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Abstract Objectives To describe histopathologic findings in the placentas of women with coronavirus disease 2019 (COVID-19) during pregnancy. Methods Pregnant women with COVID-19 delivering between March 18, 2020, and May 5, 2020, were identified. Placentas were examined and compared to historical controls and women with placental evaluation for a history of melanoma. Results Sixteen placentas from patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were examined (15 with live birth in the third trimester, 1 delivered in the second trimester after intrauterine fetal demise). Compared to controls, third trimester placentas were significantly more likely to show at least one feature of maternal vascular malperfusion (MVM), particularly abnormal or injured maternal vessels, and intervillous thrombi. Rates of acute and chronic inflammation were not increased. The placenta from the patient with intrauterine fetal demise showed villous edema and a retroplacental hematoma. Conclusions Relative to controls, COVID-19 placentas show increased prevalence of decidual arteriopathy and other features of MVM, a pattern of placental injury reflecting abnormalities in oxygenation within the intervillous space associated with adverse perinatal outcomes. Only 1 COVID-19 patient was hypertensive despite the association of MVM with hypertensive disorders and preeclampsia. These changes may reflect a systemic inflammatory or hypercoagulable state influencing placental physiology.

Events in fetal life impact long-term health outcomes. The placenta is the first organ to form and is the site of juxtaposition between the maternal and fetal circulations. Most diseases of pregnancy are caused by, impact, or are reflected in the placenta. The purpose of this review is to describe the main inflammatory processes in the placenta, discuss their immunology, and relate their short- and long-term disease associations. Acute placental inflammation (API), including maternal and fetal inflammatory responses corresponds to the clinical diagnosis of chorioamnionitis and is associated with respiratory and neurodevelopmental diseases. The chronic placental inflammatory pathologies (CPI), include chronic villitis of unknown etiology, chronic deciduitis, chronic chorionitis, eosinophilic T-cell vasculitis, and chronic histiocytic intervillositis. These diseases are less-well studied, but have complex immunology and show mechanistic impacts on the fetal immune system. Overall, much work remains to be done in describing the long-term impacts of placental inflammation on offspring health.

To explore how placental pathology is currently used by clinicians and what placental information would be most useful in the immediate hours after delivery. We used a qualitative study design to conduct in-depth, semi-structured interviews with obstetric and neonatal clinicians who provide delivery or postpartum care at an academic medical center in the US (n = 19). Interviews were transcribed and analyzed using descriptive content analysis. Clinicians valued placental pathology information yet cited multiple barriers that prevent the consistent use of pathology. Four main themes were identified. First, the placenta is sent to pathology for consistent reasons, however, the pathology report is accessed by clinicians inconsistently due to key barriers: difficult to find in the electronic medical record, understand, and get quickly. Second, clinicians value placental pathology for explanatory capability as well as for contributions to current and future care, particularly when there is fetal growth restriction, stillbirth, or antibiotic use. Third, a rapid placental exam (specifically including placental weight, infection, infarction, and overall assessment) would be helpful in providing clinical care. Fourth, placental pathology reports that connect clinically relevant findings (similar to radiology) and that are written with plain, standardized language and that non-pathologists can more readily understand are preferred. Placental pathology is important to clinicians that care for mothers and newborns (particularly those that are critically ill) after birth, yet many problems stand in the way of its usefulness. Hospital administrators, perinatal pathologists, and clinicians should work together to improve access to and contents of reports. Support for new methods to provide quick placenta information is warranted.

Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits.

Neurodevelopment shapes how children think, move, and engage with their surroundings. Understanding the pathways underlying neurodevelopmental pathophysiology in the perinatal stage can inform intervention strategies to mitigate or reduce the severity and extent of developmental brain injuries. Early risk stratification enables timely therapies and resource planning during a critical period for the developing brain. Over the past decade, attention has turned to the placenta as a uniquely informative vantage point for the identification of pregnancies at high risk for adverse neurodevelopmental outcomes. Situated at the maternal-fetal interface, the placenta functions as a dynamic record of intrauterine conditions, integrating genetic and environmental signals into distinct and quantifiable biomarkers. Emerging evidence indicates these placental biomarkers may predict later neurodevelopmental outcomes, highlighting the organ’s value in precision perinatal care. With this in mind, the objective of this scoping review will be to investigate the current use of placental biomarkers as predictors of neurodevelopmental outcomes in clinical practice, particularly the trisomies (T13, T18, T21). In the second section of this paper, we will focus on recent advancements and improvements in the use of placental biomarkers for diagnostic and prognostic purposes in other neurodevelopmental outcomes. Finally, this article concludes with a discussion of the impact of neuroplacentology in protocol development, risk stratification, and psychosocial wellness of pregnant women.

