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Objective: To evaluate the performance of first trimester biochemical markers, pregnancy-associated plasma protein-A (PAPP-A), free beta human chorionic gonadotropin (fβ-hCG), and nuchal translucency (NT) in detection of severe congenital heart defects (CHDs). Methods: During the study period from 1 January 2008 to 31 December 2011, biochemical markers and NT were measured in 31,144 women as part of voluntary first trimester screening program for Down's syndrome in Northern Finland. Data for 71 severe CHD cases and 762 controls were obtained from the hospital records and from the National Medical Birth Register, which records the birth of all liveborn and stillborn infants, and from the National Register of Congenital Malformations that receives information about all the CHD cases diagnosed in Finland. Results: Both PAPP-A and fβ-hCG multiple of median (MoM) values were decreased in all severe CHDs: 0.71 and 0.69 in ventricular septal defects (VSDs), 0.58 and 0.88 in tetralogy of Fallot cases (TOFs), 0.82 and 0.89 in hypoplastic left heart syndromes (HLHSs), and 0.88 and 0.96 in multiple defects, respectively. NT was increased in all study groups except of VSD group. ROC AUC was 0.72 for VSD when combining prior risk with PAPP-A and fβ-hCG. Adding NT did not improve the detection rate. With normal NT but decreased (

To develop a predictive risk model for early-onset pre-eclampsia (EO-PE) using maternal characteristics, combined screening markers, previously reported biomarkers for PE and mean arterial pressure (MAP).This retrospective study was conducted at Oulu University hospital between 2006 and 2010. Maternal serum from first trimester combined screening was further analyzed for alpha fetoprotein (AFP), placental growth factor (PlGF), soluble tumor necrosis factor receptor-1 (sTNFR1), retinol binding protein-4 (RBP4), a disintegrin and metalloprotease-12 (ADAM12), soluble P-selectin (sP-selectin), follistatin like-3 (FSTL3), adiponectin, angiopoietin-2 (Ang-2) and sex hormone binding globulin (SHBG). First, the training sample set with 29 cases of EO-PE and 652 controls was developed to study whether these biomarkers separately or in combination with prior risk (maternal characteristics, first trimester pregnancy associated plasma protein-A (PAPP-A) and free beta human chorionic gonadotrophin (fβ-hCG)) could be used to predict the development of EO-PE. Second, the developed risk models were validated with a test sample set of 42 EO-PE and 141 control subjects. For the test set MAP data was also available.Single marker statistically significant (ANOVA p0.05) changes between control and EO-PE pregnancies were observed with AFP, RBP4 and sTNFR1 with both training and test sample sets. Based on the test sample set performances, the best detection rate, 47% for a 10% false positive rate, was achieved with PlGF and sTNFR1 added with prior risk and MAP.Based on our results, the best first trimester biomarkers to predict the subsequent EO-PE were AFP, PlGF, RBP4 and sTNFR1. The risk models that performed best for the prediction of EO-PE included prior risk, MAP, sTNFR1 and AFP or PlGF or RBP4.

BACKGROUNDDuchenne muscular dystrophy (DMD) is a progressive, lethal X-linked neuromuscular disorder with an average worldwide incidence of 1:5000. Blood spot creatine kinase (CK) enzyme assays previously used in newborn screening programs for DMD are nonspecific because measured CK enzyme activity is attributable to 3 isoenzyme forms of CK (CK-MM, CK-MB, and CK-BB) and it is the CK-MM isoform that is found predominantly in skeletal muscle. CK-MM is increased in boys with DMD owing to muscle damage. We describe a sensitive and specific automated immunoassay for CK-MM to screen for DMD in blood spots.METHODSThe prototype assay was developed on the PerkinElmer GSP® analyzer to enable high-throughput screening. CK-MM was assayed using a solid phase, 2-site immunofluorometric system. Purified human CK-MM was used to create calibrators and controls.RESULTSThe limit of blank (LOB), detection (LOD), and quantification (LOQ) values were CONCLUSIONSCK-MM can be reliably quantified in blood spots. The development of this CK-MM assay on a commercial immunoassay analyzer would enable standardized and high-throughput newborn blood spot screening of DMD.

