Prognostic models in obstetrics: available, but far from applicable

Implementation and Effects of Risk-Dependent Obstetric Care in the Netherlands (Expect Study II): Protocol for an Impact Study

BACKGROUND

Recently, validated risk models predicting adverse obstetric outcomes combined with risk-dependent care paths have been made available for early antenatal care in the southeastern part of the Netherlands. This study will evaluate implementation progress and impact of the new approach in obstetric care.

OBJECTIVE

The objective of this paper is to describe the design of a study evaluating the impact of implementing risk-dependent care. Validated first-trimester prediction models are embedded in daily clinical practice and combined with risk-dependent obstetric care paths.

METHODS

A multicenter prospective cohort study consisting of women who receive risk-dependent care is being performed from April 2017 to April 2018 (Expect Study II). Obstetric risk profiles will be calculated using a Web-based tool, the Expect prediction tool. The primary outcomes are the adherence of health care professionals and compliance of women. Secondary outcomes are patient satisfaction and cost-effectiveness. Outcome measures will be established using Web-based questionnaires. The secondary outcomes of the risk-dependent care cohort (Expect II) will be compared with the outcomes of a similar prospective cohort (Expect I). Women of this similar cohort received former care-as-usual and were prospectively included between July 1, 2013 and December 31, 2015 (Expect I).

RESULTS

Currently, women are being recruited for the Expect Study II, and a total of 300 women are enrolled.

CONCLUSIONS

This study will provide information about the implementation and impact of a new approach in obstetric care using prediction models and risk-dependent obstetric care paths.

TRIAL REGISTRATION

Netherlands Trial Register NTR4143; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4143 (Archived by WebCite at http://www.webcitation.org/6t8ijtpd9).

Improved prediction of gestational hypertension by inclusion of placental growth factor and pregnancy associated plasma protein-a in a sample of Ghanaian women

BACKGROUND

We assessed whether adding the biomarkers Pregnancy Associated Plasma Protein-A (PAPP-A) and Placental Growth Factor (PlGF) to maternal clinical characteristics improved the prediction of a previously developed model for gestational hypertension in a cohort of Ghanaian pregnant women.

METHODS

This study was nested in a prospective cohort of 1010 pregnant women attending antenatal clinics in two public hospitals in Accra, Ghana. Pregnant women who were normotensive, at a gestational age at recruitment of between 8 and 13 weeks and provided a blood sample for biomarker analysis were eligible for inclusion. From serum, biomarkers PAPP-A and PlGF concentrations were measured by the AutoDELFIA immunoassay method and multiple of the median (MoM) values corrected for gestational age (PAPP-A and PlGF) and maternal weight (PAPP-A) were calculated. To obtain prediction models, these biomarkers were included with clinical predictors maternal weight, height, diastolic blood pressure, a previous history of gestational hypertension, history of hypertension in parents and parity in a logistic regression to obtain prediction models. The Area Under the Receiver Operating Characteristic Curve (AUC) was used to assess the predictive ability of the models.

RESULTS

Three hundred and seventy three women participated in this study. The area under the curve (AUC) of the model with only maternal clinical characteristics was 0.75 (0.64-0.86) and 0.89(0.73-1.00) for multiparous and primigravid women respectively. The AUCs after inclusion of both PAPP-A and PlGF were 0.82 (0.74-0.89) and 0.95 (0.87-1.00) for multiparous and primigravid women respectively.

CONCLUSION

Adding the biomarkers PAPP-A and PlGF to maternal characteristics to a prediction model for gestational hypertension in a cohort of Ghanaian pregnant women improved predictive ability. Further research using larger sample sizes in similar settings to validate these findings is recommended.

