Improvement of ventricular function in systolic heart failure patients with oral L-citrulline supplementation

Background: The utility of L-arginine and L-citrulline in treatment of arterial hypertension by increasing vascular oxide nitric availability has been demonstrated. Photoplethysmography, a simple and low-cost optical technique, makes it possible to assess vascular function and to detect changes in blood flow, pulse and swelling of the microvascular tissular space. The aim of the study was to evaluate the effect of L-citrulline supplementation on functional class, ejection fraction and peripheral blood flow in patients with systolic heart failure. Methods: Thirty-five stable outpatients attending the Heart Failure Clinic at the INCMNSZ underwent clinical evaluation, radioisotopic ventriculography and photoplethysmography before and at the end of 4 months. They were randomized into two groups: experimental group, with oral L-citrulline supplementation (3 g/day, n = 20) and control group, without supplementation (n = 15). Results: In the experimental group the left ventricular ejection fraction (LVEF) increased 20.3% at rest and 12.7% with stress, as well as the right ventricular ejection fraction at rest of 15.10% and 14.88% with stress. In addition, functional class improved in 35%, and the maximum amplitude time/total time (MAT/TT) index decreased 23.1%. These changes were statistically significant compared with the control group. Conclusions: Citrulline supplementation significantly improved the LVEF, the endothelial function (MAT/TT index) and functional class. Citrulline can be an important co-adjuvant in the treatment of stable and stable systolic heart failure patients

Integrating the STOP-BANG Score and Clinical Data to Predict Cardiovascular Events After Infarction A Machine Learning Study

BACKGROUND: OSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores that are obtained during the management of patients with myocardial infarction (MI). Currently, machine learning (ML) is able to select and integrate numerous variables to optimize prediction tasks. RESEARCH QUESTION: Can the integration of STOP-BANG score with clinical data and scores through ML better identify patients who experienced an in-hospital cardiovascular event after acute MI? STUDY DESIGN AND METHOD: This is a prospective observational cohort study of 124 patients with acute MI of whom the STOP-BANG score classified 34 as low (27.4%), 30 as intermediate (24.2%), and 60 as high (48.4%) OSA-risk patients who were followed during hospitalization. ML implemented feature selection and integration across 47 variables (including STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction) to identify those patients who experienced an in-hospital cardiovascular event (ie, death, ventricular arrhythmias, atrial fibrillation, recurrent angina, reinfarction, stroke, worsening heart failure, or cardiogenic shock) after definitive MI treatment. Receiver operating characteristic curves were used to compare ML performance against STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction, independently. RESULTS: There were an increasing proportion of cardiovascular events across the low, intermediate, and high OSA risk groups (P = .005). ML selected 7 accessible variables (ie, Killip class, leukocytes, GRACE score, c reactive protein, oxygen saturation, STOP-BANG score, and N-terminal prohormone of B-type natriuretic peptide); their integration outperformed all comparators (area under the curve, 0.83 [95% CI, 0.74-0.90]; P <.01). INTERPRETATION: The integration of the STOP-BANG score into clinical evaluation (considering Killip class, GRACE score, and simple laboratory values) of subjects who were admitted for an acute MI because of ML can significantly optimize the identification of patients who will experience an in-hospital cardiovascular event