Commenced in January 2007
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Edition: International
Paper Count: 3

preterm birth Related Abstracts

3 Computational Feasibility Study of a Torsional Wave Transducer for Tissue Stiffness Monitoring

Authors: Rafael Muñoz, Juan Melchor, Alicia Valera, Laura Peralta, Guillermo Rus

Abstract:

A torsional piezoelectric ultrasonic transducer design is proposed to measure shear moduli in soft tissue with direct access availability, using shear wave elastography technique. The measurement of shear moduli of tissues is a challenging problem, mainly derived from a) the difficulty of isolating a pure shear wave, given the interference of multiple waves of different types (P, S, even guided) emitted by the transducers and reflected in geometric boundaries, and b) the highly attenuating nature of soft tissular materials. An immediate application, overcoming these drawbacks, is the measurement of changes in cervix stiffness to estimate the gestational age at delivery. The design has been optimized using a finite element model (FEM) and a semi-analytical estimator of the probability of detection (POD) to determine a suitable geometry, materials and generated waves. The technique is based on the time of flight measurement between emitter and receiver, to infer shear wave velocity. Current research is centered in prototype testing and validation. The geometric optimization of the transducer was able to annihilate the compressional wave emission, generating a quite pure shear torsional wave. Currently, mechanical and electromagnetic coupling between emitter and receiver signals are being the research focus. Conclusions: the design overcomes the main described problems. The almost pure shear torsional wave along with the short time of flight avoids the possibility of multiple wave interference. This short propagation distance reduce the effect of attenuation, and allow the emission of very low energies assuring a good biological security for human use.

Keywords: Soft tissue, Shear Modulus, cervix ripening, preterm birth, shear wave elastography, torsional wave

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2 Pregnancy Outcome in Women with HIV Infection from a Tertiary Care Centre of India

Authors: Kavita Khoiwal, Vatsla Dadhwal, K. Aparna Sharma, Dipika Deka, Plabani Sarkar

Abstract:

Introduction: About 2.4 million (1.93 - 3.04 million) people are living with HIV/AIDS in India. Of all HIV infections, 39% (9,30,000) are among women. 5.4% of infections are from mother to child transmission (MTCT), 25,000 infected children are born every year. Besides the risk of mother to child transmission of HIV, these women are at risk of the higher adverse pregnancy outcome. The objectives of the study were to compare the obstetric and neonatal outcome in women who are HIV positive with low-risk HIV negative women and effect of antiretroviral drugs on preterm birth and IUGR. Materials and Methods: This is a retrospective case record analysis of 212 HIV-positive women delivering between 2002 to 2015, in a tertiary health care centre which was compared with 238 HIV-negative controls. Women who underwent medical termination of pregnancy and abortion were excluded from the study. Obstetric outcome analyzed were pregnancy induced hypertension, HIV positive intrauterine growth restriction, preterm birth, anemia, gestational diabetes and intrahepatic cholestasis of pregnancy. Neonatal outcome analysed were birth weight, apgar score, NICU admission and perinatal transmission.HIV-positiveOut of 212 women, 204 received antiretroviral therapy (ART) to prevent MTCT, 27 women received single dose nevirapine (sdNVP) or sdNVP tailed with 7 days of zidovudine and lamivudine (ZDV + 3TC), 15 received ZDV, 82 women received duovir and 80 women received triple drug therapy depending upon the time period of presentation. Results: Mean age of 212 HIV positive women was 25.72+3.6 years, 101 women (47.6 %) were primigravida. HIV positive status was diagnosed during pregnancy in 200 women while 12 women were diagnosed prior to conception. Among 212 HIV positive women, 20 (9.4 %) women had preterm delivery (< 37 weeks), 194 women (91.5 %) delivered by cesarean section and 18 women (8.5 %) delivered vaginally. 178 neonates (83.9 %) received exclusive top feeding and 34 neonates (16.03 %) received exclusive breast feeding. When compared to low risk HIV negative women (n=238), HIV positive women were more likely to deliver preterm (OR 1.27), have anemia (OR 1.39) and intrauterine growth restriction (OR 2.07). Incidence of pregnancy induced hypertension, diabetes mellitus and ICP was not increased. Mean birth weight was significantly lower in HIV positive women (2593.60+499 gm) when compared to HIV negative women (2919+459 gm). Complete follow up is available for 148 neonates till date, rest are under evaluation. Out of these 7 neonates found to have HIV positive status. Risk of preterm birth (P value = 0.039) and IUGR (P value = 0.739) was higher in HIV positive women who did not receive any ART during pregnancy than women who received ART. Conclusion: HIV positive pregnant women are at increased risk of adverse pregnancy outcome. Multidisciplinary team approach and use of highly active antiretroviral therapy can optimize the maternal and perinatal outcome.

Keywords: HIV infection, antiretroviral therapy, IUGR, preterm birth

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1 On the Influence of Sleep Habits for Predicting Preterm Births: A Machine Learning Approach

Authors: C. Fernandez-Plaza, I. Abad, E. Diaz, I. Diaz

Abstract:

Births occurring before the 37th week of gestation are considered preterm births. A threat of preterm is defined as the beginning of regular uterine contractions, dilation and cervical effacement between 23 and 36 gestation weeks. To author's best knowledge, the factors that determine the beginning of the birth are not completely defined yet. In particular, the incidence of sleep habits on preterm births is weekly studied. The aim of this study is to develop a model to predict the factors affecting premature delivery on pregnancy, based on the above potential risk factors, including those derived from sleep habits and light exposure at night (introduced as 12 variables obtained by a telephone survey using two questionnaires previously used by other authors). Thus, three groups of variables were included in the study (maternal, fetal and sleep habits). The study was approved by Research Ethics Committee of the Principado of Asturias (Spain). An observational, retrospective and descriptive study was performed with 481 births between January 1, 2015 and May 10, 2016 in the University Central Hospital of Asturias (Spain). A statistical analysis using SPSS was carried out to compare qualitative and quantitative variables between preterm and term delivery. Chi-square test qualitative variable and t-test for quantitative variables were applied. Statistically significant differences (p < 0.05) between preterm vs. term births were found for primiparity, multi-parity, kind of conception, place of residence or premature rupture of membranes and interruption during nights. In addition to the statistical analysis, machine learning methods to look for a prediction model were tested. In particular, tree based models were applied as the trade-off between performance and interpretability is especially suitable for this study. C5.0, recursive partitioning, random forest and tree bag models were analysed using caret R-package. Cross validation with 10-folds and parameter tuning to optimize the methods were applied. In addition, different noise reduction methods were applied to the initial data using NoiseFiltersR package. The best performance was obtained by C5.0 method with Accuracy 0.91, Sensitivity 0.93, Specificity 0.89 and Precision 0.91. Some well known preterm birth factors were identified: Cervix Dilation, maternal BMI, Premature rupture of membranes or nuchal translucency analysis in the first trimester. The model also identifies other new factors related to sleep habits such as light through window, bedtime on working days, usage of electronic devices before sleeping from Mondays to Fridays or change of sleeping habits reflected in the number of hours, in the depth of sleep or in the lighting of the room. IF dilation < = 2.95 AND usage of electronic devices before sleeping from Mondays to Friday = YES and change of sleeping habits = YES, then preterm is one of the predicting rules obtained by C5.0. In this work a model for predicting preterm births is developed. It is based on machine learning together with noise reduction techniques. The method maximizing the performance is the one selected. This model shows the influence of variables related to sleep habits in preterm prediction.

Keywords: Machine Learning, Noise Reduction, preterm birth, sleep habit

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