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Original Article  |  Open Access  |  10 May 2020

Risk factors adversely impacting post coronary artery bypass grafting longer-term vs. shorter-term clinical outcomes

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Vessel Plus 2020;4:12.
10.20517/2574-1209.2020.01 |  © The Author(s) 2020.
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Aim: Coronary artery bypass grafting (CABG) patients’ characteristics and surgical techniques associated with short-term (ST; < 1 year) mortality are well documented; however, the literature pinpointing factors predictive of longer-term (LT; ≥ 1 year) death rates are more limited. Thus, the CABG factors associated with ST vs. LT mortality were compared.

Methods: Using advanced PubMed search techniques, the factors associated with improved post-CABG mortality were compared for ST vs. LT prediction models; ST vs. LT models’ results were compared across three time periods: until 1997, 1998-2007, and 2007-2017.

Results: Of 156 post-CABG mortality risk models (n = 125 publications), 133 ST and 23 LT models were evaluated. Important predictors consistently included age, ejection fraction, and renal dysfunction/dialysis. The ST models more commonly identified surgical priority, gender, and prior cardiac surgery; however, the LT models more frequently included diabetes and peripheral arterial disease. Compared to ST mortality, patterns also emerged for cerebrovascular disease and chronic lung disease predicting LT mortality. As modifiable risks, body mass index or another marker of body habitus appeared in 31/133 (23%) of ST models; smoking or tobacco use was considered in only 4/133 (3%). No models evaluated compliance with ischemic heart disease guidelines. No time period-related differences were found.

Conclusion: Different risk factors predicted ST vs. LT post-CABG mortality; for LT death, debilitating chronic/complex comorbidities were more often reported. As few models focused on identifying modifiable patient risks or ischemic heart disease guideline compliance, future CABG LT risk modeling should address these knowledge gaps.


Coronary artery bypass graft surgery, risk assessment, outcomes research, survival, mortality


Over the past 60 years, much has changed in the healthcare field. Increasingly, attention is being paid to healthcare quality with the goals of improving clinical outcomes and increasing value of care delivered. A special emphasis in quality improvement has been placed on high volume procedures such as coronary artery bypass grafting (CABG). Although CABG volumes have declined from ~213,700 procedures (2011) to ~156,900 procedures (2016), it remains the most common cardiac surgical procedure performed in the United States[1-3]. To evaluate the true value of CABG, longer-term outcomes are necessary to establish the durability of the procedure. Accordingly, the baseline patient risk factors associated with short-term (< 1 year) and longer-term (≥ 1 year) CABG mortality were compared.

Interpreting CABG clinical outcomes data can often be challenging, as there may be a wide range in pre-CABG patient’s severity of coronary disease or comorbidity-related disease complexity, variations in CABG operative techniques used or post-CABG pre-discharge patient care management, as well as provider-based variations for annual CABG volumes performed. In 1972, the Department of Veterans Affairs (VA) healthcare system began internally reporting national unadjusted outcome rates (e.g., “observed” in-hospital mortality rates) for patients undergoing cardiac surgery at its institutions; these first VA reports focused upon observed CABG mortality and post-CABG complication rates[4].

After US hospitals’ CABG mortality reports were made publicly available by the Department of Health and Human Services in 1985, Congress in 1986 mandated that the VA report risk-adjusted cardiac surgery mortality rates and compare these VA rates to national standards[5]. Given these legislation-driven mandates, VA clinicians and scientists began looking for ways to “level the playing field” using statistical risk models to permit more meaningful comparisons between centers and surgeons; these risk-adjusted outcome reports were used in their local VA medical centers’ quality improvement endeavors.

Initiated in April 1987, the VA Continuous Improvement in Cardiac Surgery Program (CICSP) was founded; CICSP was one of the first registries to report risk-adjusted CABG 30-day operative mortality and major morbidity across all participating VA hospitals[4]. The VA CICSP identified a set of Veteran risk characteristics associated with CABG adverse outcomes; based on gathering 54 patients’ risk, cardiac surgical procedural details, and hospital-related outcomes, the VA CICSP calculated the “expected” mortality occurrence for each Veteran undergoing a CABG procedure. Across providers and “high-risk” patient sub-groups, therefore, “observed” to “expected” outcome rates were compared to identify opportunities to improve their local VA cardiac surgical care[6].

Some of the earliest lists of pre-CABG patient risk factors associated with mortality were developed entirely based on expert consensus. As different national, regional, and state-wide databases originally gathered different sets of patient risk factors, an early consensus conference was held to identify the minimal set of “core” risk variables required to be captured[7,8]. Given challenges encountered with CABG records’ data completeness, however, these earliest mathematical approaches to calculate risk-adjusted outcome rates made use of Bayes theorem[9]. Since the VA’s programmatic expansions in 1992, dramatic improvements were made in the VA completeness of CABG data captured; thus, logistic regression emerged as the most common analytical approach used. Other approaches have been reported, including applications of neural networks and Cox regression[10,11]. Given both the ease of clinical interpretation and superior statistical model performance, however, logistic regression remains the standard analytical approach used to predict post-CABG short-term (ST) and longer-term (LT) mortality[12-14].

Historically, the process of choosing logistic regression eligible (“candidate”) risk variables was different for each CABG registry. As this pre-selection candidate variable approach may have introduced subjectivity and biased model results, CABG risk models (such as those developed by the VA, Society of Thoracic Surgeons, and EuroSCORE teams) have been derived in recent years using a standardized approach with a core set of model eligible variables. Beyond this core set, however, each database incorporates an expanded set of population-specific risk variables in their risk modeling processes.

Over the past 30 years, nearly countless CABG risk models with various designs and complexity have been developed to predict the likelihood of death at pre-specified time periods. As the standard ST endpoint used, operative mortality was defined as death within 30 days or within the index hospitalization. As operative mortality avoids any potential post-discharge referral bias (e.g., post-CABG hospital discharge to a separate sub-acute care facility), this endpoint was determined to be the most clinically relevant performance metric; it is commonly used to assess the quality of the surgical procedure. Other models have considered LT death during longer periods of follow-up, investigating the durability of the CABG procedure and importance of other risk factors. For ST and LT published risk models, therefore, this study describes the patterns in pre-CABG factors differentially impacting ST vs. LT mortality. Until this report, these patterns had not been previously described. Moreover, this novel report identifies additional opportunities to improve future CABG risk models.


An advanced literature review was undertaken to document published risk factors associated with post-CABG mortality. In February 2019, PubMed was searched for all Medline publications using the following terms: “CABG” (Title) OR “coronary artery bypass” (Title) AND “mortality” (Title) OR “risk” (Title) OR “death” (Title) OR “survival” (Title). This yielded 1904 publications. Following a review of all articles for pre-stated inclusion/exclusion criteria, there were a total of 125 included articles with 156 CABG mortality models. Only papers reporting risk models for mortality after an isolated CABG procedure were included; inclusion criteria were otherwise left intentionally broad so as to gather a wide variety of models. Models requiring data from the postoperative period were excluded for the purpose of this review, whereas those employing only preoperative variables [as opposed to preoperative and intraoperative variables (e.g., cardiopulmonary bypass time)] were identified for sub-analysis review. For the 125 publications meeting all inclusion/exclusion criteria, their reference lists were also carefully reviewed for relevant publications to augment the original search strategy’s findings.

