TTM @ #EuAsia19

Optimal depth, timing and duration based on recent clinical trials (K Sunde)

Cardiac arrest is a complex disease

  • several different causes (many untreatable, irreversible, extreme challenging)
  • large heterogeneity
  • challenges the system due to the limited/crucial time-intervals (hypoxia/extreme ischemia)
  • large differences in quality of care within and inbetween systems (both during ALS and in post resuscitation care)
  • very high mortality

Depth and Timing

ILCOR Statement 2003 –

Unconscious adult patients with spontaneous circulation after out-of hospital cardiac arrest should be cooled to 32-34°C for 12-24 hrs when the initial rhythm was VF.

For any other rhythm, or cardiac arrest inhospital, such cooling may also be beneficial.

Outcome, timing and adverse events in therapeutic hypothermia after out-of-hospital cardiac arrest.

  • timing, speed and duration of hypothermia had no impact on outcome!

Confounding aspects regarding early/fast cooling

  • the lack of protection against a drop in core temperature is due to a larger and deeper brain injury! (link)
  • If you are really “dead” you are colder and it is very easy to cool you fast! (link)

Intra-Arrest Transnasal Evaporative Cooling: A Randomized, Prehospital, Multicenter Study (PRINCE: Pre-ROSC IntraNasal Cooling Effectiveness) link

Duration of TTM

Targeted Temperature Management for 48 vs 24 Hours and Neurologic Outcome After Out-of-Hospital Cardiac Arrest

Prolonged targeted temperature management in patients suffering from out-of-hospital cardiac arrest

Conclusion

  • Cardiac arrest is complex, with large heterogeneity and very high mortality
  • Large differences in quality of care within and inbetween systems
  • Concerning pathophysiology and TTM: depth, speed and duration impacts on the reperfusion injury/brain injury
  • We are concluding based on pragmatic trials not optimizing the intervention tested or considering the ongoing pathophysiology!
  • Outcome assessment: cognitive function/QoL years after the arrest!

Haemodynamic Management During Targeted Temperature Management (Huang CH)

Multiple reasons for haemodynamic instability post-cardiac arrest

Haemodynamic Response Correlated to Outcome – Reversible myocardial dysfunction in survivors of out-of-hospital cardiac arrest.

Cardiovascular Response & Haemodynamic Changes In Hypothermia Treatment

  • Changes in CV β-adrenoceptor (reduced response)
  • Bradycardia
  • Increase in stroke volume
  • Reduced intravascular volume during hypothermia is by 10– 35%

Lower heart rate is associated with good one-year outcome in postresuscitation patients (link)

Survivors Have Higher Mean Arterial Pressure (link)

Lowest value of DAP over the first 6 h after ICU admission for predicting unfavourable neurological outcome at 3 months (link)

Postresuscitation hemodynamics during therapeutic hypothermia after out-of-hospital cardiac arrest with ventricular fibrillation: A retrospective study

Taiwanese Protocol

ESICM Datathon: Day 3

Hemodynamics in septic shock Speaker: G. Baselli
Amazing how a ‘routine’ haemodynamic system can be viewed so differently from the point of view of an engineer/data scientist/medic

In conclusion

  1. Autonomic nervous system function and CV regulation is dynamic and hence the data needs to be dynamic and not a constant
  2. Need to pick out the meaningful physiological parameters to feed into the machine learning algorithm
  3. Important to have large open-access databases
  4. These databases need to integrate multi-scale information both in dimension and in time

Neurointensive care Speaker: A. Ercole

The concept of cerebral perfusion pressure = MAP – ICP is an example of a simple mathematical concept

We measure what we can, NOT what we should

Perhaps the autoregulation status of the TBI pt is more important – cerebrovascular pressure reactivity (PRx) Link

Data studies needs the same robustness as any other drug studies

Data Access quality and Curation for Observational Research Designs

Reinforcement learning in sepsis Speaker: M. Komorowski

The Artificial Intelligence Clinicians learns optimal treatment strategies for sepsis in intensive care

Can a computer help the clinician do the right thing?

Looked at 2 groups of treatment in septic patients – vasopressor use and fluid administration.

In general pts received more IV fluids and less vasopressors than recommended by the AI policy.

The machine considered much more variables compared to the human clinician

Conclusion

  • Reinforcement learning could provide clinically interpretable treatment suggestions
  • The models could improve outcomes in sepsis
  • Flexible framework transferable to other clinical questions

Gradient-boosted decision trees Speaker: C. Cosgriff

“Its not about doctors versus computers, its doctors with computers versus doctors without computers – @cosgriffc”

If you don’t know what gradient boost is (I don’t) but would like to find out more OR want to learn some basic coding in R/Python/SQL, have a look at kaggle.com

The power of XGBoost for the ICU is to shift towards ‘human intelligence’, supporting clinicians and intensivists (NOT REPLACING THEM)

References:

  • Introduction to Statistical Learning
  • Elements of statistical learning
  • Deep Learning with Python
  • xgBoost: A scalable tree boosting system

Clinical research and AI Speaker: R. Furlan

This is highlighted by a local project looking at the ability to diagnose syncope using natural language algorithms of EHR compared to human clinicians

Moving models to the bedside Speaker: P. Thoral

Insight into the challenges on bringing machine learning and artificial intelligence models to the bed side.

Highlights the various legislative requirement with regards to introducing system (medical software is still considered a medical device)

Need to engage the stakeholders

The future: physicians, engineers, machines Speaker: R. Barbieri

Various scenarios of the future predicted by Prof Barbieri

Armageddon

  • Technology used in warfare
  • Technology cannot overcome environmental disasters

Best Scenario

  • Technology cures, predicts and prevents diseases
  • No need for ICUs

Idiocracy

  • Technology blunts human knowledge
  • Humans lose ability to think
  • Technology takes over but are unable to make critical decisions

Oligarchy (aka BladeRunner)

  • Technology and knowledge controlled by a few
  • Progress without common wealth
  • Humans and machines start merging

Terminator

  • Machines takes over
  • Humans and human nature are irrelevant

Optimistic