Revisiting the discriminatory accuracy of traditional risk factors in preeclampsia screening

Risk factors and prediction model for new-onset hypertensive disorders of pregnancy: a retrospective cohort study

Background and aims

Hypertensive disorders of pregnancy (HDP) is a significant cause of maternal and neonatal mortality. This study aims to identify risk factors for new-onset HDP and to develop a prediction model for assessing the risk of new-onset hypertension during pregnancy.

Methods

We included 446 pregnant women without baseline hypertension from Liyang People's Hospital at the first inspection, and they were followed up until delivery. We collected maternal clinical parameters and biomarkers between 16th and 20th weeks of gestation. Logistic regression was used to determine the effect of the risk factors on HDP. For model development, a backward selection algorithm was applied to choose pertinent biomarkers, and predictive models were created based on multiple machine learning methods (generalised linear model, multivariate adaptive regression splines, random forest, and k-nearest neighbours). Model performance was evaluated using the area under the curve.

Results

Out of the 446 participants, 153 developed new-onset HDP. The HDP group exhibited significantly higher baseline body mass index (BMI), weight change, baseline systolic/diastolic blood pressure, and platelet counts than the control group. The increase in baseline BMI, weight change, and baseline systolic and diastolic blood pressure significantly elevated the risk of HDP, with odds ratios and 95% confidence intervals of 1.10 (1.03-1.17), 1.10 (1.05-1.16), 1.04 (1.01-1.08), and 1.10 (1.05-1.14) respectively. Restricted cubic spline showed a linear dose-dependent association of baseline BMI and weight change with the risk of HDP. The random forest-based prediction model showed robust performance with the area under the curve of 0.85 in the training set.

Conclusion

This study establishes a prediction model to evaluate the risk of new-onset HDP, which might facilitate the early diagnosis and management of HDP.

Multilevel modelling for measuring interaction of effects between multiple categorical variables: An illustrative application using risk factors for preeclampsia

BACKGROUND

Measuring multiple and higher-order interaction effects between multiple categorical variables proves challenging.

OBJECTIVES

To illustrate a multilevel modelling approach to studying complex interactions.

METHODS

We apply a two-level random-intercept linear regression to a binary outcome for individuals (level-1) nested within strata (level-2) defined by all observed combinations of multiple categorical exposure variables. As a pedagogic application, we analyse 36 strata defined by five risk factors of preeclampsia (parity, previous preeclampsia, chronic hypertension, multiple pregnancies, body mass index category) among 652,603 women in the Swedish Medical Birth Registry between 2002 and 2010.

RESULTS

The absolute risk of preeclampsia was 4% but was predicted to vary from 1% to 44% across strata. The stratum discriminatory accuracy was 30% according to the variance partition coefficient (VPC) and 0.73 according to the area under the receiver operating characteristic curve (AUC). While the risk heterogeneity across strata was primarily due to the main effects of the categories defining the strata, 5% of the variation was attributable to their two- and higher-way interaction effects. One stratum presented a positive interaction, and two strata presented negative interaction.

CONCLUSIONS

Multilevel modelling is an innovative tool for identifying and analysing higher-order interaction effects. Further work is needed to explore how this approach can best be applied to making causal inferences.

