Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

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

2020 ◽  
Vol 63 (1) ◽  
Author(s):  
Rayees Rahman ◽  
Arad Kodesh ◽  
Stephen Z. Levine ◽  
Sven Sandin ◽  
Abraham Reichenberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
General Population ◽  
Electronic Medical Records ◽  
False Positive ◽  
Medical Records ◽  
False Positive Rate ◽  
Autism Spectrum ◽  
Health Maintenance ◽  
C Statistic ◽  
Positive Rate

Abstract Background. Current approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, and 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. The aim of the current study was to test the ability of machine learning (ML) models applied to electronic medical records (EMRs) to predict ASD early in life, in a general population sample. Methods. We used EMR data from a single Israeli Health Maintenance Organization, including EMR information for parents of 1,397 ASD children (ICD-9/10) and 94,741 non-ASD children born between January 1st, 1997 and December 31st, 2008. Routinely available parental sociodemographic information, parental medical histories, and prescribed medications data were used to generate features to train various ML algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross-validation by computing the area under the receiver operating characteristic curve (AUC; C-statistic), sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value [PPV]). Results. All ML models tested had similar performance. The average performance across all models had C-statistic of 0.709, sensitivity of 29.93%, specificity of 98.18%, accuracy of 95.62%, false positive rate of 1.81%, and PPV of 43.35% for predicting ASD in this dataset. Conclusions. We conclude that ML algorithms combined with EMR capture early life ASD risk as well as reveal previously unknown features to be associated with ASD-risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.

Impact of Including or Removing Nuchal Translucency Measurement on the Detection and False-Positive Rates of First-Trimester Down Syndrome Screening

2015 ◽  
Vol 40 (3) ◽  
pp. 214-218 ◽  
Author(s):  
Emmanuel Spaggiari ◽  
Isabelle Czerkiewicz ◽  
Corinne Sault ◽  
Sophie Dreux ◽  
Armelle Galland ◽  
...  
Keyword(s):  
Down Syndrome ◽  
False Positive ◽  
False Positive Rate ◽  
Method Comparison ◽  
First Trimester ◽  
Nuchal Translucency ◽  
Routine Practice ◽  
Detection Rates ◽  
Significant Difference ◽  
Positive Rate

Introduction: First-trimester Down syndrome (DS) screening combining maternal age, serum markers (pregnancy-associated plasma protein-A and beta-human chorionic gonadotropin) and nuchal translucency (NT) gives an 85% detection rate for a 5% false-positive rate. These results largely depend on quality assessment of biochemical markers and of NT. In routine practice, despite an ultrasound quality control organization, NT images can be considered inadequate. The aim of the study was to evaluate the consequences for risk calculation when NT measurement is not taken into account. Material and Method: Comparison of detection and false-positive rates of first-trimester DS screening (PerkinElmer, Turku, Finland), with and without NT, based on a retrospective study of 117,126 patients including 274 trisomy 21-affected fetuses. NT was measured by more than 3,000 certified sonographers. Results: There was no significant difference in detection rates between the two strategies including or excluding NT measurement (86.7 vs. 81.8%). However, there was a significant difference in the false-positive rates (2.23 vs. 9.97%, p < 0.001). Discussion: Sonographers should be aware that removing NT from combined first-trimester screening would result in a 5-fold increase in false-positive rate to maintain the expected detection rates. This should be an incentive for maintaining quality in NT measurement.

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.

scholarly journals Using Logistic Regression to Estimate the False Positive Rate in The IDI (SoLinks)

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Anna Lin ◽  
Soon Song ◽  
Nancy Wang
Keyword(s):  
Logistic Regression ◽  
False Positive ◽  
False Positive Rate ◽  
Training Data ◽  
Model Parameters ◽  
Data Infrastructure ◽  
True Match ◽  
Positive Rate ◽  
Research Populations ◽  
The Individual

IntroductionStats NZ’s Integrated Data Infrastructure (IDI) is a linked longitudinal database combining administrative and survey data. Previously, false positive linkages (FP) in the IDI were assessed by clerical review of a sample of linked records, which was time consuming and subject to inconsistency. Objectives and ApproachA modelled approach, ‘SoLinks’ has been developed in order to automate the FP estimation process for the IDI. It uses a logistic regression model to calculate the probability that a given link is a true match. The model is based on the agreement types defined for four key linking variables – first name, last name, sex, and date of birth. Exemptions have been given to some specific types of links that we believe to be high quality true matches. The training data used to estimate the model parameters was based on the outcomes of the clerical review process over several years. ResultsWe have compared the FP rates estimated through clerical review to the ones estimated through the SoLinks model. Some SoLinks estimates fall outside the 95% confidence intervals of the clerically reviewed ones. This may be the result of the pre-defined probabilities for the specific types of links are too high. ConclusionThe automation of FP checking has saved analyst time and resource. The modelled FP estimates have been more stable across time than the previous clerical reviews. As this model estimates the probability of a true match at the individual link level, we may provide this probability to researchers so that they can calculate linked quality indicators for their research populations.

