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Carmen Ricós

Publications and source records attributed to Carmen Ricós.

8 recordsLinked to original sources
PubMed2006

Biological variation at long-term renal post-transplantation.

In laboratory testing the reference change value (RCV) is used to detect changes in a patient's clinical status, even before clinical signs are evident. In a previous study we determined the biological variation (BV) of a number of constituents during early post-transplantation in kidney recipients to calculate useful RCVs for this purpose. RCVs for creatinine and urate were identified as the most suitable and were different from those calculated from the normal population. The aim of the current study was to determine the BV components at long-term following renal transplantation to predict potential crises in transplant recipients who have been stable for a number of years. BV components for creatinine and urate were calculated in a new group of 40 kidney transplanted patients (26 men and 14 women, 29-71 years old) who had been stable for period of 4 to 7 years following transplantation (long-term post-TR group). An average of 8 samples per patient was obtained during a period of 1-2 years. Results were compared with those from our described group of recently transplanted patients (short-term post-TR group). There were no statistically significant differences between the groups with regard to within-subject variation or within-subject plus analytical variation (CVI+A) for any of the constituents studied. Distribution of CVI+A values in long-term post-TR was comparable to that of short-term post-TR values. Independence between creatinine and urate was maintained at long-term. The fact that BV components for creatinine and urate were similar in short- and long-term post-TR and that independence was maintained implies that the short-term post-TR RCV can also be applied in long-term post-TR patients. The RCV for predicting crises in this population represents an optimization of laboratory reporting and could be a valuable tool for clinical decision making.

Adult↗
PubMed2005

Power function of the reference change value in relation to cut-off points, reference intervals and index of individuality.

The reference change value, defined as RCV = 1.96 x 2(1/2) x(s(I)(2) +s A(2))(1/2), where s(I) is within-subject biological variation and s A is analytical variation, has been used for many years to take clinical decisions in patient monitoring. Furthermore, the index of individuality was defined as II = (s(I)(2) +s(A)(2))(1/2)/s(G) , where s(G) is the between-subject biological variation. This index has been simplified by later authors to s(I)/s(G) and has been used in monitoring situations to determine the utility of population-based reference intervals. Harris stated that when the index of individuality is lower than 0.6, the specific reference interval of the individual - when available - is better than the population-based reference interval. However, if a change within a patient is equivalent to the RCV applied for the significant difference between two measurements, the probability of detecting this change is only 50% (the same probability of missing it). Therefore, to obtain a higher probability of detecting a change by the RCV (e.g., 90%) the interpretation of the index of individuality has to be reconsidered. This contribution compares the power of the RCV to the use of cut-off points and population-based reference intervals. The benefits of the RCV compared to the distance to cut-off point or reference limit are also described in relation to the index of individuality.

Analysis of Variance↗
PubMed2004

Integration of data derived from biological variation into the quality management system.

BACKGROUND: [corrected] Data on within- and between-subject biological variation are available for around 250 analytes commonly used in medical laboratories. METHODS: Integration of this data into the quality system occurs at all three levels of laboratory activity: (a) Preanalytic process: biological variation provides the basis for selecting the most appropriate specimen for analysis, for defining sample stability and for deciding suitable timing between samplings; (b) analytic process: biological variation-derived goals are fundamental for designing internal quality control procedures, and for evaluating laboratory performance; and (c) postanalytic process: delta checks based on within-subject biological variation values are used for validating results and for interpreting serial results from a patient. CONCLUSION: The biological variation is a pillar for managing quality in laboratory medicine.

Blood Chemical Analysis↗
PubMed2004

Reference change values and power functions.

Repeated samplings and measurements in the monitoring of patients to look for changes are common clinical problems. The "reference change value", calculated as zp x [2 x (CVI2 + CVA2)](1/2), where zp is the z-statistic and CVI and CVA are within-subject and analytical coefficients of variation, respectively, has been used to detect whether a measured difference between measurements is statistically significant. However, a reference change value only detects the probability of false-positives (type I error), and for this reason, a model to calculate the risk of missing significant changes in serial results from individuals (probability of false-negatives) is investigated in this work by means of power functions. Therefore, when an analyte is being monitored in a patient, power functions estimate the probability of detecting a defined real change by measuring the difference. Thus, when a measured difference is the same as the calculated reference change value, then it will be detected in only 50% of situations.

Analysis of Variance↗
PubMed2004

Quality indicators and specifications for the extra-analytical phases in clinical laboratory management.

BACKGROUND: Quality management systems should cover all the steps involved in the overall testing and non-testing processes. AIM: To identify quality indicators for extra-analytical processes in the clinical laboratory and to specify acceptability limits, in order to provide a useful tool for continuous improvement of laboratory service. METHODS: A literature review by Medline search was performed using the keywords: Q-Tracks and Q-probes alone, and management, error, mistake, and indicator crossed with quality, laboratory and medicine. The indicators retrieved were organized according to the various laboratory processes. Their expression was standardized in relation to the total activity of each process reported in each paper reviewed. The magnitude of the errors reported was considered to be the current state of the art for the extra-analytical step and was proposed as the quality specification. RESULTS: Examples of indicators and specifications for the pre-analytical process: Analytical request: Error in patient identification (0.08%), request unintelligible (0.1%). SAMPLING: Requested but not collected (7%), redraws (2%). Transport and reception of samples: Inadequate transportation conditions (0.005%), hemolyzed sample (0.2%). Examples of indicators and specifications for the post-analytical process: Report validation: Test not performed (1.4%), test performed but not requested (1.1%). Intra-laboratory reports: Laboratory reporting errors (0.05%), delivery outside specified time (11%). Consulting service: average time to communicate critical values for inpatients (6 min). CONCLUSIONS: These extra-analytical indicators and their specifications, expressed in a standardized manner, constitute a preliminary basis for comparison of individual laboratory performance with the purpose of improving laboratory quality.

Clinical Laboratory Techniques↗
PubMed2004

Analytical quality specifications for common reference intervals.

Interpretation of laboratory test results requires comparison to some type of reference value or reference interval. These comparisons can be cross-sectional (population-based reference interval and cut-off values) or longitudinal (reference change value). Quality specifications for cross-sectional comparison have been established by determining the influence of analytical bias and imprecision on the percentage of the healthy population falling outside the reference limits, when sharing population-based reference intervals in a Gaussian distribution of results. Quality specifications for longitudinal comparisons are equally important and are often overlooked, since less work has been done in this area. Some criteria suggest that a difference between consecutive results designates a true change in a patient health status when the difference is higher than the within-subject biological variation plus the within-laboratory analytical variation. In this chapter we discuss the clinical considerations and laboratory-related factors that must be considered when quality specifications are applied to sharing reference comparisons. Real life experience shows that different analytical methods can produce comparable results when common quality goals are established, and quality can be achieved through a willingness to work together. Within the existing organization, the current specifications for analytical quality and a dedication to quality health care makes it possible to achieve transferability between laboratories within a geographic area.

Clinical Chemistry Tests↗
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↗