The zlog value: a decade of standardizing laboratory results
Georg Hoffmann, Frank Klawonn, Inga Trulson, Sandra Klawitter, Matthias OrthAbstract
Background
The zlog transformation was developed in 2016 as a method-independent and unit-independent standardization of laboratory results for the German Electronic Health Record (ePA). Based on the assumption of a log-normal distribution and the known lower and upper reference limits, it maps any result to a z-score on a logarithmic scale with a fixed reference interval of −1.96 to +1.96. A prerequisite for valid zlog calculation is that the reference limits entered reflect a true reference interval – not a clinical decision limit or therapeutic target – and have been properly verified for the local analytical method and population.
Content
Since its publication, the zlog concept has been adopted across a broad range of medical disciplines and analytical applications. We review 19 publications from 2017 to 2025 spanning laboratory medicine, cardiology, neonatology, machine learning, and data visualization, and discuss the growing ecosystem of software tools that support correct reference interval estimation as the necessary foundation.
Opinion
Beyond its regulatory role in the ePA, the zlog value represents a conceptual bridge between clinical and laboratory medicine, as well as data science and artificial intelligence. Color-coded longitudinal laboratory reports based on zlog values can intuitively communicate complex clinical trajectories, as illustrated by an intensive care example. Standardization at the level of individual measurements is a prerequisite for meaningful multivariate analysis, automated interpretation, and interoperable data exchange across research repositories and public health systems.
Outlook
The log-normal assumption underlying zlog is examined critically, and a more flexible Box-Cox transformation is identified as a promising but statistically challenging refinement. The zlog framework is further proposed as the natural foundation for extending univariate reference intervals to multivariate reference regions.