Evaluation of circulating placenta-related long noncoding RNAs as potential biomarkers for preeclampsia

Increasing evidence has indicated that circulating placental RNAs may reflect the pathophysiology of the placenta. In the current study, circulating placenta-related long noncoding RNAs (lncRNAs) were evaluated as potential biomarkers for preeclampsia (PE). Two parts of the case-control study were simultaneously executed, including the following sets: 52 patients with late-onset PE (LOPE) (diagnosed after 34 weeks) and 52 gestational age (GA)-matched controls; 58 patients with early-onset PE (EOPE) (diagnosed before 34 weeks) and 58 GA-matched controls. LncRNA expression was detected in the placenta analysis part, and the participants were delivered by caesarean sections. The levels of circulating placenta-associated lncRNAs were measured in the plasma analysis part and all pregnant women were included. Using microarray analysis, 163 differentially expressed lncRNAs were identified in placental samples from patients with LOPE, some of which were also detected in plasma samples from pregnant women. There were significant positive correlations between plasma, and placental expression levels of NONHSAT116812 and NONHSAT145880, which in plasma provided high diagnostic efficiencies for LOPE and EOPE. The present study demonstrated that circulating placenta-associated lncRNAs, paticularly NONHSAT116812 and NONHSAT145880 have potential as biomarkers for PE.

A systematic review of neonatal treatment intensity scores and their potential application in low-resource setting hospitals for predicting mortality, morbidity and estimating resource use

BACKGROUND

Treatment intensity scores can predict mortality and estimate resource use. They may therefore be of interest for essential neonatal care in low resource settings where neonatal mortality remains high. We sought to systematically review neonatal treatment intensity scores to (1) assess the level of evidence on predictive performance in predicting clinical outcomes and estimating resource utilisation and (2) assess the applicability of the identified models to decision making for neonatal care in low resource settings.

METHODS

We conducted a systematic search of PubMed, EMBASE (OVID), CINAHL, Global Health Library (Global index, WHO) and Google Scholar to identify studies published up until 21 December 2016. Included were all articles that used treatments as predictors in neonatal models. Individual studies were appraised using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). In addition, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used as a guiding framework to assess certainty in the evidence for predicting outcomes across studies.

RESULTS

Three thousand two hundred forty-nine articles were screened, of which ten articles were included in the review. All of the studies were conducted in neonatal intensive care units with sample sizes ranging from 22 to 9978, with a median of 163. Two articles reported model development, while eight reported external application of existing models to new populations. Meta-analysis was not possible due heterogeneity in the conduct and reporting of the identified studies. Discrimination as assessed by area under receiver operating characteristic curve was reported for in-hospital mortality, median 0.84 (range 0.75-0.96, three studies), early adverse outcome and late adverse outcome (0.78 and 0.59, respectively, one study).

CONCLUSION

Existing neonatal treatment intensity models show promise in predicting mortality and morbidity. There is however low certainty in the evidence on their performance in essential neonatal care in low resource settings as all studies had methodological limitations and were conducted in intensive care. The approach may however be developed further for low resource settings like Kenya because treatment data may be easier to obtain compared to measures of physiological status.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42016034205.

Early warning scores for detecting deterioration in adult hospital patients: a systematic review protocol

INTRODUCTION

Early warning scores (EWSs) are used extensively to identify patients at risk of deterioration in hospital. Previous systematic reviews suggest that studies which develop EWSs suffer methodological shortcomings and consequently may fail to perform well. The reviews have also identified that few validation studies exist to test whether the scores work in other settings. We will aim to systematically review papers describing the development or validation of EWSs, focusing on methodology, generalisability and reporting.

METHODS

We will identify studies that describe the development or validation of EWSs for adult hospital inpatients. Each study will be assessed for risk of bias using the Prediction model Risk of Bias ASsessment Tool (PROBAST). Two reviewers will independently extract information. A narrative synthesis and descriptive statistics will be used to answer the main aims of the study which are to assess and critically appraise the methodological quality of the EWS, to describe the predictors included in the EWSs and to describe the reported performance of EWSs in external validation.

ETHICS AND DISSEMINATION

This systematic review will only investigate published studies and therefore will not directly involve patient data. The review will help to establish whether EWSs are fit for purpose and make recommendations to improve the quality of future research in this area.

PROSPERO REGISTRATION NUMBER

CRD42017053324.

External Validation Study of First Trimester Obstetric Prediction Models (Expect Study I): Research Protocol and Population Characteristics

BACKGROUND

A number of first-trimester prediction models addressing important obstetric outcomes have been published. However, most models have not been externally validated. External validation is essential before implementing a prediction model in clinical practice.

