Review articles

Gardosi, Jason

doi:10.1515/jpme.1998.26.3.137

Cited by 16 publications

References 218 publications

(339 reference statements)

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Birthweight percentiles by gestational age in Kuwait

Alshimmiri

Al-Saleh

Alsaeid

et al. 2002

Arch Gynecol Obstet

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Our aim is develop a curve for singleton birthweight based on accurately calculated gestational age. A retrospective analysis of all singleton live births from 22-44 completed weeks of gestation during the period from September 1998 to December 2000 in the two largest birth birth centers in Kuwait was conducted. Neonates with major congenital anomalies and those with unrecorded gestational age were excluded from the study population. Total population and gender-specific birthweight percentiles according to gestational age were developed after smoothening of growth curves. A total of 35768 births were included in the development of the birthweight curve. Percentiles of birthweight for all population and by gender are presented. There was significant difference in birthweight among different ethnic groups in this population. At term, 9.8% of births are smaller than the 10th percentile and 10.0% are larger than the 90th percentile. Plotting birthweight in our population on percentile curves derived from the United States or United Kingdom would generally overestimate small for gestational age newborns and underestimate large for gestational age newborns. We conclude that the diagnosis of clinically significant birthweight abnormalities depends on the fetal growth curve used. A population specific curve of fetal growth dated by ultrasonography would provide a reliable reference for birthweight distribution.

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Birthweight percentiles by gestational age in Kuwait

Alshimmiri

Al-Saleh

Alsaeid

et al. 2002

Arch Gynecol Obstet

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Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning

et al. 2005

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Pre-eclampsia is a multi-system disorder of pregnancy with major maternal and perinatal implications. Emerging therapeutic strategies are most likely to be maximally effective if commenced weeks or even months prior to the clinical presentation of the disease. Although widespread plasma alterations precede the clinical onset of pre-eclampsia, no single plasma constituent has emerged as a sensitive or specific predictor of risk. Consequently, currently available methods of identifying the condition prior to clinical presentation are of limited clinical use. We have exploited genetic programming, a powerful data mining method, to identify patterns of metabolites that distinguish plasma from patients with pre-eclampsia from that taken from healthy, matched controls. High-resolution gas chromatography time-of-flight mass spectrometry (GC-tof-MS) was performed on 87 plasma samples from women with pre-eclampsia and 87 matched controls. Normalised peak intensity data were fed into the Genetic Programming (GP) system which was set up to produce a model that gave an output of 1 for patients and 0 for controls. The model was trained on 50% of the data generated and tested on a separate hold-out set of 50%. The model generated by GP from the GC-tof-MS data identified a metabolomic pattern that could be used to produce two simple rules that together discriminate pre-eclampsia from normal pregnant controls using just 3 of the metabolite peak variables, with a sensitivity of 100% and a specificity of 98%. Thus, preeclampsia can be diagnosed at the level of small-molecule metabolism in blood plasma. These findings justify a prospective assessment of metabolomic technology as a screening tool for pre-eclampsia, while identification of the metabolites involved may lead to an improved understanding of the aetiological basis of pre-eclampsia and thus the development of targeted therapies.

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