The purpose of the Emergency Medical Services Outcomes Project (EMSOP) is to develop a foundation and framework for out-of-hospital outcomes research. Fundamental to that purpose is the ...identification of priority conditions, risk-adjustment measures (RAMs), and outcome measures. In this third EMSOP article, we examine the topic of risk adjustment, discuss the relevance of risk adjustment for out-of-hospital outcomes research, and recommend RAMs that should be evaluated for potential use in emergency medical services (EMS) research. Risk adjustment allows better judgment about the effectiveness and quality of alternative therapies; it fosters a better comparison of potentially dissimilar groups of patients. By measuring RAMs, researchers account for an important source of variation in their studies. Core RAMs are those measures that might be necessary for out-of-hospital outcomes research involving any EMS condition. Potential core RAMs that should be evaluated for their feasibility, validity, and utility in out-of-hospital research include patient age and sex, race and ethnicity, vital signs, level of responsiveness, Glasgow Coma Scale, standardized time intervals, and EMS provider impression of the presenting condition. Potential core RAMs that could be obtained through linkage to other data sources and that should be evaluated for their feasibility, validity, and utility include principal diagnosis and patient comorbidity. We recommend that these potential core RAMs be systematically evaluated for use in risk adjustment of out-of-hospital patient groups that might be used for outcomes research. Garrison HG, Maio RF, Spaite DW, Desmond JS, Gregor MA, O'Malley PJ, Stiell IG, Cayten CG, Chew JL Jr, MacKenzie EJ, Miller DR. Emergency Medical Services Outcomes Project III (EMSOP III): the role of risk adjustment in out-of-hospital outcomes research. Ann Emerg Med. July 2002;40:79-88.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Contrast-induced nephropathy is a common complication of contrast administration in patients with chronic kidney disease and diabetes. Its pathophysiology is not well understood; similarly the role ...of intravenous or oral acetylcysteine is unclear. Randomized controlled trials to date have been conducted without detailed knowledge of the effect of acetylcysteine on renal function. We are conducting a detailed mechanistic study of acetylcysteine on normal and impaired kidneys, both with and without contrast. This information would guide the choice of dose, route, and appropriate outcome measure for future clinical trials in patients with chronic kidney disease.
We designed a 4-part study. We have set up randomised controlled cross-over studies to assess the effect of intravenous (50 mg/kg/hr for 2 hrs before contrast exposure, then 20 mg/kg/hr for 5 hrs) or oral acetylcysteine (1200 mg twice daily for 2 days, starting the day before contrast exposure) on renal function in normal and diseased kidneys, and normal kidneys exposed to contrast. We have also set up a parallel-group randomized controlled trial to assess the effect of intravenous or oral acetylcysteine on patients with chronic kidney disease stage III undergoing elective coronary angiography. The primary outcome is change in renal blood flow; secondary outcomes include change in glomerular filtration rate, tubular function, urinary proteins, and oxidative balance.
Contrast-induced nephropathy represents a significant source of hospital morbidity and mortality. Over the last ten years, acetylcysteine has been administered prior to contrast to reduce the risk of contrast-induced nephropathy. Randomized controlled trials, however, have not reliably demonstrated renoprotection; a recent large randomized controlled trial assessing a dose of oral acetylcysteine selected without mechanistic insight did not reduce the incidence of contrast-induced nephropathy. Our study should reveal the mechanism of effect of acetylcysteine on renal function and identify an appropriate route for future dose response studies and in time randomized controlled trials.
Clinical Trials.gov: NCT00558142; EudraCT: 2006-003509-18.
