A mathematical model for the estimation of human embryonic and fetal age.
Precise determination of donor age in human embryonic and fetal tissue is crucial for cell transplantation due to the existence of distinct time windows within which successful grafting is possible. This study demonstrates that between 4-12 wk postconception embryonic and fetal age can be estimated based on various morphometric parameters measured on a routine basis in suction abortion material. The greatest length, the neck-rump length, the foot length, and the proximal and distal arm and leg length were correlated with the anamnestic and ultrasonographically estimated age. Multivariate regression analyses showed a linear correlation between age and the logarithmic value of the various morphometric parameters. The best correlation was found for a mathematical model combining the limb parameters (r = 0.904; p < 0.001; n = 37). A prospective follow-up study (n = 40) was carried out to test the validity of the mathematical model. A high correlation was found between the calculated age and the estimated age based on anamnestic data (r = 0.749, p < 0.001). Outliers due to errors in the anamnestic data were readily identified by comparing anamnestic with calculated age. This method allows determination of embryonic and fetal age within and beyond the age group of the Carnegie classification and may, therefore, be useful for the needs of experimental and clinical cell transplantation.
Evtouchenko
L
L
Department of Neurosurgery, University of Bern, Inselspital, Switzerland.
Studer
L
L
Spenger
C
C
Dreher
E
E
Seiler
R W
RW
D000818:Animals; D000886:Anthropometry / Q000379:methods; D004622:Embryo, Mammalian / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D005314:Embryonic and Fetal Development; D005260:Female; D016332:Fetal Tissue Transplantation; D005333:Fetus / Q000033:anatomy & histology; D005865:Gestational Age; D006801:Humans; D008962:Models, Theoretical; D015999:Multivariate Analysis; D011247:Pregnancy; D011446:Prospective Studies; D012044:Regression Analysis; D015203:Reproducibility of Results; D016216:Ultrasonography, Prenatal