Impacts of pregnancy and menopause on COVID-19 severity: A systematic review and meta-analysis of 4.6 million women.

BACKGROUND: COVID-19 pandemic is still a public health emergency of international concern. However, whether pregnancy and menopause impact the severity of COVID-19 remain unclear. AIM: This study is performed to investigate the truth. DESIGN: Study appraisal and Synthesis follows PRISMA guideline. Meta-analysis is performed in random-effects model. METHODS: PubMed, Embase, Cochrane database, Central, CINAHL, ClinicalTrials.gov, WHO COVID-19 database, and WHO-ICTRP are searched until March 28 2023. RESULTS: In total, 57 studies (4,640,275 COVID-19 women) were analyzed. Pregnant women were at a lower risk of severe COVID-19, intensive care unit (ICU) admission and disease mortality compared to those nonpregnant women with comparable comorbidities. In contrast, pregnant women with more prepregnancy comorbidities were at a higher risk of severe COVID-19, ICU admission and invasive mechanical ventilation (IMV). In addition, pregnant women with pregnancy complications had a significantly increased risk of severe COVID-19 and ICU admission. Menopause increased COVID-19 severity, IMV requirement and disease mortality. Hormone replacement therapy (HRT) inhibited COVID-19 severity in postmenopausal women. Premenopausal and postmenopausal women had a lower chance of severe illness than age-matched men. The impact of pregnancy on COVID-19 severity was significant in Americans and Caucasians, while the effect of menopause on COVID-19 severity was only significant in Chinese. CONCLUSIONS: Pregnancy and menopause are protective and risk factors for severe COVID-19, respectively. The protective role of pregnancy on COVID-19 is minimal and could be counteracted or masked by prepregnancy or pregnancy comorbidities. The administration of estrogen and progesterone may prevent severe COVID-19.

Impact of alcohol consumption on coronavirus disease 2019 severity: A systematic review and meta-analysis.

Fear and misinformation lead to widespread myths in the coronavirus disease 2019 (COVID-19) pandemic, such as "consuming high-strength alcohol kills the virus in the inhaled air." However, whether alcohol consumption can affect COVID-19 has not been clarified yet. This study aims to investigate the impact of alcohol consumption on COVID-19 severity. PubMed, Embase, Cochrane Library, Central, CINAHL, ClinicalTrials.gov, and WHO-International Clinical Trials Registry Platform were searched until November 25, 2022. Forty studies (1,697,683 COVID-19 individuals) were analyzed. Brown (patients numbers: 1317, risk ratios [RR] = 1.58, 95% [confidence interval] CI = 1.31 to 1.90, I2 = 0.0%, p < 0.001), American (patients numbers: 3721, RR = 1.51, 95% CI = 1.30 to 1.75, I2 = 0.0%, p < 0.001), and European (patients numbers: 261,437, RR = 2.04, 95% CI = 1.96 to 2.13, I2 = 0.0%, p < 0.001) drinkers were at high risk of severe COVID-19, intensive care unit (ICU) admission, and invasive mechanical ventilation (IMV), respectively. Consistently, individuals with a drinking history were at high risk of severe COVID-19 (patients numbers: 5399, RR = 1.23, 95% CI = 1.02 to 1.48, I2 = 38.4%, p = 0.03) and ICU admission (patients numbers: 6995, RR = 1.32, 95% CI = 1.08 to 1.60, I2 = 46.6%, p = 0.01). In addition, current drinkers had an increased risk of symptomatic COVID-19. However, excessive drinkers were at high risk of COVID-19 hospitalization. Alcohol consumption intensifies COVID-19 severity and deteriorates its clinical outcomes. Here, we strongly propose that people do not drink alcohol during the COVID-19 pandemic.

Can nasal irrigation with chlorine dioxide be considered as a potential alternative therapy for respiratory infectious diseases? The example of COVID-19.

Chlorine dioxide (ClO2) is a high-level disinfectant that is safe and widely used for sterilization. Due to the limitations on preparing a stable solution, direct use of ClO2 in the human body is limited. Nasal irrigation is an alternative therapy used to treat respiratory infectious diseases. This study briefly summarizes the available evidence regarding the safety/efficacy of directly using ClO2 on the human body as well as the approach of nasal irrigation to treat COVID-19. Based on the available information, as well as a preliminary experiment that comprehensively evaluated the efficacy and safety of ClO2, 25-50 ppm was deemed to be an appropriate concentration of ClO2 for nasal irrigation to treat COVID-19. This finding requires further verification. Nasal irrigation with ClO2 can be considered as a potential alternative therapy to treat respiratory infectious diseases, and COVID-19 in particular.

Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network.

Background: The novel coronavirus disease 2019 (COVID-19) has been spread widely in the world, causing a huge threat to the living environment of people. Objective: Under CT imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images. Methods: Since a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtain, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract complicated and varied COVID-19 lesion features effectively that may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation. Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279 and a low Hausdorff distance (H) of 92.4604. Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results indicated that the proposed method has a good performance in COVID-19 lesion segmentation.

Real-time Prediction of the Daily Incidence of COVID-19 in 215 Countries and Territories Using Machine Learning: Model Development and Validation.

