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Influence of Season, Storage Temperature and Time of Sample Collection in Pancreatitis-Associated Protein-Based Algorithms for Newborn Screening for Cystic Fibrosis.

Pia MaierSumathy JeyaweerasinkamJanina EberhardLina SoueidanSusanne HämmerlingDirk KohlmüllerPatrik FeyhGwendolyn GramerSven F GarbadeGeorg Friedrich HoffmannJürgen G OkunOlaf Sommerburg
Published in: International journal of neonatal screening (2024)
Newborn screening (NBS) for cystic fibrosis (CF) based on pancreatitis-associated protein (PAP) has been performed for several years. While some influencing factors are known, there is currently a lack of information on the influence of seasonal temperature on PAP determination or on the course of PAP blood concentration in infants during the first year of life. Using data from two PAP studies at the Heidelberg NBS centre and storage experiments, we compared PAP determinations in summer and winter and determined the direct influence of temperature. In addition, PAP concentrations measured in CF-NBS, between days 21-35 and 36-365, were compared. Over a 7-year period, we found no significant differences between PAP concentrations determined in summer or winter. We also found no differences in PAP determination after 8 days of storage at 4 °C, room temperature or 37 °C. When stored for up to 3 months, PAP samples remained stable at 4 °C, but not at room temperature ( p = 0.007). After birth, PAP in neonatal blood showed a significant increasing trend up to the 96th hour of life ( p < 0.0001). During the first year of life, blood PAP concentrations continued to increase in both CF- (36-72 h vs. 36-365 d p < 0.0001) and non-CF infants (36-72 h vs. 36-365 d p < 0.0001). Seasonal effects in central Europe appear to have a limited impact on PAP determination. The impact of the increase in blood PAP during the critical period for CF-NBS and beyond on the applicability and performance of PAP-based CF-NBS algorithms needs to be re-discussed.
Keyphrases
  • cystic fibrosis
  • room temperature
  • machine learning
  • lung function
  • healthcare
  • ionic liquid
  • high resolution
  • deep learning
  • mass spectrometry
  • big data