Hot Topics Session

A collection of Tweetorials from the Hot Topics session…

PUMA project:


COVIP Study:


Clinical characteristics and outcomes in 4244 COVID-19 patients:…


CandiSEP study


High vs Low PEEP in Non-ARDS ICU patients…

Management of Acute ischaemic stroke:


Triage in COVID-19 Patients…

Mechanical ventilation in acute brain injury…


The VICTAS trial…


And the CHLORAL trial…




Link to a tweet of summary threads by Velia Marta Antonini (@FOAMecmo):



I hope you find these links and summaries useful.



Jerry Nolan talks cardiac arrest resuscitation at #LIVES2020

Watch the interview in the VOD section but here is a tweetorial summarising the discussion. I’ve included the key references…

Basics in scientific writing – a blog and tweetorial

Basics in scientific writing- Do’s and Don’ts in publishing your research

Jos M. Ltour, Ruth Endacott

Keep Calm and Submit your Paper!

Think about the journal choice (comply with guidelines)
Remove distractions

Use equator guidelines early (while writing protocol)

KISS – keep it simple and short


  • keep it short
  • avoid question marks
  • avoid rare abrbeviations



  • maximize discoverability
  • employ keywords (repeat 2-3 times in natural manner)



  • short,leading to aim of the study (1-1.5 page is enough)
  • attention-grabbing (get the reader into it)




    • use subheadings
    • settings, study population, intervention, outcomes, analysis
    • do not forget the ethics




  • this section is called Findings for qualitative studies
  • be clear and objective, do not interpret the results
  • do not overlap textwhich can be read in tables and figures
  • common mistake title of table (always on top of table) and title of figure (always on top of figure)
  • use electronic supplement materials(appendices) if you have a lot of data (long tables>2 pages)
  • give all the data -it contributes to the transparency of the study



  • you can repeat the aim of your study
  • focus on clinical implications (3 pages enough)
  • be clear, do not blur
  • pick out the most important, striking, overarching results
  • min 3 limitations – a study without limitations is not study
  • try to choose the journal which allows 4.000 -5.000 word count
  • use key messages (what is known, what this paper adds)


  • do not submit too quickly
  • read out loud
  • present to co-authors
  • ask a colleague to read it
  • co-authors -only with significant contribution to your work (use acknowledgement section)
  • check references (40% mistake rate) – use a reference manager software
  • plagiarism is an offence (self-plagiarism is unethical)
  • have ready short cover letter (not a long one selling your manuscript)
  • have ready potential reviewer names


Final words


  • Have a personal reason to write
  • Make writing meaningful
  • Look for inspiration, motivation and support from your colleagues
  • Reward yourself for the sacrifices made
  • Look after your mental health



Personalising Care: Machine learning from pressure waves (ICP)

Personalising Care: Machine learning from pressure waves (ICP)

Soojin Park, Associate Prof. Neurology

Division of Neurocritical Care, Columbia University, NYC, USA

Watch on demand:


  • Acute hydrocephalus affects ~37k pts/yr in USA
  • Rx = EVD, but 1/5th develop infection ventriculitis
  • Risk of ventriculitis ↑ with duration and frequency of CSF sampling (by which diagnosis made..)


Can we find a way of using physiological information contained in ICP waveform to develop a method for detecting ventriculitis, without having to sample CSF?

Park reminds us of the normal ICP waveform (exam revision déjà vu..)

And how it’s morphology changes with ↑ICP

This alteration in waveform morphology with ↑ICP has a biologically plausible mechanism in ventriculitis

 ⭐ Goal 1

Examine changes in ICP waveform morphologies prior to ventriculitis

  • Dataset = only patients WITH ventriculitis
  • Collaboration with group experienced in ICP waveform big data, however their pre-processing identified abnormal waveforms as artefactual!
  • ⚠Problem = vague definition of ventriculitis
  • Used ‘gold standard’ of limiting it to those with culture-positive CSF
  • n = 19 pts
  • ❗ Park mentions that CSF is cultured 3/w at this institution, perhaps not usual practice – CT: worth considering this in the context of their motivation

  • ⚠ EVDs left open to drainage most of the time, typical practice across other institutions, thus waveform only intermittently present when EVD clamped by nurse
  • ❓ Challenge = automating identification of waveforms (CT: I note solution was not to get desperate medical student to manually sift data in exchange for ‘research experience on their CV..)


  • Dominant pulses extracted using Morphological Clustering Analysis of ICP Pulse
  • Before / During / After ventriculitis (i.e culture-positive CSF)
  • Morphologically similar groups obtained by hierarchical k-means clustering
  • Dynamic Time Warping used as a ‘distance’ metric to correct for speed (HR), see below
  • Meta-clusters determined by clinicians, see figure B below.
  • Bi/triphasic (green)
  • Monophasic/tombstone (yellow)
  • Artefactual (red)
  • = supervised learning


  • Prior to ventriculitis majority of pulses had physiological tri/biphasic appearance
  • During ventriculitis this dropped from 61.8 > 22.6%, a statistically significant change, which persisted
  • ✨ Most importantly this change occurred a full day before the ventriculitis was clinically detectable

 ⭐ Goal 2

Leverage time-varying dominant pulses of ICP from hourly EVD clamping data into a detection model of ventriculitis

  • Collaboration:
  • Columbia Vangelos College of Physicians & Surgeons
  • R Adams Cowley Shock Trauma Center, University of Maryland
  • Aims:
  • Improve performance and generalisability of model to other institutions data
  • Work in submission therefore not shown
  • Collaborators sought, see email below:

Concluding Remarks

  • Example presented for ICP but process generalisable to other waveforms, of which there are many in ICU!

‍‍My thoughts:

  • I’ve often been disappointed at how little waveform data is actually stored from ICU monitors
  • Perhaps I shouldn’t be given the general lack of high-quality ICU data (see data sharing session) and huge storage requirements
  • Most of the ‘high resolution/granular/insert other buzzword here’ EHRs I’ve come across sample at a frequency ~ 1 hz (c.f. 125-250 hZ in this study)
  • Starting point for those interested in waveform data in ICU = MIMIC-III Waveform Database
  • Be warned this is truly big data

Blog by Chris Tomlinson:

Anaesthetist & Critical Care Registrar

‍ PhD Candidate at UCL UKRI Centre for AI-enabled Healthcare | LinkedIn | @tomlincr