Simple approach based on maternal characteristics and mean arterial pressure for the prediction of preeclampsia in the first trimester of pregnancy

2017 ◽  
Vol 45 (7) ◽  
Author(s):  
Rebeca Silveira Rocha ◽  
Júlio Augusto Gurgel Alves ◽  
Sammya Bezerra Maia e Holanda Moura ◽  
Edward Araujo Júnior ◽  
Alberto Borges Peixoto ◽  
...  
Keyword(s):  
Predictive Value ◽  
Skin Color ◽  
Characteristic Curve ◽  
False Positive Rate ◽  
First Trimester ◽  
Maternal Characteristics ◽  
Arterial Blood ◽  
Positive Rate ◽  
History Of ◽  
First Trimester Of Pregnancy

AbstractAim:To propose a simple model for predicting preeclampsia (PE) in the 1Methods:A prospective cohort was performed to predict PE between 11 and 13+6 weeks of gestation. The MC evaluated were maternal age, skin color, parity, previous PE, smoking, family history of PE, hypertension, diabetes mellitus and body mass index (BMI). Mean arterial blood pressure (MAP) was measured at the time of the 1Results:We analyzed 733 pregnant women; 55 developed PE, 21 of those developed preterm PE and 34 term PE. For total PE, the best model was MC+MAP, which had an area under the receiver operating characteristic curve (AUC ROC) of 0.79 [95% confidence interval (CI)=0.76–0.82]. For preterm PE, the best model was MC+MAP, with an AUC ROC of 0.84 (95% CI=0.81–0.87). For term PE, the best model was MC, with an AUC ROC of 0.75 (0.72–0.79). The MC+MAP model demonstrated a detection rate of 67% cases of preterm PE, with a false-positive rate of 10%, positive predictive value of 17% and negative predictive value of 99%.Conclusion:The MC+MAP model showed good accuracy in predicting preterm PE in the 1

Taiwanese new direction in prediction of early pregnancy preeclampsia: a prospective and non-interventional study

2020 ◽  
Author(s):  
Yen-Tin Chen ◽  
Tzu-Yi Lin ◽  
Po-Jen Cheng ◽  
Kok-Seong Chan ◽  
Hui-Yu Huang ◽  
...  
Keyword(s):  
False Positive Rate ◽  
Model Performance ◽  
First Trimester ◽  
Patient Specific ◽  
Interventional Study ◽  
High Detection Rate ◽  
Maternal Characteristics ◽  
Positive Rate ◽  
Trimester Screening ◽  
Set Up

Abstract Background First trimester screening is essential to preeclampsia (PE) prevention. Fetal Medicine Foundation (FMF) model combined maternal characteristics with mean arterial pressure (MAP), uterine artery pulsatility index (UtAPI) and placental growth factor (PlGF) to estimate risk. High detection rate (DR) was observed in Asia. The study aims to evaluate performance of screening in Taiwan.Methods This was a prospective and non-interventional study between January, 2017 and June, 2018. Data was collected from 700 pregnant women at 11+ 0-13+ 6 gestational week. Maternal characteristics were recorded. MAP, UtAPI and PlGF were measured and converted into Multiple of the Median (MoM). Patient-specific risks were calculated with FMF model. Performance of screening was examined by ROC curve and DR.Results 25 women (3.57%) contracted PE, including 8 with preterm PE (1.14%). In preterm PE, mean MoM of MAP and UtAPI were higher (1.096 vs 1.000; 1.084 vs 1.035). Mean MoM of PlGF was lower (0.927 vs 1.031). DR in preterm PE achieved 12.5%, 50.0%, 50.0% and 62.5% at false-positive rate (FPR) of 5%, 10%, 15% and 20%.Conclusion FMF model showed high DR for PE in Taiwan. Integration of PE and Down screening could set up a one-step workflow.

scholarly journals Early Prediction of Preeclampsia

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Leona C. Poon ◽  
Kypros H. Nicolaides
Keyword(s):  
Early Onset ◽  
False Positive Rate ◽  
Protein A ◽  
First Trimester ◽  
Maternal Serum ◽  
Uterine Artery Doppler ◽  
Maternal Risk ◽  
Positive Rate ◽  
First Trimester Of Pregnancy ◽  
Early Onset Preeclampsia

