Jointly modelling longitudinally measured urinary human chorionic gonadotrophin and early pregnancy outcomes

Abstract

Human chorionic gonadotrophin (hCG) is largely used to confirm pregnancy. Yet evidence shows that longitudinal hCG profiles are distinguishable between healthy and failing pregnancies. We retrospectively fitted a joint longitudinal-survival model to data from 127 (85 healthy and 42 failing pregnancies) US women, aged 18–45, who were attempting to conceive, to quantify the association between longitudinally measured urinary hCG and early miscarriage. Using subject-specific predictions, obtained uniquely from the joint model, we investigated the plausibility of adaptively monitoring early pregnancy outcomes based on updating hCG measurements. Volunteers collected daily early morning urine samples for their menstrual cycle and up to 28 days post day of missed period. The longitudinal submodel for log hCG included a random intercept and slope and fixed linear and quadratic time terms. The survival submodel included maternal age and cycle length covariates. Unit increases in log hCG corresponded to a 63.9% (HR 0.36, 95% CI 0.16, 0.47) decrease in the risk of miscarriage, confirming a strong association between hCG and miscarriage. Outputted conditional survival probabilities gave individualised risk estimates for the early pregnancy outcomes in the short term. However, longer term monitoring would require a larger sample size and prospectively followed up data, focusing on emerging extensions to the joint model, which allow assessment of the specificity and sensitivity.