dc.contributor.author Hoffmann, Georg dc.contributor.author Klawonn, Frank dc.contributor.author Lichtinghagen, Ralf dc.contributor.author Orth, Matthias dc.date.accessioned 2018-04-16T08:09:36Z dc.date.available 2018-04-16T08:09:36Z dc.date.issued 2017-01-26 dc.identifier.citation The zlog value as a basis for the standardization of laboratory results 2017, 41 (1) LaboratoriumsMedizin en dc.identifier.issn 1439-0477 dc.identifier.issn 0342-3026 dc.identifier.doi 10.1515/labmed-2017-0135 dc.identifier.uri http://hdl.handle.net/10033/621355 dc.description.abstract Abstract Background: With regard to the German E-Health Law of 2016, the German Society for Clinical Chemistry and Laboratory Medicine (DGKL) has been invited to develop a standard procedure for the storage and transmission of laboratory results. We suggest the commonly used z-transformation. Methods: This method evaluates by how many standard deviations (SDs) a given result deviates from the mean of the respective reference population. We confirm with real data that laboratory results of healthy individuals can be adjusted to a normal distribution by logarithmic transformation. Results: Thus, knowing the lower and upper reference limits LL and UL, one can transform any result x into a zlog value using the following equation: \eqalign{ {\rm{zlog}} = & {\rm{(log(x)}}-{\rm{(log(LL)}} + {\rm{log(UL))/2)\cdot3}}{\rm{.92/(log(UL)}} \cr -{\bf{ }}{\rm{log(LL))}} \cr} The result can easily be interpreted, as its reference interval (RI) is –1.96 to +1.96 by default, and very low or high results yield zlog values around –5 and +5, respectively. For intuitive data presentation, the zlog values may be transformed into a continuous color scale, e.g. from blue via white to orange. Using the inverse function, any zlog value can then be translated into the theoretical result of an analytical method with another RI: (1) $${\rm{x}} = {\rm{L}}{{\rm{L}}^{0.5 - {\rm{zlog}}/3.92}} \cdot {\rm{U}}{{\rm{L}}^{0.5 + {\rm{zlog}}/3.92}}$$ Conclusions: Our standardization proposal can easily be put into practice and may effectively contribute to data quality and patient safety in the frame of the German E-health law. We suggest for the future that laboratories should provide the zlog value in addition to the original result, and that the data transmission protocols (e.g. HL7, LDT) should contain a special field for this additional value. dc.relation.url http://www.degruyter.com/view/j/labm.2017.41.issue-s1/labmed-2017-0135/labmed-2017-0135.xml en dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/ * dc.title The zlog value as a basis for the standardization of laboratory results dc.type Article en dc.contributor.department Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7, 38124 Braunschweig, Germany. en dc.identifier.journal LaboratoriumsMedizin en html.description.abstract Abstract Background: With regard to the German E-Health Law of 2016, the German Society for Clinical Chemistry and Laboratory Medicine (DGKL) has been invited to develop a standard procedure for the storage and transmission of laboratory results. We suggest the commonly used z-transformation. Methods: This method evaluates by how many standard deviations (SDs) a given result deviates from the mean of the respective reference population. We confirm with real data that laboratory results of healthy individuals can be adjusted to a normal distribution by logarithmic transformation. Results: Thus, knowing the lower and upper reference limits LL and UL, one can transform any result x into a zlog value using the following equation: \eqalign{ {\rm{zlog}} = & {\rm{(log(x)}}-{\rm{(log(LL)}} + {\rm{log(UL))/2)\cdot3}}{\rm{.92/(log(UL)}} \cr -{\bf{ }}{\rm{log(LL))}} \cr} The result can easily be interpreted, as its reference interval (RI) is –1.96 to +1.96 by default, and very low or high results yield zlog values around –5 and +5, respectively. For intuitive data presentation, the zlog values may be transformed into a continuous color scale, e.g. from blue via white to orange. Using the inverse function, any zlog value can then be translated into the theoretical result of an analytical method with another RI: (1) $${\rm{x}} = {\rm{L}}{{\rm{L}}^{0.5 - {\rm{zlog}}/3.92}} \cdot {\rm{U}}{{\rm{L}}^{0.5 + {\rm{zlog}}/3.92}}$$ Conclusions: Our standardization proposal can easily be put into practice and may effectively contribute to data quality and patient safety in the frame of the German E-health law. We suggest for the future that laboratories should provide the zlog value in addition to the original result, and that the data transmission protocols (e.g. HL7, LDT) should contain a special field for this additional value.
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