A Mathematical Algorithm for Dried Blood Spot Quality Assessment and Results concerning Quality from a Newborn Screening Program.

ABSTRACT

BACKGROUND: In addition to newborn screening, dried blood spots (DBSs) are used for a wide variety of analytes for clinical, epidemiological, and research purposes. Guidelines on DBS collection, storage, and transport are available, but it is suggested that each laboratory should establish its own acceptance criteria. METHODS: An optical scanning device was developed to assess the quality of DBSs received in the newborn screening laboratory from 11 maternity wards between 2013 and 2018. The algorithm was adjusted to agree with the visual examination consensus of experienced laboratory personnel. Once validated, the algorithm was used to categorize DBS specimens as either proper or improper. Improper DBS specimens were further divided based on 4 types of specimen defects. RESULTS: In total, 27 301 DBSs were analyzed. Compared with an annual DBS rejection rate of about 1%, automated scanning rejected 26.96% of the specimens as having at least one defect. The most common specimen defect was multi-spotting (ragged DBS, 19.13%). Among maternity wards, improper specimen rates varied greatly between 5.70% and 49.92%. CONCLUSIONS: Improper specimen rates, as well as the dominant type of defect(s), are mainly institution-dependent, with various maternity wards consistently showing specific patterns of both parameters over time. Although validated in agreement with experienced laboratory personnel consensus, automated analysis rejects significantly more specimens. While continuous staff training, specimen quality monitoring, and problem-reporting to maternities is recommended, a thorough quality assessment strategy should also be implemented by every newborn screening laboratory. An important role in this regard may be played by automation in the form of optical scanning devices.