Dataset Information

Prediction of Small for Gestational Age Infants in Healthy Nulliparous Women Using Clinical and Ultrasound Risk Factors Combined with Early Pregnancy Biomarkers.

ABSTRACT:

Objective

Most small for gestational age pregnancies are unrecognised before birth, resulting in substantial avoidable perinatal mortality and morbidity. Our objective was to develop multivariable prediction models for small for gestational age combining clinical risk factors and biomarkers at 15±1 weeks' with ultrasound parameters at 20±1 weeks' gestation.

Methods

Data from 5606 participants in the Screening for Pregnancy Endpoints (SCOPE) cohort study were divided into Training (n = 3735) and Validation datasets (n = 1871). The primary outcomes were All-SGA (small for gestational age with birthweight <10th customised centile), Normotensive-SGA (small for gestational age with a normotensive mother) and Hypertensive-SGA (small for gestational age with an hypertensive mother). The comparison group comprised women without the respective small for gestational age phenotype. Multivariable analysis was performed using stepwise logistic regression beginning with clinical variables, and subsequent additions of biomarker and then ultrasound (biometry and Doppler) variables. Model performance was assessed in Training and Validation datasets by calculating area under the curve.

Results

633 (11.2%) infants were All-SGA, 465(8.2%) Normotensive-SGA and 168 (3%) Hypertensive-SGA. Area under the curve (95% Confidence Intervals) for All-SGA using 15±1 weeks' clinical variables, 15±1 weeks' clinical+ biomarker variables and clinical + biomarkers + biometry /Doppler at 20±1 weeks' were: 0.63 (0.59-0.67), 0.64 (0.60-0.68) and 0.69 (0.66-0.73) respectively in the Validation dataset; Normotensive-SGA results were similar: 0.61 (0.57-0.66), 0.61 (0.56-0.66) and 0.68 (0.64-0.73) with small increases in performance in the Training datasets. Area under the curve (95% Confidence Intervals) for Hypertensive-SGA were: 0.76 (0.70-0.82), 0.80 (0.75-0.86) and 0.84 (0.78-0.89) with minimal change in the Training datasets.

Conclusion

Models for prediction of small for gestational age, which combine biomarkers, clinical and ultrasound data from a cohort of low-risk nulliparous women achieved modest performance. Incorporation of biomarkers into the models resulted in no improvement in performance of prediction of All-SGA and Normotensive-SGA but a small improvement in prediction of Hypertensive-SGA. Our models currently have insufficient reliability for application in clinical practice however, they have potential utility in two-staged screening tests which include third trimester biomarkers and or fetal biometry.

SUBMITTER: McCowan LM

PROVIDER: S-EPMC5221822 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of Small for Gestational Age Infants in Healthy Nulliparous Women Using Clinical and Ultrasound Risk Factors Combined with Early Pregnancy Biomarkers.

McCowan Lesley M E LM Thompson John M D JM Taylor Rennae S RS Baker Philip N PN North Robyn A RA Poston Lucilla L Roberts Claire T CT Simpson Nigel A B NA Walker James J JJ Myers Jenny J Kenny Louise C LC

PloS one 20170109 1

<h4>Objective</h4>Most small for gestational age pregnancies are unrecognised before birth, resulting in substantial avoidable perinatal mortality and morbidity. Our objective was to develop multivariable prediction models for small for gestational age combining clinical risk factors and biomarkers at 15±1 weeks' with ultrasound parameters at 20±1 weeks' gestation.<h4>Methods</h4>Data from 5606 participants in the Screening for Pregnancy Endpoints (SCOPE) cohort study were divided into Training ...[more]

