Personalized assessment of cervical length improves prediction of spontaneous preterm birth: a standard and a percentile calculator

Gudicha, Dereje W.; Romero, Roberto; Kabiri, Doron; Hernández‐Andrade, Edgar; Pacora, Percy; Erez, Offer; Kusanovic, Juan Pedro; Jung, Eun-Jung; Paredes, Carmen; Berry, Stanley M.; Yeo, Lami; Hassan, Sonia S.; Hsu, Chaur‐Dong; Tarca, Adi L.

doi:10.1016/j.ajog.2020.09.002

Cited by 43 publications

(37 citation statements)

References 94 publications

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“…Such MoM models are akin to customized standards of estimated fetal weight 91,92 and cervical length. 93 After removing the effect of gestational age and maternal characteristics on biomarker levels, any remaining abnormality in MoM values in cases relative to controls is considered independent evidence that can be used to improve accuracy of predictions. However, our approach differs from the FMF in several aspects.…”

Section: Ajogorgmentioning

confidence: 99%

Prediction of preeclampsia throughout gestation with maternal characteristics and biophysical and biochemical markers: a longitudinal study

Tarca

Taran

Romero

et al. 2022

American Journal of Obstetrics and Gynecology

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BACKGROUND: The current approach to predict preeclampsia combines maternal risk factors and evidence from biophysical markers (mean arterial pressure, Doppler velocimetry of the uterine arteries) and maternal blood proteins (placental growth factor, soluble vascular endothelial growth factor receptor-1, pregnancy-associated plasma protein A). Such models require the transformation of biomarker data into multiples of the mean values by using population-and site-specific models. Previous studies have focused on a narrow window in gestation and have not included the maternal blood concentration of soluble endoglin, an important antiangiogenic factor up-regulated in preeclampsia. OBJECTIVE: This study aimed (1) to develop models for the calculation of multiples of the mean values for mean arterial pressure and biochemical markers; (2) to build and assess the predictive models for preeclampsia based on maternal risk factors, the biophysical (mean arterial pressure) and biochemical (placental growth factor, soluble vascular endothelial growth factor receptor-1, and soluble endoglin) markers collected throughout pregnancy; and (3) to evaluate how prediction accuracy is affected by the presence of chronic hypertension and gestational age. STUDY DESIGN: This longitudinal case-cohort study included 1150 pregnant women: women without preeclampsia with (n¼49) and without chronic hypertension (n¼871) and those who developed preeclampsia (n¼166) or superimposed preeclampsia (n¼64). Mean arterial pressure and immunoassay-based maternal plasma placental growth factor, soluble vascular endothelial growth factor receptor-1, and soluble endoglin concentrations were available throughout pregnancy (median of 5 observations per patient). A prior-risk model for preeclampsia was established by using Poisson regression based on maternal characteristics and obstetrical history. Next, multiple regression was used to fit biophysical and biochemical marker data as a function of maternal characteristics by using data collected at 8 to 15 þ6 , 16 to 19 þ6 , 20 to 23 þ6 , 24 to 27 þ6 , 28 to 31 þ6 , and 32 to 36 þ6 week intervals, and observed values were converted into multiples of the mean values. Then, multivariable prediction models for preeclampsia were fit based on the biomarker multiples of the mean data and prior-risk estimates. Separate models were derived for overall, preterm, and term preeclampsia, which were evaluated by receiver operating characteristic curves and sensitivity at fixed false-positive rates.RESULTS: (1) The inclusion of soluble endoglin in prediction models for all preeclampsia, together with the prior-risk estimates, mean arterial pressure, placental growth factor, and soluble vascular endothelial growth factor receptor-1, increased the sensitivity (at a fixed false-positive rate of 10%) for early prediction of superimposed preeclampsia, with the largest increase (from 44% to 54%) noted at 20 to 23 þ6 weeks (McNemar test, P<.05); (2) combined evidence from prior-risk estimates and biomarkers predicted preterm pree...

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Section: Ajogorgmentioning

confidence: 99%

Prediction of preeclampsia throughout gestation with maternal characteristics and biophysical and biochemical markers: a longitudinal study

Tarca

Taran

Romero

et al. 2022

American Journal of Obstetrics and Gynecology

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“…This strategy is also effective in women with a previous preterm birth and a short cervix and is as effective as cervical cerclage (23). Moreover, to implement this work in clinical practice, a calculator has been developed to calculate the risk of preterm delivery incorporating cervical length, as well as maternal characteristics such as weight, height, parity, and gestational age (24).…”

Section: Perinatology Research Branch (Prb)mentioning

confidence: 99%

World Prematurity Day: it takes an NIH village to prevent preterm birth and improve treatments for preterm infants

Bremer

Grewal

Hazra

et al. 2021

American Journal of Physiology-Lung Cellular and Molecular Phys

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“…To address the complex public health problem of preterm birth, the development of biomarkers for preterm labor is necessary 25 – 28 . Currently, risk modeling of preterm birth relies on maternal factors: a history of preterm birth 29 , of late miscarriage 30 or cervical excisional surgery 31 , a sonographic short cervix 32 , a low customized cervical length percentile 33 during the current pregnancy, amniotic fluid sludge 34 , and an abnormal cervical consistency index 35 . Biochemical markers, e.g., fFN 36 , PIGFBP-1 37 , PAMG-1 38 , inflammatory cytokines 39 , and cervical acetate 40 , as well as the vaginal microbiome 41 and maternal blood transcriptome 42 , have also been suggested as predictive of preterm birth.…”

Section: Introductionmentioning

confidence: 99%

The amniotic fluid cell-free transcriptome in spontaneous preterm labor

Bhatti

Romero

Gomez‐Lopez

et al. 2021

Sci Rep

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The amniotic fluid (AF) cell-free RNA was shown to reflect physiological and pathological processes in pregnancy, but its value in the prediction of spontaneous preterm delivery is unknown. Herein we profiled cell-free RNA in AF samples collected from women who underwent transabdominal amniocentesis after an episode of spontaneous preterm labor and subsequently delivered within 24 h (n = 10) or later (n = 28) in gestation. Expression of known placental single-cell RNA-Seq signatures was quantified in AF cell-free RNA and compared between the groups. Random forest models were applied to predict time-to-delivery after amniocentesis. There were 2385 genes differentially expressed in AF samples of women who delivered within 24 h of amniocentesis compared to gestational age-matched samples from women who delivered after 24 h of amniocentesis. Genes with cell-free RNA changes were associated with immune and inflammatory processes related to the onset of labor, and the expression of placental single-cell RNA-Seq signatures of immune cells was increased with imminent delivery. AF transcriptomic prediction models captured these effects and predicted delivery within 24 h of amniocentesis (AUROC = 0.81). These results may inform the development of biomarkers for spontaneous preterm birth.

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Personalized assessment of cervical length improves prediction of spontaneous preterm birth: a standard and a percentile calculator

Cited by 43 publications

References 94 publications

Prediction of preeclampsia throughout gestation with maternal characteristics and biophysical and biochemical markers: a longitudinal study

Prediction of preeclampsia throughout gestation with maternal characteristics and biophysical and biochemical markers: a longitudinal study

World Prematurity Day: it takes an NIH village to prevent preterm birth and improve treatments for preterm infants

The amniotic fluid cell-free transcriptome in spontaneous preterm labor

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