Comparison of three algorithms for prediction preeclampsia in the first trimester of pregnancy

First-Trimester Prediction Models Based on Maternal Characteristics for Adverse Pregnancy Outcomes: A Systematic Review and Meta-Analysis

BACKGROUND

Early risk stratification can facilitate timely interventions for adverse pregnancy outcomes, including preeclampsia (PE), small-for-gestational-age neonates (SGA), spontaneous preterm birth (sPTB) and gestational diabetes mellitus (GDM).

OBJECTIVES

To perform a systematic review and meta-analysis of first-trimester prediction models for adverse pregnancy outcomes.

SEARCH STRATEGY

The PubMed database was searched until 6 June 2024.

SELECTION CRITERIA

First-trimester prediction models based on maternal characteristics were included. Articles reporting on prediction models that comprised biochemical or ultrasound markers were excluded.

DATA COLLECTION AND ANALYSIS

Two authors identified articles, extracted data and assessed risk of bias and applicability using PROBAST.

MAIN RESULTS

A total of 77 articles were included, comprising 30 developed models for PE, 15 for SGA, 11 for sPTB and 35 for GDM. Discriminatory performance in terms of median area under the curve (AUC) of these models was 0.75 [IQR 0.69-0.78] for PE models, 0.62 [0.60-0.71] for SGA models of nulliparous women, 0.74 [0.72-0.74] for SGA models of multiparous women, 0.65 [0.61-0.67] for sPTB models of nulliparous women, 0.71 [0.68-0.74] for sPTB models of multiparous women and 0.71 [0.67-0.76] for GDM models. Internal validation was performed in 40/91 (43.9%) of the models. Model calibration was reported in 21/91 (23.1%) models. External validation was performed a total of 96 times in 45/91 (49.5%) of the models. High risk of bias was observed in 94.5% of the developed models and in 58.3% of the external validations.

CONCLUSIONS

Multiple first-trimester prediction models are available, but almost all suffer from high risk of bias, and internal and external validations were often not performed. Hence, methodological quality improvement and assessment of the clinical utility are needed.

Comparison of National Factor-Based Models for Preeclampsia Screening

OBJECTIVE

 This study aimed to compare the predictive values of the American College of Obstetricians and Gynecologists (ACOG), the National Institute for Health and Care Excellence (NICE), and the Society of Obstetricians and Gynecologists of Canada (SOGC) factor-based models for preeclampsia (PE) screening.

STUDY DESIGN

 We conducted a secondary analysis of maternal and birth data from 32 hospitals. For each delivery, we calculated the risk of PE according to the ACOG, the NICE, and the SOGC models. Our primary outcomes were PE and preterm PE (PE combined with preterm birth) using the ACOG criteria. We calculated the detection rate (DR or sensitivity), the false positive rate (FPR or 1 - specificity), the positive (PPV) and negative (NPV) predictive values of each model for PE and for preterm PE using receiver operator characteristic (ROC) curves.

RESULTS

 We used 130,939 deliveries including 4,635 (3.5%) cases of PE and 823 (0.6%) cases of preterm PE. The ACOG model had a DR of 43.6% for PE and 50.3% for preterm PE with FPR of 15.6%; the NICE model had a DR of 36.2% for PE and 41.3% for preterm PE with FPR of 12.8%; and the SOGC model had a DR of 49.1% for PE and 51.6% for preterm PE with FPR of 22.2%. The PPV for PE of the ACOG (9.3%) and NICE (9.4%) models were both superior than the SOGC model (7.6%; p < 0.001), with a similar trend for the PPV for preterm PE (1.9 vs. 1.9 vs. 1.4%, respectively; p < 0.01). The area under the ROC curves suggested that the ACOG model is superior to the NICE for the prediction of PE and preterm PE and superior to the SOGC models for the prediction of preterm PE (all with p < 0.001).

CONCLUSION

 The current ACOG factor-based model for the prediction of PE and preterm PE, without considering race, is superior to the NICE and SOGC models.

KEY POINTS

· Clinical factor-based model can predict PE in approximately 44% of the cases for a 16% false positive.. · The ACOG model is superior to the NICE and SOGC models to predict PE.. · Clinical factor-based models are better to predict PE in parous than in nulliparous..

