Long-Term Post-CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

Risk Prediction Models for Long-Term Survival after Cardiac Surgery: A Systematic Review

BACKGROUND

The reporting of alternative postoperative measures of quality after cardiac surgery is becoming increasingly important as in-hospital mortality rates continue to decline. This study aims to systematically review and assess risk models designed to predict long-term outcomes after cardiac surgery.

METHODS

The MEDLINE and Embase databases were searched for articles published between 1990 and 2020. Studies developing or validating risk prediction models for long-term outcomes after cardiac surgery were included. Data were extracted using checklists for critical appraisal and systematic review of prediction modeling studies.

RESULTS

Eleven studies were identified for inclusion in the review, of which nine studies described the development of long-term risk prediction models after cardiac surgery and two were external validation studies. A total of 70 predictors were included across the nine models. The most frequently used predictors were age (n = 9), peripheral vascular disease (n = 8), renal disease (n = 8), and pulmonary disease (n = 8). Despite all models demonstrating acceptable performance on internal validation, only two models underwent external validation, both of which performed poorly.

CONCLUSION

Nine risk prediction models predicting long-term mortality after cardiac surgery have been identified in this review. Statistical issues with model development, limited inclusion of outcomes beyond 5 years of follow-up, and a lack of external validation studies means that none of the models identified can be recommended for use in contemporary cardiac surgery. Further work is needed either to successfully externally validate existing models or to develop new models. Newly developed models should aim to use standardized long-term specific reproducible outcome measures.

Health-related quality of life impacts upon 5-year survival after coronary artery bypass surgery

BACKGROUND

Poor preoperative health-related quality of life (HRQoL) has been associated with reduced short-term survival after coronary artery bypass graft (CABG) surgery; however, its impact on long-term mortality is unknown. This study's objective was to determine if baseline HRQoL status predicts 5-year post-CABG mortality.

METHODS

This prespecified, randomized on/off bypass follow-up study (ROOBY-FS) subanalysis compared baseline patient characteristics and HRQoL scores, obtained from the Seattle Angina Questionnaire (SAQ) and Veterans RAND Short Form-36 (VR-36), between 5-year post-CABG survivors and nonsurvivors. Standardized subscores were calculated for each questionnaire. Multivariable logistic regression assessed whether HRQoL survey subcomponents independently predicted 5-year mortality (p ≤ .05).

RESULTS

Of the 2203 ROOBY-FS enrollees, 2104 (95.5%) completed baseline surveys. Significant differences between 5-year post-CABG deaths (n = 286) and survivors (n = 1818) included age, history of chronic obstructive pulmonary disease, stroke, peripheral vascular disease, renal dysfunction, diabetes, lower left ventricular ejection fraction, atrial fibrillation, depression, non-White race/ethnicity, lower education status, and off-pump CABG. Adjusting for these factors, baseline VR-36 physical component summary score (p = .01), VR-36 mental component summary score (p < .001), and SAQ physical limitation score (p = .003) were all associated with 5-year all-cause mortality.

CONCLUSIONS

Pre-CABG HRQoL scores may provide clinically relevant prognostic information beyond traditional risk models and prove useful for patient-provider shared decision-making and enhancing pre-CABG informed consent.

Machine learning algorithms for predicting mortality after coronary artery bypass grafting

Background

As the era of big data analytics unfolds, machine learning (ML) might be a promising tool for predicting clinical outcomes. This study aimed to evaluate the predictive ability of ML models for estimating mortality after coronary artery bypass grafting (CABG).

Materials and methods

Various baseline and follow-up features were obtained from the CABG data registry, established in 2005 at Tehran Heart Center. After selecting key variables using the random forest method, prediction models were developed using: Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) algorithms. Area Under the Curve (AUC) and other indices were used to assess the performance.

Results

A total of 16,850 patients with isolated CABG (mean age: 67.34 ± 9.67 years) were included. Among them, 16,620 had one-year follow-up, from which 468 died. Eleven features were chosen to train the models. Total ventilation hours and left ventricular ejection fraction were by far the most predictive factors of mortality. All the models had AUC &gt; 0.7 (acceptable performance) for 1-year mortality. Nonetheless, LR (AUC = 0.811) and XGBoost (AUC = 0.792) outperformed NB (AUC = 0.783), RF (AUC = 0.783), SVM (AUC = 0.738), and KNN (AUC = 0.715). The trend was similar for two-to-five-year mortality, with LR demonstrating the highest predictive ability.

