Comparison of the Onset and End of Specific and Major Side Effects in Iranian Teenage Participants Vaccinated With COVID-19 Vaccine: Sinopharm and Soberana

Background Clinical trials were conducted on children on side effects after vaccination. We tried to assess the frequency and onset of the main symptoms in children who were vaccinated. We aimed to evaluate early and delayed adverse effects after coronavirus disease 2019 (COVID-19) vaccine among Iranian pediatrics and adolescents in a national survey. Methods This cross-sectional study included people <18 years who received the Soberana (PastoCoVac) and Sinopharm vaccines since 2021. The basic information was gender, age, type of vaccine, and reaction after vaccination besides the main events that occurred for them. The required data were collected via a predetermined checklist by trained interviewers through phone calls by their parents or legal guardians. The independent t test and Fisher exact test were used. P values less than 0.05 were considered significant. Results A total of 11,042 participants (age range, 10-18 years) consisting of 5374 boys (47.8%) and 5768 girls (52.2%) were studied and 88.1% of the children (n = 9727) were vaccinated by Sinopharm and 11.9% (n = 1315) by Soberana. The data of kidney-related side effects had delayed improvement of side effects after the Sinopharm compared with the Soberana vaccines (P = 0.012). Cardiovascular and hematological side effects showed early-onset (P = 0.006) and delayed improvement of side effects (P = 0.002) after the Soberana vaccine compared with the Sinopharm vaccine. Neurological side effects showed delayed improvement of side effects after the Soberana vaccine compared with the Sinopharm vaccine (P = 0.027). Joint-related side effects showed early-onset (P = 0.004) and delayed improvement of side effects (P = 0.023) after the Soberana vaccine compared with the Sinopharm vaccine. Respiratory side effects showed delayed improvement of side effects after the Soberana vaccine compared with the Sinopharm vaccine (P = 0.013), and dermatological side effects showed early-onset (P = 0.050) and delayed improvement of side effects (P = 0.035) after the Soberana vaccine compared with the Sinopharm vaccine. There was not any statistically significant difference regarding gastrointestinal side effects between the 2 vaccines (P > 0.05). Conclusion The cardiovascular and hematological, joint-related (non-neurologic musculoskeletal) and dermatological side effects after the Soberana vaccine appear earlier and end later compared with the Sinopharm vaccine. Improvement of renal side effects in the Sinopharm vaccine group and improvement of neurological and respiratory side effects in the Soberana vaccine group occurred with delay compared with other vaccines.

Comparison of the Onset and End of Specific and Major Side Effects in Iranian Teenage Participants Vaccinated With COVID-19 Vaccine: Sinopharm and Soberana.

Background: Clinical trials were conducted on children on side effects after vaccination. We tried to assess the frequency and onset of the main symptoms in children who were vaccinated. We aimed to evaluate early and delayed adverse effects after coronavirus disease 2019 (COVID-19) vaccine among Iranian pediatrics and adolescents in a national survey. Methods: This cross-sectional study included people <18 years who received the Soberana (PastoCoVac) and Sinopharm vaccines since 2021. The basic information was gender, age, type of vaccine, and reaction after vaccination besides the main events that occurred for them. The required data were collected via a predetermined checklist by trained interviewers through phone calls by their parents or legal guardians. The independent t test and Fisher exact test were used. P values less than 0.05 were considered significant. Results: A total of 11,042 participants (age range, 10-18 years) consisting of 5374 boys (47.8%) and 5768 girls (52.2%) were studied and 88.1% of the children (n = 9727) were vaccinated by Sinopharm and 11.9% (n = 1315) by Soberana. The data of kidney-related side effects had delayed improvement of side effects after the Sinopharm compared with the Soberana vaccines (P = 0.012). Cardiovascular and hematological side effects showed early-onset (P = 0.006) and delayed improvement of side effects (P = 0.002) after the Soberana vaccine compared with the Sinopharm vaccine. Neurological side effects showed delayed improvement of side effects after the Soberana vaccine compared with the Sinopharm vaccine (P = 0.027). Joint-related side effects showed early-onset (P = 0.004) and delayed improvement of side effects (P = 0.023) after the Soberana vaccine compared with the Sinopharm vaccine. Respiratory side effects showed delayed improvement of side effects after the Soberana vaccine compared with the Sinopharm vaccine (P = 0.013), and dermatological side effects showed early-onset (P = 0.050) and delayed improvement of side effects (P = 0.035) after the Soberana vaccine compared with the Sinopharm vaccine. There was not any statistically significant difference regarding gastrointestinal side effects between the 2 vaccines (P > 0.05). Conclusion: The cardiovascular and hematological, joint-related (non-neurologic musculoskeletal) and dermatological side effects after the Soberana vaccine appear earlier and end later compared with the Sinopharm vaccine. Improvement of renal side effects in the Sinopharm vaccine group and improvement of neurological and respiratory side effects in the Soberana vaccine group occurred with delay compared with other vaccines.

