Quantifying biometrology operator data analysis subjectivity within flow cytometry using measurement uncertainty principles
thesisposted on 27.11.2019, 14:34 by Rebecca Grant
A recent evaluation of medical error has shown it to be the third leading cause of death in the US, following heart disease and cancer. Better reporting and decision making could tackle this, but ultimately more accurate and precise measurement, with correct interpretation could make a significant difference to this unnecessary statistic. Clinical pathology measurement platforms are complex, requiring significant standardisation efforts to reduce false positives/negatives and the impacts these have on patient safety. Cell and Gene Therapy (CGT) manufacturing processes depend upon these platforms for measurement, with Flow Cytometry (FC) used for in-process and release metrics. However, the highly subjective nature of FC data analysis requires investigation to monitor impact on manufacturing and clinical decision making.
FC standardisation efforts have reduced variation from sample preparation and setup, however, no efforts have purely focused on the final post-analytical stage, to quantify the effect of subjective analysis of data files. This research has isolated this section of FC analysis, providing better measurement precision to build up a realistic uncertainty budget for FC measurements. Through a series of participant analysis studies that build in complexity, it has been shown that as FC data becomes more complex, the uncertainty contributions from inter-operator data analysis increase from 8 % to 34 %. This increase could mean the difference between a CGT treatment being provided at the right time, being discarded when it was suitable for administration, or an unsuitable treatment administered to the patient at an unsuitable time, having costly implications for all.
This variation does not correlate with operator experience or use frequency of the instrument, but is influenced by data visualisation effects, requiring further investigation at a later date to reduce this impact. Image parameters for other CGT measurement platforms are also impacted by subjective data analysis, requiring harmonisation to ensure the subjectivity is quantifiable, standardised and reduces manufacturing and hence medical error impacts to the patient and therapeutic product.
EPSRC and MRC Centre for Doctoral Training in Regenerative Medicine
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- Mechanical, Electrical and Manufacturing Engineering