PMID: 28068394

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Project description:ObjectiveSmall for gestational age (SGA) infants comprise up to 50% of all stillbirths and a minority are detected before birth. We aimed to develop and validate early pregnancy predictive models for SGA infants.Methods5628 participants from SCOPE, a prospective study of nulliparous pregnant women, were interviewed at 15 ± 1 weeks' gestation. Fetal anthropometry, uterine and umbilical Doppler studies were performed at 20 ± 1 weeks'. The cohort was divided into training (n = 3735) and validation datasets (n = 1871). All-SGA (birthweight <10th customised centile), Normotensive-SGA (SGA with normotensive mother) and Hypertensive-SGA (SGA with mother who developed hypertension) were the primary outcomes. Multivariable analysis was performed using stepwise logistic regression firstly using clinical variables and then with clinical and ultrasound variables. Receiver operator curves were constructed and areas under the curve (AUC) calculated.Results633 infants (11.3%) in the whole cohort were SGA; 465 (8.3%) Normotensive-SGA and 165 (3.0%) Hypertensive-SGA. In the training dataset risk factors for All-SGA at 15 ± 1 weeks' included: family history of coronary heart disease, maternal birthweight <3000 g and 3000 g to 3499 g compared with ≥ 3500 g, >12 months to conceive, university student, cigarette smoking, proteinuria, daily vigorous exercise and diastolic blood pressure ≥ 80. Recreational walking ≥ 4 times weekly, rhesus negative blood group and increasing random glucose were protective. AUC for clinical risk factors was 0.63. Fetal abdominal or head circumference z scores <10(th) centile and increasing uterine artery Doppler resistance at 20 ± 1 weeks' were associated with increased risk. Addition of these parameters increased the AUC to 0.69. Clinical predictors of Normotensive and Hypertensive-SGA were sub-groups of All-SGA predictors and were quite different. The combined clinical and ultrasound AUC for Normotensive and Hypertensive-SGA were 0.69 and 0.82 respectively.ConclusionPredictors for SGA of relevance to clinical practice were identified. The identity and predictive potential differed in normotensive women and those who developed hypertension.

Project description:ObjectiveAccurate gestational-age (GA) estimation, preferably by ultrasound measurement of fetal crown-rump length before 14 weeks' gestation, is an important component of high-quality antenatal care. The objective of this study was to determine how GA can best be estimated by fetal ultrasound for women who present for the first time late in pregnancy with uncertain or unknown menstrual dates.MethodsINTERGROWTH-21st was a large, prospective, multicenter, population-based project performed in eight geographically defined urban populations. One of its principal components, the Fetal Growth Longitudinal Study, aimed to develop international fetal growth standards. Each participant had their certain menstrual dates confirmed by first-trimester ultrasound examination. Fetal head circumference (HC), biparietal diameter (BPD), occipitofrontal diameter (OFD), abdominal circumference (AC) and femur length (FL) were measured every 5 weeks from 14 weeks' gestation until delivery. For each participant, a single, randomly selected ultrasound examination was used to explore all candidate biometric variables and permutations to build models to predict GA. Regression equations were ranked based upon minimization of the mean prediction error, goodness of fit and model complexity. An automated machine learning algorithm, the Genetic Algorithm, was adapted to evaluate > 64 000 potential polynomial equations as predictors.ResultsOf the 4607 eligible women, 4321 (94%) had a pregnancy without major complications and delivered a live singleton without congenital malformations. After other exclusions (missing measurements in GA window and outliers), the final sample comprised 4229 women. Two skeletal measures, HC and FL, produced the best GA prediction, given by the equation loge (GA) = 0.03243 × (loge (HC))2 + 0.001644 × FL × loge (HC) + 3.813. When FL was not available, the best equation based on HC alone was loge (GA) = 0.05970 × (loge (HC))2 + 0.000000006409 × (HC)3 + 3.3258. The estimated uncertainty of GA prediction (half width 95% interval) was 6-7 days at 14 weeks' gestation, 12-14 days at 26 weeks' gestation and > 14 days in the third trimester. The addition of FL to the HC model led to improved prediction intervals compared with using HC alone, but no further improvement in prediction was afforded by adding AC, BPD or OFD. Equations that included other measurements (BPD, OFD and AC) did not perform better.ConclusionsAmong women initiating antenatal care late in pregnancy, a single set of ultrasound measurements combining HC and FL in the second trimester can be used to estimate GA with reasonable accuracy. We recommend this tool for underserved populations but considerable efforts should be implemented to improve early initiation of antenatal care worldwide. © 2016 Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Project description:Malaria during pregnancy is associated with an increased risk for low birth weight (<2500 grams). Distinguishing infants that are born premature (< 37 weeks) from those that are growth-restricted (less than the 10th percentile at birth) requires accurate assessment of gestational age. Where ultrasound is accessible, sonographic confirmation of gestational age is more accurate than menstrual dating. The goal was to pilot the feasibility and utility of adding ultrasound to an observational pregnancy malaria cohort.In July 2009, research staff (three mid-level clinical providers, one nurse) from The Blantyre Malaria Project underwent an intensive one-week ultrasound training to perform foetal biometry. Following an additional four months of practice and remote image review, subjects from an ongoing cohort were recruited for ultrasound to determine gestational age. Gestational age at delivery established by ultrasound was compared with postnatal gestational age assessment (Ballard examination).One hundred and seventy-eight women were enrolled. The majority of images were of good quality (94.3%, 509/540) although a learning curve was apparent with 17.5% (24/135) images of unacceptable quality in the first 25% of scans. Ultrasound was used to date 13% of the pregnancies when menstrual dates were unknown and changed the estimated gestational age for an additional 25%. There was poor agreement between the gestational age at delivery as established by the ultrasound protocol compared to that determined by the Ballard examination (bias 0.8 weeks, limits of agreement -3.5 weeks to 5.1 weeks). The distribution of gestational ages by Ballard suggested a clustering of gestational age around the mean with 87% of the values falling between 39 and 41 weeks. The distribution of gestational age by ultrasound confirmed menstrual dates was more typical. Using ultrasound confirmed dates as the gold standard, 78.5% of preterm infants were misclassified as term and 26.8% of small-for gestational age infants misclassified as appropriately grown by Ballard.Ultrasound should be strongly considered in prospective malaria studies with obstetric endpoints to confirm gestational age and avoid misclassification of infants as premature or growth-restricted. The use of ultrasound does require a significant investment of time to maintain quality image acquisition.

