Introduction:There is a lack of published data on the contemporary use and timing of antenatal corticosteroids in the United States. Our primary objective was to address this gap using clinical data from a large US birth cohort. Our secondary objective was to identify factors associated with optimally timed steroids in births <34 weeks' gestation. Methods:This retrospective study included de-identified clinical data from a quality improvement database for births at 24+0-42+6 weeks' gestation between January 1, 2017 and December 31, 2021 at 12 hospitals in the northwest United States. We evaluated the proportion of live births <34 weeks' gestation that received at least a first dose of antenatal steroids within the 7 days preceding birth ("optimally timed" steroids), and the proportion of patients given antenatal steroids who delivered at term. Annual rates of these measures were also evaluated across the study period. Associations between optimal steroid timing in preterm births <34 weeks' gestation and sociodemographic and medical characteristics were explored with a multilevel logistic regression model using hospitals as random effects. Results:Of 105,981 patients included in the analysis, 6.1% (6443) received steroids before 37 weeks' gestation. A total of 83.1% of live births <34 weeks' gestation received antenatal steroids, but only 55.4% received optimally timed steroids. The rate of optimally timed steroids in births <34 weeks' gestation was stable between 2017 and 2021. Over the same period, the proportion of births at 34+0-36+6 weeks' gestation that received steroids increased from 36.6% to 46.6%, and the proportion that received steroids and then delivered at term increased from 15.5% to 22.1% (p < 0.001). After adjusting for confounders, preeclampsia was associated with increased odds for optimally timed steroids in births <34 weeks' gestation (OR 1.46; 95% CI 1.20, 1.79) while ruptured membranes (odds ratio [OR], 0.76; 95% CI, 0.63, 0.91) and placenta previa (OR, 0.28; 95% CI, 0.16, 0.51) were associated with decreased odds. Conclusion:In our study, most patients who delivered at <34 weeks' gestation received at least one dose of antenatal corticosteroids, but this was outside of the optimal 7-day window in approximately half of cases. Quality improvement efforts should focus on timing, as well as the use of antenatal corticosteroids.

BACKGROUND: Early identification of patients at increased risk for postpartum hemorrhage (PPH) associated with severe maternal morbidity (SMM) is critical for preparation and preventative intervention. However, prediction is challenging in patients without obvious risk factors for postpartum hemorrhage with severe maternal morbidity. Current tools for hemorrhage risk assessment use lists of risk factors rather than predictive models. OBJECTIVE: To develop, validate (internally and externally), and compare a machine learning model for predicting PPH associated with SMM against a standard hemorrhage risk assessment tool in a lower risk laboring obstetric population. STUDY DESIGN: This retrospective cross-sectional study included clinical data from singleton, term births (>=37 weeks' gestation) at 19 US hospitals (2016-2021) using data from 58,023 births at 11 hospitals to train a generalized additive model (GAM) and 27,743 births at 8 held- out hospitals to externally validate the model. The outcome of interest was PPH with severe maternal morbidity (blood transfusion, hysterectomy, vascular embolization, intrauterine balloon tamponade, uterine artery ligation suture, uterine compression suture, or admission to intensive care). Cesarean birth without a trial of vaginal birth and patients with a history of cesarean were excluded. We compared the model performance to that of the California Maternal Quality Care Collaborative (CMQCC) Obstetric Hemorrhage Risk Factor Assessment Screen. RESULTS: The GAM predicted PPH with an area under the receiver- operating characteristic curve (AUROC) of 0.67 (95% CI 0.64-0.68) on external validation, significantly outperforming the CMQCC risk screen AUROC of 0.52 (95% CI 0.50-0.53). Additionally, the GAM had better sensitivity of 36.9% (95% CI 33.01-41.02) than the CMQCC screen sensitivity of 20.30% (95% CI 17.40-22.52) at the CMQCC screen positive rate of 16.8%. The GAM identified in-vitro fertilization as a risk factor (adjusted OR 1.5; 95% CI 1.2-1.8) and nulliparous births as the highest PPH risk factor (adjusted OR 1.5; 95% CI 1.4-1.6). CONCLUSION: Our model identified almost twice as many cases of PPH as the CMQCC rules-based approach for the same screen positive rate and identified in-vitro fertilization and first-time births as risk factors for PPH. Adopting predictive models over traditional screens can enhance PPH prediction.

