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Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study.

ABSTRACT: OBJECTIVE:To evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women. DESIGN AND SETTING:A population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008-13 in the Stockholm-Gotland Counties in Sweden. METHODS:Maternal social, reproductive and medical history and medical examinations (including mean arterial pressure, proteinuria, hemoglobin and capillary glucose levels) routinely collected at the first visit in antenatal care, constitute the predictive variables. Predictive models for preeclampsia were created by three methods; logistic regression models using 1) pre-specified variables (similar to the Fetal Medicine Foundation model including maternal factors and mean arterial pressure), 2) backward selection starting from the full suite of variables, and 3) a Random forest model using the same candidate variables. The performance of the British National Institute for Health and Care Excellence (NICE) binary risk classification guidelines for preeclampsia was also evaluated. The outcome measures were diagnosis of preeclampsia with delivery <34, <37, and ?37 weeks' gestation. RESULTS:A total of 2 773 (4.4%) nulliparous women subsequently developed preeclampsia. The pre-specified variables model was superior the other two models, regarding prediction of preeclampsia with delivery <34 and <37 weeks, both with areas under the curve of 0.68, and sensitivity of 30.6% (95% CI 24.5-37.2) and 29.2% (95% CI 25.2-33.4) at a 10% false positive rate, respectively. The performance of these customizable multivariable models at the chosen false positive rate, was significantly better than the binary NICE-guidelines for preeclampsia with delivery <37 and ?37 weeks' gestation. CONCLUSION:Multivariable models in early pregnancy had a modest performance, although providing advantages over the NICE-guidelines, in predicting preeclampsia in nulliparous women. Use of a machine learning algorithm (Random forest) did not result in superior prediction.

SUBMITTER: Sandstrom A

PROVIDER: S-EPMC6881002 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study.

Sandström Anna A Snowden Jonathan M JM Höijer Jonas J Bottai Matteo M Wikström Anna-Karin AK

PloS one 20191127 11

<h4>Objective</h4>To evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women.<h4>Design and setting</h4>A population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008-13 in the Stockholm-Gotland Counties in Sweden.<h4>Methods</h4>Maternal social, reproductive and medical history and medical examinations (including mean arterial pressure, proteinuria, hemogl ...[more]

PMID: 31774875

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Project description:BackgroundIn the first year after childbirth, 94% of women experience one or more major health problems (urinary incontinence, faecal incontinence, perineal pain, back pain). Difficulties in intimate partner relationships and changes affecting sexual health are also common. The aim of this study is to investigate changes in women's health from early pregnancy until four years after the birth of a first child.Methods/designThe Maternal Health Study is a longitudinal study designed to fill in some of the gaps in current research evidence regarding women's physical and psychological health and recovery after childbirth. A prospective pregnancy cohort of >1500 nulliparous women has been recruited in early pregnancy at six metropolitan public hospitals in Melbourne, Australia between April 2003 and December 2005. In the first phase of the study participants are being followed up at 30-32 weeks gestation in pregnancy, and at three, six, nine, 12 and 18 months postpartum using a combination of self-administered questionnaires and telephone interviews. Women consenting to extended follow-up (phase 2) will be followed up six and 12 months after any subsequent births and when their first child is four years old. Study instruments incorporate assessment of the frequency and severity of urinary and bowel symptoms, sexual health issues, perineal and abdominal pain, depression and intimate partner violence. Pregnancy and birth outcome data will be obtained by review of hospital case notes.DiscussionFeatures of the study which distinguish it from prior research include: the capacity to identify incident cases of morbidity and clustering of health problems; a large enough sample to detect clinically important differences in maternal health outcomes associated with the method of birth; careful exposure measurement involving manual abstraction of data from medical records in order to explore mediating factors and possible causal pathways; and use of a variety of strategies to improve ascertainment of health outcomes.

Project description:BACKGROUND:Obesity increases the risk for developing gestational diabetes mellitus (GDM) and preeclampsia (PE), which both associate with increased risk for type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) in women in later life. In the general population, metabolic syndrome (MetS) associates with T2DM and CVD. The impact of maternal MetS on pregnancy outcomes, in nulliparous pregnant women, has not been investigated. METHODS AND FINDINGS:Low-risk, nulliparous women were recruited to the multi-centre, international prospective Screening for Pregnancy Endpoints (SCOPE) cohort between 11 November 2004 and 28 February 2011. Women were assessed for a range of demographic, lifestyle, and metabolic health variables at 15 ± 1 weeks' gestation. MetS was defined according to International Diabetes Federation (IDF) criteria for adults: waist circumference ?80 cm, along with any 2 of the following: raised trigycerides (?1.70 mmol/l [?150 mg/dl]), reduced high-density lipoprotein cholesterol (<1.29 mmol/l [<50 mg/dl]), raised blood pressure (BP) (i.e., systolic BP ?130 mm Hg or diastolic BP ?85 mm Hg), or raised plasma glucose (?5.6 mmol/l). Log-binomial regression analyses were used to examine the risk for each pregnancy outcome (GDM, PE, large for gestational age [LGA], small for gestational age [SGA], and spontaneous preterm birth [sPTB]) with each of the 5 individual components for MetS and as a composite measure (i.e., MetS, as defined by the IDF). The relative risks, adjusted for maternal BMI, age, study centre, ethnicity, socioeconomic index, physical activity, smoking status, depression status, and fetal sex, are reported. A total of 5,530 women were included, and 12.3% (n = 684) had MetS. Women with MetS were at an increased risk for PE by a factor of 1.63 (95% CI 1.23 to 2.15) and for GDM by 3.71 (95% CI 2.42 to 5.67). In absolute terms, for PE, women with MetS had an adjusted excess risk of 2.52% (95% CI 1.51% to 4.11%) and, for GDM, had an adjusted excess risk of 8.66% (95% CI 5.38% to 13.94%). Diagnosis of MetS was not associated with increased risk for LGA, SGA, or sPTB. Increasing BMI in combination with MetS increased the estimated probability for GDM and decreased the probability of an uncomplicated pregnancy. Limitations of this study are that there are several different definitions for MetS in the adult population, and as there are none for pregnancy, we cannot be sure that the IDF criteria are the most appropriate definition for pregnancy. Furthermore, MetS was assessed in the first trimester and may not reflect pre-pregnancy metabolic health status. CONCLUSIONS:We did not compare the impact of individual metabolic components with that of MetS as a composite, and therefore cannot conclude that MetS is better at identifying women at risk. However, more than half of the women who had MetS in early pregnancy developed a pregnancy complication compared with just over a third of women who did not have MetS. Furthermore, while increasing BMI increases the probability of GDM, the addition of MetS exacerbates this probability. Further studies are required to determine if individual MetS components act synergistically or independently.

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.

Project description:ObjectiveMost 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.MethodsData 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.Results633 (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.ConclusionModels 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.

Dataset Information

Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study.

Publications

Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study.

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