Project description:BackgroundBoth obesity and gestational diabetes (GDM) are associated with adverse outcomes. Diet during pregnancy impacts weight gain and fetal growth. Therefore, we aimed to explore non-pharmacological treatment success depending on pre-pregnancy body weight and its association with large for gestational age (LGA) infants in women with GDM.MethodsIn our observational study we investigated 57 singleton pregnant women with GDM. All women received standard treatment, including healthy diet education and regular medical checkups. Data were collected through blood analysis, medical records and questionnaires assessing diet before conception and during pregnancy. Differences in dietary patterns were compared in normal weight and overweight/obese group using Mann-Whitney U, Wilcoxon Signed Rank Test or Kruskal-Wallis test, as appropriate. Logistic regression was used for prediction of LGA. p-value less than 0.05 was used for statistical significance.ResultsPreconceptionally, the Mann-Whitney U test showed that the normal-weight group (n = 41) more frequently consumed fruits (U = 116.5, p < 0.001), eggs (U = 189.5, p = 0.02), cheese (U = 148.0, p = 0.003) compared to the overweight/obese group (n = 16), that consumed more beef (U = 407.0, p = 0.03) and low-calorie beverages (U = 397.0, p = 0.05). During pregnancy both groups improved their diet, with no differences detected. Personality types differed only preconceptionally with regard to healthy diet. Excessive gestational weight gain did not significantly differ between body-weight groups (16.6% vs. 23.1%), neither did the incidence of LGA infants (46.2% vs. 43.8%). Significant predictors of LGA were paternal height (OR = 1.12, 95% CI 1.01-1.23), 3rd trimester HbA1c (OR = 0.50, 95% CI 0.26-0.97), unemployment (OR = 4.80, 95% CI 1.12-20.61) and diet improvement during pregnancy (OR = 1.19, 95% CI 1.02-1.39). After adjustment improvement in diet was no longer a significant predictor for LGA.ConclusionEven though dietary patterns of the participants significantly improved during pregnancy, LGA infants were born independently of pre-pregnancy weight or diet and despite good glycemic control. Further research is needed to explore social determinants of health and whether solutions outside the health sector could provide efficient means in preventing adverse pregnancy outcomes as well as improving metabolic health.