ABSTRACT Objective Low‐dose aspirin (LDA) has been shown to reduce the risk of preterm pre‐eclampsia and it has been suggested that it should be recommended for all pregnancies. However, some studies have reported an association between LDA and an increased risk of bleeding complications in pregnancy. Our aim was to evaluate the risk of placental abruption and postpartum hemorrhage (PPH) in patients for whom their healthcare provider had recommended prophylactic aspirin. Methods This multicenter cohort study included 72 598 singleton births at 19 hospitals in the USA, between January 2019 and December 2021. Pregnancies complicated by placenta previa/accreta, birth occurring at less than 24 weeks' gestation, multiple pregnancy or those with data missing for aspirin recommendation were excluded. Propensity scores were calculated using 20 features spanning sociodemographic factors, medical history, year and hospital providing care. The association between LDA recommendation and placental abruption or PPH was estimated by inverse‐probability treatment weighting using the propensity scores. Results We included 71 627 pregnancies in the final analysis. Aspirin was recommended to 6677 (9.3%) and was more likely to be recommended for pregnant individuals who were 35 years or older ( P < 0.001), had a body mass index of 30 kg/m 2 or higher ( P < 0.001), had prepregnancy hypertension ( P < 0.001) and who had a Cesarean delivery ( P < 0.001). Overall, 1.7% of the study cohort (1205 pregnancies) developed preterm pre‐eclampsia: 1.3% in the no‐aspirin and 5.8% in the aspirin group. After inverse‐probability weighting with propensity scores, aspirin was associated with increased risk of placental abruption (adjusted odds ratio (aOR), 1.44 (95% CI, 1.04–2.00)) and PPH (aOR, 1.21 (95% CI, 1.05–1.39)). The aOR translated to a number needed to harm with LDA of 79 (95% CI, 43–330) for PPH and 287 (95% CI, 127–3151) for placental abruption. Conclusions LDA recommendation in pregnancy was associated with increased risk for placental abruption and for PPH. Our results support the need for more research into aspirin use and bleeding complications in pregnancy before recommending it beyond the highest‐risk pregnancies. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Background and Aims Predicting personalized risk for adverse events following percutaneous coronary intervention (PCI) remains critical in weighing treatment options, employing risk mitigation strategies, and enhancing shared decision-making. This study aimed to employ machine learning models using pre-procedural variables to accurately predict common post-PCI complications.Methods A group of 66 adults underwent a semiquantitative survey assessing a preferred list of outcomes and model display. The machine learning cohort included 107 793 patients undergoing PCI procedures performed at 48 hospitals in Michigan between 1 April 2018 and 31 December 2021 in the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) registry separated into training and validation cohorts. External validation was conducted in the Cardiac Care Outcomes Assessment Program database of 56 583 procedures in 33 hospitals in Washington.Results Overall rate of in-hospital mortality was 1.85% (n = 1999), acute kidney injury 2.51% (n = 2519), new-onset dialysis 0.44% (n = 462), stroke 0.41% (n = 447), major bleeding 0.89% (n = 942), and transfusion 2.41% (n = 2592). The model demonstrated robust discrimination and calibration for mortality {area under the receiver-operating characteristic curve [AUC]: 0.930 [95% confidence interval (CI) 0.920-0.940]}, acute kidney injury [AUC: 0.893 (95% CI 0.883-0.903)], dialysis [AUC: 0.951 (95% CI 0.939-0.964)], stroke [AUC: 0.751 (95%CI 0.714-0.787)], transfusion [AUC: 0.917 (95% CI 0.907-0.925)], and major bleeding [AUC: 0.887 (95% CI 0.870-0.905)]. Similar discrimination was noted in the external validation population. Survey subjects preferred a comprehensive list of individually reported post-procedure outcomes.Conclusions Using common pre-procedural risk factors, the BMC2 machine learning models accurately predict post-PCI outcomes. Utilizing patient feedback, the BMC2 models employ a patient-centred tool to clearly display risks to patients and providers (https://shiny.bmc2.org/pci-prediction/). Enhanced risk prediction prior to PCI could help inform treatment selection and shared decision-making discussions. Structured Graphical Abstract (Top) Graphical representation of the overall study goal combining machine learning and patient feedback to create a patient-centred personalized risk prediction tool. (Bottom) Area under the receiver-operating characteristic curves for XGBoost model performance for in-hospital mortality, acute kidney injury (AKI), new requirement for dialysis, stroke, transfusion, and major bleeding.