Research-oriented cancer hospitals in the United States treat and study patients with a range of diseases. Measures of disease specific research productivity, and comparison to overall productivity, are currently lacking. Different institutions are specialized in research of particular diseases. To report disease specific productivity of American cancer hospitals, and propose a summary measure. We conducted a retrospective observational survey of the 50 highest ranked cancer hospitals in the 2013 US News and World Report rankings. We performed an automated search of PubMed and Clinicaltrials.gov for published reports and registrations of clinical trials (respectively) addressing specific cancers between 2008 and 2013. We calculated the summed impact factor for the publications. We generated a summary measure of productivity based on the number of Phase II clinical trials registered and the impact factor of Phase II clinical trials published for each institution and disease pair. We generated rankings based on this summary measure. We identified 6076 registered trials and 6516 published trials with a combined impact factor of 44280.4, involving 32 different diseases over the 50 institutions. Using a summary measure based on registered and published clinical trails, we ranked institutions in specific diseases. As expected, different institutions were highly ranked in disease-specific productivity for different diseases. 43 institutions appeared in the top 10 ranks for at least 1 disease (vs 10 in the overall list), while 6 different institutions were ranked number 1 in at least 1 disease (vs 1 in the overall list). Research productivity varies considerably among the sample. Overall cancer productivity conceals great variation between diseases. Disease specific rankings identify sites of high academic productivity, which may be of interest to physicians, patients and researchers.

The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta cause or reflect most abnormalities in gestation and can have life-long consequences for the mother and infant. Placental villi undergo a complex but reproducible sequence of maturation across the third-trimester. Abnormalities of villous maturation are a feature of gestational diabetes and preeclampsia, among others, but there is significant interobserver variability in their diagnosis. Machine learning has emerged as a powerful tool for research in pathology. To capture the volume of data and manage heterogeneity within the placenta, we developed GestaltNet, which emulates human attention to high-yield areas and aggregation across regions. We used this network to estimate the gestational age (GA) of scanned placental slides and compared it to a baseline model lacking the attention and aggregation functions. In the test set, GestaltNet showed a higher r2 (0.9444 vs. 0.9220) than the baseline model. The mean absolute error (MAE) between the estimated and actual GA was also better in the GestaltNet (1.0847 weeks vs. 1.4505 weeks). On whole-slide images, we found the attention sub-network discriminates areas of terminal villi from other placental structures. Using this behavior, we estimated GA for 36 whole slides not previously seen by the model. In this task, similar to that faced by human pathologists, the model showed an r2 of 0.8859 with an MAE of 1.3671 weeks. We show that villous maturation is machine-recognizable. Machine-estimated GA could be useful when GA is unknown or to study abnormalities of villous maturation, including those in gestational diabetes or preeclampsia. GestaltNet points toward a future of genuinely whole-slide digital pathology by incorporating human-like behaviors of attention and aggregation. The authors used artificial intelligence to estimate gestational age from scanned whole-slide images of placenta. They developed attention and aggregation deep learning methods because of the large volume of data and tissue heterogeneity. This study provides proof-of-concept in unannotated, unknown images with high accuracy.

Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists grade the microscopic appearance of breast tissue using the Nottingham criteria, which are qualitative and do not account for noncancerous elements within the tumor microenvironment. Here we present the Histomic Prognostic Signature (HiPS), a comprehensive, interpretable scoring of the survival risk incurred by breast tumor microenvironment morphology. HiPS uses deep learning to accurately map cellular and tissue structures to measure epithelial, stromal, immune, and spatial interaction features. It was developed using a population-level cohort from the Cancer Prevention Study-II and validated using data from three independent cohorts, including the Prostate, Lung, Colorectal, and Ovarian Cancer trial, Cancer Prevention Study-3, and The Cancer Genome Atlas. HiPS consistently outperformed pathologists in predicting survival outcomes, independent of tumor–node–metastasis stage and pertinent variables. This was largely driven by stromal and immune features. In conclusion, HiPS is a robustly validated biomarker to support pathologists and improve patient prognosis. Deep learning enables comprehensive and interpretable scoring for breast cancer prognosis prediction, outperforming pathologists in multicenter validation and providing insight on prognostic biomarkers.