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

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 (n=49) and without chronic hypertension (n=871) and those who developed preeclampsia (n=166) or superimposed preeclampsia (n=64). 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⁺⁶, 16 to 19⁺⁶, 20 to 23⁺⁶, 24 to 27⁺⁶, 28 to 31⁺⁶, and 32 to 36⁺⁶ 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⁺⁶ weeks (McNemar test, P<.05); (2) combined evidence from prior-risk estimates and biomarkers predicted preterm preeclampsia with a sensitivity (false-positive rate, 10%) of 55%, 48%, 62%, 72%, and 84% at 8 to 15⁺⁶, 16 to 19⁺⁶, 20 to 23⁺⁶, 24 to 27⁺⁶, and 28 to 31⁺⁶ week intervals, respectively; (3) the sensitivity for term preeclampsia (false-positive rate, 10%) was 36%, 36%, 41%, 43%, 39%, and 51% at 8 to 15⁺⁶, 16 to 19⁺⁶, 20 to 23⁺⁶, 24 to 27⁺⁶, 28 to 31⁺⁶, and 32 to 36⁺⁶ week intervals, respectively; (4) the detection rate for superimposed preeclampsia among women with chronic hypertension was similar to that in women without chronic hypertension, especially earlier in pregnancy, reaching at most 54% at 20 to 23⁺⁶ weeks (false-positive rate, 10%); and (5) prediction models performed comparably to the Fetal Medicine Foundation calculators when the same maternal risk factors and biomarkers (mean arterial pressure, placental growth factor, and soluble vascular endothelial growth factor receptor-1 multiples of the mean values) were used as input.

CONCLUSION

We introduced prediction models for preeclampsia throughout pregnancy. These models can be useful to identify women at risk during the first trimester who could benefit from aspirin treatment or later in pregnancy to inform patient management. Relative to prediction performance at 8 to 15⁺⁶ weeks, there was a substantial improvement in the detection rate for preterm and term preeclampsia by using data collected after 20 and 32 weeks' gestation, respectively. The inclusion of plasma soluble endoglin improves the early prediction of superimposed preeclampsia, which may be valuable when Doppler velocimetry of the uterine arteries is not available.

Early-pregnancy prediction of risk for pre-eclampsia using maternal blood leptin/ceramide ratio: discovery and confirmation

OBJECTIVE

This study aimed to develop a blood test for the prediction of pre-eclampsia (PE) early in gestation. We hypothesised that the longitudinal measurements of circulating adipokines and sphingolipids in maternal serum over the course of pregnancy could identify novel prognostic biomarkers that are predictive of impending event of PE early in gestation.

STUDY DESIGN

Retrospective discovery and longitudinal confirmation.

SETTING

Maternity units from two US hospitals.

PARTICIPANTS

Six previously published studies of placental tissue (78 PE and 95 non-PE) were compiled for genomic discovery, maternal sera from 15 women (7 non-PE and 8 PE) enrolled at ProMedDx were used for sphingolipidomic discovery, and maternal sera from 40 women (20 non-PE and 20 PE) enrolled at Stanford University were used for longitudinal observation.

OUTCOME MEASURES

Biomarker candidates from discovery were longitudinally confirmed and compared in parallel to the ratio of placental growth factor (PlGF) and soluble fms-like tyrosine kinase (sFlt-1) using the same cohort. The datasets were generated by enzyme-linked immunosorbent and liquid chromatography-tandem mass spectrometric assays.

RESULTS

Our discovery integrating genomic and sphingolipidomic analysis identified leptin (Lep) and ceramide (Cer) (d18:1/25:0) as novel biomarkers for early gestational assessment of PE. Our longitudinal observation revealed a marked elevation of Lep/Cer (d18:1/25:0) ratio in maternal serum at a median of 23 weeks' gestation among women with impending PE as compared with women with uncomplicated pregnancy. The Lep/Cer (d18:1/25:0) ratio significantly outperformed the established sFlt-1/PlGF ratio in predicting impending event of PE with superior sensitivity (85% vs 20%) and area under curve (0.92 vs 0.52) from 5 to 25 weeks of gestation.

CONCLUSIONS

Our study demonstrated the longitudinal measurement of maternal Lep/Cer (d18:1/25:0) ratio allows the non-invasive assessment of PE to identify pregnancy at high risk in early gestation, outperforming the established sFlt-1/PlGF ratio test.

Parameterization of the mid-trimester drop in blood pressure trajectory during pregnancy and its utility for predicting preeclampsia

OBJECTIVES

The purpose of this study was to parameterize mid-trimester drop in blood pressure (BP) trajectory during pregnancy and to evaluate its utility for predicting preeclampsia.

