population-based studies is needed to inform the treatment of SARS-CoV-2 infection during pregnancy and to provide health risk information to pregnant individuals. OBJECTIVE To assess the risk of perinatal complications associated with SARS-CoV-2 infection and to describe factors associated with hospitalizations. DESIGN, SETTING, AND PARTICIPANTSThis population-based cohort study included 43 886 pregnant individuals with longitudinal electronic health record data from preconception to delivery who delivered at Kaiser Permanente Northern California between March 1, 2020, and March 16, 2021. Individuals with diagnostic codes for COVID-19 that did not have a confirmatory polymerase chain reaction test for SARS-CoV-2 were excluded.EXPOSURES SARS-CoV-2 infection detected by polymerase chain reaction test (from 30 days before conception to 7 days after delivery) as a time varying exposure. MAIN OUTCOMES AND MEASURESSevere maternal morbidity including 21 conditions (eg, acute myocardial infarction, acute renal failure, acute respiratory distress syndrome, and sepsis) that occurred at any time during pregnancy or delivery; preterm birth; pregnancy hypertensive disorders; gestational diabetes; venous thromboembolism (VTE); stillbirth; cesarean delivery; and newborn birth weight and respiratory conditions. Standardized mean differences between individuals with and without SARS-CoV-2 were calculated. Cox proportional hazards regression was used to estimate the hazard ratios (HRs) and 95% CIs for the association between SARS-CoV-2 infection and perinatal complications and hospitalization and to consider the timing of SARS-CoV-2 infection relative to outcomes. RESULTSIn this study of 43 886 pregnant individuals (mean [SD] age, 30.7 [5.2] years), individuals with a SARS-CoV-2 infection (1332 [3.0%]) were more likely to be younger, Hispanic, multiparous individuals with a higher neighborhood deprivation index and obesity or chronic hypertension. After adjusting for demographic characteristics, comorbidities, and smoking status, individuals with SARS-CoV-2 infection had higher risk for severe maternal morbidity (HR, 2.45; 95% CI, 1.91-3.13), preterm birth (<37 weeks; HR, 2.08; 95% CI, 1.75-2.47), and VTE (HR, 3.08; 95% CI, 1.09-8.74) than individuals without SARS-CoV-2. SARS-CoV-2 infection was also associated with increased risk of medically indicated preterm birth (HR, 2.56; 95% CI, 2.06-3.19); spontaneous preterm birth (HR, 1.61; 95% CI, 1.22-2.13); and early (HR, 2.52; 95% CI, 1.49-4.24), moderate (HR, 2.18; 95% CI, 1.25-3.80), and late (HR, 1.95; 95% CI, 1.61-2.37) preterm birth. Among individuals with SARS-CoV-2 infection, 76 (5.7%) had a hospitalization; pregestational diabetes (HR, 7.03; 95% CI, 2.22-22.2) and Asian or Pacific Islander (HR, 2.33; 95% CI, 1.06-5.11) and Black (HR, 3.14; 95% CI, 1.24-7.93) race and ethnicity were associated with an increased risk of hospitalization. CONCLUSIONS AND RELEVANCEIn this cohort study, SARS-CoV-2 infection was associated with increased risk of severe maternal morbidity, preterm b...

Gestational diabetes (GDM) predisposes pregnant individuals to perinatal complications and long-term diabetes and cardiovascular diseases. We developed and validated metabolomic markers for GDM in a prospective test-validation study. In a case-control sample within the PETALS cohort (91 GDM, 180 non-GDM; discovery set), a random PETALS subsample (42 GDM, 372 non-GDM; validation set 1), and a case-control sample within the GLOW trial (35 GDM, 70 non-GDM; validation set 2), fasting serum untargeted metabolomics were measured by gas chromatography/time-of-flight mass spectrometry. Multivariate enrichment analysis examined metabolites-GDM associations. Ten-fold cross-validated LASSO regression identified predictive metabolomic markers at gestational weeks (GW) 10-13 and 16-19 for GDM. The purinone metabolites at GW 10-13 and 16-19, and the amino acids, amino alcohols, hexoses, indoles, and pyrimidines metabolites at GW 16-19 were positively associated with GDM risk (FDR &lt;0.05). A 17-metabolite panel at GW 10-13 outperformed the model using conventional risk factors including fasting glycemia (discovery AUC: 0.871 vs. 0.742; validation 1: 0.869 vs. 0.731; validation 2: 0.972 vs. 0.742; P &lt;0.01). Similar results were observed for a 13-metabolite panel at GW 17-19. Dysmetabolism is present early in pregnancy among individuals progressing to GDM. Multi-metabolite panels in early pregnancy can predict GDM risk beyond conventional risk factors.