Working collaboratively under the senior co-authors’ guidance, the majority of literature search screening and data extraction were performed primarily by one author (BC). To permit meaningful model comparisons, risks were classified into 91 different common clinical categories. Clinically relevant composite variables were reported based upon database-specific definitions (e.g., “critical preoperative state” and “extra-cardiac arteriopathy”). Named risk indices (e.g., “Elixhauser Comorbidity Index”) were analyzed using their assigned name as a group, rather than being recorded based upon the indices’ subcomponents. For the 125 publications evaluated, the set of risk factors identified to be associated with post-CABG ST or LT mortality were compared. Time trends in models’ risk factors reported were evaluated across three time periods until 1997, 1998-2007, and 2007-2017.


One hundred fifty-six post-CABG mortality risk models were identified within 125 different papers. In Appendix A, the full listing of these papers and models can be found in Supplementary Table 1 and Supplementary Table 2.

Of these models, 133 predicted ST CABG mortality. Operative mortality was the most commonly reported ST endpoint, defined as death occurring during the index hospitalization and/or up to 30 days after the index surgical procedure. Twenty-three LT CABG mortality models were identified. The longest period of follow-up was seven years, reported by Wu et al.[15] When looking at those models considering only preoperative (i.e., not intraoperative) risk factors, there were 75 ST models and 14 LT models (total = 89). As a pre-planned sub-analysis, risk models considering on-pump vs. off-pump CABG and only preoperative risk factors were also compared separately. This identified three ST and one LT models (total = 4). The complete listing of variables for the ST vs. LT models with frequency counts is included in Table 1.

Table 1

All data

All modelsShort-term modelsLong-term models
Variablen = 156%Variablen = 133%Variablen = 23%
Age   13486%Age   11586%Age   1252%
Left ventricular function   10467%Left ventricular function   8564%Left ventricular function   1252%
Renal failure   8856%Urgency   7859%Comb. arterial disease   939%
Comb. heart failure variables   8454%Gender   7657%Diabetes   835%
Urgency   8454%Renal failure   7355%Peripheral arterial disease   835%
Gender   8353%Comb. heart failure variables   6952%Renal failure   730%
Comb. arterial disease   7649%Repeat operation   6851%Comb. heart failure variables   730%
Comb. CHF or NYHA   7447%Comb. arterial disease   6146%Comb. CHF or NYHA   730%
Repeat operation   7347%Comb. any MI variable   6045%Lung disease   522%
Comb. any MI variable   6542%Comb. CHF or NYHA   6045%Neurologic disease   522%
History of MI   6340%History of MI   5944%Congestive heart failure   522%
Comb. critical state   6240%Comb. critical state   5844%Comb. graft variables   522%
Peripheral arterial disease   6240%Peripheral arterial disease   4937%Postoperative variables   522%
Diabetes   5435%Lung disease   4332%Body size measurements   417%
Lung disease   5233%Comb. vessel disease   4332%Comb. vessel disease   417%
Comb. vessel disease   5133%Diabetes   4030%Type of graft(s)   417%
Neurologic disease   4931%Neurologic disease   3929%Smoking status   417%
Congestive heart failure   4730%Congestive heart failure   3728%Gender   313%
Body size measurements   3925%Body size measurements   3123%Urgency   313%
Left main disease   3623%Cardiogenic shock   3023%Comb. any MI variable   313%
Cardiogenic shock   3422%NYHA class   2922%Left main disease   313%
NYHA class   3321%Left main disease   2922%Repeat operation   29%
Number of diseased vessels   3019%Number of diseased vessels   2720%NYHA class   29%
Comb. ECG or arrhythmia variables   2919%Comb. ECG or arrhythmia variables   2620%History of MI   29%
Preoperative IABP use   2717%Concurrent procedure   2620%Valve disease   29%
Concurrent procedure   2617%Preoperative IABP use   2418%Hypertension   29%
Angina   2617%Angina   2317%Comb. HTN or BP   29%
Comb. HTN or BP   2516%Comb. HTN or BP   2116%Atrial arrhythmia   29%
Comb. PCI variables   2415%Comb. PCI variables   2116%Hypercholesterolemia   29%
Hypertension   2415%Hypertension   2015%Intraoperative variables   29%
Postoperative variables   2214%Pulmonary hypertension   1914%Ventricular wall motion   14%
Comb. graft variables   2013%Non-CABG surgery   1814%Calcified aorta   14%
Pulmonary hypertension   1912%Postoperative variables   1713%Angina   14%
Valve disease   1912%Any arrhythmia   1612%Active MI   14%
Non-CABG surgery   1812%Valve disease   1511%Number of diseased vessels   14%
Any arrhythmia   1711%Comb. graft variables   1411%Diffuse/severe disease   14%
Inotropic medication   1510%Extracardiac arteriopathy   129%Number of grafts   14%
Prior/recent PCI or PTCA   1510%Inotropic medication   129%Race or ethnicity   14%
Atrial arrhythmia   138%Cardiopulmonary bypass time   129%Preoperative IABP use   14%
Type of graft(s)   138%Prior/recent PCI or PTCA   129%Inotropic medication   14%
Extracardiac arteriopathy   138%Nitroglycerin use   108%Comb. critical state   14%
Cardiopulmonary bypass time   128%Critical state   108%Cardiogenic shock   14%
Race or ethnicity   117%Preoperative diuretic use   97%Immunosuppression   14%
Ventricular or unstable arrhythmia   106%Type of graft(s)   97%Date or order of surgery   14%
Preoperative diuretic use   106%Liver disease   97%Aortic cross-clamp duration   14%
Critical state   106%Cardiomegaly   97%On- vs. off-pump CABG   14%
Liver disease   106%Atrial arrhythmia   97%Prior/recent PCI or PTCA   14%
Smoking status   106%Diffuse/severe disease   86%Comb. PCI variables   14%
Nitroglycerin use   106%PTCA failure/emergency   86%Ventricular or unstable arrhythmia   14%
Diffuse/severe disease   96%Ventricular or unstable arrhythmia   86%Comb. ECG or arrhythmia variables   14%
Immunosuppression   96%Preop intubation   86%Preoperative thrombolysis   14%
Cardiomegaly   96%Race or ethnicity   75%Left ventricular hypertrophy   14%
PTCA failure/emergency   85%On- vs. off-pump CABG   75%Cachexia or malnutrition   00%
Preop intubation   85%Endocarditis   75%Pulmonary hypertension   00%
On- vs. off-pump CABG   85%Dyspnea   65%Extracardiac arteriopathy   00%
Number of grafts   85%Number of grafts   65%Dyspnea   00%
Intraoperative variables   74%Immunosuppression   65%Type of MI   00%
Endocarditis   74%Aortic cross-clamp duration   54%Pulmonary rales   00%
Aortic cross-clamp duration   74%Intraoperative variables   54%Preoperative diuretic use   00%
Dyspnea   64%Pulmonary rales   43%Killip classification   00%
Date or order of surgery   64%Smoking status   43%Blood pressure   00%
Digoxin or digitalis use   53%Anticoagulation or antiplatelet use   43%Nitroglycerin use   00%
Hypercholesterolemia   53%Disaster, catastrophic state   43%Liver disease   00%
Disaster, catastrophic state   43%Anemia (hemoglobin, hematocrit)   43%Cardiopulmonary bypass time   00%
Pulmonary rales   43%Digoxin or digitalis use   43%Cardiomegaly   00%
Active MI   43%A published comorbidity index   43%Preoperative CPR/cardiac arrest   00%
Ventricular wall motion   43%Other preoperative comorbidities   43%Location or type of surgical center   00%
Other preoperative comorbidities   43%Ventricular wall motion   32%Center’s case frequency   00%
A published comorbidity index   43%Active MI   32%Endocarditis   00%
Anemia (hemoglobin, hematocrit)   43%Preoperative CPR/cardiac arrest   32%Abdominal aortic aneurysm   00%
Anticoagulation or antiplatelet use   43%Location or type of surgical center   32%PTCA failure/emergency   00%
Serum albumin   32%Hypercholesterolemia   32%Stent thrombosis   00%
Other preoperative labs   32%Refused blood products   32%Any family history variable   00%
Refused blood products   32%Other preoperative labs   32%Any arrhythmia   00%
Location or type of surgical center   32%Serum albumin   32%Antiarrhythmic agents   00%
Preoperative CPR/cardiac arrest   32%Cachexia or malnutrition   22%Other ECG abnormalities   00%
Preoperative thrombolysis   21%Type of MI   22%Non-CABG surgery   00%
Other ECG abnormalities   21%Date or order of surgery   22%Anticoagulation or antiplatelet use   00%
Stent thrombosis   21%Abdominal aortic aneurysm   22%PT or INR   00%
Type of MI   21%Stent thrombosis   22%Critical state   00%
Calcified aorta   21%Any family history variable   22%Disaster, catastrophic state   00%
Cachexia or malnutrition   21%Other ECG abnormalities   22%Anemia (hemoglobin, hematocrit)   00%
Recent admissions   21%Steroid use   22%Transfusion   00%
Patient education level/literacy   21%Preoperative cardiac biomarkers   22%Refused blood products   00%
Preoperative cardiac biomarkers   21%Patient education level/literacy   22%Digoxin or digitalis use   00%
Steroid use   21%Calcified aorta   11%Preop intubation   00%
Heart rate   21%Killip classification   11%Concurrent procedure   00%
Any family history variable   21%Blood pressure   11%A published comorbidity index   00%
Abdominal aortic aneurysm   21%Center’s case frequency   11%Heart rate   00%
Transfusion   11%Antiarrhythmic agents   11%Steroid use   00%
PT or INR   11%Preoperative thrombolysis   11%Preoperative cardiac biomarkers   00%
Antiarrhythmic agents   11%PT or INR   11%Other preoperative labs   00%
Center’s case frequency   11%Transfusion   11%Serum albumin   00%
Blood pressure   11%Heart rate   11%Other preoperative comorbidities   00%
Killip classification   11%ACE inhibitor use   11%ACE inhibitor use   00%
Acute mental status changes   11%ASA classification   11%Functional state   00%
Time from admission to procedure   11%Insurance type or status   11%Patient education level/literacy   00%
Left ventricular hypertrophy   11%Recent admissions   11%ASA classification   00%
Insurance type or status   11%Time from admission to procedure   11%Insurance type or status   00%
ASA classification   11%Acute mental status changes   11%Recent admissions   00%
Functional state   11%Functional state   00%Time from admission to procedure   00%
ACE inhibitor use   11%Left ventricular hypertrophy   00%Acute mental status changes   00%
Total variables (excl. combinations)   92Total variables (excl. combinations)   90Total variables (excl. combinations)   42

