Quantitative blood loss: a validation study

Objective: To determine if quantitative blood loss would correlate to predicted blood loss based on change in blood concentration of hemoglobin. Conclusion: The correlation between calculated blood using modified Brecher’s formula showed poor overall correlation to quantitative blood loss. There was a higher correlation at blood loss greater than 1500 cc which is where estimated blood loss has been shown to be most poor. Possible reasons for this poor correlation include maternal factors influencing hemoglobin levels, gestational age, error in blood loss calculation, inaccuracy of Brecher’s formula in pregnancy

Physical Activity During Pregnancy and Subsequent Risk of Preeclampsia and Gestational Hypertension: A Case Control Study

Physical activity (PA) is hypothesized to reduce the risk of preeclampsia, but few epidemiologic studies have simultaneously evaluated leisure time PA (LTPA), sedentary activity, occupational activity, and non-occupational, non-leisure time PA. Thus, we assessed the independent and combined effects of these different types of PA during pregnancy on preeclampsia and gestational hypertension risk

A retrospective study of administration of vaccination for hepatitis B among newborn infants prior to hospital discharge at a midwestern tertiary care center

Infants are at high risk of developing chronic, life-threatening disease as a result of hepatitis B virus infection. Universal vaccination of infants against hepatitis B virus, before discharge from the hospital after delivery is recommended as a measure to eradicate infection and associated mortality and morbidity. The purpose of this study was to determine rates of perinatal hepatitis B vaccine (Hep B) administration at a tertiary care center in Iowa and to assess the impact of maternal factors on Hep B uptake

Candidate gene analysis of spontaneous preterm delivery: New insights from re-analysis of a case-control study using case-parent triads and control-mother dyads

Background: Spontaneous preterm delivery (PTD) has a multifactorial etiology with evidence of a genetic contribution to its pathogenesis. A number of candidate gene case-control studies have been performed on spontaneous PTD, but the results have been inconsistent, and do not fully assess the role of how two genotypes can impact outcome. To elucidate this latter point we re-analyzed data from a previously published case-control candidate gene study, using a case-parent triad design and a hybrid design combining case-parent triads and control-mother dyads. These methods offer a robust approach to genetic association studies for PTD compared to traditional case-control designs. Methods: The study participants were obtained from the Norwegian Mother and Child Cohort Study (MoBa). A total of 196 case triads and 211 control dyads were selected for the analysis. A case-parent triad design as well as a hybrid design was used to analyze 1,326 SNPs from 159 candidate genes. We compared our results to those from a previous case-control study on the same samples. Haplotypes were analyzed using a sliding window of three SNPs and a pathway analysis was performed to gain biological insight into the pathophysiology of preterm delivery. Results: The most consistent significant fetal gene across all analyses was COL5A2. The functionally similar COL5A1 was significant when combining fetal and maternal genotypes. PON1 was significant with analytical approaches for single locus association of fetal genes alone, but was possibly confounded by maternal effects. Focal adhesion (hsa04510), Cell Communication (hsa01430) and ECM receptor interaction (hsa04512) were the most constant significant pathways. Conclusion: This study suggests a fetal association of COL5A2 and a combined fetal-maternal association of COL5A1 with spontaneous PTD. In addition, the pathway analysis implied interactions of genes affecting cell communication and extracellular matrix.publishedVersio

Initial Metabolic Profiles Are Associated with 7-Day Survival among Infants Born at 22-25 Weeks of Gestation.

OBJECTIVE:To evaluate the association between early metabolic profiles combined with infant characteristics and survival past 7 days of age in infants born at 22-25 weeks of gestation. STUDY DESIGN:This nested case-control consisted of 465 singleton live births in California from 2005 to 2011 at 22-25 weeks of gestation. All infants had newborn metabolic screening data available. Data included linked birth certificate and mother and infant hospital discharge records. Mortality was derived from linked death certificates and death discharge information. Each death within 7 days was matched to 4 surviving controls by gestational age and birth weight z score category, leaving 93 cases and 372 controls. The association between explanatory variables and 7-day survival was modeled via stepwise logistic regression. Infant characteristics, 42 metabolites, and 12 metabolite ratios were considered for model inclusion. Model performance was assessed via area under the curve. RESULTS:The final model included 1 characteristic and 11 metabolites. The model demonstrated a strong association between metabolic patterns and infant survival (area under the curve [AUC] 0.885, 95% CI 0.851-0.920). Furthermore, a model with just the selected metabolites performed better (AUC 0.879, 95% CI 0.841-0.916) than a model with multiple clinical characteristics (AUC 0.685, 95% CI 0.627-0.742). CONCLUSIONS:Use of metabolomics significantly strengthens the association with 7-day survival in infants born extremely premature. Physicians may be able to use metabolic profiles at birth to refine mortality risks and inform postnatal counseling for infants born at <26 weeks of gestation

Socioeconomic Mediators of Racial and Ethnic Disparities in Congenital Heart Disease Outcomes: A Population-Based Study in California.

