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Your first Shiny app

This post was originally published here

What is Shiny?

Shiny is an R package (install.packages("shiny")) for making your outputs interactive. Furthermore, Shiny creates web apps meaning your work can be shared online with people who don’t use R. In other words: with Shiny, R people can make websites without ever learning Javascript etc.

I am completely obsessed with Shiny and these days I end up presenting most of my work in a Shiny app.

If it’s not worth putting in a Shiny app it’s not worth doing.

Your first Shiny app

Getting started with Shiny is actually a lot easier than a lot of people make it out to be. So I created a very short (9 slides) presentation outlining my 5-step programme for your first Shiny app.

This is the app: https://riinu.shinyapps.io/shiny_testing/

This is the presentation: http://rpubs.com/riinu/shiny

And here are the steps (also included in the presentation):

STEP 0: install.packages("shiny"). Use RStudio.

STEP 1: Create a script called app.R using this skeleton:

https://gist.github.com/riinuots/c6ec0691633df2929adc7de90bdbc792

STEP 2: Copy your plot code into the renderPlot function.

STEP 3: Add a sliderInput to your User Interface (ui). A slider is just one of the many Shiny widgets you could be using: https://shiny.rstudio.com/gallery/widget-gallery.html

STEP 4: Tell your Server you wish the dplyr::filter() to use the value from the slider. All inputs from the User Interface (ui) are stored in input$variable_name: replace the 2007 with input$year.

STEP 5 (optional): Add animate = TRUE.

Press Control+Shift+Enter or the “Run App” button. You now have a Shiny app running on your computer. To deploy it to the internet, e.g. like I’ve done in the link above, see this.

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Radical but conservative liver surgery

This post was originally published here

Cutting-edge liver surgery is often associated with modern technology such as the robot. In this edition of HPB, Torzilli and colleagues provide a fascinating account of 12 years of “radical but conservative” open liver surgery.

This is extreme parenchymal-sparing hepatectomy (PSH) in 169 patients with colorectal liver metastases. In all cases, tumour was touching or infiltrating portal pedicles or hepatic veins, a situation where most surgeons would advocate a major hepatectomy where possible. The PSH by its nature results in a 0 mm resection margin when the vessel is preserved, which was the aim in many of these procedures. Although this is off-putting, the cut-edge recurrence rate was no higher than average.

PSH in the form of “easy atypicals” is performed by all HPB surgeons. There are two main differences here. First is the aim to detach tumours from intrahepatic vascular structures. For instance, hepatic veins in contact with tumour were preserved and only resected if infiltrated. Even then, they were tangentially incised if possible and reconstructed with a bovine pericardial patch. Second is the careful attention paid to identifying and using communicating hepatic veins. This is well described but used extensively here to allow complete resection of segments while avoiding congestion in the draining region.

Short-term mortality and morbidity rates are comparable with other published series. A median survival of 36 months and 5-year overall survival of around 30% is reasonable given some of these patients may not be offered surgery in certain centres. The authors describe the parenchymal sparing approach “failing” in 14 (10%) patients: 7 (5%) has recurrence at the cut edge and 8 (6%) within segments which would have been removed using a standard approach. 44% of the 55 patients with liver-only recurrence underwent re-resection.

This is not small surgery. The average operating time is 8.5 h with the longest taking 18.5 h. The 66% thoracotomy rate is also notable in an era of minimally invasive surgery and certainly differs from my own practice. This study is challenging and I look forward to the debates that should arise from it.

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Handling your .bib file (LaTex bibliography)

This post was originally published here

To create a .bib file that only includes the citations you used in the manuscript:

bibexport -o extracted_file.bib manuscript.aux

There are a few issues with this though. The command bibexport comes with the installation of TexLive, but my Windows computer (bless) does not cooperate (“bibexport is not recognised as an internal or external command…”) . So I can only use it on my Mac (luv ya).

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Effect of day of the week on mortality after emergency general surgery

This post was originally published here

Out latest paper published in the BJS describes short- and long-term outcomes after emergency surgery in Scotland. We looked for a weekend effect and didn’t find one.

  • In around 50,000 emergency general surgery patients, we didn’t find an association between day of surgery or day of admission and death rates;
  • In around 100,000 emergency surgery patients including orthopaedic and gynaecology procedures, we didn’t find an association between day of surgery or day of admission and death rates;
  • In around 500,000 emergency and planned surgery patients, we didn’t find an association between day of surgery or day of admission and death rates.

