Browsing by Author "Young, Paul"

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Open Access
Combined Vertical Ozone Profile Database
(Bodeker Scientific and Climate Change Research Institute of Victoria University, 2011) Bodeker, Greg; Hassler, Birgit; Young, Paul; Portmann, Robert
Bodeker Scientific produces a combined monthly mean vertical ozone profile database spanning the period 1979 to 2007. The database is completely filled such that there are no missing data. A publication describing the construction of this database is currently in preparation. The raw individual ozone data are sourced from the BDBP database (see The BDBP). Monthly means are calculated from individual ozone measurements extracted from the BDBP in much the same way as in Hassler et al. (2009). These are referred to as Tier 0 data. A regression model is fitted to the Tier 0 data at each of 70 pressure/altitude levels. The regression model is of the form: Ozone(t,lat) = A(t,lat) + Offset and seasonal cycle B(t,lat) x t + Linear trend C(t,lat) x EESC(t,AoA) + Age-of-air dependent equivalent effective stratospheric chlorine D(t,lat) x QBO(t) + Quasi-biennial Oscillation E(t,lat) x QBOorthog(t) + Orthogonalized QBO F(t,lat) x ENSO(t) + El-Niño Southern Oscillation G(t,lat) x Solar(t) + Solar cycle H(t,lat) x Pinatubo(t) + Mt. Pinatubo volcanic eruption R(t) Residual Regression model fit coefficients are expanded in Fourier series to account for seasonality and in Legendre polynomials in latitude to account for meridional structure in the fit coefficients. Regression model output is then used to produce 4 gap free Tier 1 data sets, viz.: Tier 1.1 (Anthropogenic): This comprises the mean annual cycle plus contributions from the EESC and linear trend basis functions. Tier 1.2 (Natural): This comprises the mean annual cycle plus contributions from the QBO, solar cycle and El Niño basis functions. Tier 1.3 (Natural & volcanoes): Tier 1.2 but now also including contributions from volcano basis functions. Tier 1.4 (All): Constructed by summing the contributions from all basis functions. There are 20 files available named CCMVal2_REF-B1_BSOzone-XX-YYY_TierZZ_T2Mz_O3.nc where: CCMVal2 indicates that these data files have been formatted to allow easy use in the CCMVal2 project. REF-B1 indicates that the time period covered is similar to that for the REF-B1 simulations. XX is either 'MR' for mixing ratio or 'ND' for number density. YYY is either 'PRS' to denote that the data are on pressure levels or 'ALT' to denote that the data are on altitude levels. ZZ denotes the Tier: '0', '1_1', '1_2', '1_3' or '1_4'. T2Mz denotes that these are monthly means in two dimensions (latitude and altitude/pressure). At present Bodeker Scientific has no financial support to maintain this database and so if there is anyway that you can contribute towards the maintenance of this database, that would be much appreciated. That said, this database is made freely available to any not-for-profit organisation or individual. If you are going to be using this database in a publication, please let me know. At the very least please include the following acknowledgement: We would like to thank Greg Bodeker (Bodeker Scientific) and Birgit Hassler (NOAA) for providing the combined vertical ozone profile database.