Overwhelmingly, age was the most common preoperative variable identified to be predictive of ST post-CABG mortality, reported in 115 of 125 (86%) of those models. Of the articles summarized, 22/156 (14.1%) did not report age as a risk factor. Across these 22 publications, the age-related variability in reporting observed appears to be due in part to their study-specific populations’ inherent risk profile. For example, articles focused upon higher risk patient sub-groups (e.g., emergent CABG patients or those experiencing an acute myocardial infarction) commonly did not report age as a post-CABG mortality model finding. Despite this observed pattern, however, there was not a single, simple explanation for the observed inconsistency in age not being reported across all models.

Age was followed by left ventricular ejection fraction (included in 64% of ST mortality models), surgical case priority or status (59%), patient gender (57%), and having undergone a prior cardiac surgical procedure before the index procedure (55%); these represented the top five most common preoperative variables for predicting ST post-CABG mortality. For LT models, the top five risk factors were age, ejection fraction, diabetes mellitus, peripheral arterial disease, and renal failure. There appeared to be a trend toward cerebrovascular disease and lung disease being more commonly reported by CABG risk models focused upon mortality beyond one year (compared with other variables within that same subset of models), perhaps suggesting debilitating chronic and complex comorbidities are more useful in prediction of LT mortality.

When the results were grouped into early, mid, and late subgroups by year of publication [Tables 2-4], age and ejection fraction remained among the most common risk factors for models throughout those time periods. No definite trends over time were observed in risk factor prevalence for the overall group or the ST or LT model subgroups, although sample size may have impacted the ability to detect such trends, particularly within the subgroups. Results were also similar when considering models that included only preoperative risk factors [Table 5] or those that considered on-pump vs. off-pump CABG [Table 6].

