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.
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.
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.
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.
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.
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.
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:
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]
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
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.
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!
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?
Epiviz is an interactive visualization tool for functional genomics data. It supports genome navigation like other genome browsers, but allows multiple visualizations of data within genomic regions using scatterplots, heatmaps and other user-supplied visualizations.
Even in this day and age when almost everything is available on internet how can this happen? Perhaps no one who was hiring or promoting this dude knew how to use internet. Someone can lie about his/her qualifications (such as getting a PhD) and no one checks before hiring for assistant or associate professor? Shouldn’t folks at NUS, WVU and VCU be ashamed of their gross negligence?
Findings from the CONFIRM-HF (Ferric CarboxymaltOse evaluatioN on perFormance in patients with IRon deficiency in coMbination with chronic Heart Failure) trial, point to a simple and safe solution for heart failure patients with iron deficiency who can experience significant and sustainable improvements in functional capacity and quality of life as well as reduced risk of hospital admission for worsening heart failure by iron supplementation.
CONFIRM-HF is a double-blind, placebo-controlled trial, which enrolled 304 stable, symptomatic heart failure patients from 41 sites across nine European countries. All patients had iron deficiency, defined as a serum ferritin level < 100 ng/mL, or between 100 and 300 ng/mL with transferrin saturation < 20%. Subjects were randomized to receive either intravenous iron (n=152), given as ferric carboxymaltose solution (FCM), or a normal saline placebo (n=152), for 52 weeks. Completion of the six-minute walk test (6MWT) was required at baseline, and the primary endpoint of the study was improvement in this test at week 24.
Compared to placebo-treated subjects, those treated with FCM completed 33 extra meters in the 6MWT at week 24 (p=0.002), 42 extra meters at week 36, and 36 extra meters at week 52 (both p<0.001) and the improvement was seen in all subgroups. Despite the reduction in hospitalizations among FCM-treated patients, the number of deaths was similar in both groups, suggesting a one-year follow-up may not be long enough to detect differences in mortality.
Adverse events were mild, and occurred at a similar rate in both groups.
ACE inhibitors are standard of therapy in patients with heart failure as clinical trials have shown mortality benefit with these drugs. A trial published in NEJM compared a new drug LCZ696 with enalapril (an ACE Inhibitor) and found this new drug to far better.
This trial randomly assigned 8442 patients with class II, III, or IV heart failure and an ejection fraction of 40% or less to receive either LCZ696 (at a dose of 200 mg twice daily) or enalapril (at a dose of 10 mg twice daily), in addition to recommended therapy.
The trial was stopped early (after a median follow-up of 27 months) because there was clear evidence of benefit of LCZ696 over enalapril. The primary outcome (a composite of death from cardiovascular causes or hospitalization for heart failure) had occurred in 914 patients (21.8%) in the LCZ696 group and 1117 patients (26.5%) in the enalapril group (hazard ratio in the LCZ696 group, 0.80; 95% confidence interval [CI], 0.73 to 0.87; P<0.001). A total of 711 patients (17.0%) receiving LCZ696 and 835 patients (19.8%) receiving enalapril died (hazard ratio for death from any cause, 0.84; 95% CI, 0.76 to 0.93; P<0.001); of these patients, 558 (13.3%) and 693 (16.5%), respectively, died from cardiovascular causes (hazard ratio, 0.80; 95% CI, 0.71 to 0.89; P<0.001). As compared with enalapril, LCZ696 also reduced the risk of hospitalization for heart failure by 21% (P<0.001) and decreased the symptoms and physical limitations of heart failure (P=0.001).
The incidence and type of adverse effects were different between the two drugs; the LCZ696 group had higher proportions of patients with hypotension and nonserious angioedema while enalapril group have higher proportions with renal impairment, hyperkalemia, and cough.