Purpose To evaluate the performance of first trimester maternal serum glycosylated (Sambucus nigra lectin-reactive) fibronectin in prediction of gestational diabetes mellitus (GDM). Methods In this case–control study, first trimester maternal serum glycosylated fibronectin and fibronectin were measured in 19 women who consequently developed GDM and in 59 control women with normal pregnancy outcomes. Adiponectin was used as a reference protein to evaluate relation of glycoprotein to SNA-lectin-reactive assay format. Samples were taken during gestational weeks 9+6–11+6. Data concerning GDM was obtained from the National Institute for Health and Welfare, which records the pregnancy outcomes of all women in Finland. Results There was no difference in maternal serum glycosylated fibronectin concentrations between women with consequent GDM [447.5 μg/mL, interquartile range (IQR) 254.4–540.9 μg/mL] and control women (437.6 μg/mL, IQR 357.1–569.1 μg/mL). Maternal serum fibronectin levels were significantly lower in GDM group (224.2 μg/mL, IQR 156.8–270.6 μg/mL), compared to the control group (264.8 μg/mL, IQR 224.6–330.6 μg/mL, p Conclusion There was no association between first trimester maternal serum glycosylated (SNA-reactive) fibronectin and GDM.

Duchenne muscular dystrophy (DMD/Duchenne) is a progressive X-linked disease and is the most common pediatric-onset form of muscular dystrophy, affecting approximately 1:5000 live male births. DNA testing for mutations in the dystrophin gene confirms the diagnosis of this disorder. This study involves assessment of screening newborns for DMD using an immunoassay for muscle-type (MM) creatine kinase (CK) isoform&mdash, the GSP Neonatal CK-MM kit. Comparisons were made with CK activity determination by fluorescence measurement. In addition, the study evaluated the effect of gestational age, age of infant at time of sampling and how stable the CK-MM was over time. This assay discriminates well between normal, unaffected and Duchenne affected populations and is suitable for Duchenne newborn screening.

Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome. Machine learning algorithms pose an adaptive alternative to develop better risk assessment models using the existing clinical variables. Two real-world data sets were used to experiment with multiple classification algorithms. Implemented models were tested with a third, real-world, data set and performance was compared to a predicate method, a commercial risk assessment software. Best performing deep neural network model gave an area under the curve of 0.96 and detection rate of 78% with 1% false positive rate with the test data. Support vector machine model gave area under the curve of 0.95 and detection rate of 61% with 1% false positive rate with the same test data. When compared with the predicate method, the best support vector machine model was slightly inferior, but an optimized deep neural network model was able to give higher detection rates with same false positive rate or similar detection rate but with markedly lower false positive rate. This finding could further improve the first trimester screening for Down syndrome, by using existing clinical variables and a large training data derived from a specific population.

Objective To evaluate the efficacy of first-trimester markers–-pregnancy-associated plasma protein A (PAPPA), free human chorionic gonadotropin β (fhCGβ), alpha-fetoprotein (AFP), placental growth factor (PlGF), and soluble tumor necrosis factor receptor-1 (sTNFR1) together with maternal characteristics (MC) for prediction of early-onset preeclampsia (EOPE). Methods During 2005-2010, the abovementioned biomarkers were analyzed with logistic regression analysis in 64 EOPE and 752 control subjects to determine whether these biomarkers separately and in combination with MC would predict development of EOPE. Results PAPPA, fhCGβ, and PlGF levels were lower, whereas AFP and sTNFR1 levels were higher in mothers with EOPE compared to controls. The combination of all markers with MC (age, weight, and smoking status) detected 48% of the mothers with EOPE, with a 10% false-positive rate (FPR). Conclusions First-trimester maternal serum levels of PAPPA, fhCGβ, AFP, PlGF, and sTNFR1, together with MC, are predictive of development of subsequent EOPE. These markers, along with MC, form a suitable panel for predicting EOPE.