METHODS

To develop parametric models for BP trajectory during pregnancy, we used data from 7923 Chinese pregnant women with 24 810 routine antenatal care visits. Then, we evaluated the utility of BP trajectory parameters for predicting clinician-diagnosed preeclampsia in a separate sample of 3524 pregnant women from a randomized controlled trial of prenatal vitamin supplementation conducted in the same area. We focused on parameters related to the mid-trimester BP drop, including the gestational age and BP value at the nadir (lowest point), change in BP, velocity, and area under curve during two periods (from 12 weeks of gestation to the nadir and from the nadir to 33 weeks of gestation).

RESULTS

All participants in our analysis had a mid-pregnancy drop in their SBP, DBP, and mean arterial pressure (MAP) trajectories. There were high correlations (|r| > 0.90) among trajectory parameters of the same BP measure. The final prediction model included selective parameters of SBP, DBP, and MAP trajectories, prepregnancy BMI and gestational age at the first antenatal care visit. The area under the receiver-operating curve for predicting preeclampsia was 0.886 (95% confidence interval 0.846--0.926) in the training dataset and 0.802 (0.708--0.895) in the validation dataset.

CONCLUSION

Our novel BP trajectory parameters are informative and can predict preeclampsia at a clinically acceptable level.

A risk factor-based predictive model for new-onset hypertension during pregnancy in Chinese Han women

BACKGROUND

Hypertensive disorders of pregnancy (HDP) is one of the leading causes of maternal and neonatal mortality, increasing the long-term incidence of cardiovascular diseases. Preeclampsia and gestational hypertension are the major components of HDP. The aim of our study is to establish a prediction model for pregnant women with new-onset hypertension during pregnancy (increased blood pressure after gestational age > 20 weeks), thus to guide the clinical prediction and treatment of de novo hypertension.

METHODS

A total of 117 pregnant women with de novo hypertension who were admitted to our hospital's obstetrics department were selected as the case group and 199 healthy pregnant women were selected as the control group from January 2017 to June 2018. Maternal clinical parameters such as age, family history and the biomarkers such as homocysteine, cystatin C, uric acid, total bile acid and glomerular filtration rate were collected at a mean gestational age in 16 to 20 weeks. The prediction model was established by logistic regression.

RESULTS

Eleven indicators have statistically significant difference between two groups (P < 0.05). These 11 factors were substituted into the logistic regression equation and 7 independent predictors were obtained. The equation expressed including 7 factors. The calculated area under the curve was 0.884(95% confidence interval: 0.848-0.921), the sensitivity and specificity were 88.0 and 75.0%. A scoring system was established to classify pregnant women with scores ≤15.5 as low-risk pregnancy group and those with scores > 15.5 as high-risk pregnancy group.

CONCLUSIONS

Our regression equation provides a feasible and reliable means of predicting de novo hypertension after pregnancy. Risk stratification of new-onset hypertension was performed to early treatment interventions in high-risk populations.

The predictive value of sonographic placental markers for adverse pregnancy outcome in women with chronic kidney disease

OBJECTIVE

To determine the rate of sonographic placental markers and their predictive value for preeclampsia and fetal growth restriction in women with chronic kidney disease (CKD).

STUDY DESIGN

A retrospective cohort study of women with CKD followed at a tertiary referral center between 2016 and 2019 (n = 86). All women underwent 2nd trimester sonographic placental examinations that included assessment of placental morphology, umbilical cord, and uterine artery Doppler. Continuous placental markers were converted to multiples on medians (MoM).

MAIN OUTCOME MEASURES

Predictive value of sonographic markers for preeclampsia and birthweight < 10th percentile.

