Overall percentage of rejection is similar to previously published data. As some of the included variables have turned out to be irrelevant, the program has been simplified from the year 2006 onwards.

BackgroundBlood samples are usually collected daily from different collection points, such hospitals and health centers, and transported to a core laboratory for testing. This paper presents a project to improve the collection routes of two of the largest clinical laboratories in Spain. These routes must be designed in a cost-efficient manner while satisfying two important constraints: (i) two-hour time windows between collection and delivery, and (ii) vehicle capacity.MethodsA heuristic method based on a genetic algorithm has been designed to solve the problem of blood sample collection. The user enters the following information for each collection point: postal address, average collecting time, and average demand (in thermal containers). After implementing the algorithm using C programming, this is run and, in few seconds, it obtains optimal (or near-optimal) collection routes that specify the collection sequence for each vehicle. Different scenarios using various types of vehicles have been considered. Unless new collection points are added or problem parameters are changed substantially, routes need to be designed only once.ResultsThe two laboratories in this study previously planned routes manually for 43 and 74 collection points, respectively. These routes were covered by an external carrier company. With the implementation of this algorithm, the number of routes could be reduced from ten to seven in one laboratory and from twelve to nine in the other, which represents significant annual savings in transportation costs.ConclusionsThe algorithm presented can be easily implemented in other laboratories that face this type of problem, and it is particularly interesting and useful as the number of collection points increases. The method designs blood collection routes with reduced costs that meet the time and capacity constraints of the problem.

Time is the main variable affecting stability in medical laboratory samples. Bibliographic studies differ in recommedations of stability limits mainly because of different specifications for maximum allowable error. Definition of a consensus stability function in specific conditions can help laboratories define stability limits using their own quality specifications.

A total of 36% of Spanish laboratories do not use autoverification, despite the general implementation of laboratory information systems, most of them, with autoverification ability. Criteria and rules for seven routine biochemical tests were obtained.

Aution Max AX-4280, an automated urine test-strip analyser, was evaluated in three centres. Method comparison, imprecision, carry-over, linearity, detection limit and drift studies were performed for glucose, protein, blood and leukocytes using Uriflet S 9UB strips. These strips enable measurement of pH, glucose, protein, blood, leukocytes, ketones, bilirubin, urobilinogen and nitrite. Specific gravity is determined by the refractive index method. Within-run and between-day imprecision, assessed using pooled urines and quality control materials, were good. No drift over 24 h or sample carry-over was observed. Method comparison with quantitative methods for glucose, protein and specific gravity yielded good correlations. Ascorbate negatively interfered with haemoglobin, glucose and nitrite measurements. Acetylsalicylic acid lowered pH, the effect being greatest when protein was absent. During the assessment period no malfunction or breakdown was reported. The Aution Max is easy to use and needs minimal maintenance.

For the majority of sample types, a decrease in preanalytical errors was confirmed. Improvements in organization, implementation of standardized procedures in the preanalytical phase, and participation in a Spanish external quality assessment scheme may have notably contributed to error reduction in this phase.

Background/Aim: Cystatin C (Cys C) is an endogenous marker of glomerular filtration rate (GFR) unaffected by body composition. The aim of the present study was to assess the utility of Cys C-based GFR prediction equations (Hoek, Larsson and Stevens) and creatinine (modification of diet in renal disease-isotope dilution mass spectrometry – MDRD-IDMS, and Cockcroft-Gault – CG) compared with ⁵¹Cr-EDTA. Methods: This study was carried out in 40 Caucasian older patients with advanced age (≥60) and chronic kidney disease stages 3–4. To assess the utility of prediction equations in relation to body composition, we measured lean mass (LM) with densitometry (DXA). Pearson’s, Bland-Altman and Lin’s coefficient (Rc) were used to study accuracy and precision. Results:⁵¹Cr-EDTA was 36.9 ± 9.2 ml/min/1.73 m² (22–60). Cys C levels were 2.2 ± 0.8 mg/l (r = 0.085; p = 0.662 LM) and creatinine 2.8 ± 1.1 mg/dl (r = 0.427; p = 0.021 LM). The most accurate equations were the Hoek, Larsson and Stevens formulae, with a bias of –0.2 (Rc 0.48), –2.9 (Rc 0.44) and 2.6 ml/min/1.73 m² (Rc 0.58). The biases obtained with MDRD-IDMS and CG were –14.6 (Rc 0.35) and –12.5 (Rc 0.40). All correlations among biases obtained with creatinine-based formulae and LM were negative and statistically significant (p < 0.05). Conclusions: The results show superiority of Cys C-based GFR formulae over the MDRD-IDMS and CG equations. This significant underestimation obtained with conventional prediction equations was directly related to the influence of LM.

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