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Key Points: Question: Can novice clinicians accurately estimate gestational age using a low-cost, battery-powered ultrasonography probe with integrated artificial intelligence (AI) image interpretation? Findings: This prospective study enrolled 400 pregnant individuals with due dates confirmed by first-trimester ultrasonography. At follow-up visits randomly assigned throughout gestation, novice clinicians using an AI-enabled device estimated gestational age as accurately as credentialed sonographers using traditional ultrasonography devices (difference, 0.2 days). Meaning: Obstetrical care in low-resource settings may benefit from reliable gestational age assessment using AI integration with point-of-care ultrasonography. Importance: Accurate assessment of gestational age (GA) is essential to good pregnancy care but often requires ultrasonography, which may not be available in low-resource settings. This study developed a deep learning artificial intelligence (AI) model to estimate GA from blind ultrasonography sweeps and incorporated it into the software of a low-cost, battery-powered device. Objective: To evaluate GA estimation accuracy of an AI-enabled ultrasonography tool when used by novice users with no prior training in sonography. Design, Setting, and Participants: This prospective diagnostic accuracy study enrolled 400 individuals with viable, single, nonanomalous, first-trimester pregnancies in Lusaka, Zambia, and Chapel Hill, North Carolina. Credentialed sonographers established the "ground truth" GA via transvaginal crown-rump length measurement. At random follow-up visits throughout gestation, including a primary evaluation window from 14 0/7 weeks' to 27 6/7 weeks' gestation, novice users obtained blind sweeps of the maternal abdomen using the AI-enabled device (index test) and credentialed sonographers performed fetal biometry with a high-specification machine (study standard). Main Outcomes and Measures: The primary outcome was the mean absolute error (MAE) of the index test and study standard, which was calculated by comparing each method's estimate to the previously established GA and considered equivalent if the difference fell within a prespecified margin of ±2 days. Results: In the primary evaluation window, the AI-enabled device met criteria for equivalence to the study standard, with an MAE (SE) of 3.2 (0.1) days vs 3.0 (0.1) days (difference, 0.2 days [95% CI, −0.1 to 0.5]). Additionally, the percentage of assessments within 7 days of the ground truth GA was comparable (90.7% for the index test vs 92.5% for the study standard). Performance was consistent in prespecified subgroups, including the Zambia and North Carolina cohorts and those with high body mass index. Conclusions and Relevance: Between 14 and 27 weeks' gestation, novice users with no prior training in ultrasonography estimated GA as accurately with the low-cost, point-of-care AI tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of GA for all pregnant people. Trial Registration: ClinicalTrials.gov Identifier: NCT05433519 This prospective diagnostic study examines the accuracy of an AI-enabled ultrasonography tool in estimating gestational age when used by those with no prior training in sonography. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article discusses a study by E. L. Ogburn and colleagues about using predicted values to estimate descriptive measures. Topics include issues with naively using predictions, the use of multiple imputation to validly estimate descriptive parameters using predictions V in place of Y, and an approach for estimating descriptive parameters using predicted values.

Background: The World Health Organization (WHO) published the WHO Labour Care Guide (LCG) in 2020 to support the implementation of its 2018 recommendations on intrapartum care. The WHO LCG promotes evidence-based labour monitoring and stimulates shared decision-making between maternity care providers and labouring women. There is a need to identify critical questions that will contribute to defining the research agenda relating to implementation of the WHO LCG. Methods: This mixed-methods prioritization exercise, adapted from the Child Health and Nutrition Research Initiative (CHNRI) and James Lind Alliance (JLA) methods, combined a metrics-based design with a qualitative, consensus-building consultation in three phases. The exercise followed the reporting guideline for priority setting of health research (REPRISE). First, 30 stakeholders were invited to submit online ideas or questions (generation of research ideas). Then, 220 stakeholders were invited to score "research avenues" (i.e., broad research ideas that could be answered through a set of research questions) against six independent and equally weighted criteria (scoring of research avenues). Finally, a technical working group (TWG) of 20 purposively selected stakeholders reviewed the scoring, and refined and ranked the research avenues (consensus-building meeting). Results: Initially, 24 stakeholders submitted 89 research ideas or questions. A list of 10 consolidated research avenues was scored by 75/220 stakeholders. During the virtual consensus-building meeting, research avenues were refined, and the top three priorities agreed upon were: (1) optimize implementation strategies of WHO LCG, (2) improve understanding of the effect of WHO LCG on maternal and perinatal outcomes, and the process and experience of labour and childbirth care, and (3) assess the effect of the WHO LCG in special situations or settings. Research avenues related to the organization of care and resource utilization ranked lowest during both the scoring and consensus-building process. Conclusion: This systematic and transparent process should encourage researchers, program implementers, and funders to support research aligned with the identified priorities related to WHO LCG. An international collaborative platform is recommended to implement prioritized research by using harmonized research tools, establishing a repository of research priorities studies, and scaling-up successful research results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background Point-of-care (POC) early infant diagnosis (EID) provides same-day results and the potential for immediate initiation of antiretroviral therapy (ART). Methods We conducted a pragmatic trial at 6 public clinics in Zambia. HIV-exposed infants were individually randomized to either (1) POC EID (onsite testing with the Alere q HIV-1/2 Detect) or (2) enhanced standard of care (SOC) EID (off-site testing at a public laboratory). Infants with HIV were referred for ART and followed for 12 months. Our primary outcome was defined as alive, in care, and virally suppressed at 12 months. Results Between March 2016 and November 2018, we randomized 4000 HIV-exposed infants to POC (n=1989) or SOC (n=2011). All but 2 infants in the POC group received same-day results, while the median time to result in the SOC group was 27 (interquartile range: 22–30) days. Eighty-one (2%; 95% confidence interval [CI]: 1.6–2.5%) infants were diagnosed with HIV. Although ART initiation was high, there were 15 (19%) deaths, 15 (19%) follow-up losses, and 31 (38%) virologic failures. By 12 months, only 20 of 81 (25%; 95% CI: 15–34%) infants with HIV were alive, in care, and virally suppressed: 13 (30%; 16–43%) infants in the POC group vs 7 (19%; 6–32%) in the SOC group (RR: 1.56;.7–3.50). Conclusions POC EID eliminated diagnostic delays and accelerated ART initiation but did not translate into definitive improvement in 12-month outcomes. In settings where centralized EID is well functioning, POC EID is unlikely to improve pediatric HIV outcomes. Clinical Trials Registration This trial is registered at https://clinicaltrials.gov (NCT02682810). [ABSTRACT FROM AUTHOR]