RESULTS

Women in the cohort had a high rate of preeclampsia (24.4%), birthweight < 10th% (26.7%), and preterm birth (30.2%). The most important markers were placental volume and uterine artery Doppler: the risk of preeclampsia was elevated in women with low placental volume (51.7% vs. 10.9%; OR = 8.79 [2.70-28.59] for preeclampsia; and 40.0% vs. 9.1%; OR = 6.67 [1.85-24.04] for preterm preeclampsia), and in women with bilateral uterine artery notching (62.5% vs. 20.8%; OR = 6.35 [1.37-29.45] for preeclampsia; and 62.5% vs. 10.4%; OR = 14.38 [1.29-71.75] for preterm preeclampsia). The combination of both markers had the strongest predictive value for preeclampsia (positive likelihood ratio = 8.25 [6.84-9.95]). Low placental volume and bilateral uterine notching were also associated with birthweight < 10th percentile.

CONCLUSION

A 2nd-trimester sonographic placental study can identify a subgroup of women with CKD who are at most risk of preeclampsia and fetal growth restriction. Such data may inform their subsequent perinatal care and assist care providers in the often challenging distinction between preeclampsia flare of underlying CKD.

Physical exercise and metformin in the prevention of pre-eclampsia: systematic review

Abstract Introduction: Pre-eclampsia is a disorder that may occur during pregnancy but is still unknown and / or multifactorial causes. Objective: To verify whether physical exercise and metformin may be helpful in preventing preeclampsia. Method: This is a systematic review of the literature in PubMed / MEDLINE, Web of Science, Scopus, LILACS and Cochrane. This review followed the critiques of the PRISMA checklist. Bias assessment was used for the Cochrane Handbook for Systematical Reviews of Interventions (Version 5.1.0) for clinical trials and the Downs and Black scale for cohort and case-control studies. Results: 17 studies were within the established criteria. The subjects evaluated were: pre-eclampsia, cardiovascular metabolic factors, physiotherapeutic therapies and the effects of physical exercise and metformin on the circulatory system. Conclusion: There is a need for adapted techniques and new protocols according to the contingencies and complications of pregnancy. During pregnancy, it is suggested a greater interdisciplinarity of knowledge among professionals and that the therapy receives adjustments against the metabolic alterations of the reproductive system. In order to prevent preeclampsia, the study suggests a program of individual exercises that include greater assistance, verification and / or comprehension of possible changes and their limits during pregnancy. As well as, the adjuvant use of metformin of 1000 mg/d in the initial phase, with the purpose of maintaining the effects of the drug due to renal clearance during pregnancy, until reaching a maximum of 1500 mg/d, to avoid side effects of the drug.

Discriminatory Accuracy of Preeclampsia Risk Factors in Primary Care

BACKGROUND

Although it is common to use risk factors in the screening for preeclampsia, they do not always accurately identify patients who truly have this condition.

AIM OF THE STUDY

To determine the discriminatory accuracy of known preeclampsia risk factors, both individually and in combination.

METHODS

We studied patients undergoing prenatal care who were diagnosed with preeclampsia or eclampsia (n = 160 cases) in primary care and those who were not (n = 430 controls). Data on history of preeclampsia, type 2 diabetes, chronic hypertension, multiple gestation, first pregnancy, pregnancy interval ≥10 years, overweight/obesity, mean arterial pressure (MAP) ≥80 mmHg, and age (<20 years and ≥40 years) were obtained using a dichotomous scale. Discriminatory accuracy indicators were true-positive (TP) and false-positive (FP) rates, positive and negative likelihood ratios (LR+ and LR-), diagnostic odds ratio (DOR), and the area under the receiver-operating characteristic (AUROC) curve; stratified by parity. The case-control status was the reference standard.

RESULTS

Certain combinations performed better than individual factors, independent of parity status. Among multiparous women, MAP ≥80 mmHg together with previous preeclampsia and overweight/obesity accumulated the greatest number of discriminatory accuracy indicators, with acceptable values: TP, 72.2%; FP, 1.5%; LR+, 48.4; LR-, 0.3; DOR, 171.6; and AUROC, 0.85.

