Lecture
Mapping Time in the Lab - From Static Values to Dynamic Chronomaps
- at -
- ICM Saal 4a
- Type: Lecture
Lecture description
Laboratory medicine still predominantly interprets results as isolated, static values, although many biological and pathological processes are inherently dynamic. Important temporal information therefore often remains hidden in routine laboratory data.
The ChronoMap approach addresses this limitation through a multi-level workflow. We present a newly implemented standalone HTML application that enables (i) computing periodograms with up to three harmonics (8, 12, and 24 h) using a Lomb–Scargle approach, (ii) fitting cosinor functions by a quantile regression model to identify concentration-dependent diurnal trajectories while integrating age and sex as covariates, and (iii) rendering ChronoMaps from the derived rhythmometrics.
The resulting time-dependent characteristics enable an objective assessment of whether follow-up blood sampling may be biased when samples are not collected at the same time of day. ChronoMaps visualize how expected physiological diurnal variation can influence measured concentrations and thereby help distinguish true longitudinal changes from apparent changes caused by differing sampling times. In this way, ChronoMaps support a more reliable interpretation of serial laboratory results whenever preanalytical timing is not strictly standardized. Importantly, the quantile regression model also permits the assessment of time-dependent dynamics at any concentration of interest, including reference limits and clinical decision limits.
By making the temporal dimension of routine measurements visible and quantifiable, the ChronoMap approach helps unlock the hidden treasure of laboratory data for more precise and time-aware longitudinal interpretation.
The ChronoMap approach addresses this limitation through a multi-level workflow. We present a newly implemented standalone HTML application that enables (i) computing periodograms with up to three harmonics (8, 12, and 24 h) using a Lomb–Scargle approach, (ii) fitting cosinor functions by a quantile regression model to identify concentration-dependent diurnal trajectories while integrating age and sex as covariates, and (iii) rendering ChronoMaps from the derived rhythmometrics.
The resulting time-dependent characteristics enable an objective assessment of whether follow-up blood sampling may be biased when samples are not collected at the same time of day. ChronoMaps visualize how expected physiological diurnal variation can influence measured concentrations and thereby help distinguish true longitudinal changes from apparent changes caused by differing sampling times. In this way, ChronoMaps support a more reliable interpretation of serial laboratory results whenever preanalytical timing is not strictly standardized. Importantly, the quantile regression model also permits the assessment of time-dependent dynamics at any concentration of interest, including reference limits and clinical decision limits.
By making the temporal dimension of routine measurements visible and quantifiable, the ChronoMap approach helps unlock the hidden treasure of laboratory data for more precise and time-aware longitudinal interpretation.
