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Technical Briefs |
1
Hosp. "Germans Trias i Pujol", Badalona (Barcelona), Spain;
2
Hosp. Gen. "Vall d' Hebron", Barcelona, Spain;
3
Centro de Asistencia Primaria "Dr. Robert", Badalona, Spain;
a address for
correspondence: Dept. of Biochem., Hosp. "Germans Trias i Pujol", Ctra. de Canyet s/n, 08916 Badalona (Barcelona), Spain, fax 343 395 42 06
Renal transplantation has come to be accepted as standard treatment for patients with terminal kidney failure. Because the physiological state of the renal transplant recipient is unstable, he or she is monitored with a well-defined protocol that is strictly followed by clinicians. Renal dysfunction is a common complication due to various problems, especially drug toxicity, and rejection is an ever-present danger (1). To detect episodes of rejection, clinicians use empirical criteria derived from experience that is mainly based on changes in creatinine concentrations. Posttransplantation monitoring, which includes frequent analysis of a number of constituents, generates large amounts of data. With this information, finding objective, early indicators that predict trends toward complications before the patient's condition is seriously compromised would be beneficial, so that preventive actions could be taken.
Data from biological variation (BV), the normal fluctuation around the homeostatic set point, has been used to evaluate the significance of changes in serial results (2). Therefore, it can provide clinicians with an indication of future patient status: A change between two consecutive observations higher than the established variation around the homeostatic set point could signal the beginning of a complication. However, the components of BV, sensitive and specific enough to characterize a "certain state of health" from the start of crisis, can be investigated only when a stable situation that denotes equilibrium has been demonstrated in the specific pathology.
The aims of this work were to delineate the stable period after renal transplantation for six serum analytes expected to reflect instability/rejection, and to calculate within- and between-subject BV, indices of individuality (II), and critical differences (CDs) between serial results and compare outcome with published data in healthy subjects to determine whether BV data can predict crises in this population.
We studied 19 patients (12 men and 7 women), 19 to 64 years old, with chronic renal insufficiency who had received an orthotopic kidney graft. Permission for enrollment in the study was obtained from all patients, as required by the Helsinki II protocol.
Serum specimens were collected according to the usual hospital follow-up protocol designed by the nephrologists for these patients, as summarized: (a) First week posttransplantation, daily; (b) from the second to fourth week, twice a week; (c) from the first to the third month, weekly; (d) from the third to the sixth month, every 15 days; (e) from the sixth to the twelfth month, once a month; (f) from the first year on, every 2 or 3 months, indefinitely.
Stability in posttransplantation patients is routinely verified through a combination of clinical, analytical, and imaging parameters: clinical normality as indicated by symptomatology, physiological constants, diuresis, weight, physical examination, etc.; analytical profile with particular attention directed to the stability of creatinine results (expected to differ <25% between two consecutive samplings); and doppler echography. The data from our 19 patients was studied for 2 years posttransplantation, and during this time there was no evidence of crisis or rejection according to the nephrologists' protocol.
The conditions of specimen collection were standardized to minimize the effect of collection. Specimens were collected into evacuated blood-collection tubes without anticoagulant. The specimens were allowed to clot at room temperature and were centrifuged at 3000g for 15 min. We separated the serum, and quantities were analyzed. The study was conducted in real time throughout.
Six serum biochemistry analytes were determined: creatinine, urea, urate, sodium, potassium, and chloride.
Establishment of the homeostatic point: The homeostatic point for each analyte was derived from the period of maximum stability.
1) Determination of the beginning of the stable period: The analytic results from each analyte in each patient were represented graphically from the beginning of the posttransplantation period. Visual inspection of the graphs showed high results in the first determinations that decreased to a point after which results remained constant over time (from negative slope to horizontal lines) in some of the analytes. This point was considered to be the beginning of the stable period and was called "point zero." Point zero and the stable pattern were seen very clearly in the creatinine analyses and were confirmed in the urea graph; however, in the remaining analytes a clear inflection point was not observed. Therefore in each patient, point zero for all the quantities studied was derived from the creatinine results.
2) Determination of the end of the stable period: Results were normalized according to the start value (3): the ratio of each result from a patient with respect to his value at point zero was calculated.
The CV of the ratios of the 19 patients (normalized CV) was calculated and the difference between the CV for each sampling day and the CV for point one (the CV at point zero is 0) were depicted on a graph, against the analytical variation of the method for each constituent. When the difference between the normalized CV was found to be higher than the analytical CV, stability was considered to have ended.
BV calculations: Analytical imprecision (s2a) was calculated through control materials, averaging the routine data for 12 months, and using the control concentration closest to the mean of the concentration values found in the 19 patients studied.
Before performing calculations with the patients' results, the Cochran test (4) was applied to exclude outlying values from the individual subjects, and the Reed test (5) to eliminate mean outlying values.
