Factors associated with delayed viral shedding in COVID-19 infected patients: A retrospective small-scale study

Insights from an N3C RECOVER EHR-based cohort study characterizing SARS-CoV-2 reinfections and Long COVID

BACKGROUND

Although the COVID-19 pandemic has persisted for over 3 years, reinfections with SARS-CoV-2 are not well understood. We aim to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection.

METHODS

We use an electronic health record study cohort of over 3 million patients from the National COVID Cohort Collaborative as part of the NIH Researching COVID to Enhance Recovery Initiative. We calculate summary statistics, effect sizes, and Kaplan-Meier curves to better understand COVID-19 reinfections.

RESULTS

Here we validate previous findings of reinfection incidence (6.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present findings that the proportion of Long COVID diagnoses is higher following initial infection than reinfection for infections in the same epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between initial infection and reinfection (chi-squared value: 25,697, p-value: <0.0001) with a medium effect size (Cramer's V: 0.20, DoF = 3). Individuals who experienced severe initial and first reinfection were older in age and at a higher mortality risk than those who had mild initial infection and reinfection.

CONCLUSIONS

In a large patient cohort, we find that the severity of reinfection appears to be associated with the severity of initial infection and that Long COVID diagnoses appear to occur more often following initial infection than reinfection in the same epoch. Future research may build on these findings to better understand COVID-19 reinfections.

The differences in virus shedding time between the Delta variant and original SARS-CoV-2 infected patients

Background

The worldwide epidemic of Coronavirus Disease 2019 (COVID-19) has evolved into multiple variants. The Delta variant is known for its ability to spread and replicate, while data are limited about the virus shedding time in patients infected by the Delta variant.

Methods

56 Delta variant and 56 original SARS-CoV-2 infected patients from Hunan, China, matched according to age and gender divided into two groups and compared the baseline characteristics and laboratory findings with appropriate statistical methods.

Results

Patients infected with the Delta variant had significantly fewer symptoms of fever (p < 0.001), fatigue (p = 0.004), anorexia (p < 0.001), shortness of breath (p = 0.004), diarrhea (p = 0.006), positive pneumonia rate of chest CT (p = 0.019) and chest CT ground glass opacities (p = 0.004) than those of patients with the original SARS-CoV-2. Patients of the Delta variant group had a significantly longer virus shedding time [41.5 (31.5, 46.75) vs. 18.5 (13, 25.75), p < 0.001] compared with the original SARS-CoV-2 group. The correlation analyses between the virus shedding time and clinical or laboratory parameters showed that the virus shedding time was positively related to the viral strain, serum creatinine and creatine kinase isoenzyme, while negatively correlated with lymphocyte count, total bilirubin and low-density lipoprotein. Finally, the viral strain and lymphocyte count were thought of as the independent risk factors of the virus shedding time demonstrated by multiple linear regression.

Conclusion

COVID-19 patients infected with the Delta variant exhibited fewer gastrointestinal symptoms and prolonged virus shedding time than those infected with the original SARS-CoV-2. Delta variant and fewer lymphocyte were correlated with prolonged virus shedding time.

Different effects of vaccine on VST in critical and non-critical COVID-19 patients: A retrospective study of 363 cases

Aim

To explore the risk factors of prolonged viral shedding time (VST) in critical/non-critical COVID-19 patients during hospitalization.

Methods

In this retrospective study, we enrolled 363 patients with SARS-CoV-2 infection admitted in a designated hospital during the COVID-19 outbreak in Nanjing Lukou International Airport. Patients were divided into critical (n = 54) and non-critical (n = 309) groups. We analyzed the relationship between the VST and demographics, clinical characteristics, medications, and vaccination histories, respectively.

Results

The median duration of VST was 24 d (IQR, 20-29) of all patients. The VST of critical cases was longer than non-critical cases (27 d, IQR, 22.0-30.0 vs. 23 d, IQR 20-28, P < 0.05). Cox proportional hazards model showed that ALT (HR = 1.610, 95%CI 1.186-2.184, P = 0.002) and EO% (HR = 1.276, 95%CI 1.042-1.563, P = 0.018) were independent factors of prolonged VST in total cases; HGB (HR = 0.343, 95%CI 0.162-0.728, P = 0.005) and ALP (HR = 0.358, 95%CI 0.133-0.968, P = 0.043) were independent factors of prolonged VST in critical cases, while EO% (HR = 1.251, 95%CI 1.015-1.541, P = 0.036) was the independent factor of prolonged VST in non-critical cases. Vaccinated critical cases showed higher levels of SARS-CoV-2-IgG (1.725 S/CO, IQR 0.3975-28.7925 vs 0.07 S/CO, IQR 0.05-0.16, P < 0.001) and longer VSTs (32.5 d, IQR 20.0-35.25 vs 23 d, IQR 18.0-30.0, P = 0.011) compared with unvaccinated critical patients. Fully vaccinated non-critical cases, however, presented higher levels of SARS-CoV-2-IgG (8.09 S/CO, IQR 1.6975-55.7825 vs 0.13 S/CO IQR 0.06-0.41, P < 0.001) and shorter VSTs (21 d, IQR 19.0-28.0 vs 24 d, IQR 21.0-28.5, P = 0.013) compared with unvaccinated non-critical patients.