Development and validation of prediction models for fetal growth restriction and birthweight: an individual participant data meta-analysis

Background

Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes.

Objectives

To assess the predictive performance of existing models predicting fetal growth restriction and birthweight, and if needed, to develop and validate new multivariable models using individual participant data.

Design

Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis.

Participants

Pregnant women at booking. External validation of existing models (9 cohorts, 441,415 pregnancies); International Prediction of Pregnancy Complications model development and validation (4 cohorts, 237,228 pregnancies).

Predictors

Maternal clinical characteristics, biochemical and ultrasound markers.

Primary outcomes

fetal growth restriction defined as birthweight <10th centile adjusted for gestational age and with stillbirth, neonatal death or delivery before 32 weeks' gestation birthweight.

Analysis

First, we externally validated existing models using individual participant data meta-analysis. If needed, we developed and validated new International Prediction of Pregnancy Complications models using random-intercept regression models with backward elimination for variable selection and undertook internal-external cross-validation. We estimated the study-specific performance (c-statistic, calibration slope, calibration-in-the-large) for each model and pooled using random-effects meta-analysis. Heterogeneity was quantified using τ2 and 95% prediction intervals. We assessed the clinical utility of the fetal growth restriction model using decision curve analysis, and health economics analysis based on National Institute for Health and Care Excellence 2008 model.

Results

Of the 119 published models, one birthweight model (Poon) could be validated. None reported fetal growth restriction using our definition. Across all cohorts, the Poon model had good summary calibration slope of 0.93 (95% confidence interval 0.90 to 0.96) with slight overfitting, and underpredicted birthweight by 90.4 g on average (95% confidence interval 37.9 g to 142.9 g). The newly developed International Prediction of Pregnancy Complications-fetal growth restriction model included maternal age, height, parity, smoking status, ethnicity, and any history of hypertension, pre-eclampsia, previous stillbirth or small for gestational age baby and gestational age at delivery. This allowed predictions conditional on a range of assumed gestational ages at delivery. The pooled apparent c-statistic and calibration were 0.96 (95% confidence interval 0.51 to 1.0), and 0.95 (95% confidence interval 0.67 to 1.23), respectively. The model showed positive net benefit for predicted probability thresholds between 1% and 90%. In addition to the predictors in the International Prediction of Pregnancy Complications-fetal growth restriction model, the International Prediction of Pregnancy Complications-birthweight model included maternal weight, history of diabetes and mode of conception. Average calibration slope across cohorts in the internal-external cross-validation was 1.00 (95% confidence interval 0.78 to 1.23) with no evidence of overfitting. Birthweight was underestimated by 9.7 g on average (95% confidence interval -154.3 g to 173.8 g).

Limitations

We could not externally validate most of the published models due to variations in the definitions of outcomes. Internal-external cross-validation of our International Prediction of Pregnancy Complications-fetal growth restriction model was limited by the paucity of events in the included cohorts. The economic evaluation using the published National Institute for Health and Care Excellence 2008 model may not reflect current practice, and full economic evaluation was not possible due to paucity of data.

Future work

International Prediction of Pregnancy Complications models' performance needs to be assessed in routine practice, and their impact on decision-making and clinical outcomes needs evaluation.

Conclusion

The International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight models accurately predict fetal growth restriction and birthweight for various assumed gestational ages at delivery. These can be used to stratify the risk status at booking, plan monitoring and management.

Study registration

This study is registered as PROSPERO CRD42019135045.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/148/07) and is published in full in Health Technology Assessment; Vol. 28, No. 14. See the NIHR Funding and Awards website for further award information.

Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China

Objective: The aim of this study was to use machine learning methods to analyze all available clinical and laboratory data obtained during prenatal screening in early pregnancy to develop predictive models in preeclampsia (PE).

Material and Methods: Data were collected by retrospective medical records review. This study used 5 machine learning algorithms to predict the PE: deep neural network (DNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF). Our model incorporated 18 variables including maternal characteristics, medical history, prenatal laboratory results, and ultrasound results. The area under the receiver operating curve (AUROC), calibration and discrimination were evaluated by cross-validation.