Project description:ObjectivesThe objectives of this study are to determine whether abnormalities on neonatal cranial ultrasound (CUS) are associated with minor motor abnormalities at 2 years' corrected age (CA) and to assess functional outcomes and resource utilization among children with minor motor abnormalities.MethodsInfants born at <27 weeks in the National Institute of Child Health and Human Development Neonatal Research Network between January 1, 2010, and December 31, 2014, who underwent neuroimaging with CUS at both <28 days and ≥28 days and were evaluated at 18 to 26 months' CA, were included. Follow-up included Bayley-3, neuromotor examination, Gross Motor Function Classification System (GMFCS) level, and parent questionnaires about special services and resource needs. Children were classified by the most severe motor abnormality at 18 to 26 months' CA as follows: none, minor, or major motor function abnormality. Minor motor abnormalities were defined as any of the following: (1) Bayley-3 motor composite, fine motor score, or gross motor score 1 to 2 SDs below the test normative means; (2) mild abnormalities of axial or extremity motor skills on standardized neuromotor examination; or (3) GMFCS level 1.ResultsA total of 809 (35%) of 2306 children had minor motor function abnormalities alone. This did not increase substantially with CUS findings (no intraventricular hemorrhage [IVH]: 37%, grade I IVH: 32%, grade II IVH: 38%, grade III/IV IVH: 30%, isolated ventriculomegaly: 33%, and cystic periventricular leukomalacia: 24%). The adjusted odds of minor axial and upper extremity function abnormalities and GMFCS level 1 were significantly higher in children with more severe CUS findings. Children with minor motor abnormalities had increased resource utilization and evidence of functional impairment compared with those without motor function abnormalities.ConclusionMinor motor abnormalities at 2 years' CA are common and cannot be predicted by neonatal CUS abnormalities alone. Minor motor abnormalities are associated with higher resource utilization and evidence of functional impairment. These findings have important implications for early counseling and follow-up planning for extremely preterm infants.

Project description:Implementation of dietary and lifestyle interventions prior to and early in pregnancy in high risk women has been shown to reduce the risk of gestational diabetes mellitus (GDM) development later in pregnancy. Although numerous risk factors for GDM have been identified, the ability to accurately identify women before or early in pregnancy who could benefit most from these interventions remains limited. As nulliparous women are an under-screened population with risk profiles that differ from their multiparous counterparts, development of a prediction model tailored to nulliparous women may facilitate timely preventive intervention and improve maternal and infant outcomes. We aimed to develop and validate a model for preconception and early pregnancy prediction of gestational diabetes mellitus based on clinical risk factors for nulliparous women. A risk prediction model was built within a large California birth cohort including singleton live birth records from 2007-2012. Model accuracy was assessed both internally and externally, within a cohort of women who delivered at University of Iowa Hospitals and Clinics between 2009-2017, using discrimination and calibration. Differences in predictive accuracy of the model were assessed within specific racial/ethnic groups. The prediction model included five risk factors: race/ethnicity, age at delivery, pre-pregnancy body mass index, family history of diabetes, and pre-existing hypertension. The area under the curve (AUC) for the California internal validation cohort was 0.732 (95% confidence interval (CI) 0.728, 0.735), and 0.710 (95% CI 0.672, 0.749) for the Iowa external validation cohort. The model performed particularly well in Hispanic (AUC 0.739) and Black women (AUC 0.719). Our findings suggest that estimation of a woman's risk for GDM through model-based incorporation of risk factors accurately identifies those at high risk (i.e., predicted risk >6%) who could benefit from preventive intervention encouraging prompt incorporation of this tool into preconception and prenatal care.

Dataset Information

Prediction of Small for Gestational Age Infants in Healthy Nulliparous Women Using Clinical and Ultrasound Risk Factors Combined with Early Pregnancy Biomarkers.

Objective

Methods

Results

Conclusion

Publications

Prediction of Small for Gestational Age Infants in Healthy Nulliparous Women Using Clinical and Ultrasound Risk Factors Combined with Early Pregnancy Biomarkers.

Similar Datasets

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