Table 2

All risk models by publication year

≤ 19971998-20072008-2017
Variablen = 41%Variablen = 54%ΔaVariablen = 61%ΔaΔb
Age   3790%Age   4481%-9%Age   5387%-3%5%
Repeat operation   2459%Left ventricular function   4074%18%Left ventricular function   4167%11%-7%
Comb. heart failure variables   2459%Renal failure   3667%25%Renal failure   3557%16%-9%
Left ventricular function   2356%Gender   3157%11%Urgency   3456%7%0%
History of MI   2254%Urgency   3056%7%Gender   3354%8%-3%
Comb. any MI variable   2254%Comb. heart failure variables   3056%-3%Comb. arterial disease   3252%13%1%
Comb. CHF or NYHA   2151%Comb. arterial disease   2852%13%Comb. heart failure variables   3049%-9%-6%
Urgency   2049%Repeat operation   2546%-12%Comb. critical state   3049%20%12%
Gender   1946%Comb. any MI variable   2546%-7%Comb. CHF or NYHA   2846%-5%0%
Renal failure   1741%Comb. CHF or NYHA   2546%-5%Peripheral arterial disease   2541%2%2%
Peripheral arterial disease   1639%Diabetes   2444%22%Repeat operation   2439%-19%-7%
Comb. arterial disease   1639%History of MI   2444%-9%Diabetes   2134%12%-10%
Lung disease   1537%Lung disease   2241%4%Comb. any MI variable   1830%-24%-17%
Comb. vessel disease   1537%Neurologic disease   2139%5%History of MI   1728%-26%-17%
Neurologic disease   1434%Peripheral arterial disease   2139%0%Cardiogenic shock   1626%12%4%
Congestive heart failure   1434%Comb. vessel disease   2139%2%Lung disease   1525%-12%-16%
Angina   1332%Comb. critical state   2037%8%NYHA class   1525%0%10%
Comb. critical state   1229%Congestive heart failure   1935%1%Comb. vessel disease   1525%-12%-14%
Body size measurements   1127%Body size measurements   1833%7%Neurologic disease   1423%-11%-16%
Left main disease   1127%Left main disease   1833%7%Congestive heart failure   1423%-11%-12%
Comb. ECG or arrhythmia variables   1127%Comb. HTN or BP   1426%11%Number of diseased vessels   1220%3%-1%
NYHA class   1024%Hypertension   1324%9%Preoperative IABP use   1220%3%5%
Diabetes   922%Cardiogenic shock   1222%8%Concurrent procedure   1118%3%1%
Number of diseased vessels   717%Postoperative variables   1222%15%Body size measurements   1016%-10%-17%
Preoperative IABP use   717%Number of diseased vessels   1120%3%Comb. PCI variables   1016%2%2%
Valve disease   615%Comb. ECG or arrhythmia variables   1120%-6%Atrial arrhythmia   1016%14%13%
Hypertension   615%Pulmonary hypertension   1019%9%Valve disease   915%0%7%
Nitroglycerin use   615%Concurrent procedure   917%2%Inotropic medication   915%7%9%
Cardiomegaly   615%NYHA class   815%-10%Angina   813%-19%4%
Cardiogenic shock   615%Preoperative IABP use   815%-2%Race or ethnicity   711%11%4%
Non-CABG surgery   615%Comb. graft variables   815%0%Left main disease   711%-15%-22%
Concurrent procedure   615%Comb. PCI variables   815%0%Postoperative variables   711%4%-11%
Diffuse/severe disease   615%Preoperative diuretic use   713%6%Extracardiac arteriopathy   711%11%0%
Comb. graft variables   615%Any arrhythmia   713%1%Comb. ECG or arrhythmia variables   711%-15%-9%
Comb. HTN or BP   615%Prior/recent PCI or PTCA   611%4%On- vs. off-pump CABG   610%10%6%
Comb. PCI variables   615%Non-CABG surgery   611%-4%Prior/recent PCI or PTCA   610%3%-1%
Cardiopulmonary bypass time   512%Intraoperative variables   611%11%Non-CABG surgery   610%-5%-1%
Any arrhythmia   512%Extracardiac arteriopathy   611%11%Critical state   610%10%2%
Pulmonary hypertension   410%Type of graft(s)   611%4%Comb. graft variables   610%-5%-5%
Pulmonary rales   410%Angina   59%-22%Pulmonary hypertension   58%-2%-10%
Number of grafts   37%Endocarditis   59%4%Hypertension   58%-6%-16%
Liver disease   37%Ventricular or unstable arrhythmia   59%2%Smoking status   58%6%1%
Aortic cross-clamp duration   37%Valve disease   47%-7%Immunosuppression   58%6%3%
Prior/recent PCI or PTCA   37%Race or ethnicity   47%7%Comb. HTN or BP   58%-6%-18%
Anemia (hemoglobin, hematocrit)   37%Smoking status   47%5%Any arrhythmia   58%-4%-5%
Preop intubation   37%Liver disease   47%0%Date or order of surgery   47%7%3%
Postoperative variables   37%Cardiopulmonary bypass time   47%-5%Type of graft(s)   47%-1%-5%
Ventricular wall motion   37%Aortic cross-clamp duration   47%0%Liver disease   35%-2%-2%
Preoperative diuretic use   37%Hypercholesterolemia   47%7%Cardiopulmonary bypass time   35%-7%-2%
Type of graft(s)   37%Critical state   47%7%Other preoperative labs   35%5%5%
Inotropic medication   37%Preop intubation   47%0%Number of grafts   23%-4%-2%
Preoperative CPR/cardiac arrest   37%Dyspnea   47%7%A published comorbidity index   23%1%1%
PTCA failure/emergency   37%Number of grafts   36%-2%Preoperative cardiac biomarkers   23%3%3%
Ventricular or unstable arrhythmia   37%Nitroglycerin use   36%-9%Patient education level/literacy   23%3%3%
Disaster, catastrophic state   37%Cardiomegaly   36%-9%Recent admissions   23%3%3%
Serum albumin   37%Immunosuppression   36%3%Dyspnea   23%3%-4%
Endocarditis   25%Active MI   36%6%PTCA failure/emergency   23%-4%-2%
Anticoagulation or antiplatelet use   25%Inotropic medication   36%-2%Stent thrombosis   23%3%3%
Digoxin or digitalis use   25%Location or type of surgical center   36%6%Ventricular or unstable arrhythmia   23%-4%-6%
Cachexia or malnutrition   25%PTCA failure/emergency   36%-2%Nitroglycerin use   12%-13%-4%
Smoking status   12%Refused blood products   36%6%Hypercholesterolemia   12%2%-6%
Immunosuppression   12%Date or order of surgery   24%4%Anticoagulation or antiplatelet use   12%-3%0%
Any family history variable   12%On- vs. off-pump CABG   24%4%Digoxin or digitalis use   12%-3%-2%
A published comorbidity index   12%Abdominal aortic aneurysm   24%4%Preop intubation   12%-6%-6%
Other preoperative comorbidities   12%Digoxin or digitalis use   24%-1%Other preoperative comorbidities   12%-1%-2%
ACE inhibitor use   12%Heart rate   24%4%Insurance type or status   12%2%2%
Type of MI   12%Steroid use   24%4%Time from admission to procedure   12%2%2%
Atrial arrhythmia   12%Other preoperative comorbidities   24%1%Intraoperative variables   12%2%-9%
Antiarrhythmic agents   12%Calcified aorta   24%4%Active MI   12%2%-4%
Other ECG abnormalities   12%Diffuse/severe disease   24%-11%Diffuse/severe disease   12%-13%-2%
Race or ethnicity   00%Atrial arrhythmia   24%1%Center’s case frequency   12%2%2%
Date or order of surgery   00%Preoperative thrombolysis   24%4%PT or INR   12%2%2%
On- vs. off-pump CABG   00%Any family history variable   12%-1%Disaster, catastrophic state   12%-6%2%
Abdominal aortic aneurysm   00%Anticoagulation or antiplatelet use   12%-3%Transfusion   12%2%2%
Hypercholesterolemia   00%Anemia (hemoglobin, hematocrit)   12%-5%Cardiomegaly   00%-15%-6%
Critical state   00%A published comorbidity index   12%-1%Aortic cross-clamp duration   00%-7%-7%
Heart rate   00%Functional state   12%2%Endocarditis   00%-5%-9%
Steroid use   00%ASA classification   12%2%Abdominal aortic aneurysm   00%0%-4%
Preoperative cardiac biomarkers   00%Left ventricular hypertrophy   12%2%Any family history variable   00%-2%-2%
Functional state   00%Acute mental status changes   12%2%Anemia (hemoglobin, hematocrit)   00%-7%-2%
Patient education level/literacy   00%Ventricular wall motion   12%-5%Heart rate   00%0%-4%
ASA classification   00%Type of MI   12%-1%Steroid use   00%0%-4%
Insurance type or status   00%Killip classification   12%2%ACE inhibitor use   00%-2%0%
Recent admissions   00%Blood pressure   12%2%Functional state   00%0%-2%
Left ventricular hypertrophy   00%Other ECG abnormalities   12%-1%ASA classification   00%0%-2%
Time from admission to procedure   00%Preoperative cardiac biomarkers   00%0%Left ventricular hypertrophy   00%0%-2%
Acute mental status changes   00%ACE inhibitor use   00%-2%Acute mental status changes   00%0%-2%
Intraoperative variables   00%Patient education level/literacy   00%0%Cachexia or malnutrition   00%-5%0%
Extracardiac arteriopathy   00%Insurance type or status   00%0%Ventricular wall motion   00%-7%-2%
Calcified aorta   00%Recent admissions   00%0%Calcified aorta   00%0%-4%
Dyspnea   00%Time from admission to procedure   00%0%Type of MI   00%-2%-2%
Active MI   00%Cachexia or malnutrition   00%-5%Pulmonary rales   00%-10%0%
Killip classification   00%Pulmonary rales   00%-10%Preoperative diuretic use   00%-7%-13%
Blood pressure   00%Preoperative CPR/cardiac arrest   00%-7%Killip classification   00%0%-2%
Location or type of surgical center   00%Center’s case frequency   00%0%Blood pressure   00%0%-2%
Center’s case frequency   00%Stent thrombosis   00%0%Preoperative CPR/cardiac arrest   00%-7%0%
Stent thrombosis   00%Antiarrhythmic agents   00%-2%Location or type of surgical center   00%0%-6%
Preoperative thrombolysis   00%PT or INR   00%0%Antiarrhythmic agents   00%-2%0%
PT or INR   00%Disaster, catastrophic state   00%-7%Other ECG abnormalities   00%-2%-2%
Transfusion   00%Transfusion   00%0%Preoperative thrombolysis   00%0%-4%
Refused blood products   00%Other preoperative labs   00%0%Refused blood products   00%0%-6%
Other preoperative labs   00%Serum albumin   00%-7%Serum albumin   00%-7%0%
Total variables (excl. combinations)   60Total variables (excl. combinations)   75Total variables (excl. combinations)   64
Table 3

