Performance of a proteomic preterm delivery predictor in a large independent prospective cohort

Markenson, Glenn; Saade, George R.; Laurent, Louise C.; Heyborne, Kent; Coonrod, Dean V.; Schoen, Corina N.; Baxter, Jason K.; Haas, David M.; Longo, Sherri; Grobman, William A.; Sullivan, Scott; Major, Carol; Wheeler, Sarahn M.; Pereira, Leonardo; Su, Emily J.; Boggess, Kim; Hawk, Angela F.; Crockett, Amy; Fox, Angela; Polpitiya, Ashoka D.; Fleischer, Tracey C.; Critchfield, Gregory C.; Burchard, Julja; Boniface, J. Jay; Lam, Garrett

doi:10.1016/j.ajogmf.2020.100140

Cited by 33 publications

(54 citation statements)

References 28 publications

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“…Specifically, in this work we will demonstrate that the protein ratio IBP4/SHBG, which demonstrated a statistically elevated risk of sPTB at threshold -1.4 in the PAPR study, and also demonstrated a statistically elevated risk of sPTB at threshold -1.4 in the Multicenter Assessment of a Spontaneous Preterm Birth Risk Predictor (TreeToP) study (NCT02787213). 11 This demonstrates the reproducibility of the protein ratio threshold in two large observational studies for predicting elevated risk of sPTB.…”

Section: Introductionmentioning

confidence: 73%

Clinical Validation of a Proteomic Biomarker Threshold for Increased Risk of Spontaneous Preterm Birth

Burchard

et al. 2021

Preprint

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Preterm births are prevalent and a leading cause of neonatal death in the United States. Despite the availability of effective interventions, to date there is not a robust and widely applicable test to identify pregnancies at high risk for spontaneous preterm birth (sPTB). Previously, a sPTB predictor based on the ratio of two proteins, IBP4/SHBG, was validated as an accurate predictor of sPTB in the observational study Proteomic Assessment of Preterm Risk (PAPR). Here it is demonstrated that the same predictor threshold associated with 2-fold increased risk of sPTB, namely −1.4, is also statistically significant for predicting elevated risk of sPTB in the observational study Multicenter Assessment of a Spontaneous Preterm Birth Risk Predictor (TreeToP).

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Section: Introductionmentioning

confidence: 73%

Clinical Validation of a Proteomic Biomarker Threshold for Increased Risk of Spontaneous Preterm Birth

Burchard

et al. 2021

Preprint

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“…A follow-up study demonstrated that the serum ratio of IBP4/SHBG was able to predict preterm delivery less than 32 weeks’ gestation (AUROC 0.71, sensitivity and specificity not reported). Of the nine subjects in their cohort that delivered prior to 32 weeks’ gestation, only one delivered spontaneously while the other eight had medically-indicated preterm births [ 14 ]. The authors also demonstrated that the ratio of IBP4/SHBF could predict a composite neonatal morbidity score using the Hassan scale of 3 or greater (AUROC 0.67) [ 12 , 14 ].…”

Section: Discussionmentioning

confidence: 99%

“…Of the nine subjects in their cohort that delivered prior to 32 weeks’ gestation, only one delivered spontaneously while the other eight had medically-indicated preterm births [ 14 ]. The authors also demonstrated that the ratio of IBP4/SHBF could predict a composite neonatal morbidity score using the Hassan scale of 3 or greater (AUROC 0.67) [ 12 , 14 ]. McElrath et al demonstrated that protein biomarkers from circulating microparticles could predict the risk for spontaneous preterm birth less than 35 weeks’ gestation (AUROC 0.74 with 70% sensitivity and 81% specificity) [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

“…A variety of "-omics" approaches (proteomics, genomics) have been used by other research groups to investigate similar endpoints [13][14][15][16][17]. Saade et al reported the ratio of two serum proteins, insulin-like growth factor-binding protein 4 (IBP4) and sex hormone-binding globulin (SHBG), measured between 19 0/7 and 20 6/7 weeks' gestation, predicted spontaneous preterm birth less than 37 weeks' gestation with an AUROC of 0.75 (75% sensitivity and 74% specificity) [13].…”

Section: Plos Onementioning

confidence: 99%

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Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation

Patil

Grotegut

Gaikwad³

et al. 2021

PLoS ONE

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Background Preterm delivery is a common pregnancy complication that can result in significant neonatal morbidity and mortality. Limited tools exist to predict preterm birth, and none to predict neonatal morbidity, from early in pregnancy. The objective of this study was to determine if the progesterone metabolites 11-deoxycorticosterone (DOC) and 16-alpha hydroxyprogesterone (16α-OHP), when combined with patient demographic and obstetric history known during the pregnancy, are predictive of preterm delivery-associated neonatal morbidity, neonatal length of stay, and risk for spontaneous preterm delivery prior to 32 weeks’ gestation. Methods and findings We conducted a cohort study of pregnant women with plasma samples collected as part of Building Blocks of Pregnancy Biobank at the Indiana University School of Medicine. The progesterone metabolites, DOC and 16α-OHP, were quantified by mass spectroscopy from the plasma of 58 pregnant women collected in the late first trimester/early second trimester. Steroid levels were combined with patient demographic and obstetric history data in multivariable logistic regression models. The primary outcome was composite neonatal morbidity as measured by the Hassan scale. Secondary outcomes included neonatal length of stay and spontaneous preterm delivery prior to 32 weeks’ gestation. The final neonatal morbidity model, which incorporated antenatal corticosteroid exposure and fetal sex, was able to predict high morbidity (Hassan score ≥ 2) with an area under the ROC curve (AUROC) of 0.975 (95% CI 0.932, 1.00), while the model without corticosteroid and fetal sex predictors demonstrated an AUROC of 0.927 (95% CI 0.824, 1.00). The Hassan score was highly correlated with neonatal length of stay (p<0.001), allowing the neonatal morbidity model to also predict increased neonatal length of stay (53 [IQR 22, 76] days vs. 4.5 [2, 31] days, above and below the model cut point, respectively; p = 0.0017). Spontaneous preterm delivery prior to 32 weeks’ gestation was also predicted with an AUROC of 0.94 (95% CI 0.869, 1.00). Conclusions Plasma levels of DOC and 16α-OHP in early gestation can be combined with patient demographic and clinical data to predict significant neonatal morbidity, neonatal length of stay, and risk for very preterm delivery, though validation studies are needed to verify these findings. Early identification of pregnancies at risk for preterm delivery and neonatal morbidity allows for timely implementation of multidisciplinary care to improve perinatal outcomes.

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“…The approach included one-time screening with a proteomic blood test (PreTRM ® , Sera Prognostics), which has demonstrated in the 19th-20th week of pregnancy clinically valid prediction of risk of sPTB <37 weeks and enrichment by its biomarkers of early PTB (both spontaneous and medically indicated) and adverse neonatal outcomes. 14 , 15 In our model, women identified as higher-risk via this test would then be offered established PTB prevention measures, such as high-intensity case management (HICM) and/or pharmacologic interventions. 16–18…”

Section: Introductionmentioning

confidence: 99%

Cost-Effectiveness of a Proteomic Test for Preterm Birth Prediction

Grabner

Burchard²,

Nguyen³

et al. 2021

CEOR

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Background Preterm birth (PTB) carries increased risk of short- and long-term health problems as well as higher healthcare costs. Current strategies using clinically accepted maternal risk factors (prior PTB, short cervix) can only identify a minority of singleton PTBs. Objective We modeled the cost-effectiveness of a risk-screening-and-treat strategy versus usual care for commercially insured pregnant US women without clinically accepted PTB risk factors. The risk-screening-and-treat strategy included use of a novel PTB prognostic blood test (PreTRM ® ) in the 19th–20th week of pregnancy, followed by treatment with a combined regimen of multi-component high-intensity-case-management and pharmacologic interventions for the remainder of the pregnancy for women assessed as higher-risk by the test, and usual care in women without higher risk. Methods We built a cost-effectiveness model using a combined decision-tree/Markov approach and a US payer perspective. We modeled 1-week cycles of pregnancy from week 19 to birth (preterm or term) and assessed costs throughout the pregnancy, and further to 12-months post-delivery in mothers and 30-months in infants. PTB rates and costs were based on >40,000 mothers and infants from the HealthCore Integrated Research Database ® with birth events in 2016. Estimates of test performance, treatment effectiveness, and other model inputs were derived from published literature. Results In the base case, the risk-screening-and-treat strategy dominated usual care with an estimated 870 fewer PTBs (20% reduction) and $54 million less in total cost ($863 net savings per pregnant woman). Reductions were projected for neonatal intensive care admissions (10%), overall length-of-stay (7%), and births <32 weeks (33%). Treatment effectiveness had the strongest influence on cost-effectiveness estimates. The risk-screening-and-treat strategy remained dominant in the majority of probabilistic sensitivity analysis simulations and model scenarios. Conclusion Use of a novel prognostic test during pregnancy to identify women at risk of PTB combined with evidence-based treatment is estimated to reduce total costs while preventing PTBs and their consequences.

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Performance of a proteomic preterm delivery predictor in a large independent prospective cohort

Cited by 33 publications

References 28 publications

Clinical Validation of a Proteomic Biomarker Threshold for Increased Risk of Spontaneous Preterm Birth

Clinical Validation of a Proteomic Biomarker Threshold for Increased Risk of Spontaneous Preterm Birth

Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation

Cost-Effectiveness of a Proteomic Test for Preterm Birth Prediction

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