Recent trends, including survival beyond 30 days, in aortic valve replacement (AVR) following the expansion of indications for transcatheter aortic valve replacement (TAVR) are not well-understood.
...The authors sought to characterize the trends in characteristics and outcomes of patients undergoing AVR.
The authors analyzed Medicare beneficiaries who underwent TAVR and SAVR in 2012 to 2019. They evaluated case volume, demographics, comorbidities, 1-year mortality, and discharge disposition. Cox proportional hazard models were used to assess the annual change in outcomes.
Per 100,000 beneficiary-years, AVR increased from 107 to 156, TAVR increased from 19 to 101, whereas SAVR declined from 88 to 54. The median interquartile range age remained similar from 77 71-83 years to 78 72-84 years for overall AVR, decreased from 84 79-88 years to 81 75-86 years for TAVR, and decreased from 76 71-81 years to 72 68-77 years for SAVR. For all AVR patients, the prevalence of comorbidities remained relatively stable. The 1-year mortality for all AVR decreased from 11.9% to 9.4%. Annual change in the adjusted odds of 1-year mortality was 0.93 (95% CI: 0.92-0.94) for TAVR and 0.98 (95% CI: 0.97-0.99) for SAVR, and 0.94 (95% CI: 0.93-0.95) for all AVR. Patients discharged to home after AVR increased from 24.2% to 54.7%, primarily driven by increasing home discharge after TAVR.
The advent of TAVR has led to about a 60% increase in overall AVR in older adults. Improving outcomes in AVR as a whole following the advent of TAVR with increased access is a reassuring trend.
•A relatively new trend detection approach has been applied.•Trends in original rainfall and its various periodic components were investigated.•Dominant periodic components for the trends of the ...original series were identified.•The sequential MK test was applied to find the trend turning points.
The existence of trends in hydro-climatic variables such as rainfall is an indication of potential climate variability and climate change and the identification of such trends in rainfall is essential for the planning and design of sustainable water resources. This study focuses on identifying existing trends in annual, seasonal and monthly rainfall at thirteen stations in the Onkaparinga catchment in South Australia during the period 1960–2010. A relatively new trend detection approach, which combines a Continuous Wavelet Transform (CWT) with the Mann Kendall (MK) test, was applied in this study. The original rainfall time series was decomposed to different periodic components using a CWT and then the MK test was applied to detect the trends. One station showed a statistically significant (at the 5% level) negative trend for annual rainfall. Winter rainfall exhibited significant positive trends at four stations. In the case of monthly rainfall, significant positive trends were observed in June (at seven stations), November (at one station) and December (at one station). The study showed that the periodic components might have significant trends even when there are no significant trends in the original data. The periodic component that dominates the trend in the original data varies from season to season. A sequential Mann–Kendall analysis was found useful for identifying the trend turning points. Most of the trends, whether positive or negative, started during the mid-1970s to mid-1980s. The technique developed in this study may also be applied for trend detection of other hydro-climatic variables in other catchments, particularly where temporal and spatial variabilities are high.
Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change ...and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran’s greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices.
