Farewell, Kate Granger

Tessa, Ben, Henry and I want to formally express our deep sadness at the recent passing of Dr Kate Granger.


There may be many of you who have never heard of her name but will be aware of the profound impact she has had on the culture of medicine and will have in years to come.  As a young doctor she was diagnosed with a rare sarcoma in 2011 and given a life expectancy of just 14 month. She managed to live another five years.


She was shocked at how the doctor who had delivered the terminal diagnosis had failed to introduce himself, could barely make eye contact and could hardly wait to leave the consulting room. And with this, the #hellomynameis campaign as born.  It reminded us that every patient, every person we interact with in a health care setting is a person first and a patient second. Kate remided us of the power of human connection, most easily forged by a caring smile and introducing ourselves with the phrase, “Hello, my name is….”.  In the days before her death Kate achieved one of 250,000 pounds for cancer charities.

In her too short life Dr Kate Granger has truly made a difference, something that we should all aspire to do.


Kate passed away, surrounded by loved ones, on July 23rd 2016, aged 34.

Articles of the month (July 2016)

Another month and another edition of the articles of the month. However, this time I have some very exciting news. I have teamed up with Casey Parker (the brilliant, smooth-talking Australian physician, not the adult film star) to produce an audio version of these summaries. You will be able to find this podcast on http://broomedocs.com/, … Continue reading "Articles of the month (July 2016)"

What you put in is what you get out… Systematic Review and Meta-analysis

Author: Yun Mak
Peer reviewers: Aidan Burrell, Chris Nickson

Nuts and Bolts of EBM Part 4

This study says this, that study says that. Wouldn’t it be great if we could somehow combine trials together to try to get closer to the truth?

We can.

However, as clinicians and burgeoning connoisseurs of ‘the evidence’, there are a few things we need to know to avoid being led astray.

Q1. What is a systematic review and what is a meta-analysis… How do they differ?

A systematic review is review of a clearly formulated question using systematic and explicit methods to identify, select and critically appraise all relevant research. Data is collected and analysed from all the studies that are included in the review.

Meta-analysis, on the other hand, is the process of using statistical methods to combine the samples and analyse the results of the included studies. The overall sample size is increased, thereby improving the statistical power of the analysis as well as the precision of the estimates of treatment effects.

Q2. What are the steps involved in performing a systematic review and meta-analysis?

In brief, the 7 key steps involved are:

  1. Formulate a review question
  2. Perform a comprehensive search of the literature for published AND unpublished evidence
  3. Select studies for inclusion
  4. Critically appraise studies
  5. Synthesize the findings from individual studies
  6. Combine study results (meta-analysis)
  7. Interpret results and provide recommendations

Q3. What are the benefits and limitations os systematic review and meta-analysis?

A systematic review of well-performed randomised controlled trials is regarded as the highest level within the evidence hierarchy (see Levels and Grades of Evidence in the LITFL CCC).


  • Increases statistical power (chance of detecting a real effect as statistically significant if it exists)
  • Increases precision (estimation of intervention effect size)
  • Summarises contemporary literature on a topic
  • May help resolve conflicting studies
  • Starting point for clinical guidelines and policy
  • Identifies topics for further research
  • Avoids Simpson’s paradox (if populations are separated in parallel into a set of descriptive categories, the population with the highest overall incidence may paradoxically have a lower incidence within each such category… this is explained much more simply on Youtube)


  • Limited by the quality of studies that comprise the review… aka “garbage in, garbage out”, biases present in individual studies may be compounded by meta-analysis and may appear to have more credibility as a result
  • Difficult when there is only a small number or trials, or small patient numbers
  • Difficult when one trial provides the majority of sample size
  • Poor quality analysis can have misleading findings
  • Susceptible to reporting bias / publication bias of individual studies
  • Cannot combine ‘apples with oranges’ – cannot combine studies that are too clinically diverse in terms of intervention, comparison or outcomes.

Q4. Describe a useful approach to critically appraise a systematic review and meta-analysis?

The University of Oxford Centre for Evidence-Based Medicine has a suggested method for critical appraisal of systematic review and meta-analysis. The Worksheet can be found here: http://www.cebm.net/wp-content/uploads/2014/06/SYSTEMATIC-REVIEW.docx

In summary, the review should be appraised using the PICO format and fulfil all 5 criteria of the FAITH tool.

  • What question (PICO) did the review address?
  • FAITH tool:
    • FIND all relevant studies
    • APPRAISE the use of appropriate inclusion criteria
    • INCLUDED all valid studies
    • TOTAL pooling of similar results
    • HETEROGENEITY and inconsistency of PICOs and results

Q5. What are funnel plots and how are they interpreted?

