scholarly journals Outcome of prenatal diagnosis of clubfoot: a single institution experience

2021 ◽  
Author(s):  
Wael Abdallah ◽  
Malek Nassar
Keyword(s):  
Risk Factors ◽  
Prenatal Diagnosis ◽  
Significant Relationship ◽  
Antenatal Diagnosis ◽  
False Positive Rate ◽  
Multiple Pregnancies ◽  
Single Institution ◽  
Maternal Characteristics ◽  
Live Births ◽  
Positive Rate

Aim: To assess the accuracy of antenatal diagnosis of clubfoot (CF), risk factors and outcomes in postnatal. Patients & methods: Maternal characteristics, sonographic signs and postnatal outcomes were evaluated in 60 patients with a prenatal diagnosis of CF between 2007 and 2020. Results: The rate of antenatal diagnosis of CF was 3.72/1000 live births. The false-positive rate was 6.67%. 66.7% of fetuses had bilateral CF and 33.3% had unilateral CF; 58.3% were isolated and 41.7% were complex; 58.3% were males and 41.7% were female; 16.7% were multiple pregnancies and 10% were cases of consanguinity. Conclusion: The accuracy of the diagnosis of CF depends on the operator’s skills. A significant relationship is demonstrated between the interruption of pregnancy, consanguinity, laterality and complexity.

False-positive rate in prenatal diagnosis of surgical anomalies

2006 ◽  
Vol 41 (4) ◽  
pp. 826-829 ◽  
Author(s):  
Alessandro Borsellino ◽  
Antonio Zaccara ◽  
Antonella Nahom ◽  
Alessandro Trucchi ◽  
Lucia Aite ◽  
...  
Keyword(s):  
Prenatal Diagnosis ◽  
False Positive ◽  
False Positive Rate ◽  
Positive Rate

Is D2 surgery necessary for clinical T1 gastric cancer with nodal swelling?

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 288-288
Author(s):  
Takeyuki Wada ◽  
Takaki Yoshikawa ◽  
Ayako Kamiya ◽  
Keichi Date ◽  
Tsutomu Hayashi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
False Positive ◽  
False Positive Rate ◽  
False Positivity ◽  
Positive Rate ◽  
D2 Surgery ◽  
Sensitivity Specificity

288 Background: D2 surgery is required for clinical T1 gastric cancer with nodal swelling, however, D2 has a higher risk for morbidity than D1/D1+. Moreover, previous study demonstrated that the false positive rate for nodal diagnosis in clinical T1 was very high. To select optimal surgery with high probability, we explored risk factors for false positivity in clinical T1 disease. Methods: Patients who underwent radical gastrectomy for clinical T1 gastric cancer between April 2015 and June 2019 were enrolled. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for nodal diagnosis were retrospectively investigated. The risk factors for false positivity were also analyzed by the following factors; age, sex, histological type, tumor size, tumor depth, location, tumor type, presence of ulcer, and timing of CT that is (1) the patients who underwent primary endoscopic mucosal dissection (ESD) but resulted in non-curative resection, then received CT to proceed to surgery (delayed CT group) or (2) the other patients who had received CT before primary surgery or before non-curative ESD (primary CT group). Results: A total of 679 patients were examined in the present study. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 83.5% (567/679), 14.3% (13/91), 94.2% (554/588), 27.7% (13/47), and 87.7% (554/632), respectively. The false positive rate was 72.3% (34/47). In univariate analysis, differentiated tumor ( p= 0.012) and delayed CT (p < 0.001) were associated with the false positivity. Multivariate analysis revealed that delayed CT (OR, 4.534; p < 0.001) was a sole significant risk factor for false positivity. False positive rate was 100% (13/13) in the delayed CT group and 61.8% (21/34) in the primary CT group ( p= 0.009). Conclusions: False positive rate was high in clinical T1 disease, especially when the patients received delayed CT after non-curative ESD. D2 surgery would be unnecessary even though nodal swelling was detected in CT after non-curative ESD.