Results: Compared with other prediction algorithms, the RF model showed the highest accuracy rate. The AUROC of RF model was 0.86 (95% CI 0.80–0.92), the accuracy was 0.74 (95% CI 0.74–0.75), the precision was 0.82 (95% CI 0.79–0.84), the recall rate was 0.42 (95% CI 0.41–0.44), and Brier score was 0.17 (95% CI 0.17–0.17).

Conclusion: The machine learning method in our study automatically identified a set of important predictive features, and produced high predictive performance on the risk of PE from the early pregnancy information.

Low-dose aspirin for prevention of preeclampsia: Implementation of the NICE guideline in Thailand

AIM

To evaluate the effectiveness of a preeclampsia (PE) screening program using the National Institute for Health and Care Excellence (NICE) guideline in pregnant Thai women.

METHODS

A total of 2552 pregnancies received antenatal care and were delivered at Songklanagarind Hospital between November 2016 and April 2020. PE screening with the NICE guideline was used to identify mothers at risk. In cases of positive screening results, a daily dose of 81 mg aspirin was prescribed. Pregnancy outcomes were compared with 2783 participants who had maternity care before the implementation of the screening program. The effectiveness of aspirin prophylaxis following the NICE guideline was assessed by a logistic regression model to compare the risk of PE development between before and after guidance.

RESULTS

The screening positive rate by NICE was 8.3%. Of these, 77.36% of the participants received aspirin prophylaxis according to the NICE recommendation. After the implementation of the PE screening program, the incidence of PE slightly decreased (from 4.31% to 3.72%, p = 0.274). The chance of PE in pregnancies who had high-risk factors was reduced after using low-dose aspirin prophylaxis, even though the difference was not statistically significant.

CONCLUSIONS

Screening with the NICE guidelines followed by prescription of low-dose aspirin (81 mg/day) was probably not an effective strategy for the prevention of PE in our population. Combining biophysical and biochemical markers to identify pregnant women who subsequently develop PE, concurrently with an increased dose of aspirin prophylaxis, may provide a better outcome in clinical practice.

Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis

BACKGROUND

Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management.

OBJECTIVES

To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers.

DESIGN

This was an individual participant data meta-analysis of cohort studies.

SETTING

Source data from secondary and tertiary care.

PREDICTORS

We identified predictors from systematic reviews, and prioritised for importance in an international survey.

PRIMARY OUTCOMES

Early-onset (delivery at < 34 weeks' gestation), late-onset (delivery at ≥ 34 weeks' gestation) and any-onset pre-eclampsia.

ANALYSIS

We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I ² and τ². A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals.

RESULTS

The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia.

LIMITATIONS

Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data.

CONCLUSION

For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings.

FUTURE WORK

Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42015029349.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 72. See the NIHR Journals Library website for further project information.

A new risk score model to predict preeclampsia using maternal factors and mean arterial pressure in early pregnancy

The purpose of this study was to establish a multivariable risk-scoring model for preeclampsia (PE) prediction based on maternal characteristics and mean arterial pressure (MAP). Multivariate logistic regression analysis from 4600 pregnancies during a 10-year period was used to create the best fitting model. Significant risk factors and weighted scores consisted of age ≥30 years (3), BMI ≥25 kg/m² (2), multifetal pregnancy (9), history of PE (9), adverse perinatal outcomes (6), pregnancy interval >10 years (5), nulliparous (5), underlying renal disease (10), chronic hypertension (6), autoimmune disease (5), diabetes (2) and MAP ≥95 mmHg (5). The model achieved an ROC area 0.771 with detection rates of 34%, 44%, 53% and 58% at 5%, 10%, 15% and 20% fixed false-positive rates, respectively. The new risk score model could be a clinically useful screening tool for PE. Pregnant women who have total scores of 9-13 (high risk) and more than 14 (very high risk) should receive aspirin prophylaxis.Impact StatementWhat is already known on this subject? Preeclampsia (PE) is the major cause of maternal and perinatal mortality and morbidity; it can be prevented by antiplatelet agents.What the results of this study add? A new model for identifying maternal at risk for PE using clinical risk factors and MAP was created. Weighted scores were defined for each variable for easy use in clinical practice. According to their probability for PE, pregnant women were classified into three subgroups: low risk (score 0-8), high risk (score 9-13) and very high risk groups (score ≥ 14). Aspirin should be prescribed to high risk and very high risk groups. For safety concerns, very high risk pregnancies should have close antenatal surveillance in a tertiary care hospital to reduce adverse outcomes during pregnancy and childbirth.What the implications are of these findings for clinical practice and/or further research? This new model for identifying pregnant women at high risk for PE has the potential to reduce the morbidity and mortality associated with this disease.