Conclusion

Various ML models showed acceptable performance for estimating CABG mortality, with LR illustrating the highest prediction performance. These models can help clinicians make decisions according to the risk of mortality in patients undergoing CABG.

Long-Term Prognosis after Coronary Artery Bypass Grafting: The Impact of Arterial Stiffness and Multifocal Atherosclerosis

The aim of the study was to study the effect of arterial stiffness and multifocal atherosclerosis on the 10-year prognosis of patients after coronary artery bypass grafting. Methods. Patients with coronary artery disease (n = 274) who underwent coronary artery bypass grafting (CABG), in whom cardio-ankle vascular index (CAVI) was assessed using the VaSera VS-1000 device and the presence of peripheral atherosclerosis in Doppler ultrasound. Groups were distinguished with normal CAVI (<9.0, n = 163) and pathological CAVI (≥9.0, n = 111). To assess the prognosis, coronary and non-coronary death, myocardial infarction, acute cerebrovascular accident/transient ischemic attack, repeated CABG, percutaneous coronary intervention, carotid endarterectomy, peripheral arterial surgery, pacemaker implantation were analyzed. Results. During the observation period, mortality was 27.7%. A fatal outcome from all causes was in 37 (22.7%) patients in the group with normal CAVI and in 39 (35.14%) in the group with pathological CAVI (p = 0.023). Death from cardiac causes was more common in the group with CAVI ≥ 9.0—in 25 cases (22.52%) than in the group with CAVI < 9.0—in 19 (11.6%, p = 0.016). The combined endpoint in patients with pathological CAVI was detected in 66 (59.46%) cases, with normal CAVI values—in 76 (46.63%) cases (p = 0.03). The presence of diabetes mellitus, multifocal atherosclerosis (p = 0.004), pathological CAVI (p = 0.063), and male gender were independent predictors of death at 10-year follow-up after CABG. The presence of multifocal atherosclerosis and pathological CAVI during the preoperative examination of patients were independent predictors of the combined endpoint development. Findings. Patients with coronary artery disease with pathological CAVI before CABG were more likely to experience adverse events and death in the long-term follow-up than patients with normal CAVI. Further studies are needed to investigate the possibility of correcting pathological CAVI after CABG after secondary prevention and the possible impact of this correction on prognosis.

Chronic Coronary Syndrome in Frail Old Population

The demographic trend of aging is associated with an increased prevalence of comorbidities among the elderly. Physical, immunological, emotional and cognitive impairment, in the context of the advanced biological age segment, leads to the maintenance and precipitation of cardiovascular diseases. Thus, more and more data are focused on understanding the pathophysiological mechanisms underlying each fragility phenotype and how they potentiate each other. The implications of inflammation, sarcopenia, vitamin D deficiency and albumin, as dimensions inherent in fragility, in the development and setting of chronic coronary syndromes (CCSs) have proven their patent significance but are still open to research. At the same time, the literature speculates on the interdependent relationship between frailty and CCSs, revealing the role of the first one in the development of the second. In this sense, depression, disabilities, polypharmacy and even cognitive disorders in the elderly with ischemic cardiovascular disease mean a gradual and complex progression of frailty. The battery of tests necessary for the evaluation of the elderly with CCSs requires a permanent update, according to the latest guidelines, but also an individualized approach related to the degree of frailty and the conditions imposed by it. By summation, the knowledge of frailty screening methods, through the use of sensitive and individualized tools, is the foundation of secondary prevention and prognosis in the elderly with CCSs. Moreover, a comprehensive geriatric assessment remains the gold standard of the medical approach of these patients. The management of the frail elderly, with CCSs, brings new challenges, also from the perspective of the treatment particularities. Sometimes the risk–benefit balance is difficult to achieve. Therefore, the holistic, individualized and updated approach of these patients remains a desired objective, by understanding and permanently acquiring knowledge on the complexity of the frailty syndrome.

Superiority of Bilateral Internal Thoracic Artery Grafting in Long-term Survival after Coronary Artery Bypass Through the Lenses of a Bedside Risk Score

BACKGROUND

Risk algorithms for the prediction of long-term survival after coronary artery bypass grafting (CABG) do not include the use of bilateral internal thoracic artery (BITA) grafting among the independent predictors.