The efficacy of combining several risk factors as a screening test

2005 ◽  
Vol 12 (4) ◽  
pp. 197-201 ◽  
Author(s):  
Nicholas J Wald ◽  
Joan K Morris ◽  
Simon Rish
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
False Positive ◽  
Detection Rate ◽  
False Positive Rate ◽  
Individual Risk ◽  
Detection Rates ◽  
Screening Performance ◽  
Positive Rate ◽  
The Individual

Objective: To determine the quantitative effect on overall screening performance (detection rate for a given false-positive rate) of using several moderately strong, independent risk factors in combination as screening markers. Setting: Theoretical statistical analysis. Methods: For the purposes of this analysis, it was assumed that all risk factors were independent, had Gaussian distributions with the same standard deviation in affected and unaffected individuals and had the same screening performance. We determined the overall screening performance associated with using an increasing number of risk factors together, with each risk factor having a detection rate of 10%, 15% or 20% for a 5% false-positive rate. The overall screening performance was estimated as the detection rate for a 5% false-positive rate. Results: Combining the risk factors increased the screening performance, but the gain in detection at a constant false-positive rate was relatively modest and diminished with the addition of each risk factor. Combining three risk factors, each with a 15% detection rate for a 5% false-positive rate, yields a 28% detection rate. Combining five risk factors increases the detection rate to 39%. If the individual risk factors have a detection rate of 10% for a 5% false-positive rate, it would require combining about 15 such risk factors to achieve a comparable overall detection rate (41%). Conclusion: It is intuitively thought that combining moderately strong risk factors can substantially improve screening performance. For example, most cardiovascular risk factors that may be used in screening for ischaemic heart disease events, such as serum cholesterol and blood pressure, have a relatively modest screening performance (about 15% detection rate for a 5% false-positive rate). It would require the combination of about 15 or 20 such risk factors to achieve detection rates of about 80% for a 5% false-positive rate. This is impractical, given the risk factors so far discovered, because there are too few risk factors and their associations with disease are too weak.

Second Trimester Screening of Preeclampsia Using Maternal Characteristics and Uterine and Ophthalmic Artery Doppler

2016 ◽  
Vol 39 (02) ◽  
pp. 190-197 ◽  
Author(s):  
Paulo Praciano de Souza ◽  
Júlio Gurgel Alves ◽  
Sammya Bezerra Maia e Holanda Moura ◽  
Edward Araujo Júnior ◽  
Wellington Martins ◽  
...  
Keyword(s):  
Pregnant Women ◽  
False Positive ◽  
Ophthalmic Artery ◽  
False Positive Rate ◽  
Categorical Variables ◽  
Second Trimester ◽  
Maternal Characteristics ◽  
Maternal Risk ◽  
Second Trimester Screening ◽  
Positive Rate

Abstract Purpose To establish the performance of a multi-parametric test including maternal risk factors and maternal uterine and ophthalmic artery Doppler in the second trimester of pregnancy for the prediction of preeclampsia (PE). Materials and Methods We performed a prospective observational cohort study with pregnant women who underwent a second trimester morphology scan. Maternal uterine and ophthalmic artery Doppler examinations were performed in 415 singleton pregnancies between 18 and 23 weeks of gestation. Additional history was obtained through participant questionnaires, and follow-up occurred to the time of discharge post-delivery. The control and PE groups were compared to continuous variables using the Kruskal-Wallis test and to categorical variables using the Chi-square and Fisher exact tests. Univariate and multivariate logistic regression analyses were performed to determine the best model for the prediction of PE. Results 40 (9.6 %) pregnant women developed PE. We observed significant differences in the body mass index (BMI) (p < 0.001), parity (p < 0.001), mean arterial pressure (MAP) (p < 0.001), and pulsatility index (PI) of uterine artery Doppler (p < 0.001) between PE and control groups. The best model for the prediction of PE included maternal characteristics, MAP, maternal uterine and ophthalmic artery Doppler with area under receiver operating characteristics (ROC) curve of 0.710 (95 % confidence interval, 613 – 0.807) with a sensitivity of 45 % to a false-positive rate of 10 % and 35 % to a false-positive rate of 5 %. Conclusion Maternal ophthalmic artery Doppler did not promote a significant increase in the PE detection rate during the second trimester scan.