Personalized predictions of adverse side effects of the COVID-19 vaccines.

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

Is it possible to reduce the rate of vertical transmission and improve perinatal outcomes by inclusion of remdesivir in treatment regimen of pregnant women with COVID–19?

Background Coronavirus disease 2019 (COVID–19) is currently one of the world's most critical health issues so far. Given the importance of appropriate treatment in pregnancy and the controversies about Remdesivir effectiveness and complications, the present study aimed to evaluate the impact of Remdesivir on maternal, fetal, and perinatal outcomes in pregnant women with COVID–19 diseases. Methods A total of 189 pregnant women with positive polymerase chain reaction (PCR) results for SARS–COV–2, and oxygen saturation [SpO2] of < 95%) were admitted to 12 hospitals affiliated with the Iran University of Medical Sciences from March 1st, 2020 to June 7th, 2021, namely the first four COVID-19 Picks in Iran. They were enrolled in this retrospective cohort study by census method and categorized into case and control groups, based on the inclusion of Remdesivir in their treatment protocol. Demographics, clinical outcomes, and pregnancy-related complications of the mothers and the neonates were compared between the two study groups. Results A comparison of 54 mothers in the case and 135 in the control group showed no demographic and clinical characteristics difference. Neonates whose mothers did not receive Remdesivir had a higher rate of positive PCR (10.2%), compared to the Remdesivir group (1.9%) with a relative risk of 0.91 reported for Remdesivir (95% CI: 0.85–0.98, P = 0.04);besides, Remdesivir resulted in fewer neonatal intensive care unit admission rates in mild/moderate COVID–19 group (RR = 0.32, 95% CI: 0.105–1.02, P = 0.03). Although neonatal death between the two groups was not statistically significant, from the clinical point seems important;1(1.9%) in the case vs. 9(7.2%) in the control group. Interestingly LOS (Length of Stay) in the hospital was longer in the case group (median of 7 vs. 3 days;P < 0.0001). Conclusion The inclusion of Remdesivir in the treatment protocol of pregnant women with COVID–19 may reduce vertical transmission and improve perinatal outcomes, thus being suggested to be considered. Supplementary Information The online version contains supplementary material available at 10.1186/s12884-023-05405-y.

Is it possible to reduce the rate of vertical transmission and improve perinatal outcomes by inclusion of remdesivir in treatment regimen of pregnant women with COVID-19?

