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BACKGROUND Estimated glomerular filtration rate (eGFR) is widely used in clinical practice. This study assessed the within-subject biological variation (CVI) of different eGFR equations in people with chronic kidney disease (CKD) and people without CKD. The aims of this study were (a) to determine the 24-h biological variation profiles of creatinine, cystatin C, and eGFR and (b) to determine whether CVI of creatinine, cystatin C, and eGFR changes on deterioration of glomerular filtration. METHODS Hourly blood samples were analyzed from 37 individuals (17 without CKD, 20 with CKD) during 24 h. Creatinine (enzymatic method) and cystatin C were measured using a Cobas 8000 (Roche Diagnostics). eGFR was estimated using the Modification of Diet in Renal Disease and the Chronic Kidney Disease Epidemiology Collaboration based on creatinine and/or cystatin C. Plasma samples were stored at −80 °C before analysis. Outlier and homogeneity analyses were checked before performing a nested ANOVA to determine biological variation. RESULTS CVI of creatinine was higher in people without CKD than in those with CKD (6.4% vs 2.5%) owing primarily to the more profound effect of meat consumption on creatinine variability in individuals with lower baseline creatinine concentrations. Unlike creatinine, cystatin C concentrations were unaffected by meat consumption. Cystatin C showed some diurnal rhythmic variation and less in people with CKD. Reference change values (RCVs) of all eGFR equations were within 13% to 20% in both study groups. CONCLUSIONS Despite differences in CVI of creatinine, the CVI and RCV of the eGFR equations were relatively similar for people with or without CKD.

Plasma concentrations of cardiac troponins, the preferred biomarkers for the diagnosis of acute myocardial infarction, are often persistently elevated in patients with chronic kidney disease (CKD). The origin of these elevations is unknown: Is it the heart, by increased release, or the kidneys, by decreased renal elimination? In clinical practice, this equivocal view on troponin elevations in patients with reduced glomerular clearance underlies countless clinical discussions among physicians and may delay rapid initiation of adequate treatment when these patients present with chest pain. In the present study, we aimed to discriminate between increased cardiac release and reduced renal elimination as the main process underlying this phenomenon. Specifically, we used the recently demonstrated rhythmic diurnal oscillation pattern of troponin T as a model to assess the contribution of impaired renal elimination to persistently elevated cardiac troponin levels in patients with CKD.1 The diurnal troponin T rhythm is characterized by gradually decreasing concentrations throughout daytime and rising concentrations during nighttime.1 If decreased renal clearance, and not increased production, is the key driver of elevated troponins in patients with CKD, the increased half-life and subsequent accumulation of cardiac troponin T will fade its diurnal rhythm. …

ObjectivesWhen monitoring patients over time, clinicians may struggle to distinguish ‘real changes’ in consecutive blood parameters from so-called natural fluctuations. In practice, they have to do so by relying on their clinical experience and intuition. We developedLabtracker+, a medical app that calculates the probability that an increase or decrease over time in a specific blood parameter is real, given the time between measurements.DesignWe presented patient cases to 135 participants to examine whether there is a difference between medical students, residents and experienced clinicians when it comes to interpreting changes between consecutive laboratory results. Participants were asked to interpret if changes in consecutive laboratory values were likely to be ‘real’ or rather due to natural fluctuations. The answers of the study participants were compared with the calculated probabilities by the appLabtracker+and the concordance rates were assessed.Setting and participantsMedical students (n=92), medical residents from the department of internal medicine (n=19) and internists (n=24) at a Dutch University Medical Centre.Primary and secondary outcome measuresConcordance rates between the study participants and the calculated probabilities by the appLabtracker+were compared. Besides, we tested whether physicians with clinical experience scored better concordance rates with the appLabtracker+than inexperienced clinicians.ResultsMedical residents and internists showed significantly better concordance rates with the calculated probabilities by the appLabtracker+than medical students, regarding their interpretation of differences between consecutive laboratory results (p=0.009 and pConclusionThe appLabtracker+could serve as a clinical decision tool in the interpretation of consecutive laboratory test results and could contribute to rapid recognition of parameter changes by physicians.

Background: Middle- and long-term biological variation data for hematological parameters have been reported in the literature. Within-day 24-h variability profiles for hematological parameters are currently lacking. However, comprehensive hour-to-hour variability data are critical to detect diurnal cyclical rhythms, and to take into account the 'time of sample collection' as a possible determinant of natural fluctuation. In this study, we assessed 24-h variation profiles for 20 hematological parameters.Methods: Blood samples were collected under standardized conditions from 24 subjects every hour for 24 h. At each measurement, 20 hematological parameters were determined in duplicate. Analytical variation (CVA), within-subject biological variation (CVI), between-subject biological variation (CVG), index of individuality (II), and reference change values (RCVs) were calculated. For the parameters with a diurnal rhythm, hour-to-hour RCVs were determined.Results: All parameters showed higher CVG than CVI. Highest CVG was found for eosinophils (46.6%; 95% CI, 34.9%-70.1%) and the lowest value was mean corpuscular hemoglobin concentration (MCHC) (3.2%; 95% CI, 2.4%-4.8%). CVI varied from 0.4% (95% CI, 0.32%-0.42%) to 20.9% (95% CI, 19.4%-22.6%) for red cell distribution width (RDW) and eosinophils, respectively. Six hematological parameters showed a diurnal rhythm.Conclusions: We present complete 24-h variability profiles for 20 hematological parameters. Hour-to-hour reference changes values may help to better discriminate between random fluctuations and true changes in parameters with rhythmic diurnal oscillations.

ObjectiveCardiac biomarkers hold promise for follow-up and management of aortic valve stenosis (AVS). When interpreting serial biomarker measurements of patients with AVS, it can be challenging to distinguish ‘real changes’ from ‘random fluctuation’. Hence, robust estimation of the biological variation of these biomarkers is essential. In the present study we assessed biological variation of B-type natriuretic peptide (BNP), N-terminus pro-brain natriuretic peptide (NT-proBNP), high-sensitivity troponin-T and high-sensitivity troponin-I (hs-TnT and hs-TnI), and ST2 in subjects with stable AVS.MethodsSerial blood sampling was performed in 25 subjects with moderate AVS—confirmed by echocardiography—and all free from acute cardiovascular events in the past 6 months. Blood samples were taken on seven standardised occasions during 1 year. Analytical variation (CVA), within-subject biological variation (CVI), between-subject biological variation (CVG), index of individuality (II) and reference change values were calculated for all cardiac biomarkers.ResultsCVI was highest for BNP (62.0%, 95% CI 52.5 to 75.4) and lowest for hs-TnI (9.2%, 95% CI 2.8 to 13.8). CVG exceeded the CVI for all biomarkers except BNP, and ranged from 19.8% (95% CI 13.8 to 33.4) for ST2 to 57.2% (95% CI 40.4 to 97.3) for hs-TnT. NT-proBNP, hs-TnT and ST2 revealed CVA A (19.7 and 14.9, respectively). All biomarkers except BNP showed marked individuality, with II ranging from 0.21 to 0.67 (BNP 1.34).ConclusionThis study provides the first biological variation estimates of cardiac biomarkers in patients with stable AVS. These estimates allow a more evidence-based interpretation of biomarker changes in the follow-up and management of patients with AVS.Trial registration numberNCT02510482