Wolf-Parkinson-White Syndrome: Diagnosis, Risk Assessment, and Therapy-An Update.

Wolf-Parkinson-White (WPW) syndrome is a disorder characterized by the presence of at least one accessory pathway (AP) that can predispose people to atrial/ventricular tachyarrhythmias and even sudden cardiac death. It is the second most common cause of paroxysmal supraventricular tachycardia in most parts of the world, affecting about 0.1-0.3% of the general population. Most patients with WPW syndrome have normal anatomy, but it may be associated with concomitant congenital heart disease or systemic diseases. Although many individuals are asymptomatic, during supraventricular arrhythmia episodes, they may experience severe symptoms, including syncope or even sudden cardiac death (mainly due to pre-excited atrial fibrillation over rapidly conducting AP). In addition to arrhythmia-related symptoms, for some specific locations of the APs with overt anterograde conduction, there might be a reduction in exercise capacity mediated by a reduction in LV systolic performance due to anomalous LV depolarization. Although it is typically diagnosed through electrocardiography (ECG), additional tests are necessary for risk assessment. Management of WPW syndrome may be quite challenging and can vary from only acknowledging the presence of the accessory pathway to pharmacological treatment or radiofrequency ablation. Early diagnosis, risk assessment, and appropriate treatment are critical steps in the management of WPW syndrome, aiming to improve the quality of life and reduce the risk of life-threatening arrhythmias.

The CHA2DS2-VASc Score Predicts New-Onset Atrial Fibrillation and Hemodynamic Complications in Patients with ST-Segment Elevation Myocardial Infarction Treated by Primary Percutaneous Coronary Intervention.

Arrhythmic and hemodynamic complications related to ST-segment elevation myocardial infarction (STEMI) represent a major clinical challenge. Several scores have been developed to predict mortality in STEMI. However, those scores almost exclusively include factors related to the acute phase of STEMI, and no score has been evaluated to date for its ability to specifically predict arrhythmic and hemodynamic complications. We, thus, aimed to assess the ability of chronic risk factors burden, as expressed by the CHA2DS2-VASc score, to predict STEMI-related arrhythmic and hemodynamic complications. Data were collected from 839 consecutive STEMI patients treated by primary percutaneous coronary interventions (pPCI). CHA2DS2-VASc and GRACE scores were calculated for all patients, and their ability to predict STEMI-related arrhythmic (i.e., new-onset atrial fibrillation (AF), ventricular tachycardia/fibrillation) and hemodynamic (i.e., cardiogenic shock, asystole) complications was assessed in univariate and multiple regression analysis. Arrhythmic and hemodynamic complications occurred in 14.8% and 10.2% of patients, respectively. Although the GRACE score outweighed the CHA2DS2-VASc score in the ability to predict STEMI-related hemodynamic complications (p < 0.0001), both scores had a similar predictive value for STEMI-related new-onset AF (p = 0.20), and both remained independent predictors of new-onset AF and of hemodynamic complications in the multiple regression analyses. A CHA2DS2-VASc score > 2 points independently predicted new-onset AF (p < 0.01) and hemodynamic complications (p = 0.04). Alongside the GRACE score, the CHA2DS2-VASc score independently predicted new-onset AF and hemodynamic complications in STEMI patients treated by pPCI. These data suggest that a combination of acute and chronic risk factors could provide additional benefit in identifying patients at risk of STEMI-related complications, who could benefit from closer follow-up and more intensive prophylactic and therapeutic strategies.

Prediction of 3-year all-cause and cardiovascular cause mortality in a prospective percutaneous coronary intervention registry: Machine learning model outperforms conventional clinical risk scores.