BACKGROUND: Advanced prediction of the daily incidence of COVID-19 can aid policy making on the prevention of disease spread, which can profoundly affect people's livelihood. In previous studies, predictions were investigated for single or several countries and territories. OBJECTIVE: We aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide. METHODS: Data of the previous daily incidence and infoveillance data (search volume data via Google Trends) from 215 individual countries and territories were collected. A random forest regression algorithm was used to train models to predict the daily new confirmed cases 7 days ahead. Several methods were used to optimize the models, including clustering the countries and territories, selecting features according to the importance scores, performing multiple-step forecasting, and upgrading the models at regular intervals. The performance of the models was assessed using the mean absolute error (MAE), root mean square error (RMSE), Pearson correlation coefficient, and Spearman correlation coefficient. RESULTS: Our models can accurately predict the daily new confirmed cases of COVID-19 in most countries and territories. Of the 215 countries and territories under study, 198 (92.1%) had MAEs <10 and 187 (87.0%) had Pearson correlation coefficients >0.8. For the 215 countries and territories, the mean MAE was 5.42 (range 0.26-15.32), the mean RMSE was 9.27 (range 1.81-24.40), the mean Pearson correlation coefficient was 0.89 (range 0.08-0.99), and the mean Spearman correlation coefficient was 0.84 (range 0.2-1.00). CONCLUSIONS: By integrating previous incidence and Google Trends data, our machine learning algorithm was able to predict the incidence of COVID-19 in most individual countries and territories accurately 7 days ahead.

Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning.

BACKGROUND: Internet search engine data, such as Google Trends, was shown to be correlated with the incidence of COVID-19, but only in several countries. We aim to develop a model from a small number of countries to predict the epidemic alert level in all the countries worldwide. METHODS: The "interest over time" and "interest by region" Google Trends data of Coronavirus, pneumonia, and six COVID symptom-related terms were searched. The daily incidence of COVID-19 from 10 January to 23 April 2020 of 202 countries was retrieved from the World Health Organization. Three alert levels were defined. Ten weeks' data from 20 countries were used for training with machine learning algorithms. The features were selected according to the correlation and importance. The model was then tested on 2830 samples of 202 countries. RESULTS: Our model performed well in 154 (76.2%) countries, of which each had no more than four misclassified samples. In these 154 countries, the accuracy was 0.8133, and the kappa coefficient was 0.6828. While in all 202 countries, the accuracy was 0.7527, and the kappa coefficient was 0.5841. The proposed algorithm based on Random Forest Classification and nine features performed better compared to other machine learning methods and the models with different numbers of features. CONCLUSIONS: Our result suggested that the model developed from 20 countries with Google Trends data and Random Forest Classification can be applied to predict the epidemic alert levels of most countries worldwide.

Investigation and Analysis of 108 Cases of Home Isolated Patients With Mild COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) began to spread across Wuhan, China, by the end of 2019, and patients were unable to be hospitalized because medical resources were limited. METHODS: A questionnaire survey was conducted among 108 participants with mild COVID-19 who have isolated at home under the guidance of doctors. The results of the questionnaire and outpatient data were integrated to evaluate participants' compliance with various epidemic prevention measures. RESULTS: During isolation, most participants were able to follow epidemic prevention measures under the guidance of doctors. After 14 d from the start of isolation, 45.37% of the participants recovered. Approximately half of the participants were relieved of symptoms, and most of them were transferred to mobile cabin hospitals to continue isolation. Three participants with worsening symptoms were transferred to the designated hospitals. There were no deaths of the participants, but there were 7 family members that were infected. CONCLUSIONS: During a period of home isolation under the guidance of a doctor, individuals can comply with epidemic prevention measures and symptoms can be improved. Scientific home isolation may be an effective way to relieve the strain of medical and social resources during the epidemic of COVID-19.

Association of Inpatient Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers With Mortality Among Patients With Hypertension Hospitalized With COVID-19.

RATIONALE: Use of ACEIs (angiotensin-converting enzyme inhibitors) and ARBs (angiotensin II receptor blockers) is a major concern for clinicians treating coronavirus disease 2019 (COVID-19) in patients with hypertension. OBJECTIVE: To determine the association between in-hospital use of ACEI/ARB and all-cause mortality in patients with hypertension and hospitalized due to COVID-19. METHODS AND RESULTS: This retrospective, multi-center study included 1128 adult patients with hypertension diagnosed with COVID-19, including 188 taking ACEI/ARB (ACEI/ARB group; median age 64 [interquartile range, 55-68] years; 53.2% men) and 940 without using ACEI/ARB (non-ACEI/ARB group; median age 64 [interquartile range 57-69]; 53.5% men), who were admitted to 9 hospitals in Hubei Province, China from December 31, 2019 to February 20, 2020. In mixed-effect Cox model treating site as a random effect, after adjusting for age, gender, comorbidities, and in-hospital medications, the detected risk for all-cause mortality was lower in the ACEI/ARB group versus the non-ACEI/ARB group (adjusted hazard ratio, 0.42 [95% CI, 0.19-0.92]; P=0.03). In a propensity score-matched analysis followed by adjusting imbalanced variables in mixed-effect Cox model, the results consistently demonstrated lower risk of COVID-19 mortality in patients who received ACEI/ARB versus those who did not receive ACEI/ARB (adjusted hazard ratio, 0.37 [95% CI, 0.15-0.89]; P=0.03). Further subgroup propensity score-matched analysis indicated that, compared with use of other antihypertensive drugs, ACEI/ARB was also associated with decreased mortality (adjusted hazard ratio, 0.30 [95% CI, 0.12-0.70]; P=0.01) in patients with COVID-19 and coexisting hypertension. CONCLUSIONS: Among hospitalized patients with COVID-19 and coexisting hypertension, inpatient use of ACEI/ARB was associated with lower risk of all-cause mortality compared with ACEI/ARB nonusers. While study interpretation needs to consider the potential for residual confounders, it is unlikely that in-hospital use of ACEI/ARB was associated with an increased mortality risk.