Long-term mortality after isolated coronary artery bypass grafting and risk factors for mortality.

BACKGROUND: Patients requiring coronary artery bypass grafting (CABG) have multiple co-morbidities which need to be considered in totality when determining surgical risks. The objective of this study is to evaluate short-term and long-term mortality rates of CABG surgery, as well as to identify the most significant risk factors for mortality after isolated CABG. METHODS: All patients with complete dataset who underwent isolated CABG between January 2008 and December 2017 were included. Univariate and multivariate Cox regression was performed to determine the risk factors for all-cause mortality. Classification and regression tree analysis was performed to identify the relative importance of these risk factors. RESULTS: 3,573 patients were included in the study. Overall mortality rate was 25.7%. In-hospital mortality rate was 1.62% overall. 30-day, 1-year, 5-year, 10-year and 14.5-year mortality rates were 1.46%, 2.94%, 9.89%, 22.79% and 36.30% respectively. Factors associated with death after adjustment for other risk factors were older age, lower body mass index (BMI), hypertension, diabetes mellitus, chronic obstructive pulmonary disease, pre-operative renal failure on dialysis, higher last pre-operative creatinine level, lower estimated glomerular filtration rate (eGFR), heart failure, lower left ventricular ejection fraction and New York Heart Association class II, III and IV. Additionally, female gender and logistic EuroSCORE were associated with death on univariate Cox analysis, but not associated with death after adjustment with multivariate Cox analysis. Using CART analysis, the strongest predictor of mortality was pre-operative eGFR < 46.9, followed by logistic EuroSCORE ≥ 2.4. CONCLUSION: Poorer renal function, quantified by a lower eGFR, is the best predictor of post-CABG mortality. Amongst other risk factors, logistic EuroSCORE, age, diabetes and BMI had a relatively greater impact on mortality. Patients with chronic kidney disease stage 3B and above are at highest risk for mortality. We hope these findings heighten awareness to optimise current medical therapy in preserving renal function upon diagnosis of any atherosclerotic disease and risk factors contributing to coronary artery disease.

Predicting Coronary Artery Disease in Primary Care: Development and Validation of a Diagnostic Risk Score for Major Ethnic Groups in Southeast Asia.

BACKGROUND: Coronary artery disease (CAD) risk prediction tools are useful decision supports. Their clinical impact has not been evaluated amongst Asians in primary care. OBJECTIVE: We aimed to develop and validate a diagnostic prediction model for CAD in Southeast Asians by comparing it against three existing tools. DESIGN: We prospectively recruited patients presenting to primary care for chest pain between July 2013 and December 2016. CAD was diagnosed at tertiary institution and adjudicated. A logistic regression model was built, with validation by resampling. We validated the Duke Clinical Score (DCS), CAD Consortium Score (CCS), and Marburg Heart Score (MHS). MAIN MEASURES: Discrimination and calibration quantify model performance, while net reclassification improvement and net benefit provide clinical insights. KEY RESULTS: CAD prevalence was 9.5% (158 of 1658 patients). Our model included age, gender, type 2 diabetes mellitus, hypertension, smoking, chest pain type, neck radiation, Q waves, and ST-T changes. The C-statistic was 0.808 (95% CI 0.776-0.840) and 0.815 (95% CI 0.782-0.847), for model without and with ECG respectively. C-statistics for DCS, CCS-basic, CCS-clinical, and MHS were 0.795 (95% CI 0.759-0.831), 0.756 (95% CI 0.717-0.794), 0.787 (95% CI 0.752-0.823), and 0.661 (95% CI 0.621-0.701). Our model (with ECG) correctly reclassified 100% of patients when compared with DCS and CCS-clinical respectively. At 5% threshold probability, the net benefit for our model (with ECG) was 0.063. The net benefit for DCS, CCS-basic, and CCS-clinical was 0.056, 0.060, and 0.065. CONCLUSIONS: PRECISE (Predictive Risk scorE for CAD In Southeast Asians with chEst pain) performs well and demonstrates utility as a clinical decision support for diagnosing CAD among Southeast Asians.

Predictors of two-year mortality in Asian patients with heart failure and preserved ejection fraction.

INTRODUCTION: Mortality in patients with heart failure and preserved ejection fraction (HFpEF) remains high. Data from Asia is lacking. We aim to study the impact of ethnicity and other predictors of mortality in patients admitted for HFpEF in a multi-ethnic Asian country. MATERIAL AND METHODS: Consecutive patients admitted to two local institutions with heart failure and ejection fraction ≥50% on transthoracic echocardiogram from Jan 2008 to Dec 2009 were included. All patients were followed-up for 2 years. Overall mortality was obtained from the national registry of deaths in our country. RESULTS: A total of 1960 patients with heart failure were included. 751 (38.3%) patients had HFpEF. Overall mortality at two years was 26.6% (n=200) compared to 37.1% (n=449) in patients with reduced ejection fraction (HR 0.618 (95% CI 0.508-0.753), p<0.001). Ethnicity did not predict mortality. On multivariable Cox regression analysis, significant predictors of two-year mortality in HFpEF patients were older age (HR 1.027 (1.011-1.044)), prior myocardial infarction (HR 1.577 (1.104-2.253)), prior stroke (HR 1.475 (1.055-2.061)), smoking (HR 1.467 (1.085-1.985)), higher creatinine levels (HR 1.002 (1.001-1.003)) and use of mineralocorticoid receptor antagonists (HR 1.884 (1.226-2.896)). Use of warfarin (HR 0.506 (0.304-0.842)) and statins (HR 0.585 (0.435-0.785)) were associated with significantly lower mortality. CONCLUSIONS: In our Asian population presenting with HFpEF, two-year mortality was 26.6%. Ethnicity did not predict mortality. Older age, prior myocardial infarction, prior stroke, smoking, and higher creatinine levels were found to be significant predictors of mortality.