A funnel plot is a scatter plot of the effect estimates from individual studies against some measure of each study’s size or precision. Traditionally the funnel plot was used to assess for the presence of publication bias or selective outcome reporting (relevant to the F part of the FAITH tool).

The standard error of the effect estimate is often chosen as a measure of the accuracy of the predictions of the study. This is plotted on the vertical axis with a reversed scale that places the larger, most powerful studies towards the top.

The effect estimates from smaller studies should scatter more widely at the bottom, with the spread narrowing among larger studies.

Funnel plot


  • The central dashed line is the fixed effect summary estimate.
  • The outer dashed lines indicate the triangular region within which 95% of studies are expected to lie in the absence of both bias and heterogeneity (fixed effect summary log odds ratio±1.96×standard error of summary log odds ratio).
  • The solid vertical line corresponds to no intervention effect.
  • A triangle centred on a fixed effect summary estimate and extending 1.96 standard errors either side will include about 95% of studies if no bias is present and the fixed effect assumption (that the true treatment effect is the same in each study) is valid.


  • The funnel plot may recommend caution in interpretation due to the presence of asymmetry or failure of the confidence intervals to contain 95% of the studies (both may suggest potential bias or heterogeneity).
  • Asymmetry traditionally was thought due to bias towards favourable treatment effects due to the lack of published results of no difference. However there are other possible sources of asymmetry:
    • Reporting biases
      • publication bias (delayed publication or location bias, e.g. foreign language papers)
      • selective outcome reporting
      • selective analysis reporting
    • Spuriously inflated effects in smaller studies
      • poor methodological design
      • fraud
      • inadequate analysis
    • True heterogeneity
    • Artefact (in some contexts, sampling variation can lead to an association between the intervention effect and its standard error)
    • Chance

NB. The article by Sterne (BMJ 2011) summarizes funnel plots and is where the CICM exam sought their answer for SAQ 2014.2 Q13.

Q6. What are forest plots and how are they interpreted?

A forest plot is a diagram that shows information from the individual studies that went into the meta-analysis and an estimate of the overall results (relevant to the T and H parts of the FAITH tool). Interpreting a forest plot requires knowledge of the two stages of performing meta-analysis.

The first stage involves the calculation of a measure of treatment effect with its 95% confidence intervals for each individual study. The second stage involves calculation of an overall treatment effect as a weighted average of the individual studies.

Greater weights are given to the results from studies that provide more information, because they are likely closer to the “true effect” we are trying to estimate. The weights are often the inverse of the variance (the square of the standard error) of the treatment effect, which relates closely to sample size.

forest plot diagram


  • The first authors and year of the primary studies included are on the left.
  • The solid vertical line corresponds to no intervention effect (OR = 1.0).
  • The black squares represent the odds ratios of the individual studies, and the horizontal lines their 95% confidence intervals. The area of the black squares reflects the weight each trial contributes in the meta-analysis. If the 95% confidence interval crosses the no intervention effect, then the study results were not statistically significant (p>0.05).
  • The overall treatment effect (calculated as a weighted average of the individual ORs) from the meta-analysis is represented as a diamond. The centre of the diamond represents the combined intervention effect, and the horizontal tips represent the 95% confidence intervals.


  • The overall results of the meta-analysis – the centre of the diamond represents the overall treatment effect. If the diamond reaches the no intervention effect, then the overall treatment effect is not statistically significant.
  • Visual assessment of heterogeneity – the vertical dashed line runs through the centre of the diamond. If this dashed line crosses the 95% confidence intervals of each individual study, then there is no significant heterogeneity.
  • Random-effects vs. fixed effects meta-analysis – the forest plot will state if the meta-analysis was performed using fixed- or random-effects. In a fixed effects model, we assume the true effect size for all studies is identical (so a large study will heavily influence the results) whereas, in a random effects model, we assess the mean of a distribution of effects, so each study is given more equal representation in the study estimate. Due to between-study heterogeneity, the true effect may differ from study to study and so a random-effects model is most often the appropriate method to use (ie. if fixed is used, heterogeneity will make it difficult to interpret).

NB. Forest plot interpretation was assessed by CICM in SAQ 2015.1 Q8.

Q7. How is heterogeneity represented in a meta-analysis?

Heterogeneity (or statistical heterogeneity) refers to variability in the intervention effects being evaluated in different studies included in a systematic review as a consequence of clinical or methodological diversity, or both, among the studies. This becomes manifest as the observed intervention effects being more different from each other than one would expect due to random error (chance) alone. 