Conclusions

Our results suggested that risk factors of prolonged VST were different between critical and non-critical COVID-19 patients. Increased level of SARS-CoV-2-IgG and vaccination did not shorten the VST and hospital stay in critical COVID-19 patients.

Predicting the negative conversion time of nonsevere COVID-19 patients using machine learning methods

Based on the patient's clinical characteristics and laboratory indicators, different machine-learning methods were used to develop models for predicting the negative conversion time of nonsevere coronavirus disease 2019 (COVID-19) patients. A retrospective analysis was performed on 376 nonsevere COVID-19 patients admitted to Wuxi Fifth People's Hospital from May 2, 2022, to May 14, 2022. The patients were divided into training set (n = 309) and test set (n = 67). The clinical features and laboratory parameters of the patients were collected. In the training set, the least absolute shrinkage and selection operator (LASSO) was used to select predictive features and train six machine learning models: multiple linear regression (MLR), K-Nearest Neighbors Regression (KNNR), random forest regression (RFR), support vector machine regression (SVR), XGBoost regression (XGBR), and multilayer perceptron regression (MLPR). Seven best predictive features selected by LASSO included: age, gender, vaccination status, IgG, lymphocyte ratio, monocyte ratio, and lymphocyte count. The predictive performance of the models in the test set was MLPR > SVR > MLR > KNNR > XGBR > RFR, and MLPR had the strongest generalization performance, which is significantly better than SVR and MLR. In the MLPR model, vaccination status, IgG, lymphocyte count, and lymphocyte ratio were protective factors for negative conversion time; male gender, age, and monocyte ratio were risk factors. The top three features with the highest weights were vaccination status, gender, and IgG. Machine learning methods (especially MLPR) can effectively predict the negative conversion time of non-severe COVID-19 patients. It can help to rationally allocate limited medical resources and prevent disease transmission, especially during the Omicron pandemic.

SARS-CoV-2 Reinfection is Preceded by Unique Biomarkers and Related to Initial Infection Timing and Severity: an N3C RECOVER EHR-Based Cohort Study

Although the COVID-19 pandemic has persisted for over 2 years, reinfections with SARS-CoV-2 are not well understood. We use the electronic health record (EHR)-based study cohort from the National COVID Cohort Collaborative (N3C) as part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. We validate previous findings of reinfection incidence (5.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present novel findings that Long COVID diagnoses occur closer to the index date for infection or reinfection in the Omicron BA epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between first infection and reinfection (chi-squared value: 9446.2, p-value: 0) with a medium effect size (Cramer's V: 0.18, DoF = 4).

Possible harm from glucocorticoid drugs misuse in the early phase of SARS-CoV-2 infection: a narrative review of the evidence

Since the publication of the RECOVERY trial, the use of glucocorticoid drugs (GC) has spread for the treatment of severe COVID-19 worldwide. However, the benefit of dexamethasone was largest in patients who received mechanical ventilation or supplemental oxygen therapy, while no benefit was found among patients without hypoxemia. In addition, a positive outcome was found in patients who received dexamethasone after several days of symptoms, while possible harm could exist if administered early. The right time interval for GC administration is still a matter of debate. Previous studies showed that an early GC use during the first phase of the disease, when viral replication peaks, may negatively affect the innate immune response through several mechanisms, such as the inhibition of pro-inflammatory and antiviral cytokine production and signaling pathway, including type I interferon, that is fundamental to counteract the virus and that was found to be impaired in several patients with life-threatening COVID-19. The GC misuse can lead to a more severe disease even in patients who do not have the established risk factors, such as obesity and cardiovascular diseases. In our focused review, we describe the role of immune response in viral infections, especially SARS-CoV-2, and discuss the potential harms of GC misuse in COVID-19.