CONCLUSIONS

Discriminatory accuracy was low for almost all individual preeclampsia risk factors. However, the accuracy improved after some factors were combined. To the best of our knowledge, this is the first study to examine the discriminatory accuracy of preeclampsia risk factors used for screening high-risk pregnancies in primary care in Mexico.

Comparison of the discriminatory accuracy of four risk criteria for preeclampsia

OBJECTIVES

Several criteria have been proposed to categorize the risk of preeclampsia, with notable differences between these criteria. We compared the discriminatory accuracy of criteria for categorizing preeclampsia risk established by four institutions, namely, the World Health Organization (WHO), National Institute for Health and Care Excellence (NICE), American College of Obstetricians and Gynecologists (ACOG), and National Center for Technological Excellence in Health (CENETEC), and estimated the concordance between these criteria.

STUDY DESIGN

We performed a secondary data analysis of 590 Mexican obstetric patients who received prenatal care in primary care between 2016 and 2017; 160 had a diagnosis of preeclampsia.

MAIN OUTCOME MEASURES

We estimated the true (TP) and false positive (FP) fractions, positive (PPV) and negative predictive values (NPV), positive (LR+) and negative (LR-) likelihood ratios, diagnostic odds ratio (DOR), area under the receiver operating characteristic curve (AUROC), and Kappa coefficient with corresponding 95% confidence intervals (CIs).

RESULTS

Only the WHO criteria, followed by the NICE criteria, had the greatest number of accuracy indicators with ideal or acceptable results: TP 83.6%, PPV 60.5%, NPV 90.3%, DOR 14.3, and AUROC 0.79 and TP 84.5%, PPV 51.0%, NPV 90.3%, DOR 9.7, and AUROC 0.74, respectively. The Kappa coefficient between WHO and NICE criteria was 0.78 (95% CI 0.71-0.85).

CONCLUSIONS

The discriminatory accuracies of the WHO and NICE criteria were superior to those of the ACOG and CENETEC criteria for classifying preeclampsia risk. Their concordance was good; thus, both criteria seem appropriate for screening preeclampsia in primary care.

The tyranny of the averages and the indiscriminate use of risk factors in public health: The case of coronary heart disease

Modern medicine is overwhelmed by a plethora of both established risk factors and novel biomarkers for diseases. The majority of this information is expressed by probabilistic measures of association such as the odds ratio (OR) obtained by calculating differences in average “risk” between exposed and unexposed groups. However, recent research demonstrates that even ORs of considerable magnitude are insufficient for assessing the ability of risk factors or biomarkers to distinguish the individuals who will develop the disease from those who will not. In regards to coronary heart disease (CHD), we already know that novel biomarkers add very little to the discriminatory accuracy (DA) of traditional risk factors. However, the value added by traditional risk factors alongside simple demographic variables such as age and sex has been the subject of less discussion. Moreover, in public health, we use the OR to calculate the population attributable fraction (PAF), although this measure fails to consider the DA of the risk factor it represents. Therefore, focusing on CHD and applying measures of DA, we re-examine the role of individual demographic characteristics, risk factors, novel biomarkers and PAFs in public health and epidemiology. In so doing, we also raise a more general criticism of the traditional risk factors’ epidemiology. We investigated a cohort of 6103 men and women who participated in the baseline (1991–1996) of the Malmö Diet and Cancer study and were followed for 18 years. We found that neither traditional risk factors nor biomarkers substantially improved the DA obtained by models considering only age and sex. We concluded that the PAF measure provided insufficient information for the planning of preventive strategies in the population. We need a better understanding of the individual heterogeneity around the averages and, thereby, a fundamental change in the way we interpret risk factors in public health and epidemiology.

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There is a plethora of differences in “average” risk between exposed and unexposed groups of individuals.

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Individual heterogeneity around average values is seldom considered in Public Health.

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Measures of discriminatory accuracy (DA) informs on the underlying individual heterogeneity.

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Most know risk factors and other categorizations associated with diseases have low DA.

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We need a fundamental change in the way we investigate risk factors and other categorizations in Public Health.