LCZ696 is an investigational combination drug consisting of two antihypertensives (blood pressure lowering drugs), valsartan and AHU-377, in a 1:1 mixture. It is being developed by Novartis. The combination is often described as a dual-acting angiotensin receptor-neprilysin inhibitor although the two effects are achieved by two different molecules. AHU-377 is a prodrug that is activated to LBQ657 by de-ethylation via esterases.LBQ657 inhibits the enzyme neprilysin, which is responsible for the degradation of atrial and brain natriuretic peptide, two blood pressure lowering peptides that work mainly by reducing blood volume.
On the financial side, projections on peak sales from the company's own bullish $2 billion to $5 billion. The highest estimate is by Deutsche's awestruck of $10 billion while the lowest is by EvaluatePharma which pegged nearer-term 2020 sales at a much more modest $1.3 billion.
Not surprisingly, increased morbidity and perhaps increased mortality, is associated with postpericardiotomy syndrome as well as post-operative development of atrial fibrillation and pericardial and pleural effusions. Colchicine may prevent these complications and COPPS-2 trial looked at this possibility.
This trial was reported in JAMA and also presented at ESC
The results were as below:
PRIMARY ENDPOINT: “The primary end point of postpericardiotomy syndrome occurred in 35 patients (19.4%) assigned to colchicine and in 53 (29.4%) assigned to placebo (absolute difference, 10.0%; 95% CI, 1.1%-18.7%; number needed to treat = 10).
SECONDARY ENDPOINT: “There were no significant differences between the colchicine and placebo groups for the secondary end points of postoperative AF (colchicine, 61 patients [33.9%]; placebo, 75 patients [41.7%]; absolute difference, 7.8%; 95% CI, −2.2% to 17.6%) or postoperative pericardial/pleural effusion (colchicine, 103 patients [57.2%]; placebo, 106 patients [58.9%]; absolute difference, 1.7%; 95% CI, −8.5% to 11.7%), although there was a reduction in postoperative AF in the prespecified on-treatment analysis (placebo, 61/148 patients [41.2%]; colchicine, 38/141 patients [27.0%]; absolute difference, 14.2%; 95% CI, 3.3%-24.7%).
ADVERSE EVENTS: “Adverse events occurred in 21 patients (11.7%) in the placebo group vs 36 (20.0%) in the colchicine group (absolute difference, 8.3%; 95% CI; 0.76%-15.9%; number needed to harm = 12), but discontinuation rates were similar”.
Aspirin is the mainstay of treatment for the prevention of cardiovascular disease. It acts by irreversibly inhibiting COX1 enzymes and hence blocking arachidonic acid-thromboxane pathway. Due to lack of a nucleus, platelets cannot generate new COX1 and hence COX1 is inhibited for the lifetime of platelets (8 days). Aspirin treatment results in marked decrease in the excretion of urinary metabolites of thromboxane; the residual excretion is thought to be of endothelial origin where presence of nucleus results in formation of new COX1.
Despite adequate aspirin therapy, a significant number of individuals continue to have higher platelet reactivity and incomplete inhibition of platelet function. Such individuals are also at increased risk of future cardiovascular events. However, the underlying mechanisms that result in higher residual platelet reactivity after aspirin treatment are unclear and are being extensively explored by several researchers.
To identify mechanism of aspirin resistance, this study examined the differences in proteome of 2 aspirin resistant and 4 aspirin sensitive individuals and found that the levels of glycoprotein IIIa were higher in aspirin resistant individuals than in those without aspirin resistance.
Due to small sample size, it is difficult to rule-out the possibility of a type I error, however, considering the role of glycoprotein IIIa in platelet biology, it is conceivable that protein may play a role in aspirin resistant although exact mechanism remains unknown.
Aspirin is perhaps the most commonly used drug world-wide for various ailments. Its prophylactic use in secondary prevention of cardiovascular diseases is well-established, however its use for cardiovascular disease prophylaxis in primary prevention has not been as clear. In particular, some recent meta-analysis has raised concern that aspirin use may not be beneficial for primary prevention of cardiovascular diseases. In support of a role of aspirin use in general population, a systematic review recently concluded:
In other words, aspirin use is beneficial for both cardiovascular and cancer standpoint, its takes three years to see a beneficial effect, and effect lasts long after aspirin use has been discontinued.