METHODS

There were analyzed 5,666 consecutive patients who underwent isolated (n=4,715 - BITA=2,792) and combined (n=951 - BITA=246) CABG. The mean follow-up was 10.3 years (interquartile range 9.9 years). All the predictors of an existing bedside risk score (BRS) were available for analysis (age, body mass index, ejection fraction, unstable hemodynamic state, left main disease, cerebrovascular disease, peripheral arterial disease, congestive heart failure, malignant ventricular arrhythmia, chronic obstructive pulmonary disease, diabetes and previous heart surgery). Furthermore, a modified BRS was constructed taking into account the use of BITA grafting and combined CABG.

RESULTS

The good discriminatory ability and satisfactory calibration of the BRS was confirmed in the isolated CABG subgroup. The modified BRS showed improved discriminatory ability and similar calibration. It showed a time-varying coefficient, and accordingly, we calculated adjusted survival predictions up to 20 years after isolated and combined CABG with or without BITA grafting. Patients with BITA grafting in the low-risk quartile showed 68.4% and 65.5% predicted survival rates at 20 years in the isolated and combined CABG subgroups respectively versus 56.4% and 52.8% among patients without BITA grafting.

CONCLUSIONS

The modified BRS is a useful simplified algorithm for clinicians in choosing treatment intervention for severe isolated or combined coronary artery disease. We clearly demonstrated the superiority of BITA grafting in long-term survival throughout the entire range of the modified BRS.

Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults' survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study's advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients' survival risk before a CABG operation, early prevention and disease management would be possible.

Effect of Outcome Measures on the Apparent Efficacy of Ablation for Atrial Fibrillation: Why "Success" is an Inappropriate Term

INTRODUCTION

Different endpoint criteria, different durations of follow-up and the completeness of follow-up can dramatically affect the perceived benefits of atrial fibrillation (AF) ablation.

METHODS

We defined three endpoints for recurrence of AF post ablation in a cohort of 200 patients with symptomatic AF, refractory to antiarrhythmic drugs (AADs). A 'Strict Endpoint' where patients were considered to have a recurrence with any symptomatic or documented recurrence for ≥30 seconds with no blanking period, and off their AADs, a 'Liberal Endpoint' where only documented recurrences after the blanking period, either on or off AADs were counted, and a 'Patient-defined Outcome endpoint' which was the same as the Liberal endpoint but allowed for up to two recurrences and one repeat ablation or DCCV during follow-up. We also surveyed 50 patients on the waiting list for an AF ablation and asked them key questions regarding what they would consider to be a successful result for them.

RESULTS

Freedom from recurrence of atrial tachyarrhythmias (AT) at 5 years was 62% for the Strict Endpoint, 73% for the Liberal Endpoint, and 80% for the Patient-defined Outcome endpoint (p<0.001). Of the 50 patients surveyed awaiting AF ablation, 70% said they would still consider the procedure a success if it required one repeat ablation or one DCCV (p=0.004), and 76% would be accepting of one or two recurrences during follow-up (p<0.001).

CONCLUSION

In this study, the majority of patients still considered AF ablation a successful treatment if they had up to two recurrences of AF, one repeat procedure or one DCCV. Furthermore, a 'Patient-defined' definition of success lead to significantly different results in this AF ablation cohort when compared to conventionally used/guideline directed measures of success.

A simple-to-use nomogram to predict long term survival of patients undergoing coronary artery bypass grafting (CABG) using bilateral internal thoracic artery grafting technique

BACKGROUND

Several risk scores have been created to predict long term mortality after coronary artery bypass grafting (CABG). Several studies demonstrated a reduction in long-term mortality following bilateral internal thoracic arteries (BITA) compared to single internal thoracic artery. However, these prediction models usually referred to long term survival as survival of up to 5 years. Moreover, none of these models were built specifically for operation incorporating BITA grafting.

METHODS

A historical cohort study of all patients who underwent isolated BITA grafting between 1996 and 2011 at Tel-Aviv Sourasky medical center, a tertiary referral university affiliated medical center with a 24-bed cardio-thoracic surgery department. Study population (N = 2,935) was randomly divided into 2 groups: learning group which was used to build the prediction model and validation group. Cox regression was used to predict death using pre-procedural risk factors (demographic data, patient comorbidities, cardiac characteristics and patient's status). The accuracy (discrimination and calibration) of the prediction model was evaluated.

