Thanks for that example. In terms of the risk to the patient what is the clinical picture? Does the cumulative injury matter the most and not whether it’s acute? In other words is there a chance that the last measured troponin could be self-contained in what really matters to the patient?
for your first point, whether it’s acute or not is important for patient management. Problem for the lab is we hardly know why a physician orders a troponin. An element not captured (at least accurately) is time from pain onset, this none changing serial hsTnI could be
taken late after the injury has occurred, and if history is good this non-changing troponin could indicate a cause besides acute cardiac ischemia (chronic injury or interference). However, I do think your last point is important, and probably why the majority of elevated troponins are investigated clinically
Thanks for this. It sounds as if one study we need to do is the have a suitable ultimate endpoint (prognosis?) and to relate a series of troponin values to it in a flexible way to learn who they can be used optimally to predict. Then remove all but the last troponin and repeat, computing the loss of predictive ability. This would measure the adequacy or inadequacy of the final measurement. Then take one of the serial measurements at random along with the elapsed time since onset of symtpoms and predict the outcome to find out how adequate that would be. But the gold standard from these 3 options would be the first, which allows us to estimate the troponin weights over time.
this sounds good, and time from pain-onset is important but is a very subjective measure. For time, a more accurate measure would be the length of time between measurements. There should be several datasets where this could be assessed & i guess the first endpoint to assess would be MI or death (at least common for most studies)
It would be good to have clinicians weigh in on these questions. The definition of Type 1 or Type 2 MI depends on a change in troponin - so what matters if the decision making discussion is along the lines of “you’ve had a heart attack therefore we will do A, B or C” then what matters is not just the last troponin.
It is indeed subjective Pete & it may not necessarily correspond with the time of myocardium damage/troponin release. I’m currently looking at a clinical cohort with multiple troponin measurements to (1) try and understand the dynamic changes in hs-cTn concentrations, (2) to see if I can fit curves that are utilise the time from symptom onset.
I wouldn’t be surprised if some of the definitions that depended on changes in troponin were merely assuming the changes were more important than the latest value. I have not seen a good example where a change measure was self-contained.
Dynamic rather than static troponin is what we generally look at when deciding whether an individual is having an event. Even if the troponin is decreasing we interpret this to mean that an event occurred, but (at least temporarily) resolved. The change in troponin often carries more weight than the absolute value of the last measure.
It carries more weight according to clinical intuition or according to data? I’ve seen no data justifying that.
Clinical intuition. I honestly never considered whether studies showed this to be a superior strategy. It makes pathophysiologic sense and is how I and all my cardiologist attendings have approached acute chest pain. Patients with hypertension and renal disease often have a chronic static low grade troponin elevation, and while that is a marker for poorer prognosis and certainly shouldn’t be ignored, it doesn’t - in my clinical experience, predict coronary stenosis requiring PCI. I would be very interested to study troponin dynamics if this has not been thoroughly explored. I would never have suspected that something so intuitive to my practice didn’t have data to back it up.
As Elias says - the change makes pathophysiological sense so is in the Universal Definition (although poorly defined in terms of magnitude or rate of change). However, I agree with you Frank that what is needed is an assessment of the measures of peak or latest value and change and how they relate to meaningful outcomes like mortality or stenosis requiring PCI. I’m part of a group who have shared data in the past, so may put this proposal to them (I expect it may need a lot of data).
Prepare to be surprised if other cardiovascular areas are any indication. A big lesson for me back during my days in Duke Cardiology was when cardiac surgeon Bob Jones hypothesized that the change from rest to exercise radionuclide ejection fraction was a predictor of amount of CAD and a predictor of CV death/MI. We found it didn’t predict either one, but that LVEF at peak exercise was the best single predictor we had ever seen - better than the entire coronary tree at predicting time to event.
To complicate mattters even further, the 4th Universal Definition of MI is doing little to help the (clinical) discrimination between Type 1 vs Type 2 MI. I cannot see that a spontaneous coronary artery dissection behaves any different to an intermittent complete coronary artery occlusion from a thrombus at the site of a plaque rupture event. Of course, the pathogenesis is entirely different, but cTn concentrations and dynamic change values will behave very similarly. Good luck in distinguishing between the different types of MI without invasive investigations
Just catching up here Tom! I agree - the guideline points strongly towards more invasive testing. Indeed, there is even a paragraph on the potential utility of invasive testing to identify atherosclerotic plaque rupture. But, if you don’t see it, are you going to do OCT? Always need to draw the line somewhere and I think given we haven’t shown benefit from investigation and subsequent treatment its right to be cautious!
I previously developed a model evaluating differences in rate of change of serial troponin and patient level characteristics for those adjudicated with type 1 and type 2 myocardial infarction, which I hoped might be useful to predict the diagnosis in practice. However, as you know, there is no independent reference standard to diagnose myocardial infarction, or indeed to distinguish type 1 and type 2 myocardial infarction. Ultimately I felt any regression model would simply reflect the variables which influenced diagnostic adjudication, rather than demonstrating a true association with one diagnosis or another. Others have undertaken and published similar analyses but I am unsure they are valid. Any suggestions or comments? Clinical utility is a separate issue.
You started off with the assumption that change is what’s important rather than most recent value. I have seen no data supporting that assumption. Ignoring for the moment the difficult problem of needed an independent gold standard diagnosis to analyze against, I suggest always started with a regression model that relates f(baseline) + f(current value) to the outcome, where f is a flexible transformation such as from a regression spline. You can see whether the slope of current is minus 1.0 times the slope of baseline, in which case change is an optimum way to capture the two. But I doubt you’ll see that. For many parameters, the current value has about 4/5 of the diagnostic/prognostic information, so any change measure will likely overweight the baseline value.
Thanks for taking the time to reply.
One of the diagnostic criteria for myocardial infarction is a rise and/or fall in cardiac troponin on serial testing - any regression model I create will have an outcome of the adjudicated diagnosis of myocardial infarction and this will undoubtedly include this change criteria. The bias associated with the inclusion of troponin within the reference standard, and a change on serial testing, seems inescapable. I think we have all discussed this issue at one stage or another…
To give some clinical context; the inclusion of change is on the basis of pathophysiological studies demonstrating troponin release and excretion are time dependent phenomena, rather than demonstration of additive statistical value in its inclusion for diagnosis. As a clinician, a single troponin value may guide prognosis but will not help suggest a diagnosis, as a plethora of conditions may cause a significant elevation. The dynamic change on serial testing indicates an acute process and can be helpful to guide further testing (such as coronary angiography for acute myocardial infarction or echocardiography for structural heart disease). If two levels are unchanged (within the imprecision of the assay) we will look for a non-acute aetiology.
Andrew I know you need to examine the change for comparison with other studies and prevailing clinical practice. But I am very worried that the way you described this means that you desire to encode the change into your thinking without questioning the very strong assumptions that change scores make. The fact that rise and fall may be important is not incompatible with the the current value being all-important. But we need to see the data to learn rather than assume.
Happy to question assumptions, I was just illustrating the current situation for clinical diagnosis and diagnostic adjudication from a cardiology viewpoint. The troponin community is certainly not short on data, it would be great to explore this with your input if you were interested.
Very interested. Thanks.