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Carr et al. Vessel Plus 2020;4:12 I http://dx.doi.org/10.20517/2574-1209.2020.01 Page 19 of 21

Currently, no risk models incorporate direct measures of adherence with published clinical practice
guidelines (e.g., the American College of Cardiology’s guidelines for treatment of coronary artery disease)
such as documenting the use of ischemic heart disease medications (e.g., pre-CABG statin use). As a
potentially novel and important future enhancement to preoperative risk stratification, adherence to
published guidelines should be considered. In general, adherence with published guidelines are increasingly
becoming a marker used to identify high-quality, high-value care providers. Adherence to published
guidelines has been shown to be suboptimal after CABG, yet adherence has been repeatedly associated
with improved cardiovascular-related mortality in various populations [18-20] . Applied proactively, guideline
adherence may provide a useful direction for future cardiac surgery mortality risk modeling endeavors.

Interestingly, none of these CABG mortality risk models identified mental health-related (e.g., psychiatric)
or socioeconomic risk factors as predictive; however, preoperative depression has been associated with
increased 5- and 10-year post-CABG mortality [21,22] . Similarly, one recent study showed a community-based
marker of socioeconomic status (e.g., the Distressed Community Index) to be predictive of in-hospital
[23]
mortality . Hence, these types of non-traditional CABG risk factors may be worthy of future exploration.

Limitations
Conducted as an advanced PubMed literature review in February 2019, this summary has identified
knowledge “gaps”, which are intended to foster future CABG risk modeling research. With collaborative
team member oversight and guidance, the majority of these data extractions were performed by a single
author (BC). Substantial overlap was documented among several risk variables (e.g., left ventricular ejection
fraction vs. congestive heart failure vs. pulmonary rales vs. diuretic use); therefore, the relative impact
of any individual risk factor could not be easily quantified. If standardized CABG quality improvement
database definitions (e.g., the Society of Thoracic Surgeons’ definitions) were uniformly utilized in the
future, however, comparing variable-specific relative rankings (e.g., identifying the “top five variables
impacting mortality” across all published models) would become possible.

Inherently, all risk variables reported were limited to the sub-group of patients’ risk characteristics uniquely
captured by each database. Although a common core of risk variables was captured, each dataset may have
contained unique risk factors relevant specifically to their patient populations. Additionally, different risk
modeling approaches (e.g., descending stepwise logistic regression) may have contributed to the variations
documented for the risk factors associated with post-CABG mortality.

In conclusion, CABG maintains an important role in the management of coronary artery disease; thus,
understanding patients’ ST and LT surgical risk and risk factors remains important to optimizing CABG
patient’s selection, treatment, and follow-up care. A wide array of CABG mortality model findings and an
equally vast diversity of analytic approaches were used, each prediction model having population-specific
benefits and drawbacks. Over the past 20 years, it appears that the majority of CABG registries have come
to a general consensus to utilize at least a core pre-CABG risk factor set. Beyond this core dataset, however,
population-relevant risk factors are commonly reported.

As always, research continues to identify new risk factors that may affect post-CABG patients’ risk; based
on these data-driven findings, areas warranting further research were identified - such as incorporating
modifiable risk factors and ischemic heart disease guideline compliance. Additionally, a new focus appears
warranted to evaluate pre-CABG wait time impacts upon surgical priority, as well as CABG risk-adjusted
outcomes. Applying the lessons learned, post-CABG mortality risk model findings may be quite different
in the future from current findings - as the post-CABG care continues to improve and the field of statistical
risk modeling advances forward.