Abnormal emotion processing is a core feature of schizophrenia spectrum disorders (SSDs) that encompasses multiple operations. While deficits in some areas have been well-characterized, we understand less about abnormalities in the emotion processing that happens through language, which is highly relevant for social life. Here, we introduce a novel method using deep learning to estimate emotion processing rapidly from spoken language, testing this approach in male-identified patients with SSDs (n=37) and healthy controls (n=51). Using free responses to evocative stimuli, we derived a measure of appropriateness, or “emotional alignment” (EA). We examined psychometric characteristics of EA and its sensitivity to a single-dose challenge of oxytocin, a neuropeptide shown to enhance the salience of socioemotional information in SSDs. Patients showed impaired EA relative to controls, and impairment correlated with poorer social cognitive skill and more severe motivation and pleasure deficits. Adding EA to a logistic regression model with language-based measures of formal thought disorder (FTD) improved classification of patients versus controls. Lastly, oxytocin administration improved EA but not FTD among patients. While additional validation work is needed, these initial results suggest that an automated assay using spoken language may be a promising approach to assess emotion processing in SSDs.

BackgroundPrevious studies have demonstrated an increased risk of cardiovascular disease (CVD) in women with a history of pregnancy loss. Less is known about whether pregnancy loss is associated with age at the onset of CVD, but this is a question of interest, as a demonstrated association of pregnancy loss with early-onset CVD may provide clues to the biological basis of the association, as well as having implications for clinical care. We conducted an age-stratified analysis of pregnancy loss history and incident CVD in a large cohort of postmenopausal women aged 50–79 years old.MethodsAssociations between a history of pregnancy loss and incident CVD were examined among participants in the Women's Health Initiative Observational Study. Exposures were any history of pregnancy loss (miscarriage and/or stillbirth), recurrent (2+) loss, and a history of stillbirth. Logistic regression analyses were used to examine associations between pregnancy loss and incident CVD within 5 years of study entry in three age strata (50–59, 69–69, and 70–79). Outcomes of interest were total CVD, coronary heart disease (CHD), congestive heart failure, and stroke. To assess the risk of early onset CVD, Cox proportional hazard regression was used to examine incident CVD before the age of 60 in a subset of subjects aged 50–59 at study entry.ResultsAfter adjustment for cardiovascular risk factors, a history of stillbirth was associated with an elevated risk of all cardiovascular outcomes in the study cohort within 5 years of study entry. Interactions between age and pregnancy loss exposures were not significant for any cardiovascular outcome; however, age-stratified analyses demonstrated an association between a history of stillbirth and risk of incident CVD within 5 years in all age groups, with the highest point estimate seen in women aged 50–59 (OR 1.99; 95% CI, 1.16–3.43). Additionally, stillbirth was associated with incident CHD among women aged 50–59 (OR 3.12; 95% CI, 1.33–7.29) and 60–69 (OR 2.06; 95% CI, 1.24–3.43) and with incident heart failure and stroke among women aged 70–79. Among women aged 50–59 with a history of stillbirth, a non-significantly elevated hazard ratio was observed for heart failure before the age of 60 (HR 2.93, 95% CI, 0.96–6.64).ConclusionsHistory of stillbirth was strongly associated with a risk of cardiovascular outcomes within 5 years of baseline in a cohort of postmenopausal women aged 50–79. History of pregnancy loss, and of stillbirth in particular, might be a clinically useful marker of cardiovascular disease risk in women.

