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Callum G Fraser

Publications and source records attributed to Callum G Fraser.

5 recordsLinked to original sources
PubMed2002

Does renal dysfunction predict mortality after acute stroke? A 7-year follow-up study.

BACKGROUND AND PURPOSE: The purpose of this study was to investigate renal function as a long-term predictor of mortality in patients hospitalized for acute stroke. METHODS: This was a cohort study done in a Scottish tertiary teaching hospital. Participants included 2042 (993 male) unselected consecutive stroke patients (mean age, 73 years) admitted to hospital within 48 hours of stroke between 1988 and 1994. Follow-up was up to 7 years. Main outcome measure was all-cause mortality. RESULTS: The total number of deaths at the end of follow-up was 1026. Most subjects (1512) had creatinine <124 micromol/L. The mean calculated creatinine clearance was 54.8 mL/min (SD, 23 mL/min). Renal function indexes were analyzed by quartiles with Cox proportional-hazards model. Stroke survivors had higher calculated creatinine clearance and lower serum creatinine, urea, and ratios of urea to creatinine. Calculated creatinine clearance > or =51.27 mL/min significantly predicted better long-term survival in these stroke patients even after adjustment for confounders (age, neurological score, ischemic heart disease, hypertension, smoking, and diuretic use). Similarly, creatinine > or =119 micromol/L "relative risk (RR), 1.59; 95% confidence interval (CI), 1.32 to 1.92", urea 6.8 to 8.9 mmol/L (RR, 1.34; 95% CI, 1.09 to 1.65) or > or =9 mmol/L (RR, 1.74; 95% CI, 1.42 to 2.13), and ratio of urea to creatinine > or =0.08573 mmol/micromol (RR, 1.24; 95% CI, 1.03 to 1.50) remained significant predictors of mortality after adjustment for confounders. CONCLUSIONS: After acute stroke, patients with reduced admission calculated creatinine clearance, raised serum creatinine and urea concentrations (even within conventional reference intervals), and raised ratio of urea to creatinine had a higher mortality risk. This finding may be used to stratify risk and target interventions, eg, the use of angiotensin-converting enzyme inhibitors.

Aged↗
PubMed2002

Combination of analytical quality specifications based on biological within- and between-subject variation.

At a conference on 'Strategies to Set Global Analytical Quality Specifications in Laboratory Medicine' in Stockholm 1999, a hierarchy of models to set analytical quality specifications was decided. The consensus agreement from the conference defined the highest level as 'evaluation of the effect of analytical performance on clinical outcomes in specific clinical settings' and the second level as 'data based on components of biological variation'. Here, the many proposals for analytical quality specifications based on biological variation are examined and the outcomes of the different models for maximum allowable combined analytical imprecision and bias are illustrated graphically. The following models were investigated. (1) The Cotlove et al. (1970) model defining analytical imprecision (%CVA) in relation to the within-subject biological variation (%CV(W-S)) as: %CVA < or = 0.5 x %CV(W-S) (where %CV is percentage coefficient of variation). (2) The Gowans et al. (1988) concept, which defines a functional relationship between analytical imprecision and bias for the maximum allowable combination of errors for the purpose of sharing common reference intervals. (3) The European Group for the Evaluation of Reagents and Analytical Systems in Laboratory Medicine (EGE Lab) Working Group concept, which combines the Cotlove model with the Gowans concept using the maximal acceptable bias. (4) The External Quality Assessment (EQA) Organizers Working Group concept, which is close to the EGE Lab Working Group concept, but follows the Gowans et al. concept of imprecision up to the limit defined by the model of Cotlove et al. (5) The 'three-level' concept classifying analytical quality into three levels: optimum, desirable and minimum. The figures created clearly demonstrated that the results obtained were determined by the basic assumptions made. When %CV(W-S) is small compared with the population-based coefficient of variation [%CV(P) = (%CV2(W-S) +%CV2(B-S))(1/2)], the EGE Lab and EQA Organizers Working Group concepts become similar. Examples of analytical quality specifications based on biological variations are listed and an application on external quality control is illustrated for plasma creatinine.

Bias↗
PubMed2002

Objective criteria for partitioning Gaussian-distributed reference values into subgroups.

BACKGROUND: The aim of this study was to develop new and useful criteria for partitioning reference values into subgroups applicable to gaussian distributions and to distributions that can be transformed to gaussian distributions. METHODS: The proposed criteria relate to percentages of the subgroups outside each of the reference limits of the combined distribution. Critical values suggested as partitioning criteria for these percentages were derived from analytical bias quality specifications for using common reference intervals throughout a geographic area. As alternative partitioning criteria to the actual percentages, these were transformed mathematically to critical distances between the reference limits of the subgroup distributions, to be applied to each pair of reference limits, the upper and the lower, at a time. The new criteria were tested using data on various plasma proteins collected from approximately 500 reference individuals, and the outcomes were compared with those given by the currently widely applied and recommended partitioning model of Harris and Boyd, the "Harris-Boyd model". RESULTS: We suggest 4.1% as the critical minimum percentage outside that would justify partitioning into subgroups, and 3.2% as the critical maximum percentage outside that would justify combining them. Percentages between these two values should be classified as marginal, implying that nonstatistical considerations are required to make the final decision on partitioning. The correlation between the critical percentages and the critical distances was mathematically precise in the new model, whereas this correlation is rather approximate in the Harris-Boyd model because focus on the difference between means in this model makes high precision hard to achieve. The application examples suggested that the new model is more radical than the Harris-Boyd model. CONCLUSIONS: New percentage and distance criteria, to be used for partitioning gaussian-distributed data, have been developed. The distance criteria, applied separately to both reference limit pairs of the subgroup distributions, seemed more reliable and correlated more accurately with the critical percentages than the distance criteria of the Harris-Boyd model. As opposed to the Harris-Boyd model, the new model is easily adjustable to new critical values of the percentages, should they need to be changed in the future.

Clinical Laboratory Techniques↗