BACKGROUND AND AIMS: Machine learning (ML) models have been proposed as a prognostic clinical tool and superiority over clinical risk scores is yet to be established. Our aim was to analyse the performance of predicting 3-year all-cause- and cardiovascular cause mortality using ML techniques and compare it with clinical scores in a percutaneous coronary intervention (PCI) population. METHODS: An all-comers patient population treated by PCI in a tertiary cardiovascular centre that have been included prospectively in the local registry between January 2016-December 2017 was analysed. The ML model was trained to predict 3-year mortality and prediction performance was compared with that of GRACE, ACEF, SYNTAX II 2020 and TIMI scores. RESULTS: A total number of 2242 patients were included with 12.1% and 14.9% 3-year cardiovascular and -all-cause mortality, respectively. The area under receiver operator characteristic curve for the ML model was higher than that of GRACE, ACEF, SYNTAX II and TIMI scores: 0.886 vs. 0.797, 0.792, 0.757 and 0.696 for 3-year cardiovascular- and 0.854 vs. 0.762, 0.764, 0.730 and 0.691 for 3-year all-cause mortality prediction, respectively (all p ≤ 0.001). Similarly, the area under precision-recall curve for the ML model was higher than that of GRACE, ACEF, SYNTAX II and TIMI scores: 0.729 vs. 0.474, 0.469, 0.365 and 0.389 for 3-year cardiovascular- and 0.718 vs. 0.483, 0.466, 0.388 and 0.395 for 3-year all-cause mortality prediction, respectively (all p ≤ 0.001). CONCLUSION: The ML model was superior in predicting 3-year cardiovascular- and all-cause mortality when compared to clinical scores in a prospective PCI registry.

Chronic kidney disease predicts atrial fibrillation in patients with ST-segment elevation myocardial infarction treated by primary percutaneous coronary intervention.

Background: Atrial fibrillation (AF) often complicates ST-segment elevation myocardial infarction (STEMI). Predictors of AF in this setting include factors related to the acute phase of STEMI and pre-existing conditions. More recently, novel AF predictors have been identified in the general population. We aimed to assess the ability of such novel factors to predict STEMI-related AF.Methods: Data were collected from STEMI patients treated by primary PCI. Factors related to the acute phase of STEMI (Killip class, heart rate, blood pressure on admission, post-PCI TIMI flow), classic (age, hypertension, heart failure, previous myocardial infarction), and more novel (body mass index [BMI], diabetes, chronic kidney disease [CKD], chronic obstructive pulmonary disease [COPD]) AF predictors were evaluated. The ability of these novel factors to predict STEMI-related AF was assessed.Results: Of the 629 studied patients, 10.5% presented STEMI-related AF. AF patients had higher Killip class on admission (p < .0001) and lower post-PCI TIMI flow (p < .01), they were older (p < .0001) and more likely to have a history of heart failure (p = .02) and myocardial infarction (p = .04). BMI, history of diabetes and COPD were similar between patients with and without AF (all p > .05), but CKD was more common in AF patients (p < .0001). In multiple regression analysis, CKD remained a strong independent predictor of STEMI-related AF (p < .0001).Conclusion: Irrespective of other factors, CKD was associated with increased risk of STEMI-related AF. CKD could be used to identify patients who will develop AF in this setting and who would benefit from closer follow-up and more intensive prophylactic strategies.

Impact of chronic obstructive pulmonary disease on in-hospital morbidity and mortality in patients with ST-segment elevation myocardial infarction treated by primary percutaneous coronary intervention.

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) presenting with ST-segment elevation myocardial infarction (STEMI) are less likely to beneficiate of primary percutaneous coronary intervention (pPCI), and have poorer prognosis. We aimed to evaluate the impact of COPD on the in-hospital outcomes of pPCI-treated STEMI patients. METHODS: Data were collected from 418 STEMI patients treated by pPCI. Inotropics and diuretics usage, cardiogenic shock, asystole, kidney dysfunction, and left ventricular ejection fraction were used as markers of hemodynamic complications. Atrial and ventricular fibrillation, conduction disorders, and antiarrhythmics usage were used as markers of arrhythmic complications. In-hospital mortality was evaluated. The associations between these parameters and COPD were assessed. RESULTS: COPD was present in 7.42% of STEMI patients. COPD patients were older (p=0.02) and less likely to receive beta-blockers (OR 0.29; 95%CI 0.13-0.64; p<0.01). They had higher Killip class on admission (p<0.001), received more often inotropics (p<0.001) and diuretics (p<0.01), and presented more often atrial (p=0.01) and ventricular fibrillation (p=0.02). Unadjusted in-hospital mortality was higher in COPD patients (OR 4.18, 95%CI 1.55-11.30, p<0.01). After adjustment for potentially confounding factors except beta-blockers, COPD remained an independent predictor of in-hospital mortality (p=0.02). After further adjustment with beta-blocker therapy, no excess mortality was noted in COPD patients. CONCLUSIONS: Despite being treated by pPCI, COPD patients with STEMI are more likely to develop hemodynamic and arrhythmic complications, and have higher in-hospital mortality. This appears to be due to lower beta-blockers usage in COPD patients. Increasing beta-blockers usage in COPD patients with STEMI may improve survival.