Short-term risk model variables by publication year

≤ 19971998-20072008-2017
Variablen = 39%Variablen = 45%ΔaVariablen = 49%ΔaΔb
Age   3692%Age   3680%-12%Age   4388%-5%8%
Repeat operation   2459%Left ventricular function   3271%15%Left ventricular function   3163%7%-8%
Comb. heart failure variables   2359%Gender   3067%20%Urgency   2959%10%-5%
Left ventricular function   2256%Renal failure   3067%25%Gender   2755%9%-12%
History of MI   2254%Urgency   2964%16%Renal failure   2755%14%-12%
Comb. any MI variable   2254%Comb. any MI variable   2556%2%Comb. critical state   2653%24%9%
Urgency   2049%Repeat operation   2453%-5%Comb. arterial disease   2449%10%0%
Comb. CHF or NYHA   2051%History of MI   2453%0%Comb. heart failure variables   2347%-12%-4%
Gender   1946%Comb. heart failure variables   2351%-7%Comb. CHF or NYHA   2143%-8%1%
Renal failure   1641%Comb. arterial disease   2249%10%Repeat operation   2041%-18%-13%
Lung disease   1537%Comb. critical state   2044%15%Peripheral arterial disease   1837%-2%1%
Peripheral arterial disease   1539%Comb. CHF or NYHA   1942%-9%Diabetes   1429%7%-11%
Comb. arterial disease   1539%Comb. vessel disease   1942%6%History of MI   1327%-27%-27%
Comb. vessel disease   1437%Diabetes   1840%18%Comb. any MI variable   1327%-27%-29%
Neurologic disease   1334%Lung disease   1840%3%Cardiogenic shock   1224%10%-2%
Angina   1332%Neurologic disease   1738%4%NYHA class   1122%-2%5%
Congestive heart failure   1334%Peripheral arterial disease   1636%-3%Congestive heart failure   1122%-12%-6%
Comb. critical state   1229%Left main disease   1636%9%Concurrent procedure   1122%8%2%
Body size measurements   1127%Body size measurements   1431%4%Lung disease   1020%-16%-20%
Comb. ECG or arrhythmia variables   1127%Congestive heart failure   1329%-5%Comb. vessel disease   1020%-16%-22%
NYHA class   1024%Comb. HTN or BP   1329%14%Neurologic disease   918%-16%-19%
Left main disease   1027%Hypertension   1227%12%Number of diseased vessels   918%1%-6%
Diabetes   822%Cardiogenic shock   1227%12%Preoperative IABP use   918%1%1%
Number of diseased vessels   717%Number of diseased vessels   1124%7%Comb. PCI variables   714%0%-3%
Preoperative IABP use   717%Pulmonary hypertension   1022%12%Body size measurements   612%-15%-19%
Valve disease   615%Comb. ECG or arrhythmia variables   1022%-5%Valve disease   612%-2%6%
Hypertension   615%Concurrent procedure   920%5%Non-CABG surgery   612%-2%-1%
Nitroglycerin use   615%NYHA class   818%-7%Critical state   612%12%3%
Cardiomegaly   615%Preoperative IABP use   818%1%Postoperative variables   612%5%-6%
Cardiogenic shock   615%Postoperative variables   818%10%Extracardiac arteriopathy   612%12%-1%
Non-CABG surgery   615%Comb. PCI variables   818%3%Inotropic medication   612%5%6%
Concurrent procedure   615%Any arrhythmia   716%3%Atrial arrhythmia   612%10%8%
Diffuse/severe disease   615%Prior/recent PCI or PTCA   613%6%Pulmonary hypertension   510%0%-12%
Comb. graft variables   615%Non-CABG surgery   613%-1%Angina   510%-22%-1%
Comb. HTN or BP   615%Extracardiac arteriopathy   613%13%On- vs. off-pump CABG   510%10%6%
Comb. PCI variables   615%Preoperative diuretic use   613%6%Comb. ECG or arrhythmia variables   510%-17%-12%
Cardiopulmonary bypass time   512%Angina   511%-21%Comb. graft variables   48%-6%-1%
Any arrhythmia   512%Endocarditis   511%6%Any arrhythmia   48%-4%-7%
Pulmonary hypertension   410%Intraoperative variables   511%11%Race or ethnicity   36%6%-3%
Pulmonary rales   410%Race or ethnicity   49%9%Left main disease   36%-21%-29%
Number of grafts   37%Liver disease   49%2%Cardiopulmonary bypass time   36%-6%-3%
Liver disease   37%Cardiopulmonary bypass time   49%-3%Prior/recent PCI or PTCA   36%-1%-7%
Prior/recent PCI or PTCA   37%Critical state   49%9%Type of graft(s)   36%-1%-1%
Anemia (hemoglobin, hematocrit)   37%Preop intubation   49%2%Other preoperative labs   36%6%6%
Preop intubation   37%Dyspnea   49%9%Hypertension   24%-11%-23%
Postoperative variables   37%Comb. graft variables   49%-6%Smoking status   24%2%2%
Preoperative diuretic use   37%Ventricular or unstable arrhythmia   49%2%Liver disease   24%-3%-5%
Type of graft(s)   37%Valve disease   37%-8%Immunosuppression   24%2%-3%
Inotropic medication   37%Nitroglycerin use   37%-8%A published comorbidity index   24%2%2%
Preoperative CPR/cardiac arrest   37%Cardiomegaly   37%-8%Preoperative cardiac biomarkers   24%4%4%
PTCA failure/emergency   37%Immunosuppression   37%4%Patient education level/literacy   24%4%4%
Ventricular or unstable arrhythmia   37%Aortic cross-clamp duration   37%-1%Dyspnea   24%4%-5%
Disaster, catastrophic state   37%Active MI   37%7%Comb. HTN or BP   24%-11%-25%
Serum albumin   37%Type of graft(s)   37%-1%PTCA failure/emergency   24%-3%-3%
Aortic cross-clamp duration   27%Inotropic medication   37%-1%Stent thrombosis   24%4%4%
Endocarditis   25%Location or type of surgical center   37%7%Number of grafts   12%-5%-2%
Anticoagulation or antiplatelet use   25%PTCA failure/emergency   37%-1%Nitroglycerin use   12%-13%-5%
Digoxin or digitalis use   25%Refused blood products   37%7%Date or order of surgery   12%2%0%
Cachexia or malnutrition   25%Number of grafts   24%-3%Hypercholesterolemia   12%2%-2%
Ventricular wall motion   27%On- vs. off-pump CABG   24%4%Anticoagulation or antiplatelet use   12%-3%0%
Smoking status   12%Abdominal aortic aneurysm   24%4%Digoxin or digitalis use   12%-3%0%
Immunosuppression   12%Hypercholesterolemia   24%4%Preop intubation   12%-5%-7%
Any family history variable   12%Steroid use   24%4%Other preoperative comorbidities   12%0%-2%
A published comorbidity index   12%Other preoperative comorbidities   24%2%Insurance type or status   12%2%2%
Other preoperative comorbidities   12%Diffuse/severe disease   24%-10%Recent admissions   12%2%2%
ACE inhibitor use   12%Atrial arrhythmia   24%2%Time from admission to procedure   12%2%2%
Type of MI   12%Smoking status   12%0%Center’s case frequency   12%2%2%
Atrial arrhythmia   12%Date or order of surgery   12%2%Ventricular or unstable arrhythmia   12%-5%-7%
Antiarrhythmic agents   12%Any family history variable   12%0%PT or INR   12%2%2%
Other ECG abnormalities   12%Anticoagulation or antiplatelet use   12%-3%Disaster, catastrophic state   12%-5%2%
Race or ethnicity   00%Anemia (hemoglobin, hematocrit)   12%-5%Transfusion   12%2%2%
Date or order of surgery   00%Digoxin or digitalis use   12%-3%Cardiomegaly   00%-15%-7%
On- vs. off-pump CABG   00%A published comorbidity index   12%0%Aortic cross-clamp duration   00%-7%-7%
Abdominal aortic aneurysm   00%Heart rate   12%2%Endocarditis   00%-5%-11%
Hypercholesterolemia   00%ASA classification   12%2%Abdominal aortic aneurysm   00%0%-4%
Critical state   00%Acute mental status changes   12%2%Any family history variable   00%-2%-2%
Heart rate   00%Ventricular wall motion   12%-5%Anemia (hemoglobin, hematocrit)   00%-7%-2%
Steroid use   00%Calcified aorta   12%2%Heart rate   00%0%-2%
Preoperative cardiac biomarkers   00%Type of MI   12%0%Steroid use   00%0%-4%
Functional state   00%Killip classification   12%2%ACE inhibitor use   00%-2%0%
Patient education level/literacy   00%Blood pressure   12%2%Functional state   00%0%0%
ASA classification   00%Other ECG abnormalities   12%0%ASA classification   00%0%-2%
Insurance type or status   00%Preoperative thrombolysis   12%2%Left ventricular hypertrophy   00%0%0%
Recent admissions   00%Preoperative cardiac biomarkers   00%0%Acute mental status changes   00%0%-2%
Left ventricular hypertrophy   00%ACE inhibitor use   00%-2%Intraoperative variables   00%0%-11%
Time from admission to procedure   00%Functional state   00%0%Cachexia or malnutrition   00%-5%0%
Acute mental status changes   00%Patient education level/literacy   00%0%Ventricular wall motion   00%-7%-2%
Intraoperative variables   00%Insurance type or status   00%0%Calcified aorta   00%0%-2%
Extracardiac arteriopathy   00%Recent admissions   00%0%Type of MI   00%-2%-2%
Calcified aorta   00%Left ventricular hypertrophy   00%0%Active MI   00%0%-7%
Dyspnea   00%Time from admission to procedure   00%0%Pulmonary rales   00%-10%0%
Active MI   00%Cachexia or malnutrition   00%-5%Preoperative diuretic use   00%-7%-13%
Killip classification   00%Pulmonary rales   00%-10%Killip classification   00%0%-2%
Blood pressure   00%Preoperative CPR/cardiac arrest   00%-7%Diffuse/severe disease   00%-15%-4%
Location or type of surgical center   00%Center’s case frequency   00%0%Blood pressure   00%0%-2%
Center’s case frequency   00%Stent thrombosis   00%0%Preoperative CPR/cardiac arrest   00%-7%0%
Stent thrombosis   00%Antiarrhythmic agents   00%-2%Location or type of surgical center   00%0%-7%
Preoperative thrombolysis   00%PT or INR   00%0%Antiarrhythmic agents   00%-2%0%
PT or INR   00%Disaster, catastrophic state   00%-7%Other ECG abnormalities   00%-2%-2%
Transfusion   00%Transfusion   00%0%Preoperative thrombolysis   00%0%-2%
Refused blood products   00%Other preoperative labs   00%0%Refused blood products   00%0%-7%
Other preoperative labs   00%Serum albumin   00%-7%Serum albumin   00%-7%0%
Total variables (excl. combinations)   60Total variables (excl. combinations)   73Total variables (excl. combinations)   61
Table 4

