No abstract

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.

Introduction Atrial fibrillation after cardiac surgery (AFCS) is associated with an increase in adverse events. The scores POAF, CHA2DS2-VASc and HATCH demonstrated a validated predictive to predict AF after CS (AFCS). Purpose To develop and validate a new risk score from the combination of the variables with highest predictive value of POAF, CHA2DS2-VASc and HATCH risk scores to predict AFCS. Methodology We conducted a single-center cohort study, performing a retrospective analysis of prospectively collected data. The study included consecutive patients undergoing CS between 2010–2016. The primary outcome was the development of new-onset AFCS during hospitalization. The variables of each score were evaluated in a multivariate regression model to determine the predictive impact. Discrimination was evaluated with area under the ROC curve (AUC-ROC) and calibration using the Hosmer-Lemeshow (HL) test. The Youden index was used to establish the best cut-off point for the score. The statistical difference between the ROC curves was evaluated with the method of DeLong et al. Results 3113 patients were included. Coronary artery bypass graft surgery 45%, valve replacement 24%, combined procedure (revascularization-valve surgery) 15%, and other procedures 16%. 21% (n=654) presented AFCS. Variables finally included in the new score were: age (≥75: 2, 65–74: 1), heart failure (2), female sex (1), hypertension (1), diabetes (1), previous stroke (1). The new score presented an AUC of 0.78 (95% CI 0.78–0.80), the rest of the scores presented lower discrimination ability (P<0.001): CHAD2DS2-VASc AUC 0.76, POAF 0.71 and HATCH 0.70. The HL test showed a p>0.05. For the new score, the best cut-off point was 2, with a sensitivity of 82% and specificity of 65.9%, presenting high negative predictive value: 92.9%. Variables OR (CI 95%) P Age (years)   65–74 3.14 (2.29–4.31) <0.001   ≥75 8.68 (6.32–11.93) <0.001 Female sex 3.36 (2.68–4.22) <0.001 Heart failure 2.45 (1.82–3.31) <0.001 Stroke/TIA 2.33 (1.45–3.76) <0.001 Hypertension 1.68 (1.28–2.2) <0.001 Diabetes 1.72 (1.31–2.25) <0.001 Conclusion From the combination of variables with higher predictive value included in the POAF, CHA2DS2-VASc, and HATCH scores, a new risk system was created to predict AFCS, presenting a greater predictive ability than the original ones. Being necessary future prospective validations.

Introduction: A Spanish real-world study in patients with severe persistent asthma who achieved asthma control after a one-year treatment with omalizumab highlighted the phenotypic heterogeneity of these patients (FENOMA study). In this subanalysis, we describe the clinical improvement in patients with severe allergic asthma in this study (positive skin test and IgE level 30-1500 IU/mL); n=240. Patients and Methods: FENOMA was an observational, multicentre, retrospective study in 345 patients achieving asthma control according to Spanish guidelines (GEMA). Baseline demographic and asthma-related characteristics were collected. Outcomes analyzed were those included in asthma control definition plus changes in background treatments and in blood eosinophil count (%) and exhaled nitric oxide fraction [FeNO]. Results: At baseline, patients were aged 45.4±15.0 years; 67% were women. Median (Q1;Q3) IgE levels were 302.5 (154.0; 553.5) IU/mL. After one-year treatment with omalizumab: 43.3% of patients had daytime symptoms vs 97.7% before treatment and 49.6% stopped taking oral corticosteroids. FEV 1 increased a median of 12.0 (4.0; 23.0)%; P <0.0001. The number of non-severe asthma exacerbations decreased a median of −4.0 (−7.0; 2.0); P <0.0001. Median unplanned visits to primary care or specialists and days of school/workplace absenteeism decreased from 4.9 (2.

The variables included in the analysis were selected as per clinical criteria and statistical criteria (variables with many missing values were excluded). There was no control group. Therefore, it is a descriptive analysis in which no comparison between the different patient groups will be presented as per their treatment response but based on their pre-treatment baseline characteristics.Of the 345 evaluable patients from the FENOMA study, 89 were not included in the cluster analysis due to missing data. Thus, 256 patients (74%) served for cluster analysis.Descriptive statistics were calculated for demographic and clinical characteristics. Continuous variables are presented as median and quartile 1 (Q1) and quartile 3 (Q3). Categorical variables are reported by using frequency and percentage.For statistical comparisons, the following tests were used, as applicable: Kruskal-Wallis test, Chisquare test, Fisher's exact test, Wilcoxon signed-rank test, and t-test. A 2-tailed P value of less than 0.05 was used to denote statistically significant differences. All statistical analyses were performed with SAS ® version 9.4 and SAS ® Enterprise Guide V7.15 (SAS Institute, Cary, NC). Variable correlation analysisIn the case of highly correlated variable groups, a representative of each group is selected, as the information that these variables would provide to the analysis would be redundant, and it may