Effective screening for the development of early onset preeclampsia (PE) can be provided in the first-trimester of pregnancy. Screening by a combination of maternal risk factors, uterine artery Doppler, mean arterial pressure, maternal serum pregnancy-associated plasma protein-A, and placental growth factor can identify about 95% of cases of early onset PE for a false-positive rate of 10%.

scholarly journals First trimester screening for pre-eclampsia

2016 ◽  
Vol 9 (3) ◽  
pp. 106-112 ◽  
Author(s):  
Stefan C Kane
Keyword(s):  
Clinical Utility ◽  
Disease Burden ◽  
False Positive Rate ◽  
First Trimester ◽  
Detection Rates ◽  
Positive Rate ◽  
Trimester Screening ◽  
First Trimester Of Pregnancy ◽  
Different Populations ◽  
Further Development

The commercial availability of tests in the first trimester of pregnancy that predict the later development of pre-eclampsia has prompted considerable debate regarding their clinical utility and the degree to which they fulfil the longstanding principles of screening. Such tests have been shown to achieve detection rates for early pre-eclampsia (requiring delivery prior to 34 weeks) of over 90%, for a false positive rate of 10%. However, their capacity to predict later onset pre-eclampsia, which accounts for the bulk of the disease burden, is much more limited. The relatively few studies validating the performance of these tests in different populations have demonstrated significant variations in performance. Moreover, prospective research confirming that the administration of aspirin to those screened to be high risk reduces the incidence of pre-eclampsia is yet to be completed, and there may be harms in restricting aspirin therapy to this group, given its broader beneficial effect. In light of these limitations, further development of these tests is recommended prior to their introduction to clinical practice.

scholarly journals First-Trimester Uterine Artery Doppler for the Prediction of Preeclampsia in Nulliparous Women: The Great Obstetrical Syndrome Study

2018 ◽  
Vol 36 (09) ◽  
pp. 930-935
Author(s):  
Suzanne Demers ◽  
Amélie Boutin ◽  
Cédric Gasse ◽  
Olivier Drouin ◽  
Mario Girard ◽  
...  
Keyword(s):  
Gestational Age ◽  
Uterine Artery ◽  
Prediction Models ◽  
False Positive Rate ◽  
First Trimester ◽  
Maternal Characteristics ◽  
Receiver Operating Characteristic Curves ◽  
Nulliparous Women ◽  
Positive Rate

Objective This study aimed to estimate the performance of first-trimester uterine artery (UtA) pulsatility index (PI) for the prediction of preeclampsia (PE). Study Design We conducted a prospective cohort study of nulliparous women with singleton gestation at 11 to 13 6/7 weeks. UtA-Doppler's was performed on both UtAs and the mean UtA-PI was reported in multiple of median (MoM) adjusted for gestational age. Using receiver operating characteristic curves and their area under the curves (AUC); we calculated the performance of UtA-PI for the prediction of PE. Proportional hazard models were used to develop prediction models combining UtA-PI and maternal characteristics. Results Out of 4,676 participants with completed follow-up, 232 (4.9%) developed PE, including 202 (4.3%) term and 30 (0.6%) preterm PE. Mean UtA-PI decreased with gestational age between 11 and 13 6/7 weeks (p < 0.001). First-trimester UtA-PI was associated with preterm (AUC: 0.69; 95% CI [confidence interval]: 0.57–0.80) but not with term (AUC: 0.52; 95% CI: 0.48–0.56) PE. UtA-PI combined with maternal characteristics could predict 45% of preterm PE at a false positive rate of 10%. Conclusion First-trimester UtA-PI decreases with gestational age between 11 and 13 6/7 weeks and is associated with the risk of preterm but not term PE.

scholarly journals Early changes of placenta-derived messenger RNA in maternal plasma – potential value for preeclampsia prediction?