To develop an externally validated Explainable Boosting Machine (EBM) to predict severe maternal morbidity (SMM) at the time of hospital admission for delivery. This retrospective study used clinical data for 155,935 births (24-43 weeks) at 20 U.S. hospitals (2016 - 2021). An Explainable Boosting Machine (EBM) was trained to predict SMM (hysterectomy, blood transfusion, DIC, amniotic fluid embolism, thromboembolism, or eclampsia) using 50 features known at admission (including demographic characteristics, obstetric and other medical factors). The model was trained using data from 106,903 births at 14 hospitals and externally validated with data from 49,032 births at 6 different hospitals. A logistic regression (LR) model was trained on the same data for comparison. SMM occurred in 2262 cases (1.5%) (Table 1). The features that were most predictive of SMM included preeclampsia/gestational hypertension, economic distress quintile (based on the area where the patient lived), self-reported race, age, height, nulliparity, labor type (induction versus spontaneous labor) and initial cervical exam on admission. Risk for SMM was lowest in pregnant people aged 21-37, and rose significantly for those < 20 years and >37 years old. SMM risk decreased as height increased and with increasing cervical dilation on the initial exam in hospital. External validation gave an AUC of 0.70 (CI 0.68 – 0.71) for EBMs, and 0.69 (CI 0.67 – 0.71) for LR. The ROC plot (Figure 1) identified 59% of births with SMM for a 25% screen positive rate. Using only the top 6 features, EBMs yielded an AUC of 0.66 (95% CI 0.65 – 0.67). Our study showed that EBMs can provide clinically useful classification of SMM risk at the time of admission for delivery and highlighted hypertensive disorders of pregnancy and sociodemographic factors that result in societal disadvantage, as important risk factors for SMM. EBM interpretability also gave insights into risk factors for SMM not traditionally considered such as maternal height.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Objective: We developed a composite index to quantify state legislation related to reproductive autonomy and examined its association with maternal and neonatal outcomes. We hypothesized that greater reproductive autonomy would be associated with lower rates of severe maternal morbidity (SMM), pregnancy-related mortality (PRM), preterm birth (PTB), and low birthweight. Design: A Delphi panel was used to inform development of the index. Restrictive policies were assigned values of-1 and enabling policies +1. Publicly available data were used to conduct a cross-sectional study among all live births in the 50 U.S. states to people aged 15 to 44 between January 1, 2016, and December 31, 2018, to examine the association between the risk index and PRM, SMM, PTB, and low birthweight. We used linear regression with state scores and quartiles, adjusted for state-level proportions of White, Black, and Hispanic live births; percent living in rural areas; percent of population foreign born; Health Resources and Services Administration spending on maternal and child health; and the Opportunity Index, a composite measure of indicators of the economy, education, and community. Results: From 2016 to 2018, there were 11,530,785 births, 2,846 pregnancy-related deaths, and 154,384 cases of SMM. The Delphi panel resulted in a summed state measure of 106 laws in 8 categories that could affect reproductive au-tonomy. In adjusted analyses, states in the most enabling (most reproductive autonomy) quartile had a 44.7 per 10,000 higher rate of SMM compared with the most restrictive quartile. However, the most enabling quartile was associated with a 9.87 per 100,000 lower rate of PRM and 0.67 per 100 lower rate of PTB compared with the most restrictive quartile (least reproductive autonomy). Conclusions: A composite policy index of reproductive autonomy was found to be associated with higher rates of SMM but lower rates of PRM and PTB. Further research is needed to understand how reproductive autonomy in the cumu-lative index may influence these and other maternal and birth outcomes. & COPY; 2023 Jacobs Institute of Women's Health, George Washington University. Published by Elsevier Inc. All rights reserved.

