The effect of the variability in fecal immunochemical test sample collection technique on clinical performance
Menée à partir de 6 898 échantillons de selles prélevés lors d'un test FIT (3 449 personnes ; âge médian : 65,3 ans ; 47 % d'hommes), cette étude évalue l'association entre la quantité de selles collectée et le taux de faux négatifs
Background: Fecal immunochemical test (FIT) performance can be affected by post-collection variables. Collection technique might also affect fecal hemoglobin concentration (f-Hb). Variation in quantity of feces collected in samples returned in a colorectal cancer (CRC) detection program, and the effects of under-sampling, were assessed.
Methods: Collection devices obtained from patients undergoing FIT were assessed for the color (in five classes) of the feces in buffer, mass, and f-Hb. Associations between these were examined in an in vitro study on Hb-spiked feces. Variables possibly associated with under-sampling were investigated using multivariable logistic regression. The effect of low sample mass on clinical performance (false-negative results) was determined.
Results: Of 6,898 samples collected by 3449 individuals (46.9% male, median age: 65.3 years), the buffer was lightest in color in 362 (5.2%), and darkest in 420 (6.1%). Samples with the lightest color had a significantly lower f-Hb compared to all darker classes (p<0.001). Mass was recorded for 650 devices: the lightest colored samples had significantly lower mass (p < 0.05). The correlation between mass and f-Hb was confirmed in vitro (r=0.897, p&a mp;lt;0.001). Low mass was not associated with age, sex, or technical factors (p>0.05). Under-sampling related to the lightest color was not associated with false negative results for CRC and advanced adenoma, but was for all neoplasia and inflammatory bowel disease.
Conclusions: Wide variation existed in the amount of feces collected. Under-sampling results in lower measured f-Hb and may increase false-negative results.
Impact: Color of sample buffer could be used to identify inadequate sampling.
Cancer Epidemiology Biomarkers & Prevention , résumé, 2019