Introduction: Postpartum depression (PPD) is a prevalent and debilitating disease that may affect medication adherence and thus maternal health and vertical transmission among women with HIV. We assessed the feasibility of a trial of interpersonal psychotherapy (IPT) versus antidepressant medication (ADM) to treat PPD and/or anxiety among postpartum women with HIV in Lusaka, Zambia. Methods: Between 29 October 2019 and 8 September 2020, we pre‐screened women 6–8 weeks after delivery with the Edinburgh Postnatal Depression Scale (EPDS) and diagnosed PPD or anxiety with the Mini International Neuropsychiatric Interview. Consenting participants were randomized 1:1 to up to 11 sessions of IPT or daily self‐administered sertraline and followed for 24 weeks. We assessed EPDS score, Clinical Global Impression‐Severity of Illness (CGI‐S) and medication side effects at each visit and measured maternal HIV viral load at baseline and final study visit. Retention, visit adherence, change in EPDS, CGI‐S and log viral load were compared between groups with t‐tests and Wilcoxon signed rank tests; we report mean differences, relative risks and 95% confidence intervals. A participant satisfaction survey assessed trial acceptability. Results: 78/80 (98%) participants were retained at the final study visit. In the context of the COVID‐19 pandemic, visit adherence was greater among women allocated to ADM (9.9 visits, SD 2.2) versus IPT (8.9 visits, SD 2.4; p = 0.06). EPDS scores decreased from baseline to final visit overall, though mean change was greater in the IPT group (−13.8 points, SD 4.7) compared to the ADM group (−11.4 points, SD 5.5; p = 0.04). Both groups showed similar changes in mean log viral load from baseline to final study visit (mean difference −0.43, 95% CI −0.32, 1.18; p = 0.48). In the IPT group, viral load decreased significantly from baseline (0.9 log copies/ml, SD 1.7) to final visit (0.2 log copies/ml, SD 0.9; p = 0.01). Conclusions: This pilot study demonstrates that a trial of two forms of PPD treatment is feasible and acceptable among women with HIV in Zambia. IPT and ADM both improved measures of depression severity; however, a full‐scale trial is required to determine whether treatment of PPD and anxiety improves maternal–infant HIV outcomes. [ABSTRACT FROM AUTHOR]

Background: A trial of progesterone to prevent preterm birth among HIV-infected Zambian women [Improving Pregnancy Outcomes with Progesterone (IPOP)] found no treatment effect, but the risk of the primary outcome was among the lowest ever documented in women with HIV. In this secondary analysis, we compare the risks of preterm birth (<37 weeks), stillbirth, and a composite primary outcome comprising the two in IPOP versus an observational pregnancy cohort [Zambian Preterm Birth Prevention Study (ZAPPS)] in Zambia, to evaluate reasons for the low risk in IPOP.Methods: Both studies enrolled women before 24 gestational weeks, during August 2015-September 2017 (ZAPPS) and February 2018-January 2020 (IPOP). We used linear probability and log-binomial regression to estimate risk differences and risk ratios (RR), before and after restriction and standardization with inverse probability weights.Results: The unadjusted risk of composite outcome was 18% in ZAPPS (N = 1450) and 9% in IPOP (N = 791) (RR = 2.0; 95% CI = 1.6, 2.6). After restricting and standardizing the ZAPPS cohort to the distribution of IPOP baseline characteristics, the risk remained higher in ZAPPS (RR = 1.6; 95% CI = 1.0, 2.4). The lower risk of preterm/stillbirth in IPOP was only partially explained by measured risk factors.Conclusions: Possible benefits in IPOP of additional monetary reimbursement, more frequent visits, and group-based care warrant further investigation. [ABSTRACT FROM AUTHOR]