Background Systematic exclusion of pregnant people from interventional clinical trials has created a public health emergency for millions of patients through a dearth of robust safety data for common drugs. Methods We harnessed an enterprise collection of 2.8 M electronic health records (EHRs) from routine care, leveraging data linkages between mothers and their babies to detect drug safety signals in this population at full scale. Our mixed-methods signal detection approach stimulates new hypotheses for post-marketing surveillance agnostically of both drugs and diseases—by identifying 1,054 drugs historically prescribed to pregnant patients; developing a quantitative, medication history-wide association study; and integrating a qualitative evidence synthesis platform using expert clinician review for integration of biomedical specificity—to test the effects of maternal exposure to diverse drugs on the incidence of neurodevelopmental defects in their children. Results We replicated known teratogenic risks and existing knowledge on drug structure-related teratogenicity; we also highlight 5 common drug classes for which we believe this work warrants updated assessment of their safety. Conclusion Here, we present roots of an agile framework to guide enhanced medication regulations, as well as the ontological and analytical limitations that currently restrict the integration of real-world data into drug safety management during pregnancy. This research is not a replacement for inclusion of pregnant people in prospective clinical studies, but it presents a tractable team science approach to evaluating the utility of EHRs for new regulatory review programs—towards improving the delicate equipoise of accuracy and ethics in assessing drug safety in pregnancy. Plain language summary The exclusion of pregnant people during clinical drug development limits our understanding of drug safety in pregnancy. However, given that many approved medications are prescribed during pregnancy, we studied large sets of data from patient electronic medical records, including mother-baby pairs, to develop these drug safety insights. From 2.8 M medical records, we identified 1,054 drugs prescribed to pregnant patients. Combining computerized analysis with expert clinical review, we confirmed signals previously associated with neurodevelopmental defects in children born to drug-exposed mothers. We subsequently identified 5 commonly prescribed drug classes for which we believe this work warrants updated assessment of their safety. Our approach is not a replacement for inclusion of pregnant people in clinical trials but presents a new application of existing medical record data to improve the assessment of drug safety in pregnancy. Challa et al. used 2.8 million electronic health records for proactive pharmacovigilance program development, by rapidly generating and validating hypotheses between medication use during pregnancy and neonatal neurodevelopmental defects. They identify five drug classes for which deeper safety signal evaluation may improve product marketing.

Assessment of placental villous maturation is among the most common tasks in perinatal pathology. However, the significance of abnormalities in morphology is unclear and interobserver variability is significant. To develop a machine learning model of placental maturation across the second and third trimesters and quantify the impact of different pathologist-diagnosed abnormalities of villous morphology. Digitize placental villous slides from more than 2500 placentas at 12.0 to 42.6 weeks. Build whole slide learning models to estimate obstetrician-determined gestational age for cases with appropriate maturation and normal morphology. Define the model output as \"placental age\" and compare it to the chronologic gestational age. Our model showed an r2 of 0.864 and mean absolute error of 1.62 weeks for placentas with appropriate maturation in the test set. Pathologist diagnosis of accelerated maturation was associated with a 2.56-week increase in placental age (±2.91 weeks, P < .001), while delayed maturation was associated with a 0.92-week decrease in placental age (±1.82 weeks, P < .001). Intrauterine fetal demise causes diverse changes to placental age, driven by the nature of the demise. We tested the impact of training a model, using all live births. The resulting r2 was 0.874 and mean absolute error was 1.73 weeks. Furthermore, by including cases with abnormal maturation in the training data, the effect size of accelerated maturation was blunted to only 0.56 ± 2.35 weeks (P < .001). We show that various abnormalities of villous maturation and morphology correlate with abnormalities in placental age. This \"no pathologist\" model could be useful in situations where pathologists are unavailable or the need for consistency outweighs the utility of expertise.