In constructing diagnostic algorithms and/or calculating probabilities of myocardial infarction (MI) for patients being investigated for possible ACS in the emergency department I have noticed that there is a wide range of prevalences of MI (~2 to 20%). My discussions with clinical specialists suggest that there are some differences because of cohort selection, but most difference is because of clinician behaviour (eg more likely to investigate if in a litigious culture) or health system (eg v low risk patients more likely to be cared for within primary care & not reach the ED). ie the assumption is that the true population prevalence of MI is similar (at least in Western countries), but that the population being assessed varies. The alluvial plots illustrate this.
The assumption is that in the low-prevalence population there are many more people with very low-risk of MI being assessed in ED that are not assessed in the high-prevalence population.
If we assume that the true population prevalence of MI is similar in both cases then when it comes to developing risk prediction models:
Would developing a risk prediction model in the high-prevalence population be more likely to be generalisable to the low-prevalence population but not vice versa? [my guess is “yes” but I may be wrong]
Under the assumption that the difference is in the numbers of very low-risk patients who don’t have MI, is there a way we can incorporate prevalence into a model or be used to adjust a model so as to improve calibration when a model is used in a cohort with a prevalence different from that in which it was derived?
Note - if we are to provide probabilities to clinicians instead of a diagnostic algorithm then we would expect that clinicians would use a very low (eg ~1%) probability to rule-out MI. However, I am concerned that if (as has been done) a model is developed and pre-specifies a decision probability threshold that this may not travel way.