Maternal COVID-19 infection and associated factors: A cross-sectional study

Trends in SARS-CoV-2 seroprevalence among pregnant women attending first antenatal care visits in Zambia: A repeated cross-sectional survey, 2021-2022

SARS-CoV-2 serosurveys help estimate the extent of transmission and guide the allocation of COVID-19 vaccines. We measured SARS-CoV-2 seroprevalence among women attending ANC clinics to assess exposure trends over time in Zambia. We conducted repeated cross-sectional SARS-CoV-2 seroprevalence surveys among pregnant women aged 15-49 years attending their first ANC visits in four districts of Zambia (two urban and two rural) during September 2021-September 2022. Serologic testing was done using a multiplex bead assay which detects IgG antibodies to the nucleocapsid protein and the spike protein receptor-binding domain (RBD). We calculated monthly SARS-CoV-2 seroprevalence by district. We also categorized seropositive results as infection alone, infection and vaccination, or vaccination alone based on anti-RBD and anti-nucleocapsid test results and self-reported COVID-19 vaccination status (vaccinated was having received ≥1 dose). Among 8,304 participants, 5,296 (63.8%) were cumulatively seropositive for SARS-CoV-2 antibodies from September 2021 through September 2022. SARS-CoV-2 seroprevalence primarily increased from September 2021 to September 2022 in three districts (Lusaka: 61.8-100.0%, Chongwe: 39.6-94.7%, Chipata: 56.5-95.0%), but in Chadiza, seroprevalence increased from 27.8% in September 2021 to 77.2% in April 2022 before gradually dropping to 56.6% in July 2022. Among 5,906 participants with a valid COVID-19 vaccination status, infection alone accounted for antibody responses in 77.7% (4,590) of participants. Most women attending ANC had evidence of prior SARS-CoV-2 infection and most SARS-CoV-2 seropositivity was infection-induced. Capturing COVID-19 vaccination status and using a multiplex bead assay with anti-nucleocapsid and anti-RBD targets facilitated distinguishing infection-induced versus vaccine-induced antibody responses during a period of increasing COVID-19 vaccine coverage in Zambia. Declining seroprevalence in Chadiza may indicate waning antibodies and a need for booster vaccines. ANC clinics have a potential role in ongoing SARS-CoV-2 serosurveillance and can continue to provide insights into SARS-CoV-2 antibody dynamics to inform near real-time public health responses.

The clinical manifestations and pregnancy outcomes of COVID-19 infection at a tertiary care hospital

Objective

To evaluate clinical presentation and pregnancy outcomes in pregnant women with Covid-19 infection in our local tertiary care from lower middle-income country.

Methods

A retrospective study was conducted at Obstetrics & Gynecology department, Sheikh Saeed Memorial Hospital (SSMH) of The Indus Hospital and Health Network (IHHN) from March 2020 to August 2021. Data of 422 admitted pregnant women with COVID-19 infection was retrieved for demographic and clinical information, laboratory tests, pregnancy outcome, and neonatal outcomes on RED-Cap and analyzed on SPSS 26. Univariate and multivariable logistic regression analyses were performed to estimate odds ratios (OR) for symptomology with categorical variables and feto-maternal outcome.

Results

Of the total 422 pregnant women, 24.4% were symptomatic, 74.7% exhibiting mild symptoms. Largely reported symptoms were fever (71.8%), cough (36.9%) and body ache (35.0%); while odds of symptomatic COVID-19 infection was less in educated pregnant women (OR 0.3; 95% CI 0.1-0.9) compared to uneducated. Amongst maternal comorbidities, odds of having symptomatic COVID-19 infection were 3.8 times (95% CI 1.1-13.0) in women with chronic hypertension and 5.5 times (95% CI 2.9-10.4) in women with diabetes. Symptomatic women had significantly greater incidence of miscarriages (p= 0.009), PPROM (p= 0.001), preterm birth (p= 0.000), preeclampsia (p= 0.000), placental abruption (p= 0.006) and maternal ICU admission (p= 0.000) than asymptomatic patients. Still birth was higher (6.4% vs 1.3%, p-value= 0.013) in symptomatic group. The odds of having severe maternal outcome were higher (OR=3.5; 95% CI 1.9-6.0) in symptomatic pregnant women.

Conclusion

Majority of pregnant women were asymptomatic. Symptomatic women with COVID-19 infection had an increased risk of adverse feto-maternal outcome.

