Purpose Several recent articles discuss the need for a definition of chronic kidney disease (CKD) that embarks age-dependency and its impact on the prevalence of CKD. The relevance is derived from the common knowledge that renal function declines with age. The aim of this study was to calculate age-dependent eGFR lower reference limits and to consider their impact on the prevalence of CKD. Methods A real-world data set from patients with inconspicuous urinalysis was used to establish two quantile regression models which were used to calculate continuous age-dependent lower reference limits of CKD–EPI, FAS and EKFC–eGFR based on either single eGFR determinations or eGFR values that are stable over a period of at least 3 months (± 10% eGFR). The derived lower reference limits were used to calculate the prevalence of CKD in a validation data set. Prevalence calculation was done once without and once with application of the chronicity criterion. Results Both models yielded age-dependent lower reference limits of eGFR that are comparable to previously published data. The model using patients with stable eGFR resulted in higher eGFR reference limits. By applying the chronicity criterion, a lower prevalence of CKD was calculated when compared to one-time eGFR measurements (CKD–EPI: 9.8% vs. 8.3%, FAS: 8.0% vs. 7.2%, EKFC: 9.0% vs. 7.1%). Conclusion The application of age-dependent lower reference intervals of eGFR together with the chronicity criterion result in a lower prevalence of CKD which supports the estimates of recently published work and the idea of introducing age-dependency into the definition of CKD.
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Objectives Conventionally, reference intervals are established by direct methods, which require a well-characterized, obviously healthy study population. This elaborate approach is time consuming, costly and has rarely been applied to steroid hormones measured by mass spectrometry. In this feasibility study, we investigate whether indirect methods based on routine laboratory results can be used to verify reference intervals from external sources. Methods A total of 11,259 serum samples were used to quantify 13 steroid hormones by mass spectrometry. For indirect estimation of reference intervals, we applied a “modified Hoffmann approach”, and verified the results with a more sophisticated statistical method (refineR). We compared our results with those of four recent studies using direct approaches. Results We evaluated a total of 81 sex- and age-specific reference intervals, for which at least 120 measurements were available. The overall agreement between indirectly and directly determined reference intervals was surprisingly good as nearly every fourth reference limit could be confirmed by narrow tolerance limits. Furthermore, lower reference limits could be provided for some low concentrated hormones by the indirect method. In cases of substantial deviations, our results matched the underlying data better than reference intervals from external studies. Conclusions Our study shows for the first time that indirect methods are a valuable tool to verify existing reference intervals for steroid hormones. A simple “modified Hoffmann approach” based on the general assumption of a normal or lognormal distribution model is sufficient for screening purposes, while the refineR algorithm may be used for a more detailed analysis.
Objectives Even though most physicians and professionals in laboratory medicine have received basic training in statistics, experience shows that a general understanding of data analysis is not yet available on a broad scale. Therefore, data literacy, data-driven decision making, and computational thinking should be implemented in future educational training. To evaluate the state of digital competence among young scientists (YS) in laboratory medicine, we launched a worldwide online survey. Methods A global online survey was conducted from 25/05/2022 to 26/06/2022 and was disseminated to YS who are listed in three large networks: YS of the DGKL, the EFLM Task Group-YS, and IFCC Task Force-YS and its corresponding members, covering a base of 53 countries. Results 119 young scientists from 40 countries participated in this survey. 80 % did not learn digital skills in their academic education but 96 % felt they needed to. Digital literacy was associated with terms such as programming, artificial intelligence and machine learning, statistics, communication, Big Data and data analytics. Conclusions The results of our survey show that more knowledge and training in the area of digital skills is not just necessary, but also wanted by young scientists. A varied learning environment consisting of tutorial articles, videos, exercises, technical articles, collection of helpful links, online meetings and in person bootcamps is crucial to meet the challenges of an international project with different languages, health systems and time zones.
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