Long-term risk model variables by publication year

≤ 19971998-20072008-2017
Variablen = 2%Variablen = 9%ΔaVariablen = 12%ΔaΔb
Age   150%Age   889%39%Age   542%-8%-47%
Diabetes   150%Left ventricular function   889%39%Left ventricular function   542%-8%-47%
Renal failure   150%Comb. heart failure variables   778%28%Gender   325%25%14%
Left ventricular function   150%Diabetes   667%17%Diabetes   325%-25%-42%
Neurologic disease   150%Renal failure   667%17%Renal failure   325%-25%-42%
Peripheral arterial disease   150%Congestive heart failure   667%17%Peripheral arterial disease   325%-25%-31%
Congestive heart failure   150%Comb. arterial disease   667%17%Comb. arterial disease   325%-25%-42%
Left main disease   150%Comb. CHF or NYHA   667%17%Comb. any MI variable   325%25%25%
Aortic cross-clamp duration   150%Peripheral arterial disease   556%6%Comb. heart failure variables   325%-25%-53%
Ventricular wall motion   150%Body size measurements   444%44%Comb. CHF or NYHA   325%-25%-42%
Comb. arterial disease   150%Lung disease   444%44%Urgency   217%17%6%
Comb. heart failure variables   150%Neurologic disease   444%-6%Lung disease   217%17%-28%
Comb. CHF or NYHA   150%Postoperative variables   444%44%Neurologic disease   217%-33%-28%
Comb. vessel disease   150%Comb. graft variables   444%44%NYHA class   217%17%17%
Total variables (excl. combinations)   10Smoking status   333%33%History of MI   217%17%17%
Type of graft(s)   333%33%Comb. vessel disease   217%-33%-6%
Left main disease   222%-28%Comb. graft variables   217%17%-28%
Hypercholesterolemia   222%22%Atrial arrhythmia   217%17%17%
Comb. vessel disease   222%-28%Body size measurements   18%8%-36%
Gender   111%11%Repeat operation   18%8%-3%
Urgency   111%11%Angina   18%8%8%
Repeat operation   111%11%Congestive heart failure   18%-42%-58%
Number of grafts   111%11%Number of diseased vessels   18%8%8%
Valve disease   111%11%Number of grafts   18%8%-3%
Hypertension   111%11%Valve disease   18%8%-3%
Date or order of surgery   111%11%Hypertension   18%8%-3%
Aortic cross-clamp duration   111%-39%Race or ethnicity   18%8%8%
Digoxin or digitalis use   111%11%Preoperative IABP use   18%8%8%
Heart rate   111%11%Smoking status   18%8%-25%
Functional state   111%11%Left main disease   18%-42%-14%
Left ventricular hypertrophy   111%11%Cardiogenic shock   18%8%8%
Intraoperative variables   111%11%Immunosuppression   18%8%8%
Calcified aorta   111%11%Date or order of surgery   18%8%-3%
Preoperative diuretic use   111%11%On- vs. off-pump CABG   18%8%8%
Comb. HTN or BP   111%11%Prior/recent PCI or PTCA   18%8%8%
Ventricular or unstable arrhythmia   111%11%Intraoperative variables   18%8%-3%
Comb. ECG or arrhythmia variables   111%11%Postoperative variables   18%8%-36%
Preoperative thrombolysis   111%11%Active MI   18%8%8%
Total variables (excl. combinations)   31Diffuse/severe disease   18%8%8%
Type of graft(s)   18%8%-25%
Comb. HTN or BP   18%8%-3%
Inotropic medication   18%8%8%
Comb. critical state   18%8%8%
Comb. PCI variables   18%8%8%
Total variables (excl. combinations)   35
Table 5