In a retrospective case-control study, we examined the levels of placental retinol-binding protein 4 (RBP4) and pregnancy-associated placental protein A (PAPP-A) in first-trimester maternal serum samples as well as maternal characteristics to predict early-onset and severe pre-eclampsia.In this retrospective case-control study, we identified females who delivered a singleton pregnancy on or after 24 weeks' gestation from 2003 to 2010 at Oulu University Hospital and had a retrospective first trimester trisomy screening, including serum PAPP-A measurement. Within this cohort, we identified 65 females who experienced early onset pre-eclampsia (EO-PE) and 742 controls who had uncomplicated deliveries. Retrospectively, we thawed all previously collected serum samples to measure placental retinol binding protein 4 (RBP4). PAPP-A and RBP4 were measured using automatic immunoassay systems and converted to multiples of the median (MoMs). Logistic regression analysis was performed to determine whether these biomarkers separately and in combination with maternal characteristics (maternal age, weight and smoking status) can be used to predict the development of early onset pre-eclampsia.The expected log(10) PAPP-A concentration and the expected log(10) RBP4 concentration in the control group were both affected by maternal weight and smoking status. The expected log(10) PAPP-A concentration was also affected by gestational age (GA). RBP4 levels in first-trimester serum were significantly higher in females who subsequently developed EO-PE outcome compared to those with normal pregnancy outcome (1.14 vs. 1.01 MoMs, p0.0001). Maternal serum PAPP-A levels from the same pregnancy period were significantly lower in the EO-PE group compared to controls (0.80 vs. 1.05 MoMs, p=0.005). The risk model including maternal characteristics with PAPP-A log(10) MoM and RBP4 log(10) MoM had the best EO-PE prediction ability. It detected 34% (23%-46%) of females with subsequent EO-PE with a 10% false positive rate.This study showed that first-trimester maternal serum RBP4 was significantly increased and that PAPP-A decreased in pregnancies that ended in EO-PE compared to normal pregnancies. Thus, these markers may be useful members in a panel of markers for the early detection of early-onset and severe pre-eclampsia.

To evaluate the performance of first-trimester measurement of fetal nuchal translucency (NT) in the detection of severe congenital heart defects (CHDs).During the study period of 1 January 2008 - 31 December 2011, NT was measured in 31,144 women as a part of voluntary first-trimester screening program for Down's syndrome in Northern Finland. NT was measured by personnel trained on the job by the experienced staff. No certification or annual audits are required in Finland. However, the recommendation is that the examiner should perform 200 scans on average per year. Severe CHD was classified as a defect requiring surgery in the first year of life or a defect that led to the termination of the pregnancy. All severe CHDs diagnosed during the study period in Northern Finland could not be included in this study since all women did not participate in the first-trimester screening and some cases were missing important data.Fourteen (17.7%) out of 79 severe CHDs were found with NT cutoff of 3.5 mm. Amongst the 79 severe CHD cases, there were 17 chromosomal abnormalities. With NT cutoffs of 2.0 and 1.5 mm the detection rates would have increased to 25.3% (n = 20) and 46.8% (n = 37). Using a randomly selected control group of 762 women with normal pregnancy outcomes, false positive rates (FPRs) were calculated. For NT cutoffs of 1.5, 2.0 and 3.5 mm, the FPRs were, 18.5, 3.3 and 0.4%, respectively.A greater than 3.5 mm NT measurement in the first-trimester ultrasound is an indication to suspect a fetal heart defect but its sensitivity to detect severe CHD is poor. In our study, only 17.7% of severe CHDs would have been detected with an NT cutoff of 3.5 mm.