           ### For x-axis label

Add a dark green-color  kernel density curve to the plot
lines(density(DAT), col="darkgreen", lwd=2) 

Add a red-color normal density curve to the plot
curve(dnorm(x, mean(DAT), sd(DAT)), add=TRUE, col="red", lwd=2) 

Below is similar to what you should expect to get:

Identifying and Developing a Research Question

Below are some of the resources that may be helpful in learning how to develop a research question

1. Coming up with a research question [Kinmond 2012]

2. Developing great research questions [Lipowski 2008]

3. When is a research question not a research question? [Mayo 2013]

Installing Packages in R

R has thousands of packages that extend its use to almost every arena of research. It is highly likely that you will not need most of these packages but it is also likely that you will need several of them (perhaps from 5 to 20) depending on what you plan to do. Most of these packages are available on the the CRAN website.

http://www.cran.r-project.org/web/packages/available_packages_by_name.html

Bioconductor is another source of R packages for bioinformatics-related research packages can be downloaded from its website.

http://www.bioconductor.org/packages/release/bioc/

To use a package, there are two steps:

First Step: Download and Install a Package – you can download a package to a local directory and install it from there using drop down menu OR you can directly install it from the CRAN repository. Depending on the R GUI user interface that you use, the exact steps may be slightly different. Often, first you have to specify which mirror you want to use; chose a mirror that is geographically closer to you for faster downloads. Then you can chose a package from the package list.

I use RStudio GUI. In Rstudio, click on the tab labeled ‘Packages’. If this tab is not visible, press Ctrl+7 and the tab will become visible (usually in the right lower quadrant of the window). From there you can chose install, then type in the name of package (if multiple packages, enter package names separated by space or a comma). Make sure that ‘install dependencies’ box is checked. Make sure that the correct repository and installation location are selected. Then click Install. You can also chose to use installation command directly from the console; the command below will install ggplot2 package:

install.packages("ggplot2")

Note that you need to install packages only once

Second Step: Loading a Package – Installing a package makes it available for later use but packages are not automatically uploaded during a session. Once you have installed a package, you will need to load that package when you need it during a session. To load a package use the function ‘library()’. the following command will load the package ggplot2.

library(ggplot2)

Some may like to use ‘require()’ function instead of ‘library()’. However, see this post for the differences between the two and why one should prefer ‘library()’ over ‘require()’

Personally, I try to load all needed packages at the beginning of a script. However, this strategy may not work if there is an overlap in the names of functions between two packages and you may see a warning “The following objects were masked from ‘package:xyz’:”. 

Some other useful commands to know

.libpaths() # – will give you location of library for packages
library() # – will show you all installed packages
search() # – will show you currently loaded packages

Some Thoughts About Clinical Research

Clinical research requires a wide range of skills. These skill include the ability to work with a wide range of people, to lead teams with people from wide and vastly different backgrounds, to design appropriate studies, to ask right questions, to understand research methods specific to the study question, to develop in-depth content expertise in the area of research focus, to get funding for research projects, to present study results at national meetings, to write manuscripts for publication in peer-review journals, and so on and so forth.

A fundamental skill for a researcher is the ability to knit together the conceptual framework for a study (theory) with appropriate measurement, with the result either supporting or opposing the conceptual framework. The theory should be based on the most current state of knowledge, the data collected should have the ability to test the theory, the statistical models should reflect both the conceptual structure hypothesized to have given rise to the data and the nature of collected data, and the inferences should be based on the data and the tested statistical models. This process is not linear, rather it is a loop in which theoretical aspects inform the collection of data and results of the data analyses help in refining the theory, which generates more testable hypothesis, additional data collection, and so on.

Most research is probabilistic, as opposed to deterministic. In other words, the results we obtain are not always certain; we have to include uncertainty in our analyses and expect some uncertainty in our results and inferences. Thus, we have to accept that our results are unlikely to be laws governing the system we plan to study and more likely to be an approximation of what we expect to find in the real world, with some uncertainty. There are many reasons and sources of this uncertainty some of which can be addressed while others may still be there despite our best attempts.

A researcher should determine whether the interest of research is to build inferences at population level or at the level of individuals unit (often a patient in clinical research). The study design, data collection and analysis, and the inferences may be quite different depending on what is the object of our interest. While we study individuals, our results usually address inferences at population level. In general, it is much easier to predict about the response at a population level, that is on an average, individuals with higher body mass index (say >30) will have higher blood glucose than (say) 126mg/dL. However, it is much difficult to predict with certainty how likely a particular individual with a BMI>30 is to have higher blood glucose level than 126 mg/dL. For a predictor to work well at an individual level, among other things effect size needs to be quite large.

Another important concept is that of causality. While we often have a conceptual model in our mind that A is caused by B, it may be quite difficult to prove except perhaps in a clinical trial setting. There are several factors that can increase the likelihood that the direction of cause and effect in our conceptual model is correct, such as temporality and biological plausibility.  However, often there remains a possibility that B is in fact caused by A or that some other unknown (or unmeasured) factor, C, may be responsible for both A and B. Hence, we often claim an association or correlation between A and B and not causality.

Starting to work with R

There may be some who have just started working with R after someone convinced them that R is the way to go. For those souls, it may be difficult to get started quickly. Below are some of the steps to use to start working with R

Step 1: Go to the CRAN webpage and download the version of R that is appropriate for your operating system - http://cran.r-project.org/

Step 2: Install R

Step 3: Download a GUI for R – While R comes with a GUI, other GUIs are much better. My favorite is RStudio, To download RStudio, go to RStudio website and download the version that is appropriate for your operating system - http://www.rstudio.com/products/rstudio/download/

Step 4: Install RStudio (or a GUI of your choice).

Step 5: Start using RStudio (or GUI of your choice)

That’s it – Good luck!

Fractional Flow Reserve–Guided PCI for Stable CAD

The utility of PCI in stable CAD is unclear. Fractional flow reserve (FFR) may be used to stratify patients between those who will benefit from PCI from those who will not.

A randomized clinical trial published in NEJM examined this question. Patients with FFR<0.8 were randomized to PCI or medical therapy. Although the primary endpoint included a soft endpoint (revascularization) there was significant decrease in the primary endpoint among patients who underwent PCI (8.1 vs 19.5%). Further, the hard endpoints (death or nonfatal myocardial infarction) were also reduced significantly in FFR patients (4.6% vs 8.0%). Quite interestingly, the individuals with FFR greater than 0.8 met primary endpoint as often as those with FFR<0.8 and PCI (8.1 vs. 9.0%) although this was not a direct comparison group.

Interesting results! ……… Perhaps practice changing?

Thunder and Spark–Cluster computing made easy (a little)

Here it is