OBJECTIVE

The objective of this paper is to describe the design of a study to externally validate existing first trimester obstetric prediction models, based upon maternal characteristics and standard measurements (eg, blood pressure), for the risk of pre-eclampsia (PE), gestational diabetes mellitus (GDM), spontaneous preterm birth (PTB), small-for-gestational-age (SGA) infants, and large-for-gestational-age (LGA) infants among Dutch pregnant women (Expect Study I). The results of a pilot study on the feasibility and acceptability of the recruitment process and the comprehensibility of the Pregnancy Questionnaire 1 are also reported.

METHODS

A multicenter prospective cohort study was performed in The Netherlands between July 1, 2013 and December 31, 2015. First trimester obstetric prediction models were systematically selected from the literature. Predictor variables were measured by the Web-based Pregnancy Questionnaire 1 and pregnancy outcomes were established using the Postpartum Questionnaire 1 and medical records. Information about maternal health-related quality of life, costs, and satisfaction with Dutch obstetric care was collected from a subsample of women. A pilot study was carried out before the official start of inclusion. External validity of the models will be evaluated by assessing discrimination and calibration.

RESULTS

Based on the pilot study, minor improvements were made to the recruitment process and online Pregnancy Questionnaire 1. The validation cohort consists of 2614 women. Data analysis of the external validation study is in progress.

CONCLUSIONS

This study will offer insight into the generalizability of existing, non-invasive first trimester prediction models for various obstetric outcomes in a Dutch obstetric population. An impact study for the evaluation of the best obstetric prediction models in the Dutch setting with respect to their effect on clinical outcomes, costs, and quality of life-Expect Study II-is being planned.

TRIAL REGISTRATION

Netherlands Trial Registry (NTR): NTR4143; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4143 (Archived by WebCite at http://www.webcitation.org/6t8ijtpd9).

External validation of preexisting first trimester preeclampsia prediction models

OBJECTIVE

To validate the increasing number of prognostic models being developed for preeclampsia using our own prospective study.

STUDY DESIGN

A systematic review of literature that assessed biomarkers, uterine artery Doppler and maternal characteristics in the first trimester for the prediction of preeclampsia was performed and models selected based on predefined criteria. Validation was performed by applying the regression coefficients that were published in the different derivation studies to our cohort. We assessed the models discrimination ability and calibration.

RESULTS

Twenty models were identified for validation. The discrimination ability observed in derivation studies (Area Under the Curves) ranged from 0.70 to 0.96 when these models were validated against the validation cohort, these AUC varied importantly, ranging from 0.504 to 0.833. Comparing Area Under the Curves obtained in the derivation study to those in the validation cohort we found statistically significant differences in several studies.

CONCLUSION

There currently isn't a definitive prediction model with adequate ability to discriminate for preeclampsia, which performs as well when applied to a different population and can differentiate well between the highest and lowest risk groups within the tested population. The pre-existing large number of models limits the value of further model development and future research should be focussed on further attempts to validate existing models and assessing whether implementation of these improves patient care.

Prediction of cesarean delivery in the term nulliparous woman: results from the prospective, multicenter Genesis study

BACKGROUND

In contemporary practice many nulliparous women require intervention during childbirth such as operative vaginal delivery or cesarean delivery (CD). Despite the knowledge that the increasing rate of CD is associated with increasing maternal age, obesity and larger infant birthweight, we lack a reliable method to predict the requirement for such potentially hazardous obstetric procedures during labor and delivery. This issue is important, as there are greater rates of morbidity and mortality associated with unplanned CD performed in labor compared with scheduled CDs. A prediction algorithm to identify women at risk of an unplanned CD could help reduced labor associated morbidity.

OBJECTIVE

In this primary analysis of the Genesis study, our objective was to prospectively assess the use of prenatally determined, maternal and fetal, anthropomorphic, clinical, and ultrasound features to develop a predictive tool for unplanned CD in the term nulliparous woman, before the onset of labor.

MATERIALS AND METHODS

The Genesis study recruited 2336 nulliparous women with a vertex presentation between 39+0 and 40+6 weeks' gestation in a prospective multicenter national study to examine predictors of CD. At recruitment, a detailed clinical evaluation and ultrasound assessment were performed. To reduce bias from knowledge of these data potentially influencing mode of delivery, women, midwives, and obstetricians were blinded to the ultrasound data. All hypothetical prenatal risk factors for unplanned CD were assessed as a composite. Multiple logistic regression analysis and mathematical modeling was used to develop a risk evaluation tool for CD in nulliparous women. Continuous predictors were standardized using z scores.

