Indirect determination of hematology reference intervals in adult patients on Beckman Coulter UniCell DxH 800 and Abbott CELL-DYN Sapphire devices
-
Jakob Zierk
,
Farhad Arzideh
und
Stefan W. Krause
Abstract
Background
Conventional establishment of reference intervals for hematological analytes is challenging due to the need to recruit healthy persons. Indirect methods address this by deriving reference intervals from clinical laboratory databases which contain large datasets of both physiological and pathological test results.
Methods
We used the “Reference Limit Estimator” (RLE) to establish reference intervals for common hematology analytes in adults aged 18–60 years. One hundred and ninety-five samples from 44,519 patients, measured on two different devices in a tertiary care center were analyzed. We examined the influence of patient cohorts with an increasing proportion of abnormal test results, compared sample selection strategies, explored inter-device differences, and analyzed the stability of reference intervals in simulated datasets with varying overlap of pathological and physiological test results.
Results
Reference intervals for hemoglobin, hematocrit, red cell count and platelet count remained stable, even if large numbers of pathological samples were included. Reference intervals for red cell indices, red cell distribution width and leukocyte count were sufficiently stable, if patient cohorts with the highest fraction of pathological samples were excluded. In simulated datasets, estimated reference limits shifted, if the pathological dataset contributed more than 15%–20% of total samples and approximated the physiological distribution. Advanced sample selection techniques did not improve the algorithm’s performance. Inter-device differences were small except for red cell distribution width.
Conclusions
The RLE is well-suited to create reference intervals from clinical laboratory databases even in the challenging setting of a adult tertiary care center. The procedure can be used as a complement for reference interval determination where conventional approaches are limited.
Acknowledgments
The authors thank Renate Kraska for data retrieval from the clinical information system.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: Supported by the Interdisciplinary Center for Clinical Research (IZKF) at the University Hospital of the University of Erlangen-Nuremberg by a “Laboratory Rotation programme” for JZ. JZ received a Microsoft Azure for Research Grant to use Microsoft Azure computing resources, which was used for the calculation of reference intervals.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2018-0771).
©2019 Walter de Gruyter GmbH, Berlin/Boston
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