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 Investigating Critical Risk Factors of Liver Cancer with Deep Neural Networks

2021 ◽  
Author(s):  
Jinpeng Li ◽  
Yaling Tao ◽  
Zhunan Li ◽  
Ting Cai
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Liver Cancer ◽  
False Positive Rate ◽  
Clinical Information ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Cancer Prediction ◽  
Epidemiological Risk ◽  
Positive Rate

The crude incidence of liver cancer ranks top five among all cancers in China, and the death rate ranks the top two. Identifying critical risk factors of liver cancer helps people adjust their lifestyles to reduce cancer risk. Launched in 2012, Early Diagnosis and Treatment of Urban Cancer project has been carried out in major cities of China, which collected a broad range of epidemiological risk factors including definite, probable and possible causes of cancer. We retrieved data from 2014 to the present and obtained 184 liver cancer cases among 55 thousand people. We explored 84 risk factors and implemented liver cancer prediction model with machine learning algorithms, where deep neural network achieved the best performance using non-clinical information (mean AUC=0.73). We analyzed model parameters to investigate critical risk factors that contribute the most to prediction. Using 50% top-ranking risk factors to train a model, the performance showed no significant difference from that using all risk factors. Using top 10% risk factors induced a sensitivity drop and a lower false positive rate. These phenomena prove that the identified risk factors are critical in liver cancer prediction. This work is a reference in public health research, and provides a scientific lifestyle guideline for individuals to prevent liver cancer based on machine learning technology.

scholarly journals Performance of Fetal Medicine Foundation Software for Pre-Eclampsia Prediction Upon Marker Customization: Cross-Sectional Study (Preprint)

2019 ◽  
Author(s):  
Karina Bilda De Castro Rezende ◽  
Antonio José Ledo Alves Cunha ◽  
Joffre Amim Jr ◽  
Wescule De Moraes Oliveira ◽  
Maria Eduarda Belloti Leão ◽  
...  
Keyword(s):  
High Risk ◽  
False Positive ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Screening Tests ◽  
Risk Scores ◽  
Cross Sectional ◽  
Fetal Medicine ◽  
Maternal Characteristics ◽  
Positive Rate

BACKGROUND FMF2012 is an algorithm developed by the Fetal Medicine Foundation (FMF) to predict pre-eclampsia on the basis of maternal characteristics combined with biophysical and biochemical markers. Afro-Caribbean ethnicity is the second risk factor, in magnitude, found in populations tested by FMF, which was not confirmed in a Brazilian setting. OBJECTIVE This study aimed to analyze the performance of pre-eclampsia prediction software by customization of maternal ethnicity. METHODS This was a cross-sectional observational study, with secondary evaluation of data from FMF first trimester screening tests of singleton pregnancies. Risk scores were calculated from maternal characteristics and biophysical markers, and they were presented as the risk for early pre-eclampsia (PE34) and preterm pre-eclampsia (PE37). The following steps were followed: (1) identification of women characterized as black ethnicity; (2) calculation of early and preterm pre-eclampsia risk, reclassifying them as white, which generated a new score; (3) comparison of the proportions of women categorized as high risk between the original and new scores; (4) construction of the receiver operator characteristic curve; (5) calculation of the area under the curve, sensitivity, and false positive rate; and (6) comparison of the area under the curve, sensitivity, and false positive rate of the original with the new risk by chi-square test. RESULTS A total of 1531 cases were included in the final sample, with 219 out of 1531 cases (14.30; 95% CI 12.5-16.0) and 182 out of 1531 cases (11.88%; 95% CI 10.3-13.5) classified as high risk for pre-eclampsia development, originally and after recalculating the new risk, respectively. The comparison of FMF2012 predictive model performance between the originally estimated risks and the estimated new risks showed that the difference was not significant for sensitivity and area under the curve, but it was significant for false positive rate. CONCLUSIONS We conclude that black ethnicity classification of Brazilian pregnant women by the FMF2012 algorithm increases the false positive rate. Suppressing ethnicity effect did not improve the test sensitivity. By modifying demographic characteristics, it is possible to improve some performance aspects of clinical prediction tests.

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.

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

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