DNI/DNR and what it means

As you probably know, doctors love DNI/DNRs and everything that clearly prescribes what to do. So the first thing we try to establish with an elderly seriously sick patient is whether intensive care or resuscitation is really an option.

Yet a DNI/DNR label might signify many things to the team:

  1. In an emergency, don’t tube, don’t CPR
  2. Avoid advanced and invasive treatments, such as operations for intracerebral hematoma
  3. If in doubt, don’t try to cure the patient, prefer comfort measures in a palliative setting.
  4. The prognosis is judged to be bad, either mortality-wise or with respect to quality of life.

Although meaning 1 is usually what is intended and agreed upon, meanings 2 and 4 are often used as reasons, and meaning 2 is sometimes implied, even if – as in our house – the difference is made explicit even by SOPs.

Concerning prognosis, we know that

  • Neurological emergencies are very hard to prognosticate in the first 24, even 72 hours
  • Even epidemiological data is scarce – the best evidence exists for mortality of intracerebral hemorrhage (e.g. ICH score) and proximal occluded cerebral arteries; quality of life is a different beast altogether.
  • Stating a prognosis early leads to self-fulfilling prophecies

Our discussion of the subject does not lead to clear procedural standards, but it sensitizes…

 

References

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p-values

Even in the most advanced discussions of modern times’ patient management, I have not once met a colleague who understood p-values. Not that I do.

In last Friday’s session we did the classical experiment (described e.g. in this article) of offering various choices of an explanation of how to interpret p-values to the audience, with actually none of them correct. Which is funny. Then we discussed the core definition. Which is simple. And worked through the examples on the Wikipedia entry (by the way: Wikipedia provides excellent articles on statistics!), such as computing p for simple random variables (such as the number of heads in n tosses of a coin). Which is hard.

Just for completeness: the p-value is the probability of obtaining the data’s test statistics or more extreme values, assuming the null hypothesis to be true.

Unfortunately we did not get far enough to discuss the Bayesian alternatives to p values and their decision theoretic applications.

Here are my take home messages:

  • p-values are a statistical property of the data.
  • You require a statistical model and have to assume the null hypothesis to be true to be able to compute them.
  • They say nothing about the truth of the null hypothesis or (even less so!) any alternative hypothesis.
  • They may increase with effect size yet small effect sizes may have the tiniest ps and vice versa.
  • They cannot be compared among studies.