RESULTS

From a total enrolled cohort of 2336 nulliparous participants, 491 (21%) had an unplanned CD. Five parameters were determined to be the best combined predictors of CD. These were advancing maternal age (odds ratio [OR], 1.21; 95% confidence interval [CI], 1.09 to 1.34), shorter maternal height (OR, 1.72; 95% CI, 1.52 to 1.93), increasing body mass index (OR, 1.29; 95% CI, 1.17 to 1.43), larger fetal abdominal circumference (OR, 1.23; 95% CI, 1.1 to 1.38), and larger fetal head circumference (OR, 1.27; 95% CI, 1.14 to 1.42). A nomogram was developed to provide an individualized risk assessment to predict CD in clinical practice, with excellent calibration and discriminative ability (Kolmogorov-Smirnov, D statistic, 0.29; 95% CI, 0.28 to 0.30) with a misclassification rate of 0.21 (95% CI, 0.19 to 0.25).

CONCLUSION

Five parameters (maternal age, body mass index, height, fetal abdominal circumference, and fetal head circumference) can, in combination, be used to better determine the overall risk of CD in nulliparous women at term. A risk score can be used to inform women of their individualized probability of CD. This risk tool may be useful for reassuring most women regarding their likely success at achieving an uncomplicated vaginal delivery as well as selecting those patients with such a high risk for CD that they should avoid a trial of labor. Such a risk tool has the potential to greatly improve planning hospital service needs and minimizing patient risk.

Prediction models: the right tool for the right problem

PURPOSE OF REVIEW

Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to understand. This article aims to provide the necessary insight for clinicians to assess the value of a prediction model that they intend to use in their clinical practice.

RECENT FINDINGS

Recent developments in prediction model research include the continuous development of new performance characteristics for prediction models, increasing insight into the limitations of old characteristics, as well as an improved understanding of the generalizability of prediction models to new populations and practices.

SUMMARY

Clinicians can assess the value of a prediction model for their practice by first identifying what the usage of the model will be. Second, they can recognize which performance characteristics are relevant to their assessment of the model. Finally, they need to decide whether the available scientific evidence sufficiently matches their clinical practice to proceed with implementation.

Perspectives, preferences and needs regarding early prediction of preeclampsia in Dutch pregnant women: a qualitative study

BACKGROUND

To improve early risk-identification in pregnancy, research on prediction models for common pregnancy complications is ongoing. Therefore, it was the aim of this study to explore pregnant women's perceptions, preferences and needs regarding prediction models for first trimester screening for common pregnancy complications, such as preeclampsia, to support future implementation.

METHOD

Ten focus groups (of which five with primiparous and five with multiparous women) were conducted (n = 45). Six focus groups were conducted in urban regions and four in rural regions. All focus group discussions were audio taped and NVIVO was used in order to facilitate the thematic analysis conducted by the researchers.

RESULTS

Women in this study had a positive attitude towards first trimester screening for preeclampsia using prediction models. Reassurance when determined as low-risk was a major need for using the test. Self-monitoring, early recognition and intensive monitoring were considered benefits of using prediction models in case of a high-risk. Women acknowledged that high-risk determination could cause (unnecessary) anxiety, but it was expected that personal and professional interventions would level out this anxiety.

CONCLUSION

Women in this study had positive attitudes towards preeclampsia screening. Self-monitoring, together with increased alertness of healthcare professionals, would enable them to take active actions to improve pregnancy outcomes. This attitude enhances the opportunities for prevention, early recognition and treatment of preeclampsia and probably other adverse pregnancy outcomes.

External validation, update and development of prediction models for pre-eclampsia using an Individual Participant Data (IPD) meta-analysis: the International Prediction of Pregnancy Complication Network (IPPIC pre-eclampsia) protocol

BACKGROUND

Pre-eclampsia, a condition with raised blood pressure and proteinuria is associated with an increased risk of maternal and offspring mortality and morbidity. Early identification of mothers at risk is needed to target management.

