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ObjectivesPreterm birth occurs in more than 10% of U.S. births and is the leading cause of U.S. neonatal deaths, with estimated annual costs exceeding $25 billion USD. Using real-world data, we modeled the potential clinical and economic utility of a prematurity-reduction program comprising screening in a racially and ethnically diverse population with a validated proteomic biomarker risk predictor, followed by case management with or without pharmacological treatment.MethodsThe ACCORDANT microsimulation model used individual patient data from a prespecified, randomly selected sub-cohort (N = 847) of a multicenter, observational study of U.S. subjects receiving standard obstetric care with masked risk predictor assessment (TREETOP; NCT02787213). All subjects were included in three arms across 500 simulated trials: standard of care (SoC, control); risk predictor/case management comprising increased outreach, education and specialist care (RP-CM, active); and multimodal management (risk predictor/case management with pharmacological treatment) (RP-MM, active). In the active arms, only subjects stratified as higher risk by the predictor were modeled as receiving the intervention, whereas lower-risk subjects received standard care. Higher-risk subjects' gestational ages at birth were shifted based on published efficacies, and dependent outcomes, calibrated using national datasets, were changed accordingly. Subjects otherwise retained their original TREETOP outcomes. Arms were compared using survival analysis for neonatal and maternal hospital length of stay, bootstrap intervals for neonatal cost, and Fisher's exact test for neonatal morbidity/mortality (significance, p

AbstractBackgroundClinical risk factors for preeclampsia (PE), including previous PE, chronic hypertension, and pregestational diabetes, are poorly predictive of PE. Preterm PE, defined as diagnosis of PE with delivery prior to 37 weeks’ gestational age (GA), is more likely to be associated with serious morbidities and difficult clinical decision making. Therefore, there remains an urgent clinical need to develop a safe, feasible, and accurate predictor of preterm PE that integrates molecular biomarkers and relevant clinical factors into a single risk assessment score that can be used to guide clinical management.Objective(s)To discover, verify, and validate a mid-trimester proteomic biomarker risk predictor for preterm PE, comprised of a composite clinical variable and a small number of maternal serum analytes.Study DesignThis was a secondary analysis of data from two large clinical trials (PAPR,NCT02787213; TREETOP,NCT01371019). PAPR subjects’ eligibility was limited to those who had consented to research into preterm birth and pregnancy complications and who had blood drawn between 180/7– 226/7weeks’ gestation. TREETOP subjects were limited to those who had blood drawn between 180/7– 206/7weeks’ gestation. PAPR subjects were assigned to a discovery cohort, and TREETOP subjects were randomly assigned to a first-phase cohort for verification (comprised of one-third of eligible subjects) and to a separate second-phase cohort for validation (comprised of the remaining two-thirds of eligible subjects). Peptides were analyzed by liquid chromatography-multiple reaction monitoring mass spectrometry measuring 77 pregnancy-related proteins and quality control proteins. Models were limited to a maximum of one additional protein ratio and a composite clinical variable, referred to as Clin3, which was deemed positive if any of three factors was true for the subject: prior PE; pre-existing hypertension; and/or pregestational diabetes. Overall classifier performance was assessed via area under the receiver operating characteristic curve (AUC).ResultsVerification yielded nine multi-component classifier models for prediction of preterm PE, all of which were subsequently validated. Classifiers exhibited greater predictive performance than clinical factors alone. Example performance metrics across a range of classifier score thresholds and GA at birth cutoffs of 37, 34 and 32 weeks for the Clin3 + inhibin subunit beta c (INHBC)/SHBG classifier, which showed the highest AUC, demonstrating a sensitivity of 89% at a specificity of 75% for prediction of early-onset preeclampsia (

The clinical management of pregnancy and spontaneous preterm birth (sPTB) relies on estimates of gestational age (GA). Our objective was to evaluate the effect of GA dating uncertainty on the observed performance of a validated proteomic biomarker risk predictor, and then to test the generalizability of that effect in a broader range of GA at blood draw. In a secondary analysis of a prospective clinical trial (PAPR; NCT01371019), we compared two GA dating categories: both ultrasound and dating by last menstrual period (LMP) (all subjects) and excluding dating by LMP (excluding LMP). The risk predictor's performance was observed at the validated risk predictor threshold both in weeks 191/7-206/7 and extended to weeks 180/7-206/7. Strict blinding and independent statistical analyses were employed. The validated biomarker risk predictor showed greater observed sensitivity of 88% at 75% specificity (increases of 17% and 1%) in more reliably dated (excluding-LMP) subjects, relative to all subjects. Excluding dating by LMP significantly improved the sensitivity in weeks 191/7-206/7. In the broader blood draw window, the previously validated risk predictor threshold significantly stratified higher and lower risk of sPTB, and the risk predictor again showed significantly greater observed sensitivity in excluding-LMP subjects. These findings have implications for testing the performance of models aimed at predicting PTB.

