ABSTRACT
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has been and will continue to be a challenge to the healthcare system worldwide. In this context, we aimed to discuss the impact of the COVID-19 pandemic on the diagnosis, timing, and prognosis of surgical treatment for active infective endocarditis (IE) during the pandemic and share our coping strategy. METHODS: A total of 39 patients were admitted for active IE in the year 2020. The number of the same period last year was 50. Medical information of these two groups was extracted from our surgical database. Data were compared between the two groups and differences with or without statistical significance were discussed. RESULTS: In the pandemic year, we admitted fewer transferred patients (64.1% vs. 80%, p = .094). Timespan for diagnosis were prolonged (60 vs. 34.5 days, p = .081). More patients were admitted in emergency (41% vs. 20%, p = .030) More patients had heart failure (74.4% vs. 40%, p = .001), sepsis (69.2% vs. 42.0%, p = .018), or cardiogenic shock (25.6% vs. 8.0%, p = .038). Overall surgical risk (EuroSCORE II) was higher (4.15% vs. 3.24%, p = .019) and more commando surgery was performed (7.7% vs. 2.0%, p = .441). However, we did not see more postoperative complications, and early mortality was not worse either (0 vs. 4%, p = .502). CONCLUSIONS: The negative impact of the COVID-19 pandemic on the clinical practice of surgical treatment for active IE was multifaceted. However, with the preservation of the effectiveness of multidisciplinary IE surgical team, the early outcomes were comparable with those in the normal years.
Subject(s)
COVID-19 , Endocarditis, Bacterial , Endocarditis , Endocarditis/surgery , Endocarditis, Bacterial/surgery , Humans , Pandemics , Postoperative Complications/epidemiology , Retrospective Studies , Treatment OutcomeABSTRACT
BACKGROUND: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, which has caused numerous deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution and population mobility on COVID-19 across China. METHODS: We obtained daily confirmed cases of COVID-19, air particulate matter (PM2.5, PM10), weather parameters such as ambient temperature (AT) and absolute humidity (AH), and population mobility scale index (MSI) in 63 cities of China on a daily basis (excluding Wuhan) from January 01 to March 02, 2020. Then, the Generalized additive models (GAM) with a quasi-Poisson distribution were fitted to estimate the effects of PM10, PM2.5 and MSI on daily confirmed COVID-19 cases. RESULTS: We found each 1 unit increase in daily MSI was significantly positively associated with daily confirmed cases of COVID-19 in all lag days and the strongest estimated RR (1.21, 95% CIs:1.14 ~ 1.28) was observed at lag 014. In PM analysis, we found each 10 µg/m3 increase in the concentration of PM10 and PM2.5 was positively associated with the confirmed cases of COVID-19, and the estimated strongest RRs (both at lag 7) were 1.05 (95% CIs: 1.04, 1.07) and 1.06 (95% CIs: 1.04, 1.07), respectively. A similar trend was also found in all cumulative lag periods (from lag 01 to lag 014). The strongest effects for both PM10 and PM2.5 were at lag 014, and the RRs of each 10 µg/m3 increase were 1.18 (95% CIs:1.14, 1.22) and 1.23 (95% CIs:1.18, 1.29), respectively. CONCLUSIONS: Population mobility and airborne particulate matter may be associated with an increased risk of COVID-19 transmission.