We also found that emergency surgery performed at weekends, or in those admitted at weekends, was performed a little quicker compared with weekdays.

More details can be found here:

Effect of day of the week on short- and long-term mortality after emergency general surgery
http://onlinelibrary.wiley.com/doi/10.1002/bjs.10507/full

bjs_dow-100

bjs_dow2-100

Press coverage

Broadcast: BBC GOOD MORNING SCOTLAND, HEART FM,

Print: DAILY TELEGRAPH, DAILY MIRROR, METRO, HERALD, HERALD (Leader), SCOTSMAN, THE NATIONAL, YORKSHIRE POST, GLASGOW EVENING TIMES

Online: BBC NEWS ONLINE, DAILY MAIL, EXPRESS.CO.UK, MIRROR.CO.UK, HERALD SCOTLAND, THE COURIER, WEBMD.BOOTS.COM, NEWS-MEDICAL.NET, NEW KERALA (India), BUSINESS STANDARD, YAHOO NEWS, ABERDEEN EVENING EXPRESS, BT.COM, MEDICAL XPRESS.

Weekend surgery press clipsDownload
Weekend surgery press clips 2Download

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Get data from ggplot()

This post was originally published here

ggplot includes built in and seamless functionality that summarises your data before plotting it. As shown in the example below, ggplot_build() can be used to access the summarised dataset.

summarised_barplot

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Publishing mortality rates for individual surgeons

This post was originally published here

This is our new analysis of an old topic.In Scotland, individual surgeon outcomes were published as far back as 2006. It wasn’t pursued in Scotland, but has been mandated for surgeons in England since 2013.

This new analysis took the current mortality data and sought to answer a simple question: how useful is this information in detecting differences in outcome at the individual surgeon level?

Well the answer, in short, is not very useful.

We looked at mortality after planned bowel and gullet cancer surgery, hip replacement, and thyroid, obesity and aneurysm surgery. Death rates are relatively low after planned surgery which is testament to hard working NHS teams up and down the country. This together with the fact that individual surgeons perform a relatively small proportion of all these procedures means that death rates are not a good way to detect under performance.

At the mortality rates reported for thyroid (0.08%) and obesity (0.07%) surgery, it is unlikely a surgeon would perform a sufficient number of procedures in his/her entire career to stand a good chance of detecting a mortality rate 5 times the national average.

Surgeon death rates are problematic in more fundamental ways. It is the 21st century and much of surgical care is delivered by teams of surgeons, other doctors, nurses, physiotherapists, pharmacists, dieticians etc. In liver transplantation it is common for one surgeon to choose the donor/recipient pair, for a second surgeon to do the transplant, and for a third surgeon to look after the patient after the operation. Does it make sense to look at the results of individuals? Why not of the team?

It is also important to ensure that analyses adequately account for the increased risk faced by some patients undergoing surgery. If my granny has had a heart attack and has a bad chest, I don’t want her to be deprived of much needed surgery because a surgeon is worried that her high risk might impact on the public perception of their competence. As Harry Burns the former Chief Medical Officer of Scotland said, those with the highest mortality rates may be the heroes of the health service, taking on patients with difficult disease that no one else will face.

We are only now beginning to understand the results of surgery using measures that are more meaningful to patients. These sometimes get called patient-centred outcome measures. Take a planned hip replacement, the aim of the operation is to remove pain and increase mobility. If after 3 months a patient still has significant pain and can’t get out for the groceries, the operation has not been a success. Thankfully death after planned hip replacement is relatively rare and in any case, might have little to do with the quality of the surgery.

Transparency in the results of surgery is paramount and publishing death rates may be a step towards this, even if they may in fact be falsely reassuring. We must use these data as part of a much wider initiative to capture the success and failures of surgery. Only by doing this will we improve the results of surgery and ensure every patient receives the highest quality of care.

Read the full article for free here.

Press coverage

Radio: LBC, Radio Forth

Print:

  • New Scientist
  • Scotsman
  • Daily Mail
  • Express
  • the I

Online:

ONMEDICA, SHROPSHIRESTAR.COM, THE BOLTON NEWS, EXPRESSANDSTAR.COM, BELFAST TELEGRAPH, AOL UK, MEDICAL XPRESS, BT.COM, EXPRESS.CO.UK

Individual surgery mortality press clippingsDownload

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R: ISO codes and country names

This post was originally published here

install.packages(‘maps’)