Background Racial/ethnic and socioeconomic disparities exist in outcomes for children with congenital heart disease. We sought to determine the influence of race/ethnicity and mediating socioeconomic factors on 1-year outcomes for live-born infants with hypoplastic left heart syndrome and dextro-Transposition of the great arteries. Methods and Results The authors performed a population-based cohort study using the California Office of Statewide Health Planning and Development database. Live-born infants without chromosomal anomalies were included. The outcome was a composite measure of mortality or unexpected hospital readmissions within the first year of life defined as >3 (hypoplastic left heart syndrome) or >1 readmissions (dextro-Transposition of the great arteries). Hispanic ethnicity was compared with non-Hispanic white ethnicity. Mediation analyses determined the percent contribution to outcome for each mediator on the pathway between race/ethnicity and outcome. A total of 1796 patients comprised the cohort (n=964 [hypoplastic left heart syndrome], n=832 [dextro-Transposition of the great arteries]) and 1315 were included in the analysis (n=477 non-Hispanic white, n=838 Hispanic). Hispanic ethnicity was associated with a poor outcome (crude odds ratio, 1.72; 95% confidence interval [CI], 1.37-2.17). Higher maternal education (crude odds ratio 0.5; 95% CI , 0.38-0.65) and private insurance (crude odds ratio, 0.65; 95% CI , 0.45-0.71) were protective. In the mediation analysis, maternal education and insurance status explained 33.2% (95% CI , 7-66.4) and 27.6% (95% CI , 6.5-63.1) of the relationship between race/ethnicity and poor outcome, while infant characteristics played a minimal role. Conclusions Socioeconomic factors explain a significant portion of the association between Hispanic ethnicity and poor outcome in neonates with critical congenital heart disease. These findings identify vulnerable populations that would benefit from resources to lessen health disparities

Low Birth Weight and Risk of Later-Life Physical Disability in Women

Background: There is strong evidence that low and high birth weight due to in-utero programming results in elevated risk for adult diseases, though less research has been performed examining the influence of birth weight and physical disability later in life. Methods: Baseline data from 76,055 postmenopausal women in the Women's Health Initiative, a large multi-ethnic cohort, were used to examine the association between self-reported birth weight category (<6 lbs, 6-7 lbs 15 oz, 8-9 lbs 15 oz, and ≥10 lbs) and the self-reported physical functioning score on the RAND 36-item Health Survey. Linear regression models were adjusted for age, education, race/ethnicity, body mass index, and a comorbidity score. Results: Unadjusted models indicate that women born in the lowest and highest birth weight categories have significantly lower physical functioning scores as compared to women born in the normal weight category (β = -2.22, p < .0001 and β = -3.56, p < .0001, respectively). After adjustments, the relationship between the lowest birth weight category and physical functioning score remained significant (β = -1.52, p < .0001); however, the association with the highest birth weight category dissipated. Conclusions: Preconception and prenatal interventions aimed at reducing the incidence of low birth weight infants may subsequently reduce the burden of later-life physical disability

Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation

Background: Globally, 15 million infants are born preterm and another 23.2 million infants are born small for gestational age (SGA). Determining burden of preterm and SGA births, is essential for effective planning, modification of health policies and targeting interventions for reducing these outcomes for which accurate estimation of gestational age (GA) is crucial. Early pregnancy ultrasound measurements, last menstrual period and post-natal neonatal examinations have proven to be not feasible or inaccurate. Proposed algorithms for GA estimation in western populations, based on routine new-born screening, though promising, lack validation in developing country settings. We evaluated the hypothesis that models developed in USA, also predicted GA in cohorts of South Asia (575) and Sub-Saharan Africa (736) with same precision.Methods: Dried heel prick blood spots collected 24-72 hours after birth from 1311 new-borns, were analysed for standard metabolic screen. Regression algorithm based, GA estimates were computed from metabolic data and compared to first trimester ultrasound validated, GA estimates (gold standard).Results: Overall Algorithm (metabolites + birthweight) estimated GA to within an average deviation of 1.5 weeks. The estimated GA was within the gold standard estimate by 1 and 2 weeks for 70.5% and 90.1% new-borns respectively. Inclusion of birthweight in the metabolites model improved discriminatory ability of this method, and showed promise in identifying preterm births. Receiver operating characteristic (ROC) curve analysis estimated an area under curve of 0.86 (conservative bootstrap 95% confidence interval (CI) = 0.83 to 0.89); P \u3c 0.001) and Youden Index of 0.58 (95% CI = 0.51 to 0.64) with a corresponding sensitivity of 80.7% and specificity of 77.6%.Conclusion: Metabolic gestational age dating offers a novel means for accurate population-level gestational age estimates in LMIC settings and help preterm birth surveillance initiatives. Further research should focus on use of machine learning and newer analytic methods broader than conventional metabolic screen analytes, enabling incorporation of region-specific analytes and cord blood metabolic profiles models predicting gestational age accurately

Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in south Asia and sub-Saharan Africa

Background: Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasonography-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMICs settings.Methods: This study uses data from AMANHI-ACT, a prospective pregnancy cohorts in Asia and Africa where early pregnancy ultrasonography estimated GA and birth weight are available and metabolite screening data in a subset of 1318 new-borns were also available. We utilized this opportunity to develop machine learning (ML) algorithms. Random Forest Regressor was used where data was randomly split into model-building and model-testing dataset. Mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate performance. Bootstrap procedures were used to estimate confidence intervals (CI) for RMSE and MAE. For pre-term birth identification ROC analysis with bootstrap and exact estimation of CI for area under curve (AUC) were performed.Results: Overall model estimated GA had MAE of 5.2 days (95% CI 4.6-6.8), which was similar to performance in SGA, MAE 5.3 days (95% CI 4.6-6.2). GA was correctly estimated to within 1 week for 85.21% (95% CI 72.31-94.65). For preterm birth classification, AUC in ROC analysis was 98.1% (95% CI 96.0-99.0; p \u3c 0.001). This model performed better than Iowa regression, AUC Difference 14.4% (95% CI 5-23.7; p = 0.002).Conclusions: Machine learning algorithms and models applied to metabolomic gestational age dating offer a ladder of opportunity for providing accurate population-level gestational age estimates in LMICs settings. These findings also point to an opportunity for investigation of region-specific models, more focused feasible analyte models, and broad untargeted metabolome investigation

Acylcarnitines and Genetic Variation in Fat Oxidation Genes in HIV-infected, Antiretroviral-treated Children With and Without Myopathy

BACKGROUND: Mitochondrial toxicity resulting in myopathy and lactic acidosis has been described in antiretroviral (ARV)-exposed patients. We hypothesized that myopathy in HIV-infected, ARV-treated children would be associated with metabolic (acylcarnitines) and genetic (variants in metabolic genes) markers of dysfunctional fatty acid oxidation (FAO). METHODS: Acylcarnitine profiles (ACP) were analyzed for 74 HIV-infected children on nucleoside reverse transcriptase inhibitor (NRTI)-containing ARV. Thirty-seven participants with ≥2 creatine kinase measurements \u3e500 IU (n = 18) or evidence of echocardiographic cardiomyopathy (n = 19) were matched with 37 participants without myopathy. Single nucleotide polymorphisms (SNPs) in FAO genes were also evaluated. RESULTS: Abnormal ACP was 73% (95% CI: 56%-86%) and 62% (95% CI: 45%-78%) in the myopathic and nonmyopathic groups, respectively. No significant association was found between myopathy and having an abnormal ACP (OR = 2.10, P = 0.22). In univariate analysis, a 1-year increase in NRTI use was associated with a 20% increase in odds of at least 1 ACP abnormality [OR (95% CI) = 1.20 (1.03-1.41); P = 0.02), and a 1-year increase in protease inhibitor use was associated with 28% increase in the odds of having at least 1 ACP abnormality [OR (95% CI) = 1.28 (1.07-1.52); P = 0.006). Three SNPs, all in the gene for the carnitine transporter ( SLC22A5 ), were associated with the cardiomyopathy phenotype. CONCLUSION: FAO appears to be altered in HIV-infected children with and without myopathy, but abnormal FAO does not fully explain myopathy in ARV-exposed children. Further study of SLC22A5 variation in ARV-exposed people is warranted carnitine transporter dysfunction-related cardiomyopathy may be treatable