ABSTRACT
Arrhythmias (ARs) are potential cardiovascular complication of COVID-19 but may also have a prognostic role. The aim of this study was to explore the prevalence and impact of cardiac ARs in hospitalized COVID-19 patients. All-comer patients admitted to the emergency department of Modena University Hospital from 16 March to 31 December 2020 and diagnosed with COVID-19 pneumonia infection were included in the study. The primary endpoint was 30-day mortality. Out of 902 patients, 637 (70.6%) presented a baseline 12-lead ECG registration; of these, 122 (19.2%) were diagnosed with ARs. Atrial fibrillation (AF, 40.2%) was the most frequent AR detected. The primary endpoint (30-day mortality) occurred in 33.6% (p < 0.001). AR-patients presented an almost 3-fold risk of mortality compared to non-AR-patients at 30d (Adj. OR = 2.8, 95%CI: 1.8-4.3, p < 0.001). After adjustment for significant baseline characteristics selected by a stepwise backward selection, AR-patients remained at increased risk of mortality (Adj. HR = 2.0, 95%CI: 1.9-2.3, p < 0.001). Sub-group analysis revealed that among ARs patients, those with AF at admission presented the highest risk of 30-day mortality (Adj. HR = 3.1, 95%CI: 2.0-4.9, p < 0.001). In conclusion, ARs are a quite common manifestation in COVID-19 patients, who are burdened by even worse prognosis. AR patients with AF presented the highest risk of mortality; thus, these patients may benefit from a more aggressive secondary preventive therapy and a closer follow up.
ABSTRACT
The coronavirus disease 2019 (COVID-19) has severely stressed the sanitary systems of all countries in the world. One of the main issues that physicians are called to tackle is represented by the monitoring of pauci-symptomatic COVID-19 patients at home and, generally speaking, everyone the access to the hospital might or should be severely reduced. Indeed, the early detection of interstitial pneumonia is particularly relevant for the survival of these patients. Recent studies on rheumatoid arthritis and interstitial lung diseases have shown that pathological pulmonary sounds can be automatically detected by suitably developed algorithms. The scope of this preliminary work consists of proving that the pathological lung sounds evidenced in patients affected by COVID-19 pneumonia can be automatically detected as well by the same class of algorithms. In particular the software VECTOR, suitably devised for interstitial lung diseases, has been employed to process the lung sounds of 28 patient recorded in the emergency room at the university hospital of Modena (Italy) during December 2020. The performance of VECTOR has been compared with diagnostic techniques based on imaging, namely lung ultrasound, chest X-ray and high resolution computed tomography, which have been assumed as ground truth. The results have evidenced a surprising overall diagnostic accuracy of 75% even if the staff of the emergency room has not been suitably trained for lung auscultation and the parameters of the software have not been optimized to detect interstitial pneumonia. These results pave the way to a new approach for monitoring the pulmonary implication in pauci-symptomatic COVID-19 patients.