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Background and Aims: Atrial fibrillation frequently occurs in the postoperative period of cardiac surgery, associated with an increase in morbidity and mortality. The scores POAF, CHA2DS2-VASc and HATCH demonstrated a validated ability to predict atrial fibrillation after cardiac surgery (AFCS). The objective is to develop and validate a risk score to predict AFCS from the combination of the variables with highest predictive value of POAF, CHA2DS2-VASc and HATCH models.Methods: We conducted a single-center cohort study, performing a retrospective analysis of prospectively collected data. The study included consecutive patients undergoing cardiac surgery in 2010-2016. The primary outcome was the development of new-onset AFCS. The variables of the POAF, CHA2DS2-VASc and HATCH scores were evaluated in a multivariate regression model to determine the predictive impact. Those variables that were independently associated with AFCS were included in the final model.Results: A total of 3113 patients underwent cardiac surgery, of which 21% presented AFCS. The variables included in the new score COM-AF were: age (≥75: 2 points, 65-74: 1 point), heart failure (2 points), female sex (1 point), hypertension (1 point), diabetes (1 point), previous stroke (2 points). For the prediction of AFCS, COM-AF presented an AUC of 0.78 (95% CI 0.76-0.80), the rest of the scores presented lower discrimination ability (P < 0.001): CHA2DS2-VASc AUC 0.76 (95% CI 0.74-0.78), POAF 0.71 (95% CI 0.69-0.73) and HATCH 0.70 (95% CI: 0, 67-0.72). Multivariable analysis demonstrated that COM-AF score was an independent predictor of AFCS: OR 1,91 (IC 95% 1,63-2,23).Conclusion: From the combination of variables with higher predictive value included in the POAF, CHA2DS2-VASc, and HATCH scores, a new risk model system called COM-AF was created to predict AFCS, presenting a greater predictive ability than the original ones. Being necessary future prospective validations. [ABSTRACT FROM AUTHOR]

The present study was conducted to investigate the obesity paradox and assess the effect of body mass index (BMI) on early and late clinical outcomes after cardiac surgery. Cohort study with a retrospective analysis of prospectively collected data. Single-institution cardiology medical center. The study comprised consecutive patients undergoing cardiac surgery from January 2009 to January 2019. Patients were divided into the following 4 groups defined by BMI: underweight (UW) (≤18.5 kg/m2): 0.5%, n = 27; normal weight (18.5-25 kg/m2): 25.7%, n = 1,393; overweight (OW) (>25-30 kg/m2): 44.7%, n = 2,423; and obese (OB) (≥30 kg/m2): 29.1%, n = 1,576. No interventions. A multivariate analysis was used to compare clinical outcomes among the different BMI groups. Overall 1-year survival of patients in the BMI categories was determined by the Kaplan-Meier method and compared using the log rank test. The study included 5,419 patients. The BMI groups were significantly different regarding presurgical variables. Mortality according to BMI exhibited a reverse J-shaped relationship: 7.4% in the UW group, 5.2% in the normal weight group, 3.2% in the OW group, and 4.3% in the OB group (p = 0.016). Low- cardiac- output syndrome and bleeding were more frequent in the UW group, whereas mediastinitis and hyperglycemia were more common in the OB group. After adjusting for other risk factors, BMI was not an independent predictor of in-hospital mortality. One-year follow-up was completed in 95% of the patients, and the analysis of long-term mortality did not show a difference among the BMI categories (p log rank = 0.16). OW patients had a lower mortality and better outcomes after cardiac surgery. However, when other preoperative variables were taken into account, BMI did not have independent effect on in-hospital and 1-year mortality. [ABSTRACT FROM AUTHOR]

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Introduction; Postoperative atrial fibrillation (POAFib) occurs in 20 to 40% of patients following cardiac surgery, and is associated with an increased perioperative morbidity and mortality. We aimed to develop and validate a simple clinical risk model for the prediction of POAFib after cardiac surgery. Methods; An analytical single center retrospective cohort study was conducted, including consecutive patients undergoing cardiac surgery between 2004 and 2017 with POAFib. To create the predictive risk score, a logistic regression model was performed using a random sample of 75% of the population. Coefficients of the model were then converted to a numerical risk score, and three groups were defined: low risk (≤1 point), intermediate risk (2-5 points) and high risk (≥6 points). The score was validated using the remaining 25% of the patients. Discrimination was evaluated through the area under the curve (AUC) ROC, and calibration using the Hosmer-Lemeshow (HL) test, calibration plots, and ratio of expected and observed events (E/O). Results; Six thousand five hundred nine patients underwent cardiac surgery: 52% coronary artery bypass grafting (CABG), 20% valve surgery, 14% combined (CABG and valve surgery) and 12% other. New-onset AF occurred in 1222 patients (18.77%). In the multivariate analysis, age, use of cardiopulmonary bypass pump, severe reduction in left ventricular ejection fraction (LVEF), chronic renal disease and heart failure were independent risk factors for POAFib, while the use of statins was a protective factor. The NOPAF score was calculated by adding points for each independent risk predictor. In the derivation cohort, the AUC was 0.71 (CI95% 0.69-0.72), and in the validation cohort the model also showed good discrimination (AUC 0.67 IC 0.64-0.70) and excellent calibration (HL P = 0.24). The E/O ratio was 1 (CI 95%: 0.89-1.12). According to the risk category, POAFib occurred in 5% of low; 11% of intermediate and 27.7% of high risk patients in the derivation cohort (P <0.001), and 5.7%; 12.6%; and 23.6% in the validation cohort respectively (P <0.001) Conclusion: From a large hospitalized population, we developed and validated a simple risk score named NOPAF, based on clinical variables that accurately stratifies the risk of POAFib. This score may help to identify high-risk patients prior to cardiac surgery, in order to strengthen postoperative atrial fibrillation prophylaxis. [ABSTRACT FROM AUTHOR]