To use interpretable machine learning to identify the most important risk factors for shoulder dystocia (SD). This retrospective study used clinical data from 82,889 singleton vaginal births (24-43 weeks' gestation) from 20 U.S. hospitals (01/2016 - 12/2021) to train and externally validate a predictive model for SD using 57 features available at the time of delivery plus birthweight (which can be estimated prenatally but is not known until birth). Using bootstrap analysis, an Explainable Boosting Machine (EBM) was trained using data from 59,665 births at 13 hospitals and externally validated using 23,224 births at 7 different hospitals. A logistic regression (LR) model was also trained for comparison. SD occurred in 3.4% of births. An AUC of 0.74 (95% CI 0.73 – 0.75) was obtained for the EBM. Sensitivity for SD was 62%, for a false positive rate (FPR) of 25%. LR yielded an AUC of 0.74 (95% CI 0.73 – 0.75). The most important features for predicting SD were birthweight, maternal height, BMI, cervical dilation at initial exam, and time from admission to full dilation. SD risk increased dramatically and linearly with birthweight and decreased as maternal height increased (Figure 1). Surprisingly, length of 2nd stage was not a strong predictor. Using only the top 5 features gave a slightly higher AUC of 0.75 (95% CI 0.74 – 0.76) and a 65% sensitivity for a 25% FPR, suggesting that eliminating some features might yield a small model with clinically useful SD prediction. AI models trained on these data yielded modest accuracy for predicting shoulder dystocia risk and EBM intelligibility highlighted that consideration of factors in addition to fetal weight, may add to the assessment of risk. EBMs also reinforced the need to accurately estimate fetal weight, as evident through birthweight's exceptionally large contribution to risk. Our results suggest that a novel, clinically useful model for predicting SD at the time of delivery might be achievable using only a small number of features.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through a better understanding of risk factors, heightened surveillance for high-risk patients, and more timely and appropriate interventions, thereby helping obstetricians deliver better care. We identify and study the most important risk factors for four types of pregnancy complications: (i) severe maternal morbidity, (ii) shoulder dystocia, (iii) preterm preeclampsia, and (iv) antepartum stillbirth. We use an Explainable Boosting Machine (EBM), a high-accuracy glass-box learning method, for the prediction and identification of important risk factors. We undertake external validation and perform an extensive robustness analysis of the EBM models. EBMs match the accuracy of other black-box ML methods, such as deep neural networks and random forests, and outperform logistic regression, while being more interpretable. EBMs prove to be robust. The interpretability of the EBM models reveal surprising insights into the features contributing to risk (e.g., maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.

Our goal was to develop a predictive model for preterm preeclampsia (preeclampsia with birth < 37 weeks' gestation) using an externally validated intelligible machine learning approach. This retrospective study used clinical data from 153,432 births at 20 U.S. hospitals (01/2016 - 12/2021). Explainable Boosting Machine (EBM) and Logistic Regression (LR) models were trained to predict preterm preeclampsia using data from 13 hospitals (110,407 births), and externally validated using data from 7 different hospitals (43,025 births). The 30 features used in the models were all available early in pregnancy and included sociodemographic characteristics, pre-pregnancy health, and previous pregnancy complications. The incidence of preterm preeclampsia was 1.9% (n=2919) (Table 1). The strongest predictors of preterm preeclampsia in the EBM were BMI, nulliparity, age, self-reported history of pre-pregnancy mental health issues, and chronic hypertension. BMI exhibited a near-linear contribution to preterm preeclampsia risk between 22 kg/m2 and 45 kg/m2, with the highest BMI being associated with the highest risk. A significant increase in risk for preterm preeclampsia was observed at around 45 years of age (Figure 1). The EBM yielded an AUC of 0.77 (95% CI 0.75 – 0.78) and outperformed the LR AUC of 0.74 (95% CI 0.73 – 0.75). An AUC of 0.72 (95% CI 0.72 – 0.73) was achieved for EBMs when trained only using the top 5 most important features. The EBM trained on all features identified 65% for preterm preeclampsia cases for a screen positive rate (FPR) of 25%. EBMs can be used to predict preterm preeclampsia using routinely collected data available early in pregnancy. EBMs also revealed the factors that contributed most to risk in our study population, namely maternal BMI, nulliparity, and age (with a sharp increase in risk at 45 years). Importantly, EBMs highlighted a near-linear relationship between increasing BMI (one of the most modifiable risk factors) and increasing risk for preterm preeclampsia.