Clinical features and independent predictors for recurrence of positive SARS-CoV-2 RNA: A propensity score-matched analysis

Patients with COVID‐19 may be recurrence positive for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) RNA after being cured and discharged from the hospital. The aim of this study was to explore independent influencing factors as markers for predicting positive SARS‐CoV‐2 RNA recurrence. The study included 601 COVID‐19 patients who were cured and discharged from the Public and Health Clinic Centre of Chengdu from January 2020 to March 2021, and the recurrence positive of patients within 6 weeks after SARS‐CoV‐2 RNA turned negative was followed up. We used propensity score matching to eliminate the influence of confounding factors, and multivariate Logistic regression analysis was used to determine the independent influencing factors for positive SARS‐CoV‐2 RNA recurrence. Multivariate Logistic regression showed that the elevated serum potassium (odds ratio [OR] = 6.537, 95% confidence interval [CI]: 1.864–22.931, p = 0.003), elevated blood chlorine (OR = 1.169, 95% CI: 1.032–1.324, p = 0.014) and elevated CD3⁺CD4⁺ count (OR = 1.003, 95% CI: 1.001–1.004, p < 0.001) were identified as independent risk factors for positive SARS‐CoV‐2 RNA recurrence (p < 0.05). The difference in virus shedding duration (OR = 1.049, 95% CI: 1.000–1.100, p = 0.05) was borderline statistically significant. For sensitivity analysis, we included virus shedding duration as a categorical variable in the model again and found that the OR value related to recurrence positively increased with delayed virus shedding duration, and the trend test showed a statistical difference (P trend = 0.03). Meanwhile, shortening of activated partial prothrombinase time (OR = 0.908, 95% CI: 0.824–1.000, p = 0.049) was identified as an independent protection factor for SARS‐CoV‐2 RNA recurrence positive. We have identified independent factors that affect the recurrence of SARS‐CoV‐2 RNA positive. It is recommended that doctors pay attention to these indicators when first admitted to the hospital.

Association between glucocorticoids treatment and viral clearance delay in patients with COVID-19: a systematic review and meta-analysis

BACKGROUND

Evidence of glucocorticoids on viral clearance delay of COVID-19 patients is not clear.

METHODS

In this systematic review and meta-analysis, we searched for studies on Medline, Embase, EBSCO, ScienceDirect, Web of Science, Cochrane Library, and ClinicalTrials.gov from 2019 to April 20, 2021. We mainly pooled the risk ratios (RRs) and mean difference (MD) for viral clearance delay and did subgroup analyses by the severity of illness and doses of glucocorticoids.

RESULTS

38 studies with a total of 9572 patients were identified. Glucocorticoids treatment was associated with delayed viral clearance in COVID-19 patients (adjusted RR 1.52, 95% CI 1.29 to 1.80, I2 = 52%), based on moderate-quality evidence. In subgroup analyses, risk of viral clearance delay was significant both for COVID-19 patients being mild or moderate ill (adjusted RR 1.86, 95% CI 1.35 to 2.57, I2 = 48%), and for patients of being severe or critical ill (adjusted RR 1.59, 95% CI 1.23 to 2.07, I2 = 0%); however, this risk significantly increased for patients taking high doses (unadjusted RR 1.85, 95% CI 1.08 to 3.18; MD 7.19, 95% CI 2.78 to 11.61) or medium doses (adjusted RR 1.86, 95% CI 0.96 to 3.62, I2 = 45%; MD 3.98, 95% CI 3.07 to 4.88, I2 = 4%), rather those taking low doses (adjusted RR 1.38, 95% CI 0.94 to 2.02, I2 = 59%; MD 1.46, 95% CI -0.79 to 3.70, I2 = 82%).

CONCLUSIONS

Glucocorticoids treatment delayed viral clearance in COVID-19 patients of taking high doses or medium doses, rather in those of taking low doses of glucocorticoids.

Risk factors for SARS-CoV-2 re-positivity in COVID-19 patients after discharge

Objective

Re-positivity of SARS-CoV-2 in discharged COVID-19 patients have been reported; however, early risk factors for SARS-CoV-2 re-positivity evaluation are limited.

Methods

This is a prospective study, a total of 145 COVID-19 patients were treated and all discharged according to the guideline criteria by Mar 11th 2020. After discharge, clinical visits and viral RT-PCR tests by the second and fourth week follow-up were carried-out. Patient demographic and clinical characteristics and laboratory data on admission and discharge were retrieved, and predictive factors for SARS-CoV-2 re-positivity were analyzed.

Results

13 out of 145 (9.0%) COVID-19 patients were confirmed re-positivity of SARS-CoV-2 by RT-PCR test. The median interval between disease onset to recurrence was 38 days. SARS-CoV-2 re-positive cases were of significantly longer virus shedding duration, notably higher body temperature, heart rate and lower TNF-α and IgG levels on admission. Covariate logistic regression analysis revealed virus shedding duration and IgG levels are independent risk factors for SARS-CoV-2 return positive after discharge.

Conclusion

Longer viral shedding duration and lower IgG levels are risk factors for re-positivity of SARS-CoV-2 for discharged COVID-19 patients.