Page 94 - Exploring the Potential of Self-Monitoring Kidney Function After Transplantation - Céline van Lint

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Chapter 4
agreement and negligible bias of 1.60 (–2.3– 5.5) μmol/L between central laboratory/Modular P800 and IDMS reference method, contrasting with the large scatter and excessive bias of –16.1 (–60.3– 28.1) μmol/L for StatSensor® compared to IDMS reference methods as presented in Figure 3A. In the case of trend monitoring, the uncertainty of a single capillary blood creatinine test result is less critical. Detection of rejection episodes after kidney transplantation reflects a monitoring purpose for which the device should be able to detect trends in sequential measurements. In the present study, we examined the suitability of StatSensor® capillary blood testing to monitor changes in renal function. For recently transplanted patients, clinicians are especially interested in sudden increases in serum creatinine of >10% as this requires further analysis and/ or intensified follow-up. Therefore, the aim of this study was to assess whether a >10% change in serum creatinine (as measured by the central laboratory method) can also be detected when using StatSensor® for trend monitoring. For validating trend detection, it does not matter whether creatinine increases or decreases. Newly transplanted patients are a suitable population group, as their creatinine levels usually decrease rapidly during the first days after kidney transplantation. A reasonable correlation (R=0.77) between changes detected by the central laboratory and the StatSensor® was found. False-negative results lead to a delayed detection of rejection and should not or hardly occur. Although false-positive findings are less problematic, they lead to extra diagnostic interventions. In this study, the StatSensor® correctly identified a difference of >10% (true positive) in 70% and a difference of ≤10% (true negative) in 67% of all cases (total agreement 68%) within the time period monitored. Although these results indicate that StatSensor®’s ability to detect changes in kidney function needs improvement, the absence of a significant difference between changes observed by the central laboratory analyzer and the StatSensor® shows that it does have potential for monitoring creatinine.
To strengthen StatSensor®’s performance, an important step should be the improvement of its analytical performance as this will impact its clinical (diagnosing and monitoring) performance too. In the meantime, two manoeuvres could offer a provisional solution. First, to decrease the number of false negative results, one could choose a cut-off percentage which is lower for StatSensor® results. For example, by lowering the StatSensor® cut-off percentage to >5%, the number of correctly identified relevant changes (>10% increase as determined by the central laboratory method) increases from 70% to 82%. However, this approach would result in an increased number of false positives. Second, with increasing the frequency of StatSensor® measurements, a more reliable trend will be obtained, as the confidence interval decreases proportionally to the square root of the number of performed measurements, given a normal distribution. At home, patients can measure their creatinine daily. By doing so, the chances of detecting rejection are increased and theoretically, the number of outpatient