Artificial intelligence MacHIne learning for the detection and treatment of atrial fibrillation guidelines in the emergency department setting (AIM HIGHER): Assessing a machine learning clinical decision support tool to detect and treat non-valvular atrial fibrillation in the emergency department.

OBJECTIVE: Advanced machine learning technology provides an opportunity to improve clinical electrocardiogram (ECG) interpretation, allowing non-cardiology clinicians to initiate care for atrial fibrillation (AF). The Lucia Atrial Fibrillation Application (Lucia App) photographs the ECG to determine rhythm detection, calculates CHA2DS2-VASc and HAS-BLED scores, and then provides guideline-recommended anticoagulation. Our purpose was to determine the rate of accurate AF identification and appropriate anticoagulation recommendations in emergency department (ED) patients ultimately diagnosed with AF. METHODS: We performed a single-center, observational retrospective chart review in an urban California ED, with an annual census of 70,000 patients. A convenience sample of hospitalized patients with AF as a primary or secondary discharge diagnosis were evaluated for accurate ED AF diagnosis and ED anticoagulation rates. This was done by comparing the Lucia App against a gold standard board-certified cardiologist diagnosis and using the American College of Emergency Physicians AF anticoagulation guidelines. RESULTS: Two hundred and ninety seven patients were enrolled from January 2016 until December 2019. The median age was 79 years and 44.1% were female. Compared to the gold standard diagnosis, the Lucia App detected AF in 98.3% of the cases. Physicians recommended guideline-consistent anticoagulation therapy in 78.5% versus 98.3% for the Lucia App. Of the patients with indications for anticoagulation and discharged from the ED, only 25.0% were started at discharge. CONCLUSION: Use of a cloud-based ECG identification tool can allow non-cardiologists to achieve similar rates of AF identification as board-certified cardiologists and achieve higher rates of guideline-recommended anticoagulation therapy in the ED.

Organic molecular markers and source contributions in a polluted municipality of north-east Italy: Extended PCA-PMF statistical approach.

Exceeding the maximum levels for environmental pollutants creates public and scientific interest for the environmental and human health impact it may have. In Northern Italy, the Po Valley, and in particular the Veneto region, is still a hotspot for air quality improvement. Several monitoring campaigns were carried out in this area to acquire information about sources of pollutants which are considered critical. For the first time, a deep study of the aerosol organic fraction was performed in the town Sernaglia della Battaglia, nearby Treviso. During three seasons of 2017, PM1 and PM2.5 samples were collected simultaneously. Organic molecular markers have been analyzed by in-situ derivatization thermal desorption gas chromatography time-of-flight mass spectrometry (IDTD-GC-TOFMS). Alkanes, polycyclic aromatic hydrocarbons, oxi-polycyclic aromatic hydrocarbons, anhydrous sugars, resins acids, triterpenoids, and acids were considered. The organic chemical composition has been analyzed based on seasonal variation and source contributions. Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) have been combined to deeply investigate the main sources of particulate organic matter. On the one hand, PCA evaluates the correlations between the organic markers and their seasonal distribution. On the other hand, the source contributions to aerosol composition are estimated by PMF. Four main emission sources were found by PMF: solid fuel combustion (coal, wood), combustion of petroleum distillates (gas and fuel oil) and exhaust gases of vehicles, industrial combustion processes, home heating, and forest fires are evaluated as the most important sources for the air quality and pollution in this municipality of Northern Italy.