BACKGROUND: Coronavirus disease 2019 (COVID-19) is currently one of the world's most critical health issues so far. Given the importance of appropriate treatment in pregnancy and the controversies about Remdesivir effectiveness and complications, the present study aimed to evaluate the impact of Remdesivir on maternal, fetal, and perinatal outcomes in pregnant women with COVID-19 diseases. METHODS: A total of 189 pregnant women with positive polymerase chain reaction (PCR) results for SARS-COV-2, and oxygen saturation [SpO2] of < 95%) were admitted to 12 hospitals affiliated with the Iran University of Medical Sciences from March 1st, 2020 to June 7th, 2021, namely the first four COVID-19 Picks in Iran. They were enrolled in this retrospective cohort study by census method and categorized into case and control groups, based on the inclusion of Remdesivir in their treatment protocol. Demographics, clinical outcomes, and pregnancy-related complications of the mothers and the neonates were compared between the two study groups. RESULTS: A comparison of 54 mothers in the case and 135 in the control group showed no demographic and clinical characteristics difference. Neonates whose mothers did not receive Remdesivir had a higher rate of positive PCR (10.2%), compared to the Remdesivir group (1.9%) with a relative risk of 0.91 reported for Remdesivir (95% CI: 0.85-0.98, P = 0.04); besides, Remdesivir resulted in fewer neonatal intensive care unit admission rates in mild/moderate COVID-19 group (RR = 0.32, 95% CI: 0.105-1.02, P = 0.03). Although neonatal death between the two groups was not statistically significant, from the clinical point seems important; 1(1.9%) in the case vs. 9(7.2%) in the control group. Interestingly LOS (Length of Stay) in the hospital was longer in the case group (median of 7 vs. 3 days; P < 0.0001). CONCLUSION: The inclusion of Remdesivir in the treatment protocol of pregnant women with COVID-19 may reduce vertical transmission and improve perinatal outcomes, thus being suggested to be considered.

Risk Communication in Early Containment of COVID-19 Pandemic in Iran: Implementation of the World Health Organization Strategic Preparedness and Response Plan (WHO-SPRP).

Background: Health systems must have functional and efficient preparedness and response plans to manage pandemics. Moreover, it is essential to adjust to changing circumstances and the dynamic character of pandemics. The World Health Organization (WHO) introduced the Strategic Preparedness and Response Plan (SPRP), emphasizing 144 measures across 10 pillars, helping governments prepare and respond to the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to determine how the Iranian health system, based on the WHO-SPRP, addresses strategic preparedness and response plan in the COVID-19 pandemic at the national level. Methods: The WHO-SPRP was adopted and translated into Persian by 2 bilingual natives. The chief of the health office requested that authorized officers complete the SPRP. Then, a meeting was held by officers of related units involved in COVID-19 management to address the SPRP at regional and national levels. Results: Our findings suggest that up to August 2020, effective risk communication and community engagement were not fully established. Our response plan lacked evidence-based information and educational messaging to consistently shape public opinion and impression of a respiratory pandemic. Conclusion: The Iranian health care system and services were almost able to address the SPRP and perform the major indicators that the WHO had proposed. However, special attention should be paid to risk communication and community engagement to empower informed decision-making by individuals and communities.

Pediatric and adolescent COVID-19 vaccination side effects: A retrospective cohort study of the Iranian teenage group in 2021.

To determine the safety and efficacy profile of teenager COVID-19 vaccination. In this retrospective cohort study, contact numbers of parents of teenagers under 18 years of age referred to a teenager vaccination centers in Tehran-Iran to receive the corona vaccine were collected, and the following information was obtained via the phones: demographic information, type of vaccine, and the number of doses received, as well as additional information like complications and required treatments. Eleven thousand forty-two subjects aged 10-18 years, mean age 14.55 ± 1.83 year including 5374 boys and 5768 girls were investigated. 88.1% received the Sinopharm and 11.9% the Soberana vaccine. General side effects, including fatigue, fever and chills, injection site pain and dizziness, and so forth happened in 2978 cases; 7421 children presented with at least one general or organ-specific side effect following vaccination, including potentially critical side effects, such as vascular injuries, respiratory complication, and so forth. 0.1% of the subject needed hospital admission. The breakthrough infection happened in 200 individuals. Our study shows that Sinopharm and Soberana (PastoCoVac) COVID-19 vaccines are generally safe with no serious side effects in less than 18 years old. COVID-19 infection and reinfection can occur after vaccination, but the incidence is actually tolerable and significantly lower than in the unvaccinated group.