scholarly journals Does the Porter formula hold its promise? A weight estimation formula for macrosomic fetuses put to the test

2019 ◽  
Vol 301 (1) ◽  
pp. 129-135
Author(s):  
Christoph Weiss ◽  
Sabine Enengl ◽  
Simon Hermann Enzelsberger ◽  
Richard Bernhard Mayer ◽  
Peter Oppelt
Keyword(s):  
Clinical Practice ◽  
False Positive ◽  
False Positive Rate ◽  
Normal Weight ◽  
Fetal Weight ◽  
Weight Estimation ◽  
Data Set ◽  
Detection Rates ◽  
Estimation Formula ◽  
Positive Rate

Abstract Purpose Estimating fetal weight using ultrasound measurements is an essential task in obstetrics departments. Most of the commonly used weight estimation formulas underestimate fetal weight when the actual birthweight exceeds 4000 g. Porter et al. published a specially designed formula in an attempt to improve detection rates for such macrosomic infants. In this study, we question the usefulness of the Porter formula in clinical practice and draw attention to some critical issues concerning the derivation of specialized formulas of this type. Methods A retrospective cohort study was carried out, including 4654 singleton pregnancies with a birthweight ≥ 3500 g, with ultrasound examinations performed within 14 days before delivery. Fetal weight estimations derived using the Porter and Hadlock formulas were compared. Results Of the macrosomic infants, 27.08% were identified by the Hadlock formula, with a false-positive rate of 4.60%. All macrosomic fetuses were detected using the Porter formula, with a false-positive rate of 100%; 99.96% of all weight estimations using the Porter formula fell within a range of 4300 g ± 10%. The Porter formula only provides macrosomic estimates. Conclusions The Porter formula does not succeed in distinguishing macrosomic from normal-weight fetuses. High-risk fetuses with a birthweight ≥ 4500 g in particular are not detected more precisely than with the Hadlock formula. For these reasons, we believe that the Porter formula should not be used in clinical practice. Newly derived weight estimation formulas for macrosomic fetuses must not be based solely on a macrosomic data set.

scholarly journals Starting the polypill: the use of a single age cut-off in males and females

2016 ◽  
Vol 24 (1) ◽  
pp. 50-53 ◽  
Author(s):  
Nicholas J Wald ◽  
Johannes M Luteijn ◽  
Joan K Morris
Keyword(s):  
Myocardial Infarction ◽  
False Positive ◽  
Detection Rate ◽  
False Positive Rate ◽  
Rate Sensitivity ◽  
Detection Rates ◽  
Preventive Medication ◽  
Positive Rate ◽  
Males And Females ◽  
First Myocardial Infarction

Objective Age screening and preventive medication for future myocardial infarction and stroke has been previously described. We aimed to ascertain whether different age cut-offs are needed for males and females. Methods We determined five parameters for each sex according to age cut-off: detection rate (sensitivity), false-positive rate, proportion of the population eligible for treatment with a polypill, proportion who benefit from taking a polypill (simvastatin 20 mg, losartan 25 mg, hydrochlorothiazide 12.5 mg, amlodipine 2.5 mg), and among these, years of life gained without a first myocardial infarction or stroke. Results Approximately one-third benefit, regardless of the age cut-off. For males and females combined, using ages 40 and 80, the detection rates are 98% and 52%, false-positive rates are 51% and 7%, population percentages eligible for treatment are 52% and 7%, and years of life gained without a first myocardial infarction or stroke are 8.4 and 3.6. Using age 50, detection rates are 93% (males) 98% (females), false-positive rates 37% (males) 40% (females), percentage of the population eligible for treatment 38% (males) 41% (females), percentage who benefit 35% (males) 33% (females), and years of life gained without an event 8.5 (males) 7.0 (females). At a given age cut-off, the sex differences are relatively small. Conclusion A single age cut-off can be used for both sexes.