  • clinical diversity – variability in the participants, interventions and outcomes studied may be described as
  • methodological diversity -variability in study design and risk of bias

Heterogeneity (Cochran Q test, χ2)

  • A small P value means the null hypothesis (of study homogeneity) should be rejected – and the studies should not be combined. Unfortunately, the power of this test is relatively low when there are few studies (which is common).
  • Traditionally a P value of 0.05 is used, however, this may be lower or higher (0.10) at the authors’ discretion.

Inconsistency (I2)

  • Some believe that because study variability is unavoidable, inconsistency is a more useful marker to assess the ability to combine these into meta-analysis. Inconsistency quantifies the percentage of variability between studies that is due to heterogeneity rather than due to chance.
  • There is no value considered too high – the original description suggested I2 values of 25%, 50% and 75% indicated low, moderate and high levels of heterogeneity.

References and links

  • Centre for Evidence-Based Medicine, University of Oxford – Systematic Reviews Critical Appraisal Worksheet. [Cited 27 July 2016] Available from URL: http://www.cebm.net/critical-appraisal/
  • Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 (March 2011) [Cited 27 July 2016] Available from URL: http://handbook.cochrane.org/
  • Dartmouth College Library Research Guides Systematic Reviews: Planning, Writing, and Supporting. [Cited 27 July 2016] Available from URL: http://researchguides.dartmouth.edu/sys-reviews
  • Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ (Clinical research ed.). 315(7109):629-34. 1997. [pubmed]
  • Nickson CP. Levels and Grades of Evidence. Lifeinthefastlane.com. [Cited 27 July 2016] Available from URL: http://lifeinthefastlane.com/ccc/levels-and-grades-of-evidence/
  • Reade MC, Delaney A, Bailey MJ, Angus DC. Bench-to-bedside review: avoiding pitfalls in critical care meta-analysis–funnel plots, risk estimates, types of heterogeneity, baseline risk and the ecologic fallacy. Critical care (London, England). 12(4):220. 2008. [pubmed]
  • Sterne JA, Sutton AJ, Ioannidis JP. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ (Clinical research ed.). 343:d4002. 2011. [pubmed]

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CICM Second Part Exam Practice SAQs 27072016

As prepared by Chris Nickson, here are the practice questions from today’s written exam practice session at The Alfred ICU, with recommended reading from Lifeinthefastlane.com’s Critical Care Compendium and other FOAM sources:


You have just completed reviewing a stable patient when a woman runs into the emergency department toward you. She is screaming for help and holding an 8 month-old baby who is cold and pulseless.

Outline your approach to the management of this situation.

Learn more here:

Paediatric Life Support


A MET call is activated when a 45-year-old man unexpectedly develops massive haematemesis on the ward. He was awaiting discharge after recovering from a community acquired pneumonia. He has a history of liver cirrhosis due to Hepatitis C virus from previous IV drug use.

He has bilateral crepitations and has SpO2 89% on 15L/min via a non-rebreather mask. He is drowsy, agitated and has BP 90/55 mmHg. He continues to profusely vomit large amounts of fresh blood.

Describe your approach to the initial management of this case.

Learn more here:

GI haemorrhage

Intubation in Upper Gastrointestinal Haemorrhage


Compare and contrast the different types of renal tubular acidosis (Type 1, Type 2 and Type 4).

Learn more here:

Renal Tubular Acidosis and Uraemic Acidosis


You can access all the previous practice questions since 2014 here:
See this link on INTENSIVE for exam resources:

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JellyBean 043 Paramedic Michael Stanley

Alternative Career. Alternative Energy. Alternative Jellybean Podcaster. Matt MacPartlin (@RollCageMedic) interviews a paramedic who has a odd view of the world especially when he is at work.

Alternative careers and alternative energy.

So you’ve completed your training. The world is ready for you. It has a plan for you. It has a lifelong career path lined up for you.

Do you want it? Sign up now, get those mortgage payments started and start saving for retirement? Or do you want to go and do something a bit different. Something alternative?

Michael Stanley ( @CRM_saves_lives ); a paramedic that has left the Ambulance, left the road that the ambulance was on and now works somewhere where he can see no land but he can see a lot of Windmills.

Thanks to Matt MacPartlin (a.k.a. @RollCageMedic ) for this one. Matt guest recorded all the Jellybeans from SMACC in Chicago last year. He makes more sense than I do so it seemed foolish not to hold on to him and try to somehow lend legitimacy to the Jellybean Podcasts. So you will be hearing a slightly different Irish accent on some of the future Jellybeans. And we will finally jump on this whole iTunes bandwagon. (I have come to the conclusion that Apple is not just a flash in the pan and it may be around for a a few months yet. Maybe I am wrong.)

JellyBean Large

Last update: Jul 27, 2016 @ 10:09 am

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