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Introduction: Reproductive policies' impact on disparities in neonatal outcomes is understudied. Thus, we aimed to assess whether an index of reproductive autonomy is associated with black-white disparities in preterm birth (PTB) and low birthweight (LBW).Methods: We used publicly available state-level PTB and LBW data for all live-births among persons aged 15-44 from January 1, 2016, to December 31, 2018. The independent measure was an index of state laws characterizing each state's reproductive autonomy, ranging from 5 (most restrictive) to 43 (most enabling), used continuously and as quartiles. Linear regression was performed to evaluate the association between both the index score (continuous, primary analysis; quartiles, secondary analysis) and state-level aggregated black-white disparity rates in PTB and LBW per 100 live births.Results: Among 10,297,437 black (n=1,829,051 [17.8%]) and white (n=8,468,386 [82.2%]) births, rates of PTB and LBW were 6.46 and 8.24 per 100, respectively. Regression models found that every 1-U increase in the index was associated with a -0.06 (confidence interval [CI]: -0.10 to -0.01) and -0.05 (CI: -0.08, to -0.01) per 100 lower black-white disparity in PTB and LBW rates (p<0.05, p<0.01), respectively. The most enabling quartiles were associated with -1.21 (CI: -2.38 to -0.05) and -1.62 (CI: -2.89 to -0.35) per 100 lower rates of the black-white disparity in LBW, compared with the most restrictive quartile (both p<0.05).Conclusion: Greater reproductive autonomy is associated with lower rates of state-level disparities in PTB and LBW. More research is needed to better understand the importance of state laws in shaping racialized disparities, reproductive autonomy, and birth outcomes.

To use explainable AI to identify the most important risk factors for antepartum stillbirth. This retrospective study used data from 153,432 singleton births (20-43 weeks' gestation) at 20 U.S. hospitals (01/2016 - 12/2021). An Explainable Boosting Machine (EBM) was trained to predict antepartum stillbirth using 30 features available early in pregnancy including patient demographics and medical history. Births at 13 hospitals (n=105,174) were used to train the EBM and births at 7 different hospitals (n=48,258) were used for external validation. A logistic regression (LR) model was also trained on the data for comparison. Stillbirth occurred in 610 (0.4%) pregnancies. The strongest predictor of stillbirth was BMI – risk was highest for patients with Class I obesity (Figure 1). Area of residence was also important. Risk was lowest for patients living in the most prosperous areas (DistressQuintile 1), and gradually rose to the highest risk for patients living in the most economically deprived areas (DistressQuintile 5). Maternal age, IVF, self-reported race, and nulliparity were also among the strongest predictors. Surprisingly, shorter maternal height was associated with increased stillbirth risk. EBMs yielded an AUC of 0.70 (95% CI 0.67 – 0.73), comparable to the LR AUC of 0.69 (95% CI 0.66 – 0.72). Sensitivity was 59% at a 20% screen positive rate. Experiments with other AI models on the same data suggested it is difficult to obtain higher sensitivity using only features available in early pregnancy. Our study demonstrates how the intelligibility of EBMs can reveal surprising risk factors, in this case highlighting maternal BMI and particularly Class I obesity as a crucial risk factor. Why risk was highest in Class 1 obesity is uncertain but it could be speculated that patients with Class 2 or 3 obesity receive different clinical management and increased surveillance thus mitigating stillbirth risk. Like other adverse pregnancy outcomes, socioeconomic factors are important for stillbirth.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Low dose aspirin (LDA) has been shown to reduce the risk for preterm pre-eclampsia. Some studies have reported an increased risk for bleeding complications in pregnancy. Our aim was to evaluate the risk of abruption and postpartum hemorrhage (PPH) associated with LDA. This retrospective study included 73,694 births at 19 U.S. hospitals (01/2019-12/2021). Births were excluded if they were associated with multiple pregnancy (n=2209), placenta previa/accreta (n=380) or had missing data for LDA recommendation (n=590). Propensity scores were calculated using 19 features spanning sociodemographic factors, medical history, year of birth, and hospital providing care. The association between LDA and abruption or PPH was estimated by inverse probability weighting using the propensity scores. We included 71,627 births in the final analysis; LDA was recommended to 6677 (9.3%). LDA was more likely to be recommended to older pregnant individuals, those with higher BMI, pre-existing medical complications, or giving birth at a level III hospital, and those who had an operative birth (cesarean or operative vaginal) (Table 1). Overall, 1,205 (1.7%) developed preterm preeclampsia: 1.3% in the no LDA and 5.7% in the LDA group. After inverse probability weighting with propensity scores, LDA was associated with increased risk of abruption (adjusted OR 1.53; 95% CI 1.37 – 1.71) and PPH (adjusted OR 1.21; 95% CI, 1.16– 1.26). LDA was associated with increased risk for abruption and with PPH. Limitations of the study include the lack of data on aspirin dose and timing in pregnancy, as well as the possibility of unaccounted confounding. However, our results support the need for more research into LDA and bleeding complications in pregnancy before recommending LDA beyond the highest risk pregnancies.