Accuracy of cardiac auscultation in detection of neonatal congenital heart disease by general paediatricians

2019 ◽  
Vol 29 (5) ◽  
pp. 679-683
Author(s):  
Qu-ming Zhao ◽  
Conway Niu ◽  
Fang Liu ◽  
Lin Wu ◽  
Xiao-jing Ma ◽  
...  
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Cardiac Auscultation ◽  
Detection Rates ◽  
Maternity Hospitals ◽  
High Detection ◽  
Positive Rate ◽  
Timely Fashion ◽  
A Prospective Study

AbstractBackground:Challenges remain in the judgement of pathological murmurs in newborns at maternity hospitals, and there are still many simple major CHD patients in developing countries who are not diagnosed in a timely fashion. This study aimed to evaluate the accuracy of cardiac auscultation on neonatal CHD by general paediatricians.Methods:We conducted a prospective study at three hospitals. All asymptomatic newborns underwent auscultation, pulse oximetry monitoring, and echocardiography. Major CHD was classified and confirmed through follow-up. We evaluated the accuracy of various degrees of murmurs for detecting major CHD to determine the most appropriate standards and time of auscultation.Results:A total of 6750 newborns were included. The median age of auscultation was 43 hours. Cardiac murmurs were identified in 6.6% of newborns. For all CHD, 44.4% had varying degrees of murmurs. A murmur of grade ≥2 used as a reference standard for major CHD had a sensitivity of 89.58%. The false positive rate of murmurs of grade ≥2 for detecting major CHD was significantly negatively related to auscultation time, with 84.4% of false positives requiring follow-up for non-major CHD cardiac issues. Auscultation after 27 hours of life could reduce the false positive rate of major CHD from 2.7 to 0.9%.Conclusions:With appropriate training, maternity hospital’s paediatricians can detect major CHD with high detection rates with an acceptable false positive rate.

scholarly journals What are steering pictures are worth? Using image-based steering features to detect drowsiness on rural roads

2019 ◽  
Vol 63 (1) ◽  
pp. 2041-2045
Author(s):  
Kartik Mutya ◽  
Jayesh Shah ◽  
Anthony D. McDonald ◽  
Jaycelyn Jefferson
Keyword(s):  
False Positive ◽  
Low Cost ◽  
False Positive Rate ◽  
Steering Wheel ◽  
Recurrence Plots ◽  
Drowsy Driving ◽  
Detection Rates ◽  
Detection Algorithms ◽  
Angle Data ◽  
Positive Rate

Drowsy driving is a persistent and significant problem on today’s roadways. Mitigation technologies may help resolve the problem, but their success depends on effective detection algorithms. Steering-based algorithms are a promising direction for such effective algorithms because of their low cost and ease of implementation. However, steering-based approaches are often limited by high false positive detection rates. The goal of this study was to assess if image-based steering features and convolutional neural networks can reduce these high false positive rates. The analysis investigated two methods for transforming steering wheel angle data into images, Markov Transition Field and recurrence plots. Area under the ROC curve and false positive rate results suggest that both approaches nominally improve detection performance and reduce false positives relative to a benchmark, with some evidence that recurrence plots have the highest performance.

THE MODEL SCREENING PREECLAMPSIA AT 11+0 TO 13+6 WEEKS OF GESTATION

2015 ◽  
pp. 162-172
Author(s):  
Ngoc Thanh Cao ◽  
Vu Quoc Huy Nguyen ◽  
Van Duc Vo ◽  
Quang Vinh Truong ◽  
Viet Nhan Nguyen ◽  
...  
Keyword(s):  
False Positive ◽  
Gestational Hypertension ◽  
False Positive Rate ◽  
Outcome Data ◽  
Maternal Factors ◽  
Hospital Visit ◽  
Detection Rates ◽  
Maternal Characteristics ◽  
Screening Study ◽  
Positive Rate

Objective: Screening preeclampsia at 11+0-13+6 gestational by combine maternal characteristics, MAP, PAPP-A and UtA-PI. Materials and methods: Prospective screening study for preeclampsia in pregnant attending their first hospital visit at 11-13 weeks 6 of gestation. The performance of screening for PE and GH by combinations of maternal characteristics, uterine artery with the lowest pulsatility index (L-PI), mean arterial pressure (MAP) and serum PAPP-A was determined. Results: Of 2,998 patients with complete outcome data, there were 3.74% of hypertension disorder, and 2.84% cases of pre-eclampsia. The study show a poor results screening for PE by maternal factors only. In screening for PE by combine maternal factors, MAP and L-PI, the estimated detection rates were 18,2% and 45,5% for HG, 45,6% and 57,9% for late PE at a fixed false positive rate of 5% and 10%, respectively.For early PE, in screening by combine maternal characteristics, L-PI, MAP and serum PAPP-A, the the estimated detection rates were 81,8% and 90,9% at at a fixed false positive rate of 5% and 10%, respectively. Conclusion: Effective prediction of early PE can be achieved at 11–13+6 weeks’ gestation by combine maternal characteristics, L-PI, MAP and serum PAPP-A. Key words: preeclampsia; gestational hypertension; screening; PAPP-A, UtA-PI, MAP

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