In January 2017, CDC identified a cluster of Salmonella enterica serotype Newport infections with isolates sharing an indistinguishable pulsed-field gel electrophoresis (PFGE) pattern, JJPX01.0010 ...(pattern 10), through PulseNet, the national molecular subtyping network for foodborne disease surveillance. This report summarizes the investigation by CDC, state and local health and agriculture departments, and the U.S. Department of Agriculture's Food Safety and Inspection Service (USDA-FSIS) and discusses the possible role of dairy cows as a reservoir for strains of Salmonella that persistently cause human illness. This investigation combined epidemiologic and whole genome sequencing (WGS) data to link the outbreak to contaminated ground beef; dairy cows were hypothesized to be the ultimate source of Salmonella contamination.
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BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, ODKLJ, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
This study was motivated by the use in air pollution epidemiology and health burden assessment of data simulated at 5 km × 5 km horizontal resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry ...transport model. Thus the focus of the model–measurement comparison statistics presented here was on the health-relevant metrics of annual and daily means of NO2, O3, PM2. 5, and PM10 (daily maximum 8 h running mean for O3). The comparison was temporally and spatially comprehensive, covering a 10-year period (2 years for PM2. 5) and all non-roadside measurement data from the UK national reference monitor network, which applies consistent operational and QA/QC procedures for each pollutant (44, 47, 24, and 30 sites for NO2, O3, PM2. 5, and PM10, respectively). Two important statistics highlighted in the literature for evaluation of air quality model output against policy (and hence health)-relevant standards – correlation and bias – together with root mean square error, were evaluated by site type, year, month, and day-of-week. Model–measurement statistics were generally better than, or comparable to, values that allow for realistic magnitudes of measurement uncertainties. Temporal correlations of daily concentrations were good for O3, NO2, and PM2. 5 at both rural and urban background sites (median values of r across sites in the range 0.70–0.76 for O3 and NO2, and 0.65–0.69 for PM2. 5), but poorer for PM10 (0.47–0.50). Bias differed between environments, with generally less bias at rural background sites (median normalized mean bias (NMB) values for daily O3 and NO2 of 8 and 11 %, respectively). At urban background sites there was a negative model bias for NO2 (median NMB = −29 %) and PM2. 5 (−26 %) and a positive model bias for O3 (26 %). The directions of these biases are consistent with expectations of the effects of averaging primary emissions across the 5 km × 5 km model grid in urban areas, compared with monitor locations that are more influenced by these emissions (e.g. closer to traffic sources) than the grid average. The biases are also indicative of potential underestimations of primary NOx and PM emissions in the model, and, for PM, with known omissions in the model of some PM components, e.g. some components of wind-blown dust. There were instances of monthly and weekday/weekend variations in the extent of model–measurement bias. Overall, the greater uniformity in temporal correlation than in bias is strongly indicative that the main driver of model–measurement differences (aside from grid versus monitor spatial representivity) was inaccuracy of model emissions – both in annual totals and in the monthly and day-of-week temporal factors applied in the model to the totals – rather than simulation of atmospheric chemistry and transport processes. Since, in general for epidemiology, capturing correlation is more important than bias, the detailed analyses presented here support the use of data from this model framework in air pollution epidemiology.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Myogenin (Myog) is a muscle-specific basic helix-loop-helix transcription factor that plays an essential role in the specification and differentiation of myoblasts. The
myogenin genes from the tiger ...pufferfish,
Takifugu rubripes, and green-spotted pufferfish,
Tetraodon nigroviridis, were cloned and a comparative genomic analysis performed. The gene encoding myogenin is composed of three exons and has a relatively similar genomic structure in
T. rubripes,
T. nigroviridis and human. Introns 1 and 2 were approximately 2-fold and 8-fold longer respectively in human than pufferfish.
Myogenin is located in a 100 kb region of conserved synteny between these organisms, corresponding to chromosome 1 in human, chromosome 11 in
T. nigroviridis and scaffold 208 in
T. rubripes. Pufferfish myogenin contained a serine-rich region at the carboxyl terminus that is highly conserved amongst teleosts. During embryonic development of
T. rubripes,
myogenin was expressed in a rostral–caudal gradient in the developing somites and subsequently during the pharyngula period in the pectoral fin bud primordia, jaw muscles and extraocular muscle precursors. In
T. rubripes, the time required to form a somite pair during the linear phase of somitogenesis (≡
somite-interval) was 122 min, 97 min and 50 min in embryos incubated at 15, 18 and 21 °C, respectively.
