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. Early miscarriage, defined in the UK as loss before week 13, is a frequent complication of pregnancy 1. It affects 12% to 24% of clinically confirmed pregnancies, not counting those losses which occur prior to the date of the missed period-so-called biochemical pregnancies 2. Women who suffer from a miscarriage are more likely to report symptoms associated with depression, with affected women ranging from 20% to a high of 55% 3. Though the majority of losses are self-resolving, those that are not may require diagnostic tests, hospital treatment, surgical intervention and follow-up care 2. This provides an incentive to identify potential early losses as early as possible by exploring more patient-centred monitoring strategies. The recently published priorities for research within miscarriage ranked highest the identification of effective interventions to prevent miscarriage 4. This encompasses the plausibility of using biomarkers to track pregnancy progression through viability or miscarriage. Several potential biomarkers have been identified to predict miscarriage, with human chorionic gonadotrophin (hCG) a strong contender 5. The hormone tends to rise rapidly and reliably in early pregnancy, doubling every 1.5 days in the first 5 weeks post conception and then every 3.5 days from week 7, before plateauing around week 10 5,6. Its use is more prevalent in tracking early pregnancy progress in an in vitro fertilisation (IVF) population and for identifying ectopic pregnancies 7. However, evidence suggests that longitudinal profiles of hCG can be utilised t...

Changes in Thoroughbred speed and stride characteristics over successive race starts and their association with musculoskeletal injury

“…19,45 As joint models were developed so that the association and dependence between the longitudinal marker and time to event are considered, the joint model is able to account for measurement error, the intermittent nature of observations, as well as missing data. 29,46,47 This study demonstrates that a reduction in speed and stride length, but not stride frequency, in the final sectional of a race over multiple starts may predict MSI in racehorses. A wider implementation of inertial sensor technology to monitor horses in training and racing will provide valuable datasets for further investigation.…”

“…While more computationally intensive, we chose the joint model framework for this study due to its reduced bias and improved precision over simpler approaches 19,45 . As joint models were developed so that the association and dependence between the longitudinal marker and time to event are considered, the joint model is able to account for measurement error, the intermittent nature of observations, as well as missing data 29,46,47 …”

“… 19 , 45 As joint models were developed so that the association and dependence between the longitudinal marker and time to event are considered, the joint model is able to account for measurement error, the intermittent nature of observations, as well as missing data. 29 , 46 , 47 …”