Inflammation and Immune Reactions in the Fetus as a Response to COVID-19 in the Mother

Background: Contracting COVID-19 during pregnancy can harm both the mother and the unborn child. Pregnant women are highly likely to develop respiratory viral infection complications with critical conditions caused by physiological changes in the immune and cardiopulmonary systems. Asymptomatic COVID-19 in pregnant women may be accompanied by fetal inflammatory response syndrome, which has adverse consequences for the newborn's life and health. Purpose: To conduct an inflammatory response assessment of the fetus due to the effects of COVID-19 on the mother during pregnancy by determining pro-inflammatory cytokines, cell markers, T regulatory cells, T cell response, evaluation of cardiac function, and thymus size. Materials and methods: A prospective study included pregnant women (n = 92). The main group consisted of 62 pregnant women with COVID-19 infection: subgroup 1-SARS-CoV-2 PCR-positive pregnant women 4-6 weeks before delivery (n = 30); subgroup 2-SARS-CoV-2 PCR-positive earlier during pregnancy (n = 32). The control group consisted of 30 healthy pregnant women. In all pregnant women, the levels of circulating cytokines and chemokines (IL-1α, IL-6, IL-8, IL-10, GM-CSF, TNF-α, IFN-γ, MIP-1β, and CXCL-10) were determined in the peripheral blood and after delivery in the umbilical cord blood, and an analysis was performed of the cell markers on dendritic cells, quantitative and functional characteristics of T regulatory cells, and specific T cell responses. The levels of thyroxine and thyroid-stimulating hormone were determined in the newborns of the studied groups, and ultrasound examinations of the thymus and echocardiography of the heart were also performed. Results: The cord blood dendritic cells of newborns born to mothers who suffered from COVID-19 4-6 weeks before delivery (subgroup 1) showed a significant increase in CD80 and CD86 expression compared to the control group (p = 0.023). In the umbilical cord blood samples of children whose mothers tested positive for COVID-19 4-6 weeks before delivery (subgroup 1), the CD4+CCR7+ T cells increased with a concomitant decrease in the proportion of naive CD4+ T cells compared with the control group (p = 0.016). Significantly higher levels of pro-inflammatory cytokines and chemokines were detected in the newborns of subgroup 1 compared to the control group. In the newborns of subgroup 1, the functional activity of T regulatory cells was suppressed, compared with the newborns of the control group (p < 0.001). In all pregnant women with a severe coronavirus infection, a weak T cell response was detected in them as well as in their newborns. In newborns whose mothers suffered a coronavirus infection, a decrease in thymus size, transient hypothyroxinemia, and changes in functional parameters according to echocardiography were revealed compared with the newborns of the control group. Conclusions: Fetal inflammatory response syndrome can occur in infants whose mothers suffered from a COVID-19 infection during pregnancy and is characterized by the activation of the fetal immune system and increased production of pro-inflammatory cytokines. The disease severity in a pregnant woman does not correlate with SIRS severity in the neonatal period. It can vary from minimal laboratory parameter changes to the development of complications in the organs and systems of the fetus and newborn.

Predicting Mortality in Hospitalized COVID-19 Patients in Zambia: An Application of Machine Learning

The coronavirus disease 2019 (COVID-19) has wreaked havoc globally, resulting in millions of cases and deaths. The objective of this study was to predict mortality in hospitalized COVID-19 patients in Zambia using machine learning (ML) methods based on factors that have been shown to be predictive of mortality and thereby improve pandemic preparedness. This research employed seven powerful ML models that included decision tree (DT), random forest (RF), support vector machines (SVM), logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and XGBoost (XGB). These classifiers were trained on 1,433 hospitalized COVID-19 patients from various health facilities in Zambia. The performances achieved by these models were checked using accuracy, recall, F1-Score, area under the receiver operating characteristic curve (ROC_AUC), area under the precision-recall curve (PRC_AUC), and other metrics. The best-performing model was the XGB which had an accuracy of 92.3%, recall of 94.2%, F1-Score of 92.4%, and ROC_AUC of 97.5%. The pairwise Mann-Whitney U-test analysis showed that the second-best model (GB) and the third-best model (RF) did not perform significantly worse than the best model (XGB) and had the following: GB had an accuracy of 91.7%, recall of 94.2%, F1-Score of 91.9%, and ROC_AUC of 97.1%. RF had an accuracy of 90.8%, recall of 93.6%, F1-Score of 91.0%, and ROC_AUC of 96.8%. Other models showed similar results for the same metrics checked. The study successfully derived and validated the selected ML models and predicted mortality effectively with reasonably high performance in the stated metrics. The feature importance analysis found that knowledge of underlying health conditions about patients' hospital length of stay (LOS), white blood cell count, age, and other factors can help healthcare providers offer lifesaving services on time, improve pandemic preparedness, and decongest health facilities in Zambia and other countries with similar settings.