High Rates of Placental Inflammation Among Samples Collected by the MOMI Consortium.

BACKGROUND: The Multi-Omics for Mothers and Infants (MOMI) consortium aims to improve birth outcomes. Preterm birth is a major obstetric complication globally causing significant infant and childhood morbidity and mortality. OBJECTIVES: We analyzed placental samples (basal plate, placenta/chorionic villi and/or the chorionic plate) collected by the 5 MOMI sites: The Alliance for Maternal and Newborn Health Improvement (AMANHI) Bangladesh, AMANHI Pakistan, AMANHI Tanzania, The Global Alliance to Prevent Prematurity and Stillbirth (GAPPS) Bangladesh and GAPPS Zambia. The goal was to analyze the morphology and gene expression of samples collected from preterm and uncomplicated term births. STUDY DESIGN: The teams provided biopsies from 166 singleton preterm (<37 weeks) and 175 term (≥37 weeks) deliveries. They were formalin-fixed and paraffin embedded. Tissue sections from these samples were stained with hematoxylin and eosin and subjected to morphological analyses. Other placental biopsies (n = 35 preterm, 21 term) were flash frozen, which enabled RNA purification for bulk transcriptomics. RESULTS: The morphological analyses revealed a surprisingly high rate of inflammation involving the basal plate, placenta/chorionic villi and/or the chorionic plate. The rate in chorionic villus samples, likely attributable to chronic villitis, ranged from 25% (Pakistan site) to 60% (Zambia site) of cases. Leukocyte infiltration in this location vs. the basal plate or chorionic plate correlated with preterm birth. Our transcriptomic analyses identified 267 genes as differentially expressed (DE) between placentas from preterm vs. term births (123 upregulated, 144 downregulated). Mapping the DE genes onto single cell RNA-seq data from human placentas suggested that all the component cell types, either singly or in subsets, contributed to the observed dysregulation. Consistent with the histopathological findings, GO (Gene Ontology) analyses highlighted leukocyte infiltration/activation and inflammatory responses in both the fetal and maternal compartments. CONCLUSION: The relationship between placental inflammation and preterm birth is appreciated in developed countries. Here, we show that this link also exists in developing geographies. Also, among the participating sites, we found geographic- and/or population-based differences in placental inflammation and preterm birth, suggesting the importance of local factors.

Acceptability of a Randomized Trial of Anti-depressant Medication or Interpersonal Therapy for Treatment of Perinatal Depression in Women with HIV.

Postpartum depression (PPD) affects nearly 20% of postpartum women in Sub-Saharan Africa (SSA), where HIV prevalence is high. Depression is associated with worse HIV outcomes in non-pregnant adults and mental health disorders may worsen HIV outcomes for postpartum women and their infants. PPD is effectively treated with psychosocial or pharmacologic interventions; however, few studies have evaluated the acceptability of treatment modalities in SSA. We analyzed interviews with 23 postpartum women with HIV to assess the acceptability of two depression treatments provided in the context of a randomized trial. Most participants expressed acceptability of treatment randomization and study visit procedures. Participants shared perceptions of high treatment efficacy of their assigned intervention. They reported ongoing HIV and mental health stigma in their communities and emphasized the importance of social support from clinic staff. Our findings suggest a full-scale trial of PPD treatment will be acceptable among women with HIV in Zambia.

M-estimation for common epidemiological measures: introduction and applied examples.

M-estimation is a statistical procedure that is particularly advantageous for some comon epidemiological analyses, including approaches to estimate an adjusted marginal risk contrast (i.e. inverse probability weighting and g-computation) and data fusion. In such settings, maximum likelihood variance estimates are not consistent. Thus, epidemiologists often resort to bootstrap to estimate the variance. In contrast, M-estimation allows for consistent variance estimates in these settings without requiring the computational complexity of the bootstrap. In this paper, we introduce M-estimation and provide four illustrative examples of implementation along with software code in multiple languages. M-estimation is a flexible and computationally efficient estimation procedure that is a powerful addition to the epidemiologist's toolbox.

