<i>Tests using single markers</i>

Cuckle, Howard; Wald, Nicholas

doi:10.1093/acprof:oso/9780192628268.003.0001

Cited by 22 publications

(15 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also believe that the predictive equation is preferable to a static glucose cut point of 160 mg/dl because, given comparable high specificity, it is much more sensitive. PPV and OAPR are measures of the performance of a diagnostic test that depend on the prevalence of the disease in the screened population and on the sensitivity and specificity of the test (22,25,26). However, unlike sensitivity and specificity, they are not properties of the screening test itself, but of its application.…”

Section: Diabetes Screening Equationmentioning

confidence: 99%

A Multivariate Logistic Regression Equation to Screen for Diabetes

2002

View full text Add to dashboard Cite

OBJECTIVE -To develop and validate an empirical equation to screen for diabetes.RESEARCH DESIGN AND METHODS -A predictive equation was developed using multiple logistic regression analysis and data collected from 1,032 Egyptian subjects with no history of diabetes. The equation incorporated age, sex, BMI, postprandial time (self-reported number of hours since last food or drink other than water), and random capillary plasma glucose as independent covariates for prediction of undiagnosed diabetes. These covariates were based on a fasting plasma glucose level Ն126 mg/dl and/or a plasma glucose level 2 h after a 75-g oral glucose load Ն200 mg/dl. The equation was validated using data collected from an independent sample of 1,065 American subjects. Its performance was also compared with that of recommended and proposed static plasma glucose cut points for diabetes screening.RESULTS -The predictive equation was calculated with the following logistic regression parameters: P ϭ 1/(1 Ϫ e Ϫx ), where x ϭ Ϫ10.0382 ϩ [0.0331 (age in years) ϩ 0.0308 (random plasma glucose in mg/dl) ϩ 0.2500 (postprandial time assessed as 0 to Ն8 h) ϩ 0.5620 (if female) ϩ 0.0346 (BMI)]. The cut point for the prediction of previously undiagnosed diabetes was defined as a probability value Ն0.20. The equation's sensitivity was 65%, specificity 96%, and positive predictive value (PPV) 67%. When applied to a new sample, the equation's sensitivity was 62%, specificity 96%, and PPV 63%.CONCLUSIONS -This multivariate logistic equation improves on currently recommended methods of screening for undiagnosed diabetes and can be easily implemented in a inexpensive handheld programmable calculator to predict previously undiagnosed diabetes. Diabetes Care 25:1999 -2003, 2002S creening for undiagnosed diabetes is controversial. In 1978, the American Diabetes Association (ADA), the Centers for Disease Control and Prevention, and the National Institutes of Health recommended against screening for diabetes in nonpregnant adults (1). In 1989 and again in 1996, the U.S. Preventive Services Task Force recommended against screening for diabetes in nonpregnant adults (1,2), and in 2001, the ADA recommended against community screening for diabetes (3). Several recent studies have shown that age, sex, BMI, and current metabolic status affect blood glucose levels and have raised concerns about the performance of diabetes screening tests (4 -8).The performance of all screening tests is dependent on the threshold or cut point used to define a positive test. In diabetes screening, choosing a higher glucose cut point reduces sensitivity (probability of a positive screening test given disease) but improves specificity (probability of a negative screening test given absence of disease). Choosing a lower glucose cut point improves sensitivity but reduces specificity. Because the optimal cut point for a positive test may depend on age, sex, BMI, and the time since last food or drink, we propose an alternative approach to interpreting capillary glucose screening tests by deve...

show abstract

Section: Diabetes Screening Equationmentioning

confidence: 99%

A Multivariate Logistic Regression Equation to Screen for Diabetes

2002

View full text Add to dashboard Cite

show abstract

“…The LR associated with the PLAC1 MoM distribution was calculated as reported by Cuckle and Wald [19] . Successively, the a priori + MAP risk was combined with the LR obtained by the PLAC1 distribution in order to calculate a patient-specific risk for PE.…”

Section: Methodsmentioning

confidence: 99%

Circulating mRNA for the PLAC1 Gene as a Second Trimester Marker (14-18 Weeks' Gestation) in the Screening for Late Preeclampsia

et al. 2014

View full text Add to dashboard Cite

Objective: To develop a model for prediction of late preeclampsia (PE; which develops at or after 34 weeks' gestation) based on maternal history and characteristics, mean arterial pressure (MAP), and circulating levels of mRNA for the placenta-specific 1 (PLAC1) gene in maternal plasma at 14-18 weeks' gestation. Method: This was a screening study of singleton pregnancies at 14-18 weeks' gestation including 43 women that subsequently developed PE and 200 that were unaffected by PE. A Gaussian model was fitted to the log distribution of the multiple of the median (log MoM) PLAC1 mRNA in the PE group and in the unaffected group. Likelihood ratios for log MoM of circulating levels of mRNA for the PLAC1 gene were used to combine the a priori risk from maternal characteristics with MAP to produce patient-specific risks for each case. Results: Screening by maternal characteristics (including BMI, woman's mother's history of PE, previous PE, and parity) (a priori risk) and MAP detected 46.8% of all cases of late PE at a fixed false-positive rate (FPR) of 10%. The addition of PLAC1 yielded a detection rate (DR) of 62.8% at the same level of FPR. PLAC1 alone yielded a DR of 30.2%. Conclusion: In late PE,molecular markers can be used to improve the DR of screening and can be a valid option for the biochemical approach.

show abstract

“…24,26 Uterine artery Doppler screening meets all the requirements for a worthwhile screening program, as outlined by Cuckle and Wald. 30 Furthermore, the false positive rate of 5%, which is commonly used in obstetric screening studies as a result of the Down syndrome screening program, may not be the best cutoff for screening for pre-eclampsia since the aim of screening is to help stratify the intensity of subsequent antenatal surveillance, and such a classification does not carry a direct risk to the mother or the fetus. Therefore, in screening for preeclampsia, it would be reasonable to maximize the detection rate at a cost of a higher screen-positive rate.…”

Section: Uterine Artery Velocimetry As a Screening Testmentioning

confidence: 99%

Screening for Pre-eclampsia

Theodora¹

2008

DSJUOG

View full text Add to dashboard Cite

Pre-eclampsia complicates approximately 4 to 5% of pregnancies and it is a main cause of maternal mortality and neonatal morbidity. It is defined as the raise of arterial blood pressure (systolic BP > 140 mmHg and/or diastolic BP > 90 mm Hg), combined with proteinuria (> 300 mg/24 hr). The cause of this clinical state has not been completely clear. Different trials have shown that a generalized endothelial dysfunction takes place, due to genetic and immunological factors. The result of this dysfunction is the disturbance of the special kind of angiogenesis which takes place during implantation and placental growth.Clinical trials have shown that pregnant women who demonstrate high resistance in uteroplacental flow diagnosed with Doppler ultrasonography in uterine arteries are in higher risk for pre-eclampsia.

show abstract

Tests using single markers

Cited by 22 publications

References 2 publications

A Multivariate Logistic Regression Equation to Screen for Diabetes

A Multivariate Logistic Regression Equation to Screen for Diabetes

Circulating mRNA for the PLAC1 Gene as a Second Trimester Marker (14-18 Weeks' Gestation) in the Screening for Late Preeclampsia

Screening for Pre-eclampsia

Contact Info

Product

Resources

About