Background Gestational diabetes (GDM) is prevalent and benefits from timely and effective treatment, given the short window to impact glycemic control. Clinicians face major barriers to choosing effectively among treatment modalities [medical nutrition therapy (MNT) with or without pharmacologic treatment (antidiabetic oral agents and/or insulin)]. We investigated whether clinical data at varied stages of pregnancy can predict GDM treatment modality. Methods Among a population-based cohort of 30,474 pregnancies with GDM delivered at Kaiser Permanente Northern California in 2007–2017, we selected those in 2007–2016 as the discovery set and 2017 as the temporal/future validation set. Potential predictors were extracted from electronic health records at different timepoints (levels 1–4): (1) 1-year preconception to the last menstrual period, (2) the last menstrual period to GDM diagnosis, (3) at GDM diagnosis, and (4) 1 week after GDM diagnosis. We compared transparent and ensemble machine learning prediction methods, including least absolute shrinkage and selection operator (LASSO) regression and super learner, containing classification and regression tree, LASSO regression, random forest, and extreme gradient boosting algorithms, to predict risks for pharmacologic treatment beyond MNT. Results The super learner using levels 1–4 predictors had higher predictability [tenfold cross-validated C-statistic in discovery/validation set: 0.934 (95% CI: 0.931–0.936)/0.815 (0.800–0.829)], compared to levels 1, 1–2, and 1–3 (discovery/validation set C-statistic: 0.683–0.869/0.634–0.754). A simpler, more interpretable model, including timing of GDM diagnosis, diagnostic fasting glucose value, and the status and frequency of glycemic control at fasting during one-week post diagnosis, was developed using tenfold cross-validated logistic regression based on super learner-selected predictors. This model compared to the super learner had only a modest reduction in predictability [discovery/validation set C-statistic: 0.825 (0.820–0.830)/0.798 (95% CI: 0.783–0.813)]. Conclusions Clinical data demonstrated reasonably high predictability for GDM treatment modality at the time of GDM diagnosis and high predictability at 1-week post GDM diagnosis. These population-based, clinically oriented models may support algorithm-based risk-stratification for treatment modality, inform timely treatment, and catalyze more effective management of GDM.

ImportanceThe COVID-19 pandemic accelerated the use of telemedicine. However, data on the integration of telemedicine in prenatal health care and health outcomes are sparse.ObjectiveTo evaluate a multimodal model of in-office and telemedicine prenatal health care implemented during the COVID-19 pandemic and its association with maternal and newborn health outcomes.Design, Setting, and ParticipantsThis cohort study of pregnant individuals using longitudinal electronic health record data was conducted at Kaiser Permanente Northern California, an integrated health care system serving a population of 4.5 million people. Individuals who delivered a live birth or stillbirth between July 1, 2018, and October 21, 2021, were included in the study. Data were analyzed from January 2022 to May 2023.ExposureExposure levels to the multimodal prenatal health care model were separated into 3 intervals: unexposed (T1, birth delivery between July 1, 2018, and February 29, 2020), partially exposed (T2, birth delivery between March 1, 2020, and December 5, 2020), and fully exposed (T3, birth delivery between December 6, 2020, and October 31, 2021).Main Outcomes and MeasuresPrimary outcomes included rates of preeclampsia and eclampsia, severe maternal morbidity, cesarean delivery, preterm birth, and neonatal intensive care unit (NICU) admission. The distributions of demographic and clinical characteristics, care processes, and health outcomes for birth deliveries within each of the 3 intervals of interest were assessed with standardized mean differences calculated for between-interval contrasts. Interrupted time series analyses were used to examine changes in rates of perinatal outcomes and its association with the multimodal prenatal health care model. Secondary outcomes included gestational hypertension, gestational diabetes, depression, venous thromboembolism, newborn Apgar score, transient tachypnea, and birth weight.ResultsThe cohort included 151 464 individuals (mean [SD] age, 31.3 [5.3] years) who delivered a live birth or stillbirth. The mean (SD) number of total prenatal visits was similar in T1 (9.41 [4.75] visits), T2 (9.17 [4.50] visits), and T3 (9.15 [4.66] visits), whereas the proportion of telemedicine visits increased from 11.1% (79 214 visits) in T1 to 20.9% (66 726 visits) in T2 and 21.3% (79 518 visits) in T3. NICU admission rates were 9.2% (7014 admissions) in T1, 8.3% (2905 admissions) in T2, and 8.6% (3615 admissions) in T3. Interrupted time series analysis showed no change in NICU admission risk during T1 (change per 4-week interval, −0.22%; 95% CI, –0.53% to 0.09%), a decrease in risk during T2 (change per 4-week interval, −0.91%; 95% CI, –1.77% to −0.03%), and an increase in risk during T3 (change per 4-week interval, 1.75%; 95% CI, 0.49% to 3.02%). There were no clinically relevant changes between T1, T2, and T3 in the rates of risk of preeclampsia and eclampsia (change per 4-week interval, 0.76% [95% CI, 0.39% to 1.14%] for T1; −0.19% [95% CI, –1.19% to 0.81%] for T2; and −0.80% [95% CI, –2.13% to 0.55%] for T3), severe maternal morbidity (change per 4-week interval , 0.12% [95% CI, 0.40% to 0.63%] for T1; −0.39% [95% CI, –1.00% to 1.80%] for T2; and 0.99% [95% CI, –0.88% to 2.90%] for T3), cesarean delivery (change per 4-week interval, 0.06% [95% CI, –0.11% to 0.23%] for T1; −0.03% [95% CI, –0.49% to 0.44%] for T2; and −0.05% [95% CI, –0.68% to 0.59%] for T3), preterm birth (change per 4-week interval, 0.23% [95% CI, –0.11% to 0.57%] for T1; −0.37% [95% CI, –1.29% to 0.55%] for T2; and −0.15% [95% CI, –1.41% to 1.13%] for T3), or secondary outcomes.Conclusions and RelevanceThese findings suggest that a multimodal prenatal health care model combining in-office and telemedicine visits performed adequately compared with in-office only prenatal health care, supporting its continued use after the pandemic.

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