Models containing only preoperative data

All modelsShort-term modelsLong-term models
Variablen = 89%Variablen = 75%Variablen = 14%
Left ventricular function6270%Left ventricular function5269%Renal failure1179%
Renal failure5461%Urgency4661%Left ventricular function1071%
Comb. arterial disease5258%Gender4560%Diabetes964%
Comb. heart failure variables5258%Repeat operation4459%Comb. arterial disease964%
Gender5157%Renal failure4357%Comb. heart failure variables964%
Urgency5056%Comb. arterial disease4357%Peripheral arterial disease857%
Repeat operation4854%Comb. heart failure variables4357%Comb. CHF or NYHA857%
Peripheral arterial disease4551%History of MI3851%Lung disease750%
Comb. CHF or NYHA4449%Comb. any MI variable3851%Gender643%
History of MI4247%Peripheral arterial disease3749%Neurologic disease643%
Comb. any MI variable4247%Comb. CHF or NYHA3648%Comb. vessel disease643%
Lung disease4146%Comb. critical state3648%Body size measurements536%
Comb. critical state4045%Lung disease3445%Congestive heart failure536%
Diabetes3742%Comb. vessel disease3141%Left main disease536%
Comb. vessel disease3742%Diabetes2837%Urgency429%
Neurologic disease3236%Neurologic disease2635%Repeat operation429%
Left main disease2831%Left main disease2331%History of MI429%
Congestive heart failure2730%Cardiogenic shock2331%Comb. any MI variable429%
Cardiogenic shock2730%Congestive heart failure2229%Hypertension429%
Body size measurements2629%Body size measurements2128%Comb. HTN or BP429%
Number of diseased vessels2326%Number of diseased vessels2027%Race or ethnicity429%
NYHA class2124%NYHA class1824%Comb. critical state429%
Hypertension1921%Comb. ECG or arrhythmia variables1723%Smoking status429%
Comb. HTN or BP1921%Hypertension1520%Cardiogenic shock429%
Comb. ECG or arrhythmia variables1921%Comb. HTN or BP1520%NYHA class321%
Comb. PCI variables1820%Comb. PCI variables1520%Number of diseased vessels321%
Valve disease1618%Valve disease1317%Valve disease321%
Preoperative IABP use1517%Preoperative IABP use1216%Preoperative IABP use321%
Prior/recent PCI or PTCA1416%Prior/recent PCI or PTCA1115%Inotropic medication321%
Inotropic medication1315%Any arrhythmia1115%Immunosuppression321%
Any arrhythmia1213%Inotropic medication1013%Date or order of surgery321%
Pulmonary hypertension1011%Pulmonary hypertension1013%Prior/recent PCI or PTCA321%
Race or ethnicity1011%Nitroglycerin use811%Comb. PCI variables321%
Preoperative diuretic use89%Preoperative diuretic use79%Atrial arrhythmia321%
Nitroglycerin use89%Cardiomegaly79%Comb. ECG or arrhythmia variables214%
Smoking status89%Race or ethnicity68%Extracardiac arteriopathy17%
Atrial arrhythmia89%Extracardiac arteriopathy68%Preoperative diuretic use17%
Extracardiac arteriopathy78%Liver disease68%Diffuse/severe disease17%
Liver disease78%Atrial arrhythmia57%Liver disease17%
Cardiomegaly78%Smoking status45%On- vs. off-pump CABG17%
Immunosuppression78%Immunosuppression45%Any arrhythmia17%
Diffuse/severe disease56%Diffuse/severe disease45%Ventricular or unstable arrhythmia17%
Digoxin or digitalis use56%Digoxin or digitalis use45%Hypercholesterolemia17%
Dyspnea44%Dyspnea45%Digoxin or digitalis use17%
Pulmonary rales44%Pulmonary rales45%Functional state17%
Date or order of surgery44%Critical state45%Recent admissions17%
On- vs. off-pump CABG44%On- vs. off-pump CABG34%Cachexia or malnutrition00%
Ventricular or unstable arrhythmia44%Ventricular or unstable arrhythmia34%Ventricular wall motion00%
Critical state44%Ventricular wall motion34%Pulmonary hypertension00%
Ventricular wall motion33%PTCA failure/emergency34%Calcified aorta00%
PTCA failure/emergency33%Anticoagulation or antiplatelet use34%Dyspnea00%
Hypercholesterolemia33%Anemia (hemoglobin, hematocrit)34%Type of MI00%
Anticoagulation or antiplatelet use33%A published comorbidity index34%Active MI00%
Anemia (hemoglobin, hematocrit)33%Other preoperative labs34%Pulmonary rales00%
A published comorbidity index33%Hypercholesterolemia23%Killip classification00%
Other preoperative labs33%Cachexia or malnutrition23%Number of grafts00%
Cachexia or malnutrition22%Type of MI23%Type of graft(s)00%
Type of MI22%Active MI23%Comb. graft variables00%
Active MI22%Preoperative CPR/cardiac arrest23%Blood pressure00%
Preoperative CPR/cardiac arrest22%Endocarditis23%Nitroglycerin use00%
Endocarditis22%Stent thrombosis23%Cardiopulmonary bypass time00%
Stent thrombosis22%Other ECG abnormalities23%Cardiomegaly00%
Other ECG abnormalities22%Disaster, catastrophic state23%Preoperative CPR/cardiac arrest00%
Disaster, catastrophic state22%Preop intubation23%Location or type of surgical center00%
Preop intubation22%Steroid use23%Center’s case frequency00%
Steroid use22%Preoperative cardiac biomarkers23%Aortic cross-clamp duration00%
Preoperative cardiac biomarkers22%Date or order of surgery11%Endocarditis00%
Recent admissions22%Recent admissions11%Abdominal aortic aneurysm00%
Calcified aorta11%Calcified aorta11%PTCA failure/emergency00%
Killip classification11%Killip classification11%Stent thrombosis00%
Location or type of surgical center11%Location or type of surgical center11%Any family history variable00%
Any family history variable11%Any family history variable11%Antiarrhythmic agents00%
Antiarrhythmic agents11%Antiarrhythmic agents11%Other ECG abnormalities00%
Non-CABG surgery11%Non-CABG surgery11%Non-CABG surgery00%
Preoperative thrombolysis11%Preoperative thrombolysis11%Anticoagulation or antiplatelet use00%
PT or INR11%PT or INR11%Preoperative thrombolysis00%
Transfusion11%Transfusion11%PT or INR00%
Serum albumin11%Serum albumin11%Critical state00%
ACE inhibitor use11%ACE inhibitor use11%Disaster, catastrophic state00%
Functional state11%ASA classification11%Anemia (hemoglobin, hematocrit)00%
ASA classification11%Insurance type or status11%Transfusion00%
Insurance type or status11%Acute mental status changes11%Refused blood products00%
Acute mental status changes11%Functional state00%Preop intubation00%
Number of grafts00%Number of grafts00%Concurrent procedure00%
Type of graft(s)00%Type of graft(s)00%A published comorbidity index00%
Comb. graft variables00%Comb. graft variables00%Heart rate00%
Blood pressure00%Blood pressure00%Steroid use00%
Cardiopulmonary bypass time00%Cardiopulmonary bypass time00%Preoperative cardiac biomarkers00%
Center’s case frequency00%Center’s case frequency00%Other preoperative labs00%
Aortic cross-clamp duration00%Aortic cross-clamp duration00%Serum albumin00%
Abdominal aortic aneurysm00%Abdominal aortic aneurysm00%Other preoperative comorbidities00%
Refused blood products00%Refused blood products00%ACE inhibitor use00%
Concurrent procedure00%Concurrent procedure00%Patient education level/literacy00%
Heart rate00%Heart rate00%ASA classification00%
Other preoperative comorbidities00%Other preoperative comorbidities00%Insurance type or status00%
Patient education level/literacy00%Patient education level/literacy00%Left ventricular hypertrophy00%
Left ventricular hypertrophy00%Left ventricular hypertrophy00%Time from admission to procedure00%
Time from admission to procedure00%Time from admission to procedure00%Acute mental status changes00%
Intraoperative variables00%Intraoperative variables00%Intraoperative variables00%
Postoperative variables00%Postoperative variables00%Postoperative variables00%
Total variables (excl. combinations)76Total variables (excl. combinations)75Total variables (excl. combinations)39
Table 6