2015 ◽  
Vol 23 (4) ◽  
pp. 431-438
Author(s):  
Sebastian Surugiu ◽  
Adina Chis ◽  
Codruta Mare ◽  
Horea Matei ◽  
Florin Stamatian
Keyword(s):  
Messenger Rna ◽  
False Positive Rate ◽  
Protein A ◽  
First Trimester ◽  
Maternal Plasma ◽  
Plasma Potential ◽  
Multivariate Model ◽  
Clinical Onset ◽  
Positive Rate ◽  
First Trimester Of Pregnancy

Abstract Objective: the pourpose of the study was to determine if there are any differences between placenta derived plasmatic levels of messenger RNA in normal and future preeclamptic pregnancies and if these placental transcripts can predict preeclampsia long before clinical onset Study design: we compared plasmatic expression of two placental transcripts from 12 women who ultimately developed preeclampsia with 224 controlled subjects, at the end of the first trimester of pregnancy. After multiplse-of-the-median conversion of markers we developed a multivariate model using logistic regression to determine preeclampsia risk. Results: we found lower multiples of the median values for both placental transcripts (mRNA corresponding to placental growth factor and pregnancy associated plasmatic protein A) in cases who ultimately developed preeclampsia and the multivariate model we obtained offered a preeclampsia detection rate of 75% at 10% false positive rate. Conclusion: specific early changes of placenta-derived messenger RNA could be used as preeclampsia predictors.

First trimester prediction of maternal glycemic status

2015 ◽  
Vol 43 (3) ◽  
Author(s):  
Rinat Gabbay-Benziv ◽  
Lauren E. Doyle ◽  
Miriam Blitzer ◽  
Ahmet A. Baschat
Keyword(s):  
Risk Score ◽  
Predictive Value ◽  
Challenge Test ◽  
Area Under The Curve ◽  
First Trimester ◽  
Tolerance Test ◽  
Oral Glucose ◽  
Operating Characteristics ◽  
Maternal Characteristics ◽  
Predictive Algorithm

AbstractTo predict gestational diabetes mellitus (GDM) or normoglycemic status using first trimester maternal characteristics.We used data from a prospective cohort study. First trimester maternal characteristics were compared between women with and without GDM. Association of these variables with sugar values at glucose challenge test (GCT) and subsequent GDM was tested to identify key parameters. A predictive algorithm for GDM was developed and receiver operating characteristics (ROC) statistics was used to derive the optimal risk score. We defined normoglycemic state, when GCT and all four sugar values at oral glucose tolerance test, whenever obtained, were normal. Using same statistical approach, we developed an algorithm to predict the normoglycemic state.Maternal age, race, prior GDM, first trimester BMI, and systolic blood pressure (SBP) were all significantly associated with GDM. Age, BMI, and SBP were also associated with GCT values. The logistic regression analysis constructed equation and the calculated risk score yielded sensitivity, specificity, positive predictive value, and negative predictive value of 85%, 62%, 13.8%, and 98.3% for a cut-off value of 0.042, respectively (ROC-AUC – area under the curve 0.819, CI – confidence interval 0.769–0.868). The model constructed for normoglycemia prediction demonstrated lower performance (ROC-AUC 0.707, CI 0.668–0.746).GDM prediction can be achieved during the first trimester encounter by integration of maternal characteristics and basic measurements while normoglycemic status prediction is less effective.

scholarly journals Identification of newborns at risk for autism using electronic medical records and machine learning

2019 ◽  
Author(s):  
Rayees Rahman ◽  
Arad Kodesh ◽  
Stephen Z Levine ◽  
Sven Sandin ◽  
Abraham Reichenberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Positive Predictive Value ◽  
Electronic Medical Records ◽  
Predictive Value ◽  
False Positive ◽  
Medical Records ◽  
False Positive Rate ◽  
Autism Spectrum ◽  
Positive Rate

AbstractImportanceCurrent approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, where most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome.ObjectiveDevelop a machine learning (ML) method predicting the diagnosis of ASD in offspring in a general population sample, using parental electronic medical records (EMR) available before childbirthDesignPrognostic study of EMR data within a single Israeli health maintenance organization, for the parents of 1,397 ASD children (ICD-9/10), and 94,741 non-ASD children born between January 1st, 1997 through December 31st, 2008. The complete EMR record of the parents was used to develop various ML models to predict the risk of having a child with ASD.Main outcomes and measuresRoutinely available parental sociodemographic information, medical histories and prescribed medications data until offspring’s birth were used to generate features to train various machine learning algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross validation, by computing C statistics, sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value, PPV).ResultsAll ML models tested had similar performance, achieving an average C statistics of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85% for predicting ASD in this dataset.Conclusion and relevanceML algorithms combined with EMR capture early life ASD risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.Key pointsQuestionCan autism risk in children be predicted using the pre-birth electronic medical record (EMR) of the parents?FindingsIn this population-based study that included 1,397 children with autism spectrum disorder (ASD) and 94,741 non-ASD children, we developed a machine learning classifier for predicting the likelihood of childhood diagnosis of ASD with an average C statistic of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85%.MeaningThe results presented serve as a proof-of-principle of the potential utility of EMR for the identification of a large proportion of future children at a high-risk of ASD.