Objective: To validate a serum biomarker developed in the USA for preterm birth (PTB) risk stratification in Viet Nam. Methods: Women with singleton pregnancies (n = 5000) were recruited between 19+0-23+6 weeks' gestation at Tu Du Hospital, Ho Chi Minh City. Maternal serum was collected from 19+0-22+6 weeks' gestation and participants followed to neonatal discharge. Relative insulin-like growth factor binding protein 4 (IGFBP4) and sex hormone binding globulin (SHBG) abundances were measured by mass spectrometry and their ratio compared between PTB cases and term controls. Discrimination (area under the receiver operating characteristic curve, AUC) and calibration for PTB <37 and <34 weeks' gestation were tested, with model tuning using clinical factors. Measured outcomes included all PTBs (any birth ≤37 weeks' gestation) and spontaneous PTBs (birth ≤37 weeks' gestation with clinical signs of initiation of parturition). Results: Complete data were available for 4984 (99.7%) individuals. The cohort PTB rate was 6.7% (n = 335). We observed an inverse association between the IGFBP4/SHBG ratio and gestational age at birth (p = 0.017; AUC 0.60 [95% CI, 0.53-0.68]). Including previous PTB (for multiparous women) or prior miscarriage (for primiparous women) improved performance (AUC 0.65 and 0.70, respectively, for PTB <37 and <34 weeks' gestation). Optimal performance (AUC 0.74) was seen within 19-20 weeks' gestation, for BMI >21 kg/m2 and age 20-35 years. Conclusion: We have validated a novel serum biomarker for PTB risk stratification in a very different setting to the original study. Further research is required to determine appropriate ratio thresholds based on the prevalence of risk factors and the availability of resources and preventative therapies. [ABSTRACT FROM AUTHOR]

Objectives: To address the disproportionate burden of preterm birth (PTB) in low- and middle-income countries, this study aimed to (1) verify the performance of the United States-validated spontaneous PTB (sPTB) predictor, comprised of the IBP4/SHBG protein ratio, in subjects from Bangladesh, Pakistan and Tanzania enrolled in the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, and (2) discover biomarkers that improve performance of IBP4/SHBG in the AMANHI cohort., Study Design: The performance of the IBP4/SHBG biomarker was first evaluated in a nested case control validation study, then utilized in a follow-on discovery study performed on the same samples. Levels of serum proteins were measured by targeted mass spectrometry. Differences between the AMANHI and U.S. cohorts were adjusted using body mass index (BMI) and gestational age (GA) at blood draw as covariates. Prediction of sPTB < 37 weeks and < 34 weeks was assessed by area under the receiver operator curve (AUC). In the discovery phase, an artificial intelligence method selected additional protein biomarkers complementary to IBP4/SHBG in the AMANHI cohort., Results: The IBP4/SHBG biomarker significantly predicted sPTB < 37 weeks ( n  = 88 vs. 171 terms ≥ 37 weeks) after adjusting for BMI and GA at blood draw (AUC= 0.64, 95% CI: 0.57-0.71, p  < .001). Performance was similar for sPTB < 34 weeks ( n  = 17 vs. 184 ≥ 34 weeks): AUC = 0.66, 95% CI: 0.51-0.82, p  = .012. The discovery phase of the study showed that the addition of endoglin, prolactin, and tetranectin to the above model resulted in the prediction of sPTB < 37 with an AUC= 0.72 (95% CI: 0.66-0.79, p -value < .001) and prediction of sPTB < 34 with an AUC of 0.78 (95% CI: 0.67-0.90, p  < .001)., Conclusion: A protein biomarker pair developed in the U.S. may have broader application in diverse non-U.S. populations.