Models considering on- vs. off- pump CABG

All modelsShort-term modelsLong-term models
Variablen = 4%Variablen = 3%Variablen = 1%
Age   4100%Age   3100%Age   1100%
On- vs. off-pump CABG   4100%On- vs. off-pump CABG   3100%History of MI   1100%
Gender   250%Gender   267%Comb. any MI variable   1100%
Renal failure   250%Renal failure   267%On- vs. off-pump CABG   1100%
Urgency   250%Urgency   267%Total variables (excl. combinations)   3
History of MI   250%Comb. critical state   267%
Comb. any MI variable   250%Body size measurements   133%
Comb. critical state   250%Diabetes   133%
Body size measurements   125%Left ventricular function   133%
Diabetes   125%Lung disease   133%
Left ventricular function   125%Pulmonary hypertension   133%
Lung disease   125%Repeat operation   133%
Pulmonary hypertension   125%Neurologic disease   133%
Repeat operation   125%Peripheral arterial disease   133%
Neurologic disease   125%Comb. arterial disease   133%
Peripheral arterial disease   125%NYHA class   133%
Comb. arterial disease   125%History of MI   133%
NYHA class   125%Active MI   133%
Active MI   125%Comb. any MI variable   133%
Preoperative diuretic use   125%Preoperative diuretic use   133%
Comb. heart failure variables   125%Comb. heart failure variables   133%
Comb. CHF or NYHA   125%Comb. CHF or NYHA   133%
Number of diseased vessels   125%Number of diseased vessels   133%
Comb. vessel disease   125%Comb. vessel disease   133%
Hypertension   125%Hypertension   133%
Comb. HTN or BP   125%Comb. HTN or BP   133%
Race or ethnicity   125%Race or ethnicity   133%
Preoperative IABP use   125%Preoperative IABP use   133%
Inotropic medication   125%Inotropic medication   133%
Left main disease   125%Left main disease   133%
Cardiogenic shock   125%Cardiogenic shock   133%
Any arrhythmia   125%Any arrhythmia   133%
Comb. ECG or arrhythmia variables   125%Comb. ECG or arrhythmia variables   133%
Steroid use   125%Steroid use   133%
Total variables (excl. combinations)   26Total variables (excl. combinations)   26


Across the post-CABG follow-up periods, different pre-CABG risk factors predictive of mortality were documented. This literature search revealed dozens of logistic regression models, each reporting different patient risk factors associated with time-varying post-CABG mortality endpoints. As documented by the tables, the ST models found the patient’s risk variables related to their severity of coronary disease (e.g., more commonly reported be important predictors), whereas patient’s chronic comorbidities (e.g., diabetes, cerebrovascular disease, or pulmonary disease) appeared to be more frequently associated with LT post-CABG mortality. Following one-year post-CABG, life expectancy appears to be most strongly impacted by non-cardiac comorbidities than cardiac factors or surgical processes of care. While optimizing CABG patient selection and surgical techniques may be important ST, optimal management of non-cardiac comorbidities may improve post-CABG patients’ LT survival. Moreover, across all follow-up time periods, a patient’s age, ejection fraction, and renal function (e.g., creatinine or dialysis dependence) were important predictors of post-CABG mortality; these were consistently reported for the ST and LT mortality time periods.

A special sub-analysis was performed for the sub-group of models comprised of preoperative risk factors along with a variable indicating the on-pump vs. off-pump surgical technique. Although there were 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 be prioritized to evaluate the impact of differential pre-CABG waiting periods[16].

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 markers of optimal medical management during the pre- and postoperative periods[17].

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 mortality[23]. Hence, these types of non-traditional CABG risk factors may be worthy of future exploration.


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.


Authors’ contributions

Wrote the initial study protocol, under the oversight and leadership of Grover FL: Carr BM, Shroyer ALW

Prepared the research-related materials to obtain an official determination of “not research” by the Northport VA Medical Center’s Research and Development office: Shroyer ALW

Performed the detailed data after implementing the advanced literature search strategy, acquisition with active involvement by Grover FL and Shroyer ALW: Carr BM

Ran the initial data analyses and prepared the initial set of tables and figures: Carr BM

Aided in the interpretation as well as the full co-author team worked collaboratively to assure a comprehensive search strategy: Grover FL, Shroyer ALW

The first draft of this article was written jointly by Carr BM and Shroyer ALW, with revisions provided by Grover FL, all co-authors provided their final approval.

Availability of data and materials

This study’s data file, including data extracted for each reference listed, is available as an online-only supplement (Appendix A).

Financial support and sponsorship


Conflicts of interest

All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate

The Northport VA Medical Center’s Research and Development Office determined that this study was “not research”; this “not research” determination was dated September 12, 2019.

Consent for publication

Not applicable.


© The Author(s) 2020.


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Carr BM, Grover FL, Shroyer ALW. Risk factors adversely impacting post coronary artery bypass grafting longer-term vs. shorter-term clinical outcomes. Vessel Plus 2020;4:12.

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Carr BM, Grover FL, Shroyer ALW. Risk factors adversely impacting post coronary artery bypass grafting longer-term vs. shorter-term clinical outcomes. Vessel Plus. 2020; 4: 12.

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Brendan M. Carr, Frederick L. Grover, Annie Laurie W. Shroyer. 2020. "Risk factors adversely impacting post coronary artery bypass grafting longer-term vs. shorter-term clinical outcomes" Vessel Plus. 4: 12.

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Carr, BM.; Grover FL.; Shroyer ALW. Risk factors adversely impacting post coronary artery bypass grafting longer-term vs. shorter-term clinical outcomes. Vessel Plus. 2020, 4, 12.

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