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Page 18 of 21 Carr et al. Vessel Plus 2020;4:12 I http://dx.doi.org/10.20517/2574-1209.2020.01

minor differences in the pre-CABG patients’ risk factor frequency (which may have been associated with
provider-based off-pump patient selection criteria), the pre-CABG patient risk factors identified were
extremely similar to the overall findings, as reported above. Given the smaller number of on-pump vs.
off-pump CABG mortality risk model comparisons reported, however, these findings may have limited
generalizability.

When reviewing the frequency distribution of preoperative model risk variables, it is striking how very
few modifiable (as opposed to non-modifiable) patient risk factors have been identified with a post-CABG
mortality impact. As an inherently non-modifiable risk factor, the risk for post-CABG mortality increases
as a patient’s age increases. Perhaps by the time a patient is being evaluated for a CABG procedure, the
negative prognostic impact for the most common preoperative risk factors, such as diabetes mellitus
and poor left ventricular ejection fraction, may be difficult to reverse or otherwise counteract in the ST;
however, these impacts can be seen in LT models.

In contrast, several of these reported patient risk factors have potential to be mitigated. As an example,
body mass index or another marker of body habitus (e.g., height, weight, or body surface area) was
included in 31/133 (23%) of ST models considering only preoperative risk factors. Similarly, a measure of
smoking or tobacco use was considered in only 4/133 (3%). Although it is a well-known fact that these 2
risk factors represent important drivers for a patient developing ischemic heart disease, their significance
in predicting post-CABG mortality risk appears likely confounded with presence of diabetes mellitus and
poor renal function, which may also be sequela of obesity or diabetes.

Although these risk models may be helpful to enhance the providers’ discussions with patients during
the informed consent process or support provider discussions as to treatment-related risks for adverse
events, the currently published CABG mortality risk models fall short of providing clinicians with
useful information to optimize postoperative care consults, to ensure continuity of post-discharge care,
or to enhance LT patients’ survival. While it would likely not be surprising to most clinicians that these
modifiable risk factors are important considerations, the manner presented in LT risks models may give the
impression that LT post-CABG mortality risk is set in stone at the time of surgery, rather than an evolving
risk that can be mitigated or exacerbated at any time. Using follow-up time-period-based risks (e.g.,
hemoglobin A1c management or continued tobacco use), therefore, future sequential modeling approaches
may be needed to help better guide post-CABG follow-up care decisions and to optimize LT post-CABG
survival.

One risk factor that is potentially modifiable, but not in the traditional sense, is operative urgency or
priority, meaning whether a given procedure was performed in the elective vs. urgent or even emergent
manner with an unstable patient. As clinically relevant examples, it is important to know when to intervene
in patients with active angina or acute myocardial infarction. While operating in a time sensitive manner
under potentially suboptimal conditions may be unavoidable, the fact that priority or status variables have
been identified so frequently as ST mortality risk factors would suggest that future research funding should
[16]
be prioritized to evaluate the impact of differential pre-CABG waiting periods .

A limited number of CABG mortality models found preoperative medications such as nitrates, anti-platelet
agents, angiotensin converting enzyme inhibitor, or anti-arrhythmic medication were associated with
mortality. Given risk assessment inconsistencies, some of these medications (e.g., nitrates) may have been
markers for the severity of coronary disease or preoperative instability. Other medications may, in fact, be
[17]
markers of optimal medical management during the pre- and postoperative periods .