METHODS AND FINDINGS

The learning (1,468 patients) and validation (1,467 patients) groups had similar preoperative characteristics and similar survival. Older age, diabetes mellitus, chronic obstructive lung disease, congestive heart failure, chronic renal failure, old MI, ejection fraction ≤30%, pre-operative use of intra-aortic balloon, and peripheral vascular disease, were significant predictors of mortality and were used to build the prediction model. The area under the ROC curves for 5, 10, and 15-year survival ranged between 0.742 and 0.762 for the learning group and between 0.766 and 0.770 for the validation group. The prediction model showed good calibration performance in both groups. A nomogram was built in order to introduce a simple-to-use tool for prediction of 5, 10, and 15-year survival.

CONCLUSIONS

A simple-to-use validated model can be used for a prediction of 5, 10, and 15-year mortality after CABG using the BITA grafting technique.

Preoperative factors associated with worsening in health-related quality of life following coronary artery bypass grafting in the Randomized On/Off Bypass (ROOBY) trial

For advanced coronary disease, coronary artery bypass graft (CABG) surgery generally improves patients' symptoms and long-term survival. Unfortunately, some patients experience worse health-related quality of life (HRQL) after CABG. The objective of this study is to report the frequency and risk factors associated with 1-year post-CABG HRQL deterioration.

METHODS

From 2002 to 2007, 2203 "Randomized On/Off Bypass" (ROOBY) trial patients randomly received either off-pump or on-pump CABG at 18 VA medical centers. Subjects completed both baseline and 1-year Seattle Angina Questionnaire (SAQ) and the Veterans Rand 36 (VR-36) questionnaires to assess HRQL. Using previously published criteria, the rates of clinically significant changes were determined for the SAQ [angina frequency (AF), physical limitation (PL), and quality of life (QoL)] and VR36 [mental component score (MCS) and physical component score (PCS)] subscales. Multivariate regression models were then used to identify pre-CABG patient characteristics associated with worsened 1-year HRQL status for each subscale.

RESULTS

Over 80% of patients had an improvement or no change in SAQ and VR-36 subscale scores 1 year after CABG. The HRQL scale-specific deterioration rates were 4.5% SAQ-AF, 16.8% SAQ-PL, 4.9% SAQ-QoL, 19.4% VR36-MCS, and 13.5% VR36-PCS. Predictors of 1-year HRQL deterioration were diabetes and smoking for the SAQ-AF; diabetes, chronic obstructive pulmonary disease (COPD), and peripheral vascular disease (PVD) for SAQ-PL; COPD and depression for the SAQ-QoL; diabetes for VR36-PCS, and history of stroke and depression for VR36-MCS. The baseline score was an independent predictor for worsening in all the subscales studied.

CONCLUSIONS

Among VA patients, less than 20% experienced worse HRQL 1 year after CABG. For patients with low symptom burden at baseline, diabetes, smoking, depression, PVD, COPD, and a prior stroke, clinicians should be more cautious in pre-CABG counseling as to their anticipated HRQL improvements.

Effect of multimorbidity on quality of life in adult with cardiovascular disease: a cross-sectional study

BACKGROUND

The aim of present study was to describe the effect of multimorbidity on Health-Related Quality of Life (HRQoL) in patients with coronary artery disease (CAD).

METHODS

A cross-sectional study with a simple sampling method of 296 patients undergoing coronary artery bypass surgery in a referral hospital of the northern part of Iran was conducted between April, 2015 and September, 2016. Multimorbidity was defined as the presence of at least two chronic diseases based on self-reporting and medical records. HRQoL was measured using the 36-item short form (SF-36) health status survey. We used analysis of variance (ANOVA) to assess the effect of multimorbidity on mental and physical component of HRQoL.

RESULTS

Approximately, 69% of CAD patients had at least one other disease like diabetes or hypertension. Patients without multimorbidity compared with patients with multimorbidity were significantly older (p = 0.012) and more educated (p = 0.002). Both physical and mental component score of HRQoL was better in patients without any morbidity (48.82 vs. 43.93 with 95%CI of mean difference: 3.37-6.42 and 54.85 vs. 50.44 with 95% CI of mean difference: 1.68-7.15, respectively). Both physical and mental component score was significantly lower in female and lower educated patients (physical mean score 43.07 vs. 46.54 with P = .001 and 42.53 vs. 46.82 with P < .001 and mental mean score 49.98 vs. 52.65 with P = .055 and 49.80 vs. 52.75 with P = .022 for sex and education, respectively). Also, two-way ANOVA showed that regards to morbidity, physical component score was grater in patients with lower education level than higher education level (P < .001).

CONCLUSION

The findings of this study suggest that women, lower education level and overweight reported lower quality of life. HRQoL is affected by multimorbidity among CAD patients specially in less educated.