Linear mixed model analysis to evaluate correlations between remdesivir adverse effects with age and gender of patients with mild Covid-19 pneumonia.

We aimed to assess longitudinal changes in clinical indexes of corona disease 2019 (Covid-19) patients with mild pulmonary infection during 5 days of remdesivir therapy and determine the effect of age and gender on remdesivir adverse effects (AE). Patients' clinical data including inflammatory markers, liver and renal function tests, and heart rate (HR) were extracted from medical records. Linear mixed model (LMM) was used to analyze longitudinal changes in patients' clinical indexes. Gender and age were inserted in LMM as covariates to find their correlation with AE and clinical indexes. Of 84 patients, 35 patients met our criteria for the study. There were significant increases in mean levels of white blood cell (WBC; p = 0.005), alanine aminotransferase (ALT; p = 0.001), aspartate aminotransferase (p = 0.001), blood urea nitrogen (BUN; p = 0.001), and creatinine (p = 0.006), whereas mean levels of erythrocyte sedimentation rate (p = 0.005), C-reactive protein (p = 0.001), alkaline phosphatase (p = 0.001), and potassium (p = 0.003) decreased significantly. Estimated glomerular filtration rate (p = 0.001) and HR (p = 0.001) showed a notable decline over the course of treatment. LMM analysis showed that mean changes in WBC (ß = 0.94, p = 0.029), creatinine (ß = 0.12, p = 0.020), and HR (ß = 6.47, p = 0.008) were greater in males than in females. Also, age of patients had a significant effect on the mean changes of WBC (ß = -0.02, p = 0.023), sodium (ß = -0.06, p = 0.010), BUN (ß = 0.23, p = 0.001), and HR (ß = -0.29, p = 0.001). Despite no renal and liver dysfunction, Covid-19 patients with mild pulmonary infection may develop some remdesivir AE and attributed side effects might be affected by gender and age of patients.

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU.

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies. Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission. Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP). Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset. Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.

Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning.

Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however. Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals. Results: The SPM yielded ROC-AUCs of 0.53-0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at www.aicovid.net. Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months.

Accuracy of the pre-hospital triage tools (qSOFA, NEWS, and PRESEP) in predicting probable COVID-19 patients' outcomes transferred by Emergency Medical Services.

BACKGROUND: This study aimed to evaluate the efficiency of pre-hospital triage tools including the qSOFA, NEWS, and PRESEP in determining the prognosis of probable COVID-19 patients. METHODS: In this diagnostic accuracy study, all probable COVID-19 patients older than 16-year-old who were transferred to the hospital by the Tehran Emergency Medical Services (EMS) during the first month of the pandemic, entered to the study. The scores of qSOFA, NEWS, and PRESEP were calculated using data gathered while providing pre-hospital care. The primary outcome was death; and the secondary outcomes were ICU admission, length of stay in the ICU, and length of hospital stay. RESULTS: The data of 557 individuals with the mean age of 56.93±18.31 were analyzed of whom 67.5% were males. The area under the ROC curve (AUC) of qSOFA, NEWS, and PRESEP for ICU admission was 0.553, 0.557, and 0.551, respectively. The AUC of qSOFA, NEWS, and PRESEP for death was 0.596, 0.566, and 0.604, respectively. The best obtained cut-off point for qSOFA was a score >0 (the sensitivity and specificity were 25.0 and 85.68%, respectively), for NEWS was a score >2 (the sensitivity and specificity were 83.61 and 32.67%, respectively), and for PRESEP was a score >1 (the sensitivity and specificity were 54.10 and 55.56%, respectively). CONCLUSION: Based on the findings of the current study, it is likely that the available pre-hospital triage tools (qSOFA, NEWS, and PRESEP) do not have proper efficacy to predict death, ICU admission, and disease severity of COVID-19 patients.