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Background: Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes., Methods: An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness., Results: The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH., Conclusions: We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19., (© 2023. The Author(s).)

Background: Chamas for Change (Chamas) is a group-based health education and microfinance program for pregnant and postpartum women that aims to address inequities contributing to high rates of maternal and infant mortality in rural western Kenya. In this prospective matched cohort study, we evaluated the association between Chamas participation and facility-based delivery. We additionally explored the effect of participation on promoting other positive maternal, newborn and child health (MNCH) behaviors., Methods: We prospectively compared outcomes between a cohort of Chamas participants and controls matched for age, parity, and prenatal care location. Between October-December 2012, government-sponsored community health volunteers (CHV) recruited pregnant women attending their first antenatal care (ANC) visits at rural health facilities in Busia County to participate in Chamas. Women enrolled in Chamas agreed to attend group-based health education and microfinance sessions for one year; controls received the standard of care. We used descriptive analyses, multivariable logistic regression models, and random effect models to compare outcomes across cohorts 12 months following enrollment, with α set to 0.05., Results: Compared to controls (n = 115), a significantly higher proportion of Chamas participants (n = 211) delivered in a health facility (84.4% vs. 50.4%, p < 0.001), attended at least four ANC visits (64.0% vs. 37.4%, p < 0·001), exclusively breastfed to six months (82.0% vs. 47.0%, p < 0·001), and received a CHV home visit within 48 h postpartum (75.8% vs. 38.3%, p < 0·001). In multivariable models, Chamas participants were over five times as likely as controls to deliver in a health facility (OR 5.49, 95% CI 3.12-9.64, p < 0.001). Though not significant, Chamas participants experienced a lower proportion of stillbirths (0.9% vs. 5.2%), miscarriages (5.2% vs. 7.8%), infant deaths (2.8% vs. 3.4%), and maternal deaths (0.9% vs. 1.7%) compared to controls., Conclusions: Chamas participation was associated with increased odds of facility-based delivery compared to the standard of care in rural western Kenya. Larger proportions of program participants also practiced other positive MNCH behaviors. Our findings demonstrate Chamas' potential to achieve population-level MNCH benefits; however, a larger study is needed to validate this observed effect., Trial Registration: ClinicalTrials.gov, NCT03188250 (retrospectively registered 31 May 2017).