Myogenin mRNA transcripts were quantified using qPCR and normalised to the highest level of expression. Peak
myogenin expression occurred later with respect to developmental stage (standardised using somite-intervals) and was over 2-fold higher at 21 °C than at either 18 or 15 °C. Changes in the relative timing and intensity of
myogenin expression are a potential mechanism for explaining thermal plasticity of muscle phenotype in larvae via effects on the differentiation programme.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Background: Keratinocyte growth factor (KGF) is a stromally derived growth factor of the fibroblast growth factor (FGF) family with paracrine effects targeted to influence the growth and ...differentiation of epithelia. Regional and temporal changes in KGF expression play important roles in the development and maintenance of epithelial structures and in epithelial wound healing. Differing patterns of expression of KGF by fibroblasts in the gingival region could therefore be related to the observed regional variation in the differentiation and behavior of gingival epithelia.
Methods: The in vitro and in vivo patterns of expression of KGF mRNA in human gingival and periodontal fibroblasts were examined using reverse transcription polymerase chain reactions (RT‐PCR) and in situ hybridization with digoxigenin‐labeled riboprobes. The patterns observed for human gingiva were compared with those for human skin and for murine tissues.
Results: Gingival and periodontal fibroblasts showed expression of KGF transcripts in vitro, and the degree of expression was markedly influenced by the presence of retinoic acid, an agent known to influence patterns of epithelial differentiation. Sections of human and murine gingiva and skin showed regionally variable expression of transcripts with the cells expressing KGF in the subepithelial, rather than the deeper, connective tissues and periodontium.
Conclusions: The results point to a role of KGF in the maintenance of normal growth and differentiation of gingival epithelia. A lack of KGF expression by periodontal fibroblasts in vivo is expected to hinder apical epithelial migration and thus stabilize the epithelial attachment. The effects of retinoic acid (RA) on KGF expression in vitro provide an indirect mechanism by which RA may regulate the growth and differentiation of gingival epithelia. J Periodontol 2001;72:445‐453.
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BFBNIB, CMK, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The combination of T, N, and M classifications into stage groupings is meant to facilitate a number of activities including: the estimation of prognosis and the comparison of therapeutic ...interventions among similar groups of cases. We tested the UICC/AJCC fifth edition stage grouping and six other TNM-based groupings proposed for head and neck cancer for their ability to meet these expectations in laryngeal cancer using data from Ontario, Canada, and the area of Southern Norway surrounding Oslo. We defined four criteria to assess each grouping scheme: (1) the subgroups defined by T, N, and M comprising a given group within a grouping scheme have similar survival rates (hazard consistency); (2) the survival rates differ among the groups (hazard discrimination); (3) the prediction of cure is high (outcome prediction); and (4) the distribution of patients among the groups is balanced. We previously identified or derived a measure for each criterion, and the findings were summarized using a scoring system. The range of scores was from 0 (best) to 7 (worst). The data sets were population-based, with 861 cases from Ontario and 642 cases from Southern Norway. Clinical stage assignment was used and the outcome of interest was cause-specific survival. Summary scores across the seven schemes had similar ranges: 0.9 to 5.1 in Ontario and 1.8 to 5.7 in Southern Norway, but the ranking varied. Summing the scores across the two datasets, the TANIS-7 scheme (Head & Neck 1993;15:497-503) ranked first, and was ranked high in both datasets (first and second, respectively). The UICC/AJCC scheme ranked sixth out of seven schemes, and its ranking was fifth and seventh, respectively. UICC/AJCC stage groupings were defined without empirical investigation. When tested, this scheme did not perform best. Our results suggest that the usefulness of the TNM system could be enhanced by optimizing the design of stage groupings through empirical investigation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
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