METHODS/DESIGN

We aim to systematically review the existing literature to identify prediction models for pre-eclampsia. We have established the International Prediction of Pregnancy Complication Network (IPPIC), made up of 72 researchers from 21 countries who have carried out relevant primary studies or have access to existing registry databases, and collectively possess data from more than two million patients. We will use the individual participant data (IPD) from these studies to externally validate these existing prediction models and summarise model performance across studies using random-effects meta-analysis for any, late (after 34 weeks) and early (before 34 weeks) onset pre-eclampsia. If none of the models perform well, we will recalibrate (update), or develop and validate new prediction models using the IPD. We will assess the differential accuracy of the models in various settings and subgroups according to the risk status. We will also validate or develop prediction models based on clinical characteristics only; clinical and biochemical markers; clinical and ultrasound parameters; and clinical, biochemical and ultrasound tests.

DISCUSSION

Numerous systematic reviews with aggregate data meta-analysis have evaluated various risk factors separately or in combination for predicting pre-eclampsia, but these are affected by many limitations. Our large-scale collaborative IPD approach encourages consensus towards well developed, and validated prognostic models, rather than a number of competing non-validated ones. The large sample size from our IPD will also allow development and validation of multivariable prediction model for the relatively rare outcome of early onset pre-eclampsia.

TRIAL REGISTRATION

The project was registered on Prospero on the 27 November 2015 with ID: CRD42015029349.

Prevention of Hypertensive Disorders of Pregnancy: a Novel Application of the Polypill Concept

Nearly all of the annual 287,000 global maternal deaths are preventable. Hypertensive disorders of pregnancy (HDP) are among the major causes. A novel fixed-dose combination pill or polypill to prevent cardiovascular disease is a promising strategy for prevention of HDP. The aim of this study was to identify eligible candidates for a polypill for the prevention of HDP. A comprehensive review of systematic reviews on drug and dietary interventions to prevent HDP was conducted. Interventions were evaluated based on efficacy, dose, route of administration, and side effects. Fourteen interventions were assessed. Low-dose aspirin and calcium were identified as candidates for a polypill, with risk reduction estimations for pregnancy-induced hypertension and preeclampsia ranging between 10 and 62 %, depending on patient population characteristics including a priori risk, and gestation age at start of intervention. Their effect may be augmented through the addition of vitamin D, vitamin B12, and folic acid. The effect and optimal composition needs to be evaluated in future trials. Given the persistent burden of maternal and perinatal mortality associated with HDP, prevention of these disorders is key-especially in low-resource settings. The polypill approach with a combination of aspirin, calcium, vitamin D, vitamin B12, and folic acid is a promising strategy to improve maternal and perinatal health outcomes.

Antenatal blood pressure for prediction of pre-eclampsia, preterm birth, and small for gestational age babies: development and validation in two general population cohorts

STUDY QUESTION

Can routine antenatal blood pressure measurements between 20 and 36 weeks' gestation contribute to the prediction of pre-eclampsia and its associated adverse outcomes?

METHODS

This study used repeated antenatal measurements of blood pressure from 12 996 women in the Avon Longitudinal Study of Parents and Children (ALSPAC) to develop prediction models and validated these in 3005 women from the Southampton Women's Survey (SWS). A model based on maternal early pregnancy characteristics only (BMI, height, age, parity, smoking, existing and previous gestational hypertension and diabetes, and ethnicity) plus initial mean arterial pressure was compared with a model additionally including current mean arterial pressure, a model including the deviation of current mean arterial pressure from a stratified normogram, and a model including both at different gestational ages from 20-36 weeks.

STUDY ANSWER AND LIMITATIONS

The addition of blood pressure measurements from 28 weeks onwards improved prediction models compared with use of early pregnancy risk factors alone, but they contributed little to the prediction of preterm birth or small for gestational age. Though multiple imputation of missing data was used to increase the sample size and minimise selection bias, the validation sample might have been slightly underpowered as the number of cases of pre-eclampsia was just below the recommended 100. Several risk factors were self reported, potentially introducing measurement error, but this reflects how information would be obtained in clinical practice.

WHAT THIS STUDY ADDS

The addition of routinely collected blood pressure measurements from 28 weeks onwards improves predictive models for pre-eclampsia based on blood pressure in early pregnancy and other characteristics, facilitating a reduction in scheduled antenatal care.

FUNDING, COMPETING INTERESTS, DATA SHARING

UK Wellcome Trust, US National Institutes of Health, and UK Medical Research Council. Other funding sources for authors are detailed in the full online paper. With the exceptions of CM-W, HMI, and KMG there were no competing interests.