Deep learning to estimate gestational age from fly-to cineloop videos: A novel approach to ultrasound quality control.

OBJECTIVE: Low-cost devices have made obstetric sonography possible in settings where it was previously unfeasible, but ensuring quality and consistency at scale remains a challenge. In the present study, we sought to create a tool to reduce substandard fetal biometry measurement while minimizing care disruption. METHODS: We developed a deep learning artificial intelligence (AI) model to estimate gestational age (GA) in the second and third trimester from fly-to cineloops-brief videos acquired during routine ultrasound biometry-and evaluated its performance in comparison to expert sonographer measurement. We then introduced random error into fetal biometry measurements and analyzed the ability of the AI model to flag grossly inaccurate measurements such as those that might be obtained by a novice. RESULTS: The mean absolute error (MAE) of our model (±standard error) was 3.87 ± 0.07 days, compared to 4.80 ± 0.10 days for expert biometry (difference -0.92 days; 95% CI: -1.10 to -0.76). Based on simulated novice biometry with average absolute error of 7.5%, our model reliably detected cases where novice biometry differed from expert biometry by 10 days or more, with an area under the receiver operating characteristics curve of 0.93 (95% CI: 0.92, 0.95), sensitivity of 81.0% (95% CI: 77.9, 83.8), and specificity of 89.9% (95% CI: 88.1, 91.5). These results held across a range of sensitivity analyses, including where the model was provided suboptimal truncated fly-to cineloops. CONCLUSIONS: Our AI model estimated GA more accurately than expert biometry. Because fly-to cineloop videos can be obtained without any change to sonographer workflow, the model represents a no-cost guardrail that could be incorporated into both low-cost and commercial ultrasound devices to prevent reporting of most gross GA estimation errors.

Weight changes and adverse pregnancy outcomes with dolutegravir- and tenofovir alafenamide fumarate-containing antiretroviral treatment regimens during pregnancy and postpartum.

BACKGROUND: We evaluated associations between antepartum weight change and adverse pregnancy outcomes and between antiretroviral therapy (ART) regimens and week-50 postpartum body mass index in IMPAACT 2010. METHODS: Women with HIV-1 in 9 countries were randomized 1:1:1 at 14-28 weeks gestational age (GA) to start dolutegravir(DTG)+emtricitabine(FTC)/tenofovir alafenamide fumarate(TAF) versus DTG+FTC/tenofovir disoproxil fumarate(TDF) versus efavirenz (EFV)/FTC/TDF. Insufficient antepartum weight gain was defined using IOM guidelines. Cox-proportional hazards regression models were used to evaluate the association between antepartum weight change and adverse pregnancy outcomes: stillbirth (≥20 weeks GA), preterm delivery (<37 weeks GA), small for gestational age (SGA<10th percentile), and a composite of these endpoints. RESULTS: 643 participants were randomized: 217 in DTG+FTC/TAF, 215 in DTG+FTC/TDF, and 211 in EFV/FTC/TDF arms. Baseline medians were: GA 21.9 weeks, HIV RNA 903 copies/mL, CD4 count 466 cells/uL. Insufficient weight gain was least frequent with DTG+FTC/TAF (15.0%) versus DTG+FTC/TDF (23.6%) and EFV/FTC/TDF (30.4%). Women in the DTG+FTC/TAF arm had the lowest rate of composite adverse pregnancy outcome. Low antepartum weight gain was associated with higher hazard of composite adverse pregnancy outcome (HR 1.44, 95%CI 1.04, 2.00) and SGA (HR 1.48, 95%CI 0.99, 2.22). More women in the DTG+FTC/TAF arm had body mass index ≥25 kg/m2 at 50 weeks postpartum (54.7%) versus the DTG+FTC/TDF (45.2%) and EFV/FTC/TDF (34.2%) arms. CONCLUSIONS: Antepartum weight gain on DTG regimens was protective against adverse pregnancy outcomes traditionally associated with insufficient weight gain, supportive of guidelines recommending DTG-based ART for women starting ART during pregnancy. Interventions to mitigate postpartum weight gain are needed.

Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review.

Deep learning (DL) has been widely investigated in breast ultrasound (US) for distinguishing between benign and malignant breast masses. This systematic review of test diagnosis aims to examine the accuracy of DL, compared to human readers, for the diagnosis of breast cancer in the US under clinical settings. Our literature search included records from databases including PubMed, Embase, Scopus, and Cochrane Library. Test accuracy outcomes were synthesized to compare the diagnostic performance of DL and human readers as well as to evaluate the assistive role of DL to human readers. A total of 16 studies involving 9238 female participants were included. There were no prospective studies comparing the test accuracy of DL versus human readers in clinical workflows. Diagnostic test results varied across the included studies. In 14 studies employing standalone DL systems, DL showed significantly lower sensitivities in 5 studies with comparable specificities and outperformed human readers at higher specificities in another 4 studies; in the remaining studies, DL models and human readers showed equivalent test outcomes. In 12 studies that assessed assistive DL systems, no studies proved the assistive role of DL in the overall diagnostic performance of human readers. Current evidence is insufficient to conclude that DL outperforms human readers or enhances the accuracy of diagnostic breast US in a clinical setting. Standardization of study methodologies is required to improve the reproducibility and generalizability of DL research, which will aid in clinical translation and application.

Leveraging External Validation Data: The Challenges of Transporting Measurement Error Parameters.

Approaches to address measurement error frequently rely on validation data to estimate measurement error parameters (e.g., sensitivity and specificity). Acquisition of validation data can be costly, thus secondary use of existing data for validation is attractive. To use these external validation data, however, we may need to address systematic differences between these data and the main study sample. Here, we derive estimators of the risk and the risk difference that leverage external validation data to account for outcome misclassification. If misclassification is differential with respect to covariates that themselves are differentially distributed in the validation and study samples, the misclassification parameters are not immediately transportable. We introduce two ways to account for such covariates: (1) standardize by these covariates or (2) iteratively model the outcome. If conditioning on a covariate for transporting the misclassification parameters induces bias of the causal effect (e.g., M-bias), the former but not the latter approach is biased. We provide proof of identification, describe estimation using parametric models, and assess performance in simulations. We also illustrate implementation to estimate the risk of preterm birth and the effect of maternal HIV infection on preterm birth. Measurement error should not be ignored and it can be addressed using external validation data via transportability methods.

Accounting for nonmonotone missing data using inverse probability weighting.

Inverse probability weighting can be used to correct for missing data. New estimators for the weights in the nonmonotone setting were introduced in 2018. These estimators are the unconstrained maximum likelihood estimator (UMLE) and the constrained Bayesian estimator (CBE), an alternative if UMLE fails to converge. In this work we describe and illustrate these estimators, and examine performance in simulation and in an applied example estimating the effect of anemia on spontaneous preterm birth in the Zambia Preterm Birth Prevention Study. We compare performance with multiple imputation (MI) and focus on the setting of an observational study where inverse probability of treatment weights are used to address confounding. In simulation, weighting was less statistically efficient at the smallest sample size and lowest exposure prevalence examined (n = 1500, 15% respectively) but in other scenarios statistical performance of weighting and MI was similar. Weighting had improved computational efficiency taking, on average, 0.4 and 0.05 times the time for MI in R and SAS, respectively. UMLE was easy to implement in commonly used software and convergence failure occurred just twice in >200 000 simulated cohorts making implementation of CBE unnecessary. In conclusion, weighting is an alternative to MI for nonmonotone missingness, though MI performed as well as or better in terms of bias and statistical efficiency. Weighting's superior computational efficiency may be preferred with large sample sizes or when using resampling algorithms. As validity of weighting and MI rely on correct specification of different models, both approaches could be implemented to check agreement of results.

