Indirect determination of pediatric blood count reference intervals

Abstract

Background: Determination of pediatric reference intervals (RIs) for laboratory quantities, including hematological quantities, is complex. The measured quantities vary by age, and obtaining samples from healthy children is difficult. Many widely used RIs are derived from small sample numbers and are split into arbitrary discrete age intervals. Use of intra-laboratory RIs specific to the examined population and analytical device used is not yet fully established. Indirect methods address these issues by deriving RIs from clinical laboratory databases which contain large datasets of both healthy and pathological samples. Methods: A refined indirect approach was used to create continuous age-dependent RIs for blood count quantities and sodium from birth to adulthood. The dataset for each quantity consisted of 60,000 individual samples from our clinical laboratory. Patient samples were separated according to age, and a density function of the proportion of healthy samples was estimated for each age group. The resulting RIs were merged to obtain continuous RIs from birth to adulthood. Results: The obtained RIs were compared to RIs generated by identical laboratory instruments, and to population-specific RIs created using conventional methods. This comparison showed a high concordance of reference limits and their age-dependent dynamics. Conclusions: The indirect approach reported here is well-suited to create continuous, intra-laboratory RIs from clinical laboratory databases and showed that the RIs generated are comparable to those created using established methods. The procedure can be transferred to other laboratory quantities and can be used as an alternative method for RI determination where conventional approaches are limited.

Reference intervals for iron-related blood parameters: results from a population-based cohort study (LIFE Child)

“…With the increase in concentrations up to the age of 11 years, as described, and the subsequent start of a gender-based progression characterized by relative constant concentrations in girls and a strong increase in boys, hemoglobin follows a typical trend confirmed by previous studies [10,12,17,19,35,36]. This can generally be attributed to growth during childhood development.…”

“…If one uses the cut-off values for hemoglobin and ferritin issued by WHO [41] for diagnosing iron deficiency anemia as a basis for comparison, they harmonize with the results observed by us only partially, as they do with the results of the KiGGS [16] and Zierk et al [17] studies.…”

“…With the beginning of puberty, the differences can be explained on the basis of gender, particularly as a result of hormonal factors (erythropoietin stimulation due to androgens, or erythropoietin inhibition due to estrogens) and the onset of menstruation (blood loss) in girls. The studies of KiGGS [16] and Zierk et al [17] lend themselves to comparison due to geographic proximity, timeliness and the description of continuous results. When comparing the reference intervals computed in this study with those of KiGGS (P 3-P 97), one sees mean deviations from the lower reference limits of 0.17 g/dL (maximum deviation: 0.38 g/dL) for boys and 0.07 g/dL (maximum deviation: 1.66 g/dL) for girls.…”

Evidence that neurovascular coupling underlying the BOLD effect increases with age during childhood

“…M is a function of baseline hematocrit (Hct), cerebral blood volume (CBV 0 ), and oxygen extraction fraction (OEF 0 ), as well as other participant‐independent parameters such as main magnetic field strength and MRI TE [Chiarelli et al, ]: where A is a constant related to the main magnetic field strength and the susceptibility difference between blood and tissue, and dHb 0 is the baseline deoxyhemoglobin concentration. Hematocrit increases by approximately 10%–15% during the developmental period [Orkin et al, ; Zierk et al, ]. However, baseline OEF decreases by approximately the same proportion [Takahashi et al, ].…”

An unsupervised learning method to identify reference intervals from a clinical database

“…For example, a basic method may classify any results more than two standard deviations from the mean as outliers, while a more complex method may model the results as a sum of two Gaussian distributions: one from the outlier results, and one from the non-outlier results [11]. These indirect approaches are often used to calculate reference intervals for pediatric and geriatric populations, where data is sparse [22, 23]. The IFCC recommends that the characteristics of the reference population are clearly defined [13], which is not achievable using these purely statistical approaches.…”