Preeclampsia Risk Prediction Model for Chinese Pregnant Patients With Systemic Lupus Erythematosus

OBJECTIVE

To screen for a high risk of preeclampsia in women with systemic lupus erythematosus (SLE).

METHODS

A total of 513 antenatal care records of pregnant patients with SLE were obtained, and the data were randomly assigned to either a development set (n = 342) or a validation set (n = 171). Preeclampsia predictors were identified with stepwise regression, and a coefficient B of each variable was used to establish a prediction model and risk scoring system. Goodness-of-fit was assessed by the Hosmer-Lemeshow and Omnibus tests, and the area under the receiver operating characteristic curve (area under the curve) was used to assess discrimination. Validation was performed using the validation set.

RESULTS

The preeclampsia incidence was 14.4% in the pregnant patients with SLE. A mean arterial pressure (MAP) ≥96.5 mm Hg (odds ratio [OR] 213.15 [95% confidence interval (95% CI) 24.39-999.99]), prepregnancy hypertension (OR 18.19 [95% CI 2.67-125.01]), a hematologic disorder (OR 4.13 [95% CI 1.03-16.67]), positive IgM anticardiolipin antibodies (aCLs) (OR 19.85 [95% CI 1.11-333.33]), serum albumin <31.5 grams/liter (OR 9.88 [95% CI 2.07-47.62]), serum uric acid ≥303 μmoles/liter (OR 5.58 [95% CI 1.40-22.22]), and 24-hour urinary protein ≥0.286 grams (OR 14.39 [95% CI 2.43-83.33]) were selected for the preeclampsia prediction model. The area under the curve was 0.975. Preeclampsia prediction model scores >4 indicated a high risk of preeclampsia. For the validation set, the preeclampsia prediction accuracy was 93.6% (sensitivity 88.5%, specificity 94.5%).

CONCLUSION

A model for predicting the risk of preeclampsia in pregnant patients with SLE was established on the basis of MAP, prepregnancy hypertension, hematologic disorders, IgM aCLs, albumin, uric acid, and 24-hour urinary protein. The model had good predictive efficiency and can help clinicians improve pregnancy outcomes in high-risk women with early interventions.

Screening for pre-eclampsia: Performance of National Institute for Health and Care Excellence guidelines versus American College of Obstetricians and Gynecologists recommendations

AIM

The purpose of this study was to compare the accuracy of the National Institute for Health and Care Excellence (NICE) guidelines and the American College of Obstetricians and Gynecologists (ACOG) recommendations for pre-eclampsia (PE) screening.

METHODS

This retrospective study included 4600 Thai pregnant women who received maternity care between January 2006 and December 2015 at Songklanagarind Hospital, a tertiary care center in southern Thailand. The medical data of each participant were assessed using the NICE and ACOG criteria to identify maternal risk for PE. The sensitivity, specificity, positive likelihood ratio and negative likelihood ratio for detecting pregnancies complicated with PE according to each guideline were calculated. Receiver operating characteristic (ROC) curves were constructed to compare the predictive performance.

RESULTS

Pre-eclampsia was found in 167 cases (3.63%). The ACOG recommendations achieved ROC area 0.70 (58.1% sensitivity with 82.4% specificity) for PE at any gestation and ROC area 0.66 (62.9% sensitivity with 69.0% specificity) for PE which required delivery before 37 weeks' gestation. ROC areas based on the NICE guidelines were 0.64 (35.3% sensitivity with 92.5% specificity) and 0.59 (34.8% sensitivity with 83.4% specificity) for all PE and preterm PE, respectively. The ACOG criteria were significantly more accurate than the NICE criteria for detecting maternal risk for all PE, preterm PE and nulliparity cases (P values <0.05).

