At a basic level, the SMR is a measure of how good your unit is at preventing acute illnesses from killing patients. This seems like an important metric, and indeed much is made of the SMR in the critical care community, at least locally. Units with well-developed media machinery use favourable SMR data to advertise their supremacy as centres of excellence; units which perform poorly use damning SMR data as a kick in the arse required to stimulate policy change.
The college loves SMR. Specifically, they like to discuss which it might be abnormally high, and what are its limitations as a measure of the quality of care.
More information can be derived from the following resources:
Statistical epidemiology literature appears at first glance to be written in machine code. Some attempt has been made to decompile these materials into a human-readable format.
The SMR, in a nushell:
The above definition is probably enough to satisfy the minimum requirements for the college.
In short,
SMR = observed number of deaths / expected number of deaths
In order to calculate this, one requires three main variables:
How does one know that the SMR is statistically significant? Confidence intervals can be calculated if one is that interested. NM-IBIS has a handy explanation of how to calculate the 95%CI using standard error. This sort of thing is probably sufficiently esoteric to not appear in the exam.
The SMRs of many ICUs can then be plotted on a funnel-like graph to compare their performance.
This graph was from Question 24 from the second paper of 2015. ICU "A" is clearly having some sort of crisis. The dashed lines represent the boundaries of the 95% CI for a given workload, i.e. the acceptable range of expected mortality. The width of the funnel represent the precision of the SMR estimate. Lines funnel out into wild hyperbolae at very small case numbers because of the imprecision of mortality measurements in such situations. If the ICU only admits 10 patients a year and all of them happen to die in spite of having a low-ish APACHE score, this would give an enormously high SMR - but it my represent random noise, and the CI would be impossibly wide. As the case load becomes larger, the precision of the SMR improves. The anatomy of the institutional performance funnel plot (and the related "caterpillar plot") is better discussed by Spiegelhalter (2005).
Perhaps. A variety of factors influence the SMR, of which several are completely unrelated to the ICU's performance (leaving aside the problem of defining quality of care through mortality statistics).
Some devilry is causing your SMR to rise in spite of the fact that you are still clearly awesome. This can be for a number of reasons. To take it back to basics, either the mortality measurement is wrong, or the ICU is failing because of some internal or external factors
Unfair mortality measurements should not be a problem, as there is no serious situation in which mortality might get over-reported (i.e. some patients walking around after ICU discharge who are formally dead according to the official record). An under-reporting of mortality is much more likely, and might occur in resource-poor environments where public record-keeping is rudimentary. Such dodgy records might still list the person as alive, whereas they died shortly after ICU discharge and nobody bothered to file the paperwork.
Unfair comparison group mortality / APACHE mortality estimate is usually the culprit.
Issues external to the ICU
Issues internal to the ICU (i.e. genuine under-performance)
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