Re-evaluating diagnostic thresholds for intrahepatic cholestasis of pregnancy: case-control and cohort study.

OBJECTIVE: To determine the optimal total serum bile acid (TSBA) threshold and sampling time for accurate intrahepatic cholestasis of pregnancy (ICP) diagnosis. DESIGN: Case-control, retrospective cohort studies. SETTING: Antenatal clinics, clinical research facilities. POPULATION: Women with ICP or uncomplicated pregnancies. METHODS: Serial TSBA measurements were performed pre-/postprandially in 42 women with ICP or uncomplicated pregnancy. Third-trimester non-fasting TSBA reference ranges were calculated from 561 women of black, south Asian and white ethnicity. Rates of adverse perinatal outcomes for women with ICP but peak non-fasting TSBA below the upper reference range limit were compared with those in healthy populations. MAIN OUTCOME MEASURES: Sensitivity and specificity of common TSBA thresholds for ICP diagnosis, using fasting and postprandial TSBA. Calculation of normal reference ranges of non-fasting TSBA. RESULTS: Concentrations of TSBA increased markedly postprandially in all groups, with overlap between healthy pregnancy and mild ICP (TSBA <40 µmol/l). The specificity of ICP diagnosis was higher when fasting, but corresponded to <30% sensitivity for diagnosis of mild disease. Using TSBA ≥40 µmol/l to define severe ICP, fasting measurements identified 9% (1/11), whereas non-fasting measurements detected over 91% with severe ICP. The highest upper limit of the non-fasting TSBA reference range was 18.3 µmol/l (95% confidence interval: 15.0-35.6 µmol/l). A re-evaluation of published ICP meta-analysis data demonstrated no increase in spontaneous preterm birth or stillbirth in women with TSBA <19 µmol/l. CONCLUSIONS: Postprandial TSBA levels are required to identify high-risk ICP pregnancies (TSBA ≥40 µmol/l). The postprandial rise in TSBA in normal pregnancy indicates that a non-fasting threshold of ≥19 µmol/l would improve diagnostic accuracy. TWEETABLE ABSTRACT: Non-fasting bile acids improve the diagnostic accuracy of intrahepatic cholestasis of pregnancy diagnosis.

Competing-risks model for prediction of small-for-gestational-age neonate from biophysical and biochemical markers at 11-13 weeks' gestation.

OBJECTIVE: To develop a new competing-risks model for the prediction of a small-for-gestational-age (SGA) neonate, based on maternal factors and biophysical and biochemical markers at 11-13 weeks' gestation. METHODS: This was a prospective observational study in 60 875 women with singleton pregnancy undergoing routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation. All pregnancies had pregnancy-associated plasma protein-A and placental growth factor (PlGF) measurements, 59 001 had uterine artery pulsatility index (UtA-PI) measurements and 58 479 had mean arterial pressure measurements; 57 131 cases had complete data for all biomarkers. We used a previously developed competing-risks model for the joint distribution of gestational age (GA) at delivery and birth-weight Z-score, according to maternal demographic characteristics and medical history. The likelihoods of the biophysical markers were developed by fitting folded-plane regression models, a technique that has already been used in previous studies for the likelihoods of biochemical markers. The next step was to modify the prior distribution by the likelihood, according to Bayes' theorem, to obtain individualized distributions for GA at delivery and birth-weight Z-score. We used the 57 131 cases with complete data to assess the discrimination and calibration of the model for predicting SGA with, without or independently of pre-eclampsia, by different combinations of maternal factors and biomarkers. RESULTS: The distribution of biomarkers, conditional to both GA at delivery and birth-weight Z-score, was best described by folded-plane regression models. These continuous two-dimensional likelihoods update the joint distribution of birth-weight Z-score and GA at delivery that has resulted from a competing-risks approach; this method allows application of user-defined cut-offs. The best biophysical predictor of preterm SGA was UtA-PI and the best biochemical marker was PlGF. The prediction of SGA was consistently better for increasing degree of prematurity, greater severity of smallness, coexistence of PE and increasing number of biomarkers. The combination of maternal factors with all biomarkers predicted 34.3%, 48.6% and 59.1% of all cases of a SGA neonate with birth weight < 10th percentile delivered at ≥ 37, < 37 and < 32 weeks' gestation, at a 10% false-positive rate. The respective values for birth weight < 3rd percentile were 39.9%, 53.2% and 64.4%, and for birth weight < 3rd percentile with pre-eclampsia they were 46.3%, 66.8% and 80.4%. The new model was well calibrated. CONCLUSIONS: This study has presented a single continuous two-dimensional model for prediction of SGA for any desired cut-offs of smallness and GA at delivery, laying the ground for a personalized antenatal plan for predicting and managing SGA, in the milieu of a new inverted pyramid of prenatal care. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.