Combination HIV prevention during pregnancy and the post-partum period in Malawi and Zambia: a mathematical modelling analysis.

INTRODUCTION: Despite widespread success in reducing vertical HIV transmission, most antenatal care (ANC) programmes in eastern and southern Africa have not emphasized primary prevention of maternal HIV acquisition during pregnancy and lactation/breastfeeding. We hypothesized that combination HIV prevention interventions initiated alongside ANC could substantially reduce maternal HIV incidence. METHODS: We constructed a multi-state model describing male-to-female HIV transmission in steady heterosexual partnerships during pregnancy and lactation/breastfeeding, with initial conditions based on population distribution estimates for Malawi and Zambia in 2020. We modelled individual and joint increases in three HIV prevention strategies at or soon after ANC initiation: (1) HIV testing of male partners, resulting in HIV diagnosis and less condomless sex among those with previously undiagnosed HIV; (2) initiation (or re-initiation) of suppressive antiretroviral therapy (ART) for male partners with diagnosed but unsuppressed HIV; and (3) adherent pre-exposure prophylaxis (PrEP) for HIV-negative female ANC patients with HIV-diagnosed or unknown-status male partners. We estimated the percentage of within-couple, male-to-female HIV transmissions that could be averted during pregnancy and lactation/breastfeeding with these strategies, relative to base-case conditions in which 45% of undiagnosed male partners become newly HIV diagnosed via testing, 75% of male partners with diagnosed but unsuppressed HIV initiate/re-initiate ART and 0% of female ANC patients start PrEP. RESULTS: Increasing uptake of any single strategy by 20 percentage points above base-case levels averted 10%-11% of maternal HIV acquisitions during pregnancy and lactation/breastfeeding in the model. Joint uptake increases of 20 percentage points in two interventions averted an estimated 19%-23% of transmissions, and with a 20-percentage-point increase in uptake of all three interventions, 29% were averted. Strategies achieving 95% male testing, 90% male ART initiation/re-initiation and 40% female PrEP use reduced incident infections by 45%. CONCLUSIONS: Combination HIV prevention strategies provided alongside ANC and sustained through the post-partum period could substantially reduce maternal HIV incidence during pregnancy and lactation/breastfeeding in eastern and southern Africa.

Comparative predictive power of serum vs plasma proteomic signatures in feto-maternal medicine.

BACKGROUND: Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures. OBJECTIVE: This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome. STUDY DESIGN: Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins. RESULTS: A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42-0.79; P=3.5×10-6) than the serum signature (R=0.45; confidence interval, 0.18-0.66; P=2.2×10-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34-0.75; P=4.8×10-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27-0.72; P=2.6×10-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins. CONCLUSION: Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.

Reducing Bias in Estimates of Per Protocol Treatment Effects: A Secondary Analysis of a Randomized Clinical Trial.

This secondary analysis of a randomized clinical trial evaluates ways of reducing bias in estimates of per protocol treatment effects.

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries.

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers.

BACKGROUND: Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data. METHODS: We derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. RESULTS: Samples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh). CONCLUSIONS: Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data.

Missing Outcome Data in Epidemiologic Studies.

Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology.

Maternal mid-upper arm circumference to predict small for gestational age: Findings in a Zambian cohort.

