17 resultados para C111


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Introduction: The prevalence of 13 comorbid conditions and smoking status at the time of starting renal replacement therapy (RRT) in England, Wales and Northern Ireland are described. Methods: Adult patients starting RRT between 2002 and 2007 in centres reporting to the UK Renal Registry (UKRR) and with data on comorbidity (n¼13,293) were included. The association of comorbidity with patient demographics, treatment modality, haemoglobin, renal function at start of RRT and subsequent listing for kidney transplantation were studied. Association between comorbidities and mortality at 90 days and one year after 90 days from start of RRT was explored using Cox regression. Results: Completeness of data on comorbidity returned to the UKRR remained poor. Of patients with data, 52% had one or more comorbidities. Diabetes mellitus and ischaemic heart disease were the most common conditions seen in 28.9% and 22.5% of patients respectively. Comorbidities became more common with increasing age (up to the 65–74 age group), were more common amongst Whites and were associated with a lower likelihood of pre-emptive transplantation, a greater likelihood of starting on haemodialysis (rather than peritoneal dialysis) and a lower likelihood of being listed for kidney transplantation. In multivariable survival analysis, malignancy and ischaemic/neuropathic ulcers were the strongest predictors of poor survival at 1 year after 90 days from start of RRT. Conclusions: The majority of patients had at least one comorbid condition and comorbidity is an important predictor of early mortality on RRT.

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In recent years, the issue of life expectancy has become of upmost importance to pension providers, insurance companies and the government bodies in the developed world. Significant and consistent improvements in mortality rates and, hence, life expectancy have led to unprecedented increases in the cost of providing for older ages. This has resulted in an explosion of stochastic mortality models forecasting trends in mortality data in order to anticipate future life expectancy and, hence, quantify the costs of providing for future aging populations. Many stochastic models of mortality rates identify linear trends in mortality rates by time, age and cohort, and forecast these trends into the future using standard statistical methods. The modeling approaches used failed to capture the effects of any structural change in the trend and, thus, potentially produced incorrect forecasts of future mortality rates. In this paper, we look at a range of leading stochastic models of mortality and test for structural breaks in the trend time series.

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The Climatological Database for the World's Oceans: 1750-1854 (CLIWOC) project, which concluded in 2004, abstracted more than 280,000 daily weather observations from ships' logbooks from British, Dutch, French, and Spanish naval vessels engaged in imperial business in the eighteenth and nineteenth centuries. These data, now compiled into a database, provide valuable information for the reconstruction of oceanic wind field patterns for this key period that precedes the time in which anthropogenic influences on climate became evident. These reconstructions, in turn, provide evidence for such phenomena as the El Niño-Southern Oscillation and the North Atlantic Oscillation. Of equal importance is the finding that the CLIWOC database the first coordinated attempt to harness the scientific potential of this resource represents less than 10 percent of the volume of data currently known to reside in this important but hitherto neglected source.

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The topography of the visual evoked magnetic response (VEMR) to a pattern onset stimulus was studied in five normal subjects using a single channel BTi magnetometer. Topographic distributions were analysed at regular intervals following stimulus onset (chronotopograpby). Two distinct field distributions were observed with half field stimulation: (1) activity corresponding to the C11 m which remains stable for an average of 34 msec and (2) activity corresponding to the C111 m which remains stable for about 50 msec. However, the full field topography of the largest peak within the first 130 msec does not have a predictable latency or topography in different subjects. The data suggest that the appearance of this peak is dependent on the amplitude, latency and duration of the half field C11 m peaks and the efficiency of half field summation. Hence, topographic mapping is essential to correctly identify the C11 m peak in a full field response as waveform morphology, peak latency and polarity are not reliable indicators. © 1993.

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This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

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This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

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This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

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This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

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This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

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This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

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This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

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This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.