BACKGROUND AND OBJECTIVES: Mothers who are Black, Indigenous, and people of color (BIPOC) are disproportionately impacted by substance use in pregnancy and less likely to breastfeed. Our objectives were to assess relationships between substance use in pregnancy and exclusive breastfeeding at discharge (EBF) and race/ethnicity and EBF, and determine the extent to which substance use influences the relationship between race/ethnicity and EBF. METHODS: This is a retrospective cohort study of term mother-infant dyads using 2016 to 2019 data from a Northwest quality improvement collaborative, Obstetrical Care Outcomes Assessment Program. Stepwise and stratified multivariable logistic regression analyses were conducted to determine associations between independent variables consisting of characteristics, including maternal race/ethnicity and substance use, and the dependent variable, EBF. RESULTS: Our sample consisted of 84,742 dyads, 69.5% of whom had EBF. The adjusted odds of EBF for non-Hispanic Black and Hispanic mothers were half, and for American Indian/Alaska Native mothers two-thirds, that of White mothers (aOR [95% CI]: 0.52 [0.48, 0.57], 0.51 [0.48, 0.54], 0.64 [0.55, 0.76], respectively). Substance use did not mediate the association between race/ethnicity and EBF, but it modified the association. Among those reporting nicotine or marijuana use, Hispanic mothers were half as likely as White mothers were to exclusively breastfeed. Other factors associated with a lower likelihood of EBF included public or no insurance, rural setting, C-section, NICU admission, and LBW. CONCLUSIONS: Disparities in EBF related to race/ethnicity and substance use were pronounced in this study, particularly among Hispanic mothers with nicotine or marijuana use.

Background The COVID-19 pandemic is dominated by variant viruses; the resulting impact on disease severity remains unclear. Using a retrospective cohort study, we assessed the hospitalization risk following infection with seven SARS-CoV-2 variants. Methods Our study includes individuals with positive SARS-CoV-2 RT-PCR in the Washington Disease Reporting System with available viral genome data, from December 1, 2020 to January 14, 2022. The analysis was restricted to cases with specimens collected through sentinel surveillance. Using a Cox proportional hazards model with mixed effects, we estimated hazard ratios (HR) for hospitalization risk following infection with a variant, adjusting for age, sex, calendar week, and vaccination. Findings 58,848 cases were sequenced through sentinel surveillance, of which 1705 (2.9%) were hospitalized due to COVID-19. Higher hospitalization risk was found for infections with Gamma (HR 3.20, 95%CI 2.40-4.26), Beta (HR 2.85, 95%CI 1.56-5.23), Delta (HR 2.28 95%CI 1.56-3.34) or Alpha (HR 1.64, 95%CI 1.29-2.07) compared to infections with ancestral lineages; Omicron (HR 0.92, 95%CI 0.56-1.52) showed no significant difference in risk. Following Alpha, Gamma, or Delta infection, unvaccinated patients show higher hospitalization risk, while vaccinated patients show no significant difference in risk, both compared to unvaccinated, ancestral lineage cases. Hospitalization risk following Omicron infection is lower with vaccination. Conclusion Infection with Alpha, Gamma, or Delta results in a higher hospitalization risk, with vaccination attenuating that risk. Our findings support hospital preparedness, vaccination, and genomic surveillance. Summary Hospitalization risk following infection with SARS-CoV-2 variant remains unclear. We find a higher hospitalization risk in cases infected with Alpha, Beta, Gamma, and Delta, but not Omicron, with vaccination lowering risk. Our findings support hospital preparedness, vaccination, and genomic surveillance.