Sex and age difference in risk factor distribution, trend, and long-term outcome of patients undergoing isolated coronary artery bypass graft surgery

Sattartabar, Babak; Ajam, Ali; Pashang, Mina; Jalali, Arash; Sadeghian, Saeed; Mortazavi, Seyedeh Hamideh; Mansourian, Soheil; Bagheri, Jamshid; Karimi, Abbas; Hosseini, Kaveh

doi:10.1186/s12872-021-02273-2

Cited by 16 publications

(15 citation statements)

References 35 publications

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“…The importance of EF on CABG survival has also been repeatedly reported in other studies ( 28 , 29 ), some of which revealed a dose-response relationship between decreasing EF and overall risk of death ( 28 ). This is also the case with the role of our other selected features in estimating long-term CABG survival, including age ( 30 , 31 ), glucose and lipid profile ( 32 – 35 ), Hb ( 36 , 37 ), Cr ( 38 , 39 ), and BMI ( 31 ).…”

Section: Discussionmentioning

confidence: 86%

Machine learning algorithms for predicting mortality after coronary artery bypass grafting

Khalaji

Behnoush

Jameie

et al. 2022

Front. Cardiovasc. Med.

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BackgroundAs 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 methodsVarious 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.ResultsA 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 > 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.ConclusionVarious 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.

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Section: Discussionmentioning

confidence: 86%

Machine learning algorithms for predicting mortality after coronary artery bypass grafting

Khalaji

Behnoush

Jameie

et al. 2022

Front. Cardiovasc. Med.

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“…In patients with stable CAD with conservative treatment, it has been shown that the absence of a decrease in CAVI after six months was associated with an unfavorable prognosis [ 36 ]. The traditional fight against risk factors (smoking, dyslipidemia, diabetes mellitus, arterial hypertension [ 2 ]) is manifested, among other things, by an improvement in the elasticity of the vascular wall, but it is not clear how pronounced this can be in patients after CABG; such studies have not been conducted. The question also remains open—is it possible to influence the long-term prognosis after CABG if it is possible to achieve a decrease in CAVI in the course of therapeutic and prophylactic interventions.…”

Section: Discussionmentioning

confidence: 99%

“…Long-term results after coronary artery bypass grafting depend not only on the completeness of revascularization, on the initial severity of coronary and myocardial lesions, but also on comorbidity (diabetes mellitus, arterial hypertension, diseases of the lungs, kidneys, etc.) [ 1 , 2 ]. In addition, of importance is the state of the vascular system as a whole, which can also affect the course of the disease after coronary artery bypass grafting (CABG).…”

Section: Introductionmentioning

confidence: 99%

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

Sumin

Щеглова

Ivanov

et al. 2022

JCM

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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.

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“…The results are similar to those of some previous studies [ 7 – 9 ]. It has been suggested that females have a higher risk of adverse cardiovascular events and death after CABG [ 10 , 11 ]. However, gender was not a confounding factor during the data analysis of this study.…”

Section: Discussionmentioning

confidence: 99%

Comparison of in-hospital outcomes after coronary artery bypass graft surgery in elders and younger patients: a multicenter retrospective study

Zhang

Wang

et al. 2023

J Cardiothorac Surg

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Objectives We aimed to identify in-hospital outcomes in young (≤ 65 years) and old (> 65 years) patients after coronary artery bypass grafting (CABG) by analyzing the effect of age on adverse events after on-pump or off-pump CABG. Methods Patients older than 65 years were defined as older patients and others were defined as younger patients. The qualitative data were compared by chi-square or Fisher's exact tests. The quantitative data were compared by the two-sample independent t-test or Mann–Whitney U test. Multifactor binary logistic regression was used to control for confounders and to investigate the effect of age on dichotomous outcome variables such as death. Results In the on-pump CABG population, the postoperative in-hospital mortality, the incidence of postoperative symptomatic cerebral infarction (POSCI) and postoperative atrial fibrillation (POAF) was higher in older patients than in younger patients (P value < 0.05), and age > 65 years was associated with postoperative in-hospital mortality (OR = 2.370, P value = 0.031), POSCI (OR = 5.033, P value = 0.013), and POAF (OR = 1.499, P value < 0.001). In the off-pump CABG population, the incidence of POAF was higher in older patients than in younger patients (P value < 0.05), and age > 65 years was associated with POAF (OR = 1.392, P value = 0.011). Conclusion In-hospital outcomes after CABG are strongly influenced by age. In on-pump CABG, the risk of postoperative death, POSCI, and POAF was higher in older patients, and in off-pump CABG, the risk of POAF was higher in older patients.

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Sex and age difference in risk factor distribution, trend, and long-term outcome of patients undergoing isolated coronary artery bypass graft surgery

Cited by 16 publications

References 35 publications

Machine learning algorithms for predicting mortality after coronary artery bypass grafting

Machine learning algorithms for predicting mortality after coronary artery bypass grafting

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

Comparison of in-hospital outcomes after coronary artery bypass graft surgery in elders and younger patients: a multicenter retrospective study

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