Factors associated with low birthweight in term pregnancies: A matched case-control study from rural Pakistan

Low birthweight (LBW) remains a significant public health problem in Pakistan and further understanding of factors associated with LBW is required. We conducted a hospital-based matched case control study to identify risk factors associated with LBW in a rural district of Pakistan. We found that illiteracy (AOR: 2.68; 95% CI: 1.59 - 4.38), nulliparity (AOR: 1.82; 95% CI: 1.26-2.44), having a previous miscarriage/abortion (AOR: 1.22; 95% CI: 1.06-2.35), having \u3c 2 antenatal care (ANC) visits during last pregnancy (AOR: 2.43; 95% CI: 1.34-2.88), seeking ANC in third trimester (AOR: 3.62; 95% CI : 2.14-5.03), non-use of iron folic acid during last pregnancy (AOR: 2.72; 95% CI: 1.75-3.17), having hypertension during last pregnancy (AOR: 1.42; 95% CI: 1.13-2.20), being anemic (AOR: 2.67; 95% CI: 1.65-5.24) and having postpartum weight o

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Protocol for a randomised controlled trial of a decision aid for the management of pain in labour and childbirth [ISRCTN52287533]

BACKGROUND: Women report fear of pain in childbirth and often lack complete information on analgesic options prior to labour. Preferences for pain relief should be discussed before labour begins. A woman's antepartum decision to use pain relief is likely influenced by her cultural background, friends, family, the media, literature and her antenatal caregivers. Pregnant women report that information about analgesia was most commonly derived from hearsay and least commonly from health professionals. Decision aids are emerging as a promising tool to assist practitioners and their patients in evidence-based decision making. Decision aids are designed to assist patients and their doctors in making informed decisions using information that is unbiased and based on high quality research evidence. Decision aids are non-directive in the sense that they do not aim to steer the user towards any one option, but rather to support decision making which is informed and consistent with personal values. METHODS/DESIGN: We aim to evaluate the effectiveness of a Pain Relief for Labour decision aid, with and without an audio-component, compared to a pamphlet in a three-arm randomised controlled trial. Approximately 600 women expecting their first baby and planning a vaginal birth will be recruited for the trial. The primary outcomes of the study are decisional conflict (uncertainty about a course of action), knowledge, anxiety and satisfaction with decision-making and will be assessed using self-administered questionnaires. The decision aid is not intended to influence the type of analgesia used during labour, however we will monitor health service utilisation rates and maternal and perinatal outcomes. This study is funded by a competitive peer-reviewed grant from the Australian National Health and Medical Research Council (No. 253635). DISCUSSION: The Pain Relief for Labour decision aid was developed using the Ottawa Decision Support Framework and systematic reviews of the evidence about the benefits and risks of the non-pharmacological and pharmacological methods of pain relief for labour. It comprises a workbook and worksheet and has been developed in two forms – with and without an audio-component (compact disc). The format allows women to take the decision aid home and discuss it with their partner

Record linkage to obtain birth outcomes for the evaluation of screening biomarkers in pregnancy: a feasibility study

<p>Abstract</p> <p>Background</p> <p>Linking population health data to pathology data is a new approach for the evaluation of predictive tests that is potentially more efficient, feasible and efficacious than current methods. Studies evaluating the use of first trimester maternal serum levels as predictors of complications in pregnancy have mostly relied on resource intensive methods such as prospective data collection or retrospective chart review. The aim of this pilot study is to demonstrate that record-linkage between a pathology database and routinely collected population health data sets provides follow-up on patient outcomes that is as effective as more traditional and resource-intensive methods. As a specific example, we evaluate maternal serum levels of PAPP-A and free <it>β</it>-hCG as predictors of adverse pregnancy outcomes, and compare our results with those of prospective studies.</p> <p>Methods</p> <p>Maternal serum levels of PAPP-A and free <it>β</it>-hCG for 1882 women randomly selected from a pathology database in New South Wales (NSW) were linked to routinely collected birth and hospital databases. Crude relative risks were calculated to investigate the association between low levels (multiples of the median ≤ 5^thpercentile) of PAPP-A or free <it>β</it>-hCG and the outcomes of preterm delivery (<37 weeks), small for gestational age (<10^thpercentile), fetal loss and stillbirth.</p> <p>Results</p> <p>Using only full name, sex and date of birth for record linkage, pregnancy outcomes were available for 1681 (89.3%) of women included in the study. Low levels of PAPP-A had a stronger association with adverse pregnancy outcomes than a low level of free <it>β</it>-hCG which is consistent with results in published studies. The relative risk of having a preterm birth with a low maternal serum PAPP-A level was 3.44 (95% CI 1.96–6.10) and a low free <it>β</it>-hCG level was 1.31 (95% CI 0.55–6.16).</p> <p>Conclusion</p> <p>This study provides data to support the use of record linkage for outcome ascertainment in studies evaluating predictive tests. Linkage proportions are likely to increase if more personal identifiers are available. This method of follow-up is a cost-efficient technique and can now be applied to a larger cohort of women.</p