The ANOVA test (6) was used to estimate within-subject
(s2i) plus analytical variation
(s2i+a), expressed as the weighted mean of
variances from the 19 patients. Within-subject (intraindividual) BV
(s2i) was calculated by a subtraction step with
the two previous variables (s2i+a -
s2a). Between-subject (group) BV
(s2g) was obtained by subtracting the
within-subject plus analytical variation from the total variation
(s2t) found by using all data from all
patients:
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= 0.05) was used to detect only
significant increases:
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To derive BV data, the stable period within the pathology has to first be defined: The more precisely the stable period is delineated, the more robust will be the indicators produced. The beginning of posttransplantation stability was determined by examining the slopes of the quantities studied. We considered that a clear inflection (and subsequent maintenance) of the slope in more than one quantity simultaneously would signal the beginning of stability. For all 19 patients, creatinine and urea showed this pattern and were considered valid for our purposes.
The beginning of the stable period was found between the first and second month posttransplantation, although the exact moment when it occurred was not necessarily the same in all patients. The fact that at this time the nephrologists' protocol reduces the required analyses to a frequency of one per week indicates that the empirically based criteria also perceive stability at this time.
To minimize interindividual variation and to facilitate the detection
of relative changes among patients, all results were normalized
(3)(7)(8). Fig. 1
shows the difference between the normalized CV at each sampling
day and the CV at point one, in relation to the analytical CV (parallel
line to the x-axis) for creatinine. The period of stability
was considered to conclude when the CV of the difference was higher
than the analytical CV. This occurred within an interval of eight
determinations (that starts between 1 and 2 months after surgery), when
follow-up protocol analyses are performed once a week, and is
maintained for an average of 3 months, depending on the patient.
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Table 1
shows the components of analytical and biological variation
found in this study, expressed in terms of CVs. Data from healthy
subjects obtained by averaging results from previous works
(9)(10) are also exhibited.
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When studying the components of BV, the analytical component (CVa) is lower than half the within-subject component (CVi) for all constituents studied (except chloride and sodium), demonstrating that the use of "real time" analytical data is appropiate for studying BV. Browning et al. (11) recommended that when studying BV, the analytical component should be <20% of the total variance found. The analytical difficulties for chloride and sodium are similar to those found by other authors in similar studies (12)(13).
BV data from healthy subjects have been compiled in two well-known articles (9)(10). Few works have dealt with BV in pathological status. Fraser and colleagues reported that the within-subject and between-subject variation for certain analytes in patients with renal dysfunction and cardiac infarction are the same as in healthy subjects (13)(14). Hölzel (15)(16), however, shows discrepancies in specific pathological situations. Our data show that within-subject variation was higher in the kidney graft recipients than in the healthy population, being most evident in the creatinine, potassium, urate, and urea results. We found no differences in the between-subject variation, except for potassium.
We studied BV to determine if analytical results from the routine monitoring protocol could be used as predictors of functional alteration in renal transplant recipients. To know which constituents are suitable as early indicators of negative evolution in pathological situations, the II are determined. Creatinine, urate, and urea, with II of ~0.6, are suitable for monitoring (2).
Harris and colleagues proposed a formula derived from within-subject variation to interpret CDs in serial results (17)(18). We found that CDs were ~28% in creatinine, urate, and urea. This figure is very close to the 25% criteria used by the nephrologist in their protocol. Moreover, none of the 19 patients surpassed this difference over the study period, indicating that no significant changes occurred.
The other constituents provided no information for the purpose of predicting functional alteration. Thus, an interpretation of serial results from the combination of creatinine, urate, and urea analyses could be a method for early detection of possible crises in posttransplantation patients.
In conclusion, this work describes a model for studying BV in a nonhealthy state by using available laboratory data. It shows a method for demonstrating stability within the pathology and for deriving the components of BV.
Acknowledgments
We thank Per Hyltoft Petersen for his invaluable help in the normalization section of the study, and in improving the orientation of this work.
References
The following articles in journals at HighWire Press have cited this article:
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  A. K. Aarsand, P. H. Petersen, and S. Sandberg Estimation and Application of Biological Variation of Urinary {delta}-Aminolevulinic Acid and Porphobilinogen in Healthy Individuals and in Patients with Acute Intermittent Porphyria. Clin. Chem., April 1, 2006; 52(4): 650 - 656. [Abstract] [Full Text] [PDF]
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J. Trape, J. Perez de Olaguer, J. Buxo, and L. Lopez Biological Variation of Tumor Markers and Its Application in the Detection of Disease Progression in Patients with Non-Small Cell Lung Cancer Clin. Chem., January 1, 2005; 51(1): 219 - 222. [Full Text] [PDF]
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J. Trape, J. M. Botargues, F. Porta, C. Ricos, J. M. Badal, R. Salinas, M. Sala, and A. Roca Reference Change Value for {alpha}-Fetoprotein and Its Application in Early Detection of Hepatocellular Carcinoma in Patients with Hepatic Disease Clin. Chem., July 1, 2003; 49(7): 1209 - 1211. [Full Text] [PDF]
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C. Biosca, C. Ricos, R. Lauzurica, R. Galimany, and P. Hyltoft Petersen Reference Change Value Concept Combining Two Delta Values to Predict Crises in Renal Posttransplantation Clin. Chem., December 1, 2001; 47(12): 2146 - 2148. [Full Text] [PDF]
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