Abstract
Abstract Laboratory tests are essential to assess the health status and to guide patient care in individuals of all ages. The interpretation of quantitative test results requires availability of appropriate reference intervals, and reference intervals in children have to account for the extensive physiological dynamics with age in many biomarkers. Creation of reference intervals using conventional approaches requires the sampling of healthy individuals, which is opposed by ethical and practical considerations in children, due to the need for a large number of blood samples from healthy children of all ages, including neonates and young infants. This limits the availability and quality of pediatric reference intervals, and ultimately negatively impacts pediatric clinical decision-making. Data mining approaches use laboratory test results and clinical information from hospital information systems to create reference intervals. The extensive number of available test results from laboratory information systems and advanced statistical methods enable the creation of pediatric reference intervals with an unprecedented age-related accuracy for children of all ages. Ongoing developments regarding the availability and standardization of electronic medical records and of indirect statistical methods will further improve the benefit of data mining for pediatric reference intervals.
Highlights
Direct methods to create reference intervalsThe classical approach (so called direct method) to create reference intervals is both straightforward and challenging: laboratory testing is performed in a sufficiently large group of healthy individuals, and the 2.5 and 97.5th percentiles of test results are reported as lower and upper reference limits [3, 4]
Laboratory tests are essential to assess the health status and to guide patient care in individuals of all ages
Ongoing developments regarding the availability and standardization of electronic medical records and of indirect statistical methods will further improve the benefit of data mining for pediatric reference intervals
Summary
The classical approach (so called direct method) to create reference intervals is both straightforward and challenging: laboratory testing is performed in a sufficiently large group of healthy individuals, and the 2.5 and 97.5th percentiles of test results are reported as lower and upper reference limits [3, 4]. Zierk et al.: Data mining of pediatric reference intervals children -especially young infants and neonates- is associated with pain and stress, and results in blood loss (albeit minor), without individual benefit for participating healthy children in case of reference interval studies These factors restrict the availability of high-quality reference intervals that stratify perfectly for all relevant covariates [7], the Canadian CALIPER study and others have dramatically improved the situation [8,9,10]. Ethical considerations are pronounced in these very young children, who are considered to require the highest possible level of protection from interventions from which they do not directly benefit (i.e. blood drawing for the establishment of reference intervals) This results in the dilemma that for the most vulnerable pediatric subpopulations, which are overrepresented in clinical-decision making due to their disproportionate share in pediatric morbidity and mortality, availability and quality of reference intervals are worst. The challenges associated with creating reference intervals using direct methods often result in reference intervals that do not stratify as accurately as required for all relevant covariates, resulting in the use of inappropriate (e.g. not adequately age-matched) reference intervals due to the lack of more appropriate alternatives
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