Additive effect of LRP8/APOER2 R952Q variant to APOE ε2/ε3/ε4 genotype in modulating apolipoprotein E concentration and the risk of myocardial infarction: a case-control study

BACKGROUND: The R952Q variant in the low density lipoprotein receptor-related protein 8 (LRP8)/apolipoprotein E receptor 2 (ApoER2) gene has been recently associated with familial and premature myocardial infarction (MI) by means of genome-wide linkage scan/association studies. We were interested in the possible interaction of the R952Q variant with another established cardiovascular genetic risk factor belonging to the same pathway, namely apolipoprotein E (APOE) epsilon2/epsilon3/epsilon4 genotype, in modulating apolipoprotein E (ApoE) plasma levels and risk of MI. METHODS: In the Italian cohort used to confirm the association of the R952Q variant with MI, we assessed lipid profile, apolipoprotein concentrations, and APOE epsilon2/epsilon3/epsilon4 genotype. Complete data were available for a total of 681 subjects in a case-control setting (287 controls and 394 patients with MI). RESULTS: Plasma ApoE levels decreased progressively across R952Q genotypes (mean levels +/- SD = RR: 0.045 +/- 0.020, RQ: 0.044 +/- 0.014, QQ: 0.040 +/- 0.008 g/l; P for trend = 0.047). Combination with APOE genotypes revealed an additive effect on ApoE levels, with the highest level observed in RR/non-carriers of the E4 allele (0.046 +/- 0.021 g/l), and the lowest level in QQ/E4 carriers (0.035 +/- 0.009 g/l; P for trend = 0.010). QQ/E4 was also the combined genotype with the most significant association with MI (OR 3.88 with 95\%CI 1.08-13.9 as compared with RR/non-carriers E4). CONCLUSION: Our data suggest that LRP8 R952Q variant may have an additive effect to APOE epsilon2/epsilon3/epsilon4 genotype in determining ApoE concentrations and risk of MI in an Italian population

Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

Abstract: Background: Despite advances in the care of women and their babies in the past century, an estimated 1.7 million babies are born still each year throughout the world. A robust method to estimate a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform decision-making around the timing of birth to reduce the risk of stillbirth from 35 weeks of gestation in Australia, a high-resource setting. Methods: This is a protocol for a cross-sectional study of all late-pregnancy births in Australia (2005–2015) from 35 weeks of gestation including 5188 stillbirths among 3.1 million births at an estimated rate of 1.7 stillbirths per 1000 births. A multivariable logistic regression model will be developed in line with current TransparentReporting of a multivariable prediction model forIndividualPrognosis orDiagnosis (TRIPOD) guidelines to estimate the gestation-specific probability of stillbirth with prediction intervals. Candidate predictors were identified from systematic reviews and clinical consultation and will be described through univariable regression analysis. To generate a final model, elimination by backward stepwise multivariable logistic regression will be performed. The model will be internally validated using bootstrapping with 1000 repetitions and externally validated using a temporally unique dataset. Overall model performance will be assessed with R2, calibration, and discrimination. Calibration will be reported using a calibration plot with 95% confidence intervals (α = 0.05). Discrimination will be measured by the C-statistic and area underneath the receiver-operator curves. Clinical usefulness will be reported as positive and negative predictive values, and a decision curve analysis will be considered. Discussion: A robust method to predict a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for obstetric use, few have been subject to internal and external validation and many fail to meet recommended reporting standards. In developing a risk prediction model for late-gestation stillbirth with both providers and pregnant women in mind, we endeavor to develop a validated model for clinical use in Australia that meets current reporting standards

Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

Abstract: Background: Despite advances in the care of women and their babies in the past century, an estimated 1.7 million babies are born still each year throughout the world. A robust method to estimate a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform decision-making around the timing of birth to reduce the risk of stillbirth from 35 weeks of gestation in Australia, a high-resource setting. Methods: This is a protocol for a cross-sectional study of all late-pregnancy births in Australia (2005–2015) from 35 weeks of gestation including 5188 stillbirths among 3.1 million births at an estimated rate of 1.7 stillbirths per 1000 births. A multivariable logistic regression model will be developed in line with current TransparentReporting of a multivariable prediction model forIndividualPrognosis orDiagnosis (TRIPOD) guidelines to estimate the gestation-specific probability of stillbirth with prediction intervals. Candidate predictors were identified from systematic reviews and clinical consultation and will be described through univariable regression analysis. To generate a final model, elimination by backward stepwise multivariable logistic regression will be performed. The model will be internally validated using bootstrapping with 1000 repetitions and externally validated using a temporally unique dataset. Overall model performance will be assessed with R2, calibration, and discrimination. Calibration will be reported using a calibration plot with 95% confidence intervals (α = 0.05). Discrimination will be measured by the C-statistic and area underneath the receiver-operator curves. Clinical usefulness will be reported as positive and negative predictive values, and a decision curve analysis will be considered. Discussion: A robust method to predict a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for obstetric use, few have been subject to internal and external validation and many fail to meet recommended reporting standards. In developing a risk prediction model for late-gestation stillbirth with both providers and pregnant women in mind, we endeavor to develop a validated model for clinical use in Australia that meets current reporting standards

Prevalence and determinants of unintended pregnancies amongst women attending antenatal clinics in Pakistan

Background: Unintended pregnancies are a global public health concern and contribute significantly to adverse maternal and neonatal health, social and economic outcomes and increase the risks of maternal deaths and neonatal mortality. In countries like Pakistan where data for the unintended pregnancies is scarce, studies are required to estimate its accurate prevalence and predictors using more specific tools such as the London Measure of Unplanned Pregnancies (LMUP). Methods: We conducted a hospital based cross sectional survey in two tertiary care hospitals in Pakistan. We used a pre tested structured questionnaire to collect the data on socio-demographic characteristics, reproductive history, awareness and past experience with contraceptives and unintended pregnancies using six item the LMUP. We used Univariate and multivariate analysis to explore the association between unintended pregnancies and predictor variables and presented the association as adjusted odds ratios. We also evaluated the psychometric properties of the Urdu version of the LMUP. Results: Amongst 3010 pregnant women, 1150 (38.2%) pregnancies were reported as unintended. In the multivariate analysis age \u3c 20 years (AOR 3.5 1.1-6.5), being illiterate (AOR 1.9 1.1-3.4), living in a rural setting (1.7 1.2-2.3), having a pregnancy interval of = \u3c 12 months (AOR 1.7 1.4-2.2), having a parity of \u3e2 (AOR 1.4 1.2-1.8), having no knowledge about contraceptive methods (AOR 3.0 1.7-5.4) and never use of contraceptive methods (AOR 2.3 1.4-5.1) remained significantly associated with unintended pregnancy. The Urdu version of the LMUP scale was found to be acceptable, valid and reliable with the Cronbach\u27s alpha of 0.85. Conclusions: This study explores a high prevalence of unintended pregnancies and important factors especially those related to family planning. Integrated national family program that provides contraceptive services especially the modern methods to women during pre-conception and post-partum would be beneficial in averting unintended pregnancies and their related adverse outcomes in Pakistan