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Fever and antipyresis in critically ill patients with known or suspected infection
(Te Herenga Waka—Victoria University of Wellington, 2016) Young, Paul; Beasley, Richard; Miller, John
BACKGROUND Fever is common in critically ill patients. It is unknown whether it is a marker of illness severity, a protective host response, a modifiable risk factor for morbidity and mortality, or a combination of these things. Paracetamol therapy to treat fever in intensive care unit (ICU) patients with likely infection is common but its effects are unknown. METHODS Study one We evaluated the independent association between peak temperature in the first 24 hours after ICU admission and in-hospital mortality according to whether there was an admission diagnosis of infection using a database of admissions to 129 ICUs in Australia and New Zealand (ANZ) (n=269,078). Subsequently, we sought to confirm or refute the ANZ database findings using a validation cohort of admissions to 201 ICUs in the United Kingdom (UK) (n=366,973). Study two We randomly assigned 700 ICU patients with fever (body temperature ≥38°) and likely infection to either 1 gm of intravenous paracetamol or placebo six-hourly until ICU discharge, fever resolution, antimicrobial therapy cessation, or death. The primary outcome was alive-ICU-free days to day 28. Study three We conducted a post-hoc exploratory analysis of the study two database to evaluate the association between paracetamol administration and physiological parameters and ICU support requirements. RESULTS Study one A total of 29,083 of 269,078 ANZ patients (10.8%) and 103,191 of 366,973 UK patients (28.1%) were categorised as having an infection. In the ANZ cohort, adjusted in-hospital mortality risk progressively decreased with increasing peak temperature in patients with infection. Relative to the risk at 36.5–36.9°C, the lowest risk was at 39–39.4°C (adjusted Odds Ratio (OR) 0.56; 95% confidence interval [CI], 0.48 to 0.66). In patients without infection, the adjusted mortality risk progressively increased above 39.0°C (adjusted OR 2.07 at ≥40.0°C; 95% CI, 1.68 to 2.55). In the UK cohort, findings were similar with adjusted ORs at corresponding temperatures of 0.77 (95% CI, 0.71 to 0.85) and 1.94 (95% CI, 1.60 to 2.34) for infection and non-infection groups, respectively. Study two There was no significant difference in alive-ICU-free days to day 28: 23 days (interquartile range [IQR], 13 to 25) for paracetamol patients vs. 22 days (IQR, 11.5 to 25) for placebo patients (absolute difference, 0 days; 96.2% CI, 0 to 1; P=0.07). Paracetamol was associated with a shorter median ICU length of stay in survivors than placebo: 3.5 days (IQR, 1.9 to 6.9) vs. 4.3 days (IQR, 2.1 to 8.9) (exponent, 0.84; 95% CI, 0.70 to 0.99, P=0.01) and a longer median ICU length of stay in non-survivors: 10.4 days (IQR, 4.1 to 16.9) vs. 4.0 days (IQR, 1.7 to 9.4) (exponent, 2.12; 95% CI, 1.43 to 3.13; P<0.001). A total of 55 of 345 paracetamol patients (15.9%) and 57 of 344 placebo patients (16.6%) died by 90 day (odds ratio, 0.98; 95% CI, 0.80 to 1.20; P=0.82). Study three Allocation to paracetamol was associated with more rapid abatement of fever than allocation to placebo (hazard ratio 0.74 (95% CI 0.64 to 0.87), P<0.001). The proportion of patients where the highest temperature recorded on the day following randomisation was <38°C was 222/339 (65%) in the paracetamol group and 148/333 (44.4%) in the placebo group (relative risk 1.53 (95% CI 1.31 to 1.79), P<0.001). Allocation to paracetamol was associated with reduced heart rate compared with allocation to placebo. The largest observed heart rate effect was at 12 hours post randomisation (paracetamol group 91±19 bpm vs. placebo group 97±20 bpm; absolute difference, -7.2 bpm (95% CI, -9.5 to -4.9); P<0.001.) Allocation to paracetamol was not associated with any significant change in mean arterial pressure, minute ventilation, ICU support requirements, or antimicrobial use. CONCLUSIONS Elevated peak temperature in the first 24 hours in ICU was independently associated with increased in-hospital mortality in critically ill patients without infection and with decreased in-hospital mortality in critically ill patients with infection. However, although paracetamol had a mild antipyretic effect among critically ill adults with likely infection, early treatment of fever using paracetamol did not change the number of days patients spent alive and free from needing intensive care. The apparently contradictory findings of study one and study two can be reconciled in different ways. Because of its design, the retrospective cohort study is unavoidably subject to residual confounding and the absence of fever in patients with infections may be a marker of illness severity that is incompletely accounted for in the adjusted analyses. Alternatively or additionally, it is possible that paracetamol has beneficial effects in this patient population that offset any detrimental effects related to reduction in body temperature. The divergent association of paracetamol with ICU length of stay among survivors compared non-survivors in study two should be interpreted with caution. However, it raises the possibility that use of paracetamol to treat fever may have sped up recovery and delayed death in this population. Further investigation is required to confirm or refute this hypothesis. This body of work is of major significance because it is “practice informing”. Paracetamol is the world’s most commonly administered medicine and is often used in current clinical practice to treat fever in ICU patients with infections. The findings of the randomised controlled trial, in particular, provide guidance to clinicians about the consequences of using paracetamol to treat fever.