Effect of Right-Lateral Versus Left-Lateral Tilt Position on Compression of the Inferior Vena Cava in Pregnant Women Determined by Magnetic Resonance Imaging.

BACKGROUND: Despite the existing dogma that women undergoing cesarean delivery under spinal anesthesia should be positioned with a 15° left-lateral tilt, the patients were actually positioned in a right-lateral tilt position in several of the original studies. The superiority of right versus left positioning for optimal inferior vena cava volume is unknown. We used magnetic resonance imaging to compare the effects of right-lateral and left-lateral tilt positions on abdominal aortic and inferior vena cava volumes in pregnant women. METHODS: Thirteen women with singleton pregnancies and gestational age 31-39 weeks underwent magnetic resonance imaging while in the supine position, and in the left-lateral (15° and 30°) and right-lateral tilt (15° and 30°) positions, which were maintained by placing a 1.5-m-long piece of polyethylene foam under either side of the body. Abdominal aorta and inferior vena cava volume were measured between the L1-L2 disk and L3-L4 disk levels using magnetic resonance images. RESULTS: Aortic volume did not differ significantly among any of the positions examined. Mean inferior vena cava volume was significantly greater in the 30° left-lateral tilt position than in the 15° right-lateral tilt (10.7 ± 7.5 vs 5.9 ± 5.1 mL; mean difference, 4.8; 95% CI, 1.2-8.5; P = .002) and 30° right-lateral tilt (10.7 ± 7.5 vs 5.9 ± 2.5 mL; mean difference, 4.8; 95% CI, 1.2-8.4; P = .002) positions. Mean inferior vena cava volume in the 15° left-lateral tilt position did not differ significantly from that in the 15° right-lateral tilt (mean difference, 0.4; 95% CI, -3.2 to 4.0; P = 1.000) or 30° right-lateral tilt (mean difference, 0.4; 95% CI, -3.3 to 4.0; P = 1.000) positions. Mean inferior vena cava volume in the supine position only differed significantly from that in the 30° left-lateral tilt position (5.2 ± 3.8 vs 10.7 ± 7.5 mL; mean difference, 5.5; 95% CI, 1.8-9.1; P < .001). The greatest inferior vena cava volume was observed in the 30° left-lateral tilt position in 9 of 13 subjects (70%), and in the 30° right-lateral tilt in 3 subjects (23%). CONCLUSIONS: The 30° left-lateral tilt position most consistently reduced inferior vena cava compression by the gravid uterus compared with the supine position. Mean inferior vena cava volume in pregnant women was not increased at either angle of the right-lateral tilt position compared with the 30° left-lateral tilt position. However, in a subset of patients, the 30° right-lateral tilt position achieved the optimal inferior vena cava volume. Further investigation to understand this variability is warranted.

Optimum designs for non-linear mixed effects models in the presence of covariates.

In this article, we present a new method for optimizing designs of experiments for non-linear mixed effects models, where a categorical factor with covariate information is a design variable combined with another design factor. The work is motivated by the need to efficiently design preclinical experiments in enzyme kinetics for a set of Human Liver Microsomes. However, the results are general and can be applied to other experimental situations where the variation in the response due to a categorical factor can be partially accounted for by a covariate. The covariate included in the model explains some systematic variability in a random model parameter. This approach allows better understanding of the population variation as well as estimation of the model parameters with higher precision.

A determinant-based criterion for working correlation structure selection in generalized estimating equations.

In generalized estimating equations (GEE), the correlation between the repeated observations on a subject is specified with a working correlation matrix. Correct specification of the working correlation structure ensures efficient estimators of the regression coefficients. Among the criteria used, in practice, for selecting working correlation structure, Rotnitzky-Jewell, Quasi Information Criterion (QIC) and Correlation Information Criterion (CIC) are based on the fact that if the assumed working correlation structure is correct then the model-based (naive) and the sandwich (robust) covariance estimators of the regression coefficient estimators should be close to each other. The sandwich covariance estimator, used in defining the Rotnitzky-Jewell, QIC and CIC criteria, is biased downward and has a larger variability than the corresponding model-based covariance estimator. Motivated by this fact, a new criterion is proposed in this paper based on the bias-corrected sandwich covariance estimator for selecting an appropriate working correlation structure in GEE. A comparison of the proposed and the competing criteria is shown using simulation studies with correlated binary responses. The results revealed that the proposed criterion generally performs better than the competing criteria. An example of selecting the appropriate working correlation structure has also been shown using the data from Madras Schizophrenia Study.

Digitalized to reach and track: a retrospective comparison between traditional and conditional estimate of vaccination coverage and dropout rates using e-Tracker data below one-year children in Bangladesh during-COVID and pre-COVID period.

Background: With an impressive track record in expanding childhood immunization and an inclination to adopt digitalization in healthcare service delivery, Expanded Program on Immunization (EPI) Bangladesh piloted the e-Tracker intervention in Moulvibazar district and Dhaka South City Corporation (Zone-5) from 2019 till the end of 2021. Methods: We retrieved and analyzed the digitalized e-Tracker data of 114,194 infants born between January 1, 2019 and December 31, 2020, with help from Health Management Information System (HMIS) and UNICEF Bangladesh. Childhood vaccination coverage and dropout rates were determined using a 'Traditional approach' traditionally used by WHO and a 'Conditional technique' with a modified denominator. Using a multiple logistic regression model, we examined the effects of COVID-19, birth-cohorts, mother education, and location on vaccination rates (coverages & dropouts) to aid with informed decision-making by the policymakers. Findings: The conditional estimation method yielded a lower full vaccination coverage during pre-COVID period than the national and global reported coverage derived using the 'traditional method' (73.4% vs. 89.0% & 81.0%). As expected, while the coverage has decreased, the dropout rate increased "during-COVID" compared to the "pre-COVID" period. However, dropouts were estimated lower in the 'conditional method.' The average age (in months) for getting BCG was higher in Moulvibazar (∼2.5 months) than that in Dhaka (∼1.4 months). All birth-cohorts from 'the during-COVID period had about 30% lower odds of getting fully vaccinated than those from the 'pre-COVID' period. Interpretations: Age-cohort-specific analysis showed a decline in coverage rates before and during COVID, but e-Tracker didn't have enough data to draw additional conclusions. The server only stored the child's gender, the caregiver's monthly salary, and the mother's education. It didn't track any other factors related to dropout rates. The e-Tracker is an excellent tool for measuring real coverage and should be scaled nationwide. Funding: UNICEF, Bangladesh.