OBJECTIVE: To compare the performance of mid upper arm circumference (MUAC) and body mass index (BMI) for prediction of small for gestational age (SGA) in Zambia. METHODS: This is a secondary analysis of an ongoing clinical cohort that included women with a single gestation and MUAC measured before 24 weeks of pregnancy. We assessed relationships between maternal MUAC and birth weight centile using regression. The performance of MUAC and BMI to predict SGA was compared using receiver operating characteristic curves and the effect of maternal HIV was investigated in sub-group analyses. RESULTS: Of 1117 participants, 847 (75%) were HIV-negative (HIV-) and 270 (24%) were HIV-positive (HIV+). Seventy-four (7%) delivered severe SGA infants (<3rd centile), of whom 56 (76%) were HIV- and 18 (24%) were HIV+ (odds ratio [OR] 1.01, 95% confidence interval [CI] 0.58-1.75). MUAC was associated with higher birth weight centile (+1.2 centile points, 95% CI 0.7-1.6; P < 0.001); this relationship was stronger among HIV+ women (+1.7 centile points, 95% CI 0.8-2.6; P < 0.001) than HIV- women (+0.9 centile points, 95% CI 0.4-1.4; P = 0.001). The discriminatory power was similar, albeit poor (area under the curve [AUC] < 0.7), between MUAC and BMI for the prediction of SGA. In stratified analysis, MUAC and BMI showed excellent discrimination predicting severe SGA among HIV+ (AUC 0.83 and 0.81, respectively) but not among HIV- women (AUC 0.64 and 0.63, respectively). CONCLUSION: Maternal HIV infection increased the discrimination of both early pregnancy MUAC and BMI for prediction of severe SGA in Zambia. CLINICAL TRIAL NUMBER: ClinicalTrials.gov (NCT02738892).

Different effects for different questions: An illustration using short cervix and the risk of preterm birth.

OBJECTIVE: To illustrate the difference between exposure effects and population attributable effects. METHODS: We examined the effect of mid-pregnancy short cervical length (<25 mm) on preterm birth using data from a prospective cohort of pregnant women in Lusaka, Zambia. Preterm birth was live birth or stillbirth before 37 weeks of pregnancy. For estimation, we used multivariable regression and parametric g-computation. RESULTS: Among 1409 women included in the analysis, short cervix was rare (2.4%); 13.6% of births were preterm. Exposure effect estimates were large (marginal risk ratio 2.86, 95% confidence interval [CI] 1.80-4.54), indicating that the preterm birth risk was substantially higher among women with a short cervix compared with women without a short cervix. However, the population attributable effect estimates were close to the null (risk ratio 1.06, 95% CI 1.02-1.10), indicating that an intervention to counteract the impact of short cervix on preterm birth would have minimal effect on the population risk of preterm birth. CONCLUSION: Although authors often refer to "the" effect, there are actually different types of effects, as we have illustrated here. In planning research, it is important to consider which effect to estimate to ensure that the estimate aligns with the research objective.

Inertial Measurement Unit-Assisted Ultrasonic Tracking System for Ultrasound Probe Localization.

Ultrasonic tracking is a promising technique in indoor object localization. However, limited success has been reported in dynamic orientational and positional ultrasonic tracking for ultrasound (US) probes due to its instability and relatively low accuracy. This article aims at developing an inertial measurement unit (IMU)-assisted ultrasonic tracking system that enables a high accuracy positional and orientational localization. The system was designed with the acoustic pressure field simulation of the transmitter, receiver configuration, position-variant error simulation, and sensor fusion. The prototype was tested in a tracking volume required in typical obstetric sonography within the typical operation speed ranges (slow mode and fast mode) of US probe movement. The performance in two different speed ranges was evaluated against a commercial optical tracking device. The results show that the proposed IMU-assisted US tracking system achieved centimeter-level positional tracking accuracy with the mean absolute error (MAE) of 12 mm and the MAE of orientational tracking was less than 1°. The results indicate the possibility of implementing the IMU-assisted ultrasonic tracking system in US probe localization.

Effect of weekly 17-hydroxyprogesterone caproate on small for gestational age among pregnant women with HIV in Zambia.

The IPOP trial demonstrated a reduced risk of severe small for gestational age among infants born to women with HIV who received weekly intramuscular 17 alpha-hydroxyprogesterone caproate. This secondary analysis examined the 17P treatment effect in subgroups of maternal BMI, parity, timing of antiretroviral therapy (ART) initiation, and ART regimen. We found that 17P was more effective among nulliparous women, women who started ART before pregnancy, and those taking protease inhibitors.