scholarly journals Role of Maternal Serum Glycodelin as Predictor of Ectopic Pregnancy in First Trimester

2018 ◽  
Vol 7 (4) ◽  
pp. 467-470
Author(s):  
Wasan Wajdi Ibrahim ◽  
Afraa Mahjoob Al-Naddawi ◽  
Hayder A. Fawzi
Keyword(s):  
Ectopic Pregnancy ◽  
Predictive Value ◽  
First Trimester ◽  
Maternal Serum ◽  
Intrauterine Pregnancy ◽  
Receiver Operator Curve Analysis ◽  
Medical City ◽  
First Trimester Of Pregnancy ◽  
Control Study

Objectives: Assessment of glycodelin (GD) as a marker for unruptured ectopic pregnancy (EP) in the first trimester of pregnancy. Materials and Methods: This case-control study was conducted during June 2016 to May 2017 in the Obstetrics and Gynecological Department of Baghdad University at Baghdad teaching hospital/medical city complex. In this study, 100 pregnant women in their first trimester of pregnancy were included after clinical and ultrasonic findings. Results: Based on the results, GD levels in EP were significantly lower than those with normal intrauterine pregnancy (1.58 ± 1.18 vs. 30.1 ± 11.9). In addition, using receiver operator curve analysis, the cut-off GD level of 9.5 and less had acceptable validity results (100% sensitivity, 100% specificity, 95% positive predictive value, 100% negative predictive value, and accuracy 100%) to predict EP. Conclusions: In general, serum GD is considered as an excellent predictor of unruptured EP.

Venous Flow Coupler in Head and Neck Free Flap Reconstruction

2020 ◽  
pp. 019459982095309
Author(s):  
Scott H. Troob ◽  
Quinn Self ◽  
Deniz Gerecci ◽  
Macgregor Hodgson ◽  
Javier González-Castro ◽  
...  
Keyword(s):  
Free Flap ◽  
Predictive Value ◽  
False Positive ◽  
False Positive Rate ◽  
Case Series ◽  
Arterial Flow ◽  
Retrospective Case ◽  
Venous Flow ◽  
Retrospective Case Series ◽  
Positive Rate

Objective To describe the utility of venous flow couplers in monitoring free tissue flaps in the immediate postoperative setting. Study Design Retrospective case series. Setting Otolaryngology department at a single tertiary care institution. Methods A retrospective case series of free flap reconstructions in which venous flow couplers were employed to supplement flap monitoring. All free flap cases performed over the past 4 years were reviewed. Inclusion criteria were venous flow coupler and arterial flow Doppler monitored for 5 days postoperatively. Results From July 2014 through May 2018, the venous flow coupler was used with the arterial flow Doppler and clinical monitoring in 228 cases. Eleven cases did not meet criteria for inclusion; thus, 217 cases were analyzed. Twenty cases (9.2%) returned to the operating room with concern for flap compromise, and 16 were salvaged. The combination of venous flow coupler and arterial flow Doppler identified 19 of these flaps. Venous flow couplers identified 5 compromised flaps before there was an arterial signal change, and all were salvaged. Additionally, there was a 24.1% false-positive rate when 2 venous flow couplers were used in parallel. For the venous flow coupler, the positive predictive value was 64.3% and the negative predictive value, 98.9%. The false-positive rate in the series was 5.1%. The sensitivity was 90% and the specificity, 94.9%. Conclusion The venous flow coupler is able to detect venous thrombosis in the absence of arterial thrombosis and may contribute to improved flap salvage rates.

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