CONCLUSION

For Thai pregnant women, screening for PE with maternal risk factors according to the ACOG recommendations was more effective in identifying high-risk pregnancies than the NICE guidelines.

Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia

Background

We developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia.

Methods

The BPJS Kesehatan dataset have been preprocessed using a nested case-control design into preeclampsia/eclampsia (n = 3318) and normotensive pregnant women (n = 19,883) from all women with one pregnancy. The dataset provided 95 features consisting of demographic variables and medical histories started from 24 months to event and ended by delivery as the event. Six algorithms were compared by area under the receiver operating characteristics curve (AUROC) with a subgroup analysis by time to the event. We compared our model to similar prediction models from systematically reviewed studies. In addition, we conducted a text mining analysis based on natural language processing techniques to interpret our modeling results.

Findings

The best model consisted of 17 predictors extracted by a random forest algorithm. Nine∼12 months to the event was the period that had the best AUROC in external validation by either geographical (0.88, 95% confidence interval (CI) 0.88–0.89) or temporal split (0.86, 95% CI 0.85–0.86). We compared this model to prediction models in seven studies from 869 records in PUBMED, EMBASE, and SCOPUS. This model outperformed the previous models in terms of the precision, sensitivity, and specificity in all validation sets.

Interpretation

Our low-cost model improved preliminary prediction to decide pregnant women that will be predicted by the models with high specificity and advanced predictors.

Funding

This work was supported by grant no. MOST108-2221-E-038-018 from the Ministry of Science and Technology of Taiwan.

Prediction of pre-eclampsia in nulliparous women using routinely collected maternal characteristics: a model development and validation study

Background

Guidelines recommend identifying in early pregnancy women at elevated risk of pre-eclampsia. The aim of this study was to develop and validate a pre-eclampsia risk prediction model for nulliparous women attending routine antenatal care “the Western Sydney (WS) model”; and to compare its performance with the National Institute of Health and Care Excellence (NICE) risk factor-list approach for classifying women as high-risk.

Methods

This retrospective cohort study included all nulliparous women who gave birth in three public hospitals in the Western-Sydney-Local-Health-District, Australia 2011–2014. Using births from 2011 to 2012, multivariable logistic regression incorporated established maternal risk factors to develop and internally validate the WS model. The WS model was then externally validated using births from 2013 to 2014, assessing its discrimination and calibration. We fitted the final WS model for all births from 2011 to 2014, and compared its accuracy in predicting pre-eclampsia with the NICE approach.

Results

Among 12,395 births to nulliparous women in 2011–2014, there were 293 (2.4%) pre-eclampsia events. The WS model included: maternal age, body mass index, ethnicity, multiple pregnancy, family history of pre-eclampsia, autoimmune disease, chronic hypertension and chronic renal disease. In the validation sample (6201 births), the model c-statistic was 0.70 (95% confidence interval 0.65–0.75). The observed:expected ratio for pre-eclampsia was 0.91, with a Hosmer-Lemeshow goodness-of-fit test p-value of 0.20. In the entire study sample of 12,395 births, 374 (3.0%) women had a WS model-estimated pre-eclampsia risk ≥8%, the pre-specified risk-threshold for considering aspirin prophylaxis. Of these, 54 (14.4%) developed pre-eclampsia (sensitivity 18% (14–23), specificity 97% (97–98)). Using the NICE approach, 1173 (9.5%) women were classified as high-risk, of which 107 (9.1%) developed pre-eclampsia (sensitivity 37% (31–42), specificity 91% (91–92)). The final model showed similar accuracy to the NICE approach when using lower risk-threshold of ≥4% to classify women as high-risk for pre-eclampsia.

Conclusion

The WS risk model that combines readily-available maternal characteristics achieved modest performance for prediction of pre-eclampsia in nulliparous women. The model did not outperform the NICE approach, but has the advantage of providing individualised absolute risk estimates, to assist with counselling, inform decisions for further testing, and consideration of aspirin prophylaxis.

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.