No association of vaccination with inactivated COVID-19 vaccines before conception with pregnancy complications and adverse birth outcomes: A cohort study of 5457 Chinese pregnant women.

Data on the safety of inactivated COVID-19 vaccines in pregnant women is limited and monitoring pregnancy outcomes is required. We aimed to examine whether vaccination with inactivated COVID-19 vaccines before conception was associated with pregnancy complications or adverse birth outcomes. We conducted a birth cohort study in Shanghai, China. A total of 7000 healthy pregnant women were enrolled, of whom 5848 were followed up through delivery. Vaccine administration information was obtained from electronic vaccination records. Relative risks (RRs) of gestational diabetes mellitus (GDM), hypertensive disorders in pregnancy (HDP), intrahepatic cholestasis of pregnancy (ICP), preterm birth (PTB), low birth weight (LBW), and macrosomia associated with COVID-19 vaccination were estimated by multivariable-adjusted log-binomial analysis. After exclusion, 5457 participants were included in the final analysis, of whom 2668 (48.9%) received at least two doses of an inactivated vaccine before conception. Compared with unvaccinated women, there was no significant increase in the risks of GDM (RR = 0.80, 95% confidence interval [CI], 0.69, 0.93), HDP (RR = 0.88, 95% CI, 0.70, 1.11), or ICP (RR = 1.61, 95% CI, 0.95, 2.72) in vaccinated women. Similarly, vaccination was not significantly associated with any increased risks of PTB (RR = 0.84, 95% CI, 0.67, 1.04), LBW (RR = 0.85, 95% CI, 0.66, 1.11), or macrosomia (RR = 1.10, 95% CI, 0.86, 1.42). The observed associations remained in all sensitivity analyses. Our findings suggested that vaccination with inactivated COVID-19 vaccines was not significantly associated with an increased risk of pregnancy complications or adverse birth outcomes.

Association of COVID-19 vaccination before conception with maternal thyroid function during early pregnancy: A single-center study in China.

Despite the high vaccination coverage, potential COVID-19 vaccine-induced adverse effects, especially in pregnant women, have not been fully characterized. We examined the association between COVID-19 vaccination before conception and maternal thyroid function during early pregnancy. We conducted a retrospective cohort study in Shanghai, China. A total of 6979 pregnant women were included. Vaccine administration was obtained from electronic vaccination records. Serum levels of thyroid hormone were measured by fluorescence and chemiluminescence immunoassays. Among the 6979 included pregnant women, 3470 (49.7%) received at least two doses of an inactivated vaccine. COVID-19 vaccination had a statistically significant association with both maternal serum levels of free thyroxine (FT4) and thyroid stimulating hormone (TSH). Compared with unvaccinated pregnant women, the mean FT4 levels were lower in pregnant women who had been vaccinated within 3 months before the date of conception by 0.27 pmol/L (ß = -0.27, 95% confidence interval [CI], -0.42, -0.12), and the mean TSH levels were higher by 0.08 mIU/L (ß = 0.08, 95% CI, 0.00, 0.15). However, when the interval from vaccination to conception was prolonged to more than 3 months, COVID-19 vaccination was not associated with serum FT4 or TSH levels. Moreover, we found that COVID-19 vaccination did not significantly associate with maternal hypothyroidism. Our study suggested that vaccination with inactivated COVID-19 vaccines before conception might result in a small change in maternal thyroid function, but this did not reach clinically significant levels.

Severity detection of COVID-19 infection with machine learning of clinical records and CT images.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment. OBJECTIVE: This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features. METHOD: P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data. RESULTS: The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers. CONCLUSION: This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.

Association of COVID-19 vaccination before conception with maternal liver function during early pregnancy: a cohort study of 7745 Chinese pregnant women.

ABSTRACTMulticenter case series has reported patients with hepatic injury following COVID-19 vaccination, which raised concern for the possibility of vaccine-induced liver dysfunction. We aimed to assess the impact of COVID-19 vaccination on liver function of pregnant women, who may be uniquely susceptible to vaccine-induced liver dysfunction. We conducted a retrospective cohort study at a tertiary hospital in Shanghai, China. Vaccine administration was obtained from the electronic vaccination record. Serum levels of alanine transaminase (ALT), aspartate transaminase (AST), total bile acid (TBA) and total bilirubin (TBIL) in early pregnancy were determined by enzymatic methods. Among the 7745 included pregnant women, 3832 (49.5%) received at least two doses of an inactivated vaccine. COVID-19 vaccination was significantly associated with higher levels of maternal serum TBA. Compared with unvaccinated pregnant women, the mean TBA levels increased by 0.17 µmol/L (ß = 0.17, 95% CI, 0.04, 0.31) for women who had been vaccinated within 3 months before the date of conception. Moreover, COVID-19 vaccination was significantly associated with an increased risk of maternal hyperbileacidemia. The risk of hyperbileacidemia increased by 113% (RR = 2.13; 95% CI, 1.17-3.87) for pregnant women who had been vaccinated within 3 months before conception compared with unvaccinated pregnant women. However, when the interval from complete vaccination to conception was prolonged to more than 3 months, COVID-19 vaccination was not associated with serum TBA levels or maternal hyperbileacidemia. Our findings suggest that vaccination with inactivated COVID-19 vaccines more than 3 months before conception have no detrimental effects on maternal liver function in early pregnancy.

Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images.

This paper aims to develop an automatic method to segment pulmonary parenchyma in chest CT images and analyze texture features from the segmented pulmonary parenchyma regions to assist radiologists in COVID-19 diagnosis. A new segmentation method, which integrates a three-dimensional (3D) V-Net with a shape deformation module implemented using a spatial transform network (STN), was proposed to segment pulmonary parenchyma in chest CT images. The 3D V-Net was adopted to perform an end-to-end lung extraction while the deformation module was utilized to refine the V-Net output according to the prior shape knowledge. The proposed segmentation method was validated against the manual annotation generated by experienced operators. The radiomic features measured from our segmentation results were further analyzed by sophisticated statistical models with high interpretability to discover significant independent features and detect COVID-19 infection. Experimental results demonstrated that compared with the manual annotation, the proposed segmentation method achieved a Dice similarity coefficient of 0.9796, a sensitivity of 0.9840, a specificity of 0.9954, and a mean surface distance error of 0.0318 mm. Furthermore, our COVID-19 classification model achieved an area under curve (AUC) of 0.9470, a sensitivity of 0.9670, and a specificity of 0.9270 when discriminating lung infection with COVID-19 from community-acquired pneumonia and healthy controls using statistically significant radiomic features. The significant features measured from our segmentation results agreed well with those from the manual annotation. Our approach has great promise for clinical use in facilitating automatic diagnosis of COVID-19 infection on chest CT images.