COVID-19 vaccines breakthrough infection and adverse reactions in medical students: a nationwide survey in Iran.

Introduction: There are different types of COVID-19 vaccines approved worldwide. Since no national studies focus on vaccine-related adverse reactions and breakthrough cases, this study aimed to investigate the rate of adverse events and COVID-19 infection in medical students in Iran. Methods: This retrospective cohort study included Iranian medical students who received two doses of COVID-19 vaccines. The medical team gathered the demographic characteristics, comorbidities, type of vaccine, adverse events following vaccination, and history of COVID-19 infection data through a phone interview. The frequency of adverse events and breakthrough infection was stratified by vaccine type (ChAdOx1-S, Gam-COVID-Vac, and BIBP-CorV). Results: A total of 3,591 medical students enrolled in this study, of which 57.02% were females, with a mean age of 23.31 + 4.87. A PCR-confirmed and suspicious-for-COVID-19 breakthrough infection rate of 4.51 and 7.02% was detected, respectively. There was no significant relation between breakthrough infection and gender, BMI, blood groups, and comorbidities. However, there was a significant difference in breakthrough infection rate among different types of vaccines (p = 0.001) and history of COVID-19 infection (p = 0.001). A total of 16 participants were hospitalized due to COVID-19 infection after vaccination for reasons such as dyspnea, abnormal imaging, or decreased oxygen saturation. No severe infection or death was observed in the studied population. Conclusion: Vaccination prevented severe COVID-19 infection, although a high breakthrough infection rate was evident among Iranian medical students during the Delta variant's peak. Vaccine effectiveness may be fragile during emerging new variants and in high-exposure settings. Moreover, adverse events are rare, and the benefits of vaccination outweigh the side effects. However, many limitations challenged this study, and the results should be cautious.

Metabolic health's central role in chronic kidney disease progression: a 20-year study of obesity-metabolic phenotype transitions.

This study investigates the risk of chronic kidney disease (CKD) across four metabolic phenotypes: Metabolically Healthy-No Obesity (MH-NO), Metabolically Unhealthy-No obesity (MU-NO), Metabolically Healthy-Obesity (MH-O), and Metabolically Unhealthy-Obesity (MU-O). Data from the Tehran Lipid and Glucose Study, collected from 1999 to 2020, were used to categorize participants based on a BMI ≥ 30 kg/m2 and metabolic health status, defined by the presence of three or four of the following components: high blood pressure, elevated triglycerides, low high-density lipoprotein, and high fasting blood sugar. CKD, characterized by a glomerular filtration rate < 60 ml/min/1.72 m2. The hazard ratio (HR) of CKD risk was evaluated using Cox proportional hazard models. The study included 8731 participants, with an average age of 39.93 years, and identified 734 incidents of CKD. After adjusting for covariates, the MU-O group demonstrated the highest risk of CKD progression (HR 1.42-1.87), followed by the MU-NO group (HR 1.33-1.67), and the MH-O group (HR 1.18-1.54). Persistent MU-NO and MU-O posed the highest CKD risk compared to transitional states, highlighting the significance of exposure during early adulthood. These findings emphasize the independent contributions of excess weight and metabolic health, along with its components, to CKD risk. Therefore, preventive strategies should prioritize interventions during early-adulthood.

Atorvastatin Effect on COVID-19 Outcomes: A Propensity Score Matched Study on Hospitalized Patients.

BACKGROUND: This study investigated the association of atorvastatin use on survival, need for intensive care unit (ICU) admission, and length of hospital stay (LOS) among COVID-19 inpatients. MATERIALS AND METHODS: A retrospective study was conducted between March 20th, 2020, and March 18th, 2021, on patients with confirmed COVID-19 admitted to three hospitals in Tehran, Iran. The unadjusted and adjusted effects of atorvastatin on COVID-19 prognosis were investigated. Propensity score matching (PSM) was used to achieve a 1:1 balanced dataset with a caliper distance less than 0.1 and the nearest neighbor method without replacement. RESULTS: Of 4322 COVID-19 patients, 2136 (49.42%) were treated with atorvastatin. After PSM, 1245 atorvastatin inpatients and 1245 controls were included with a median age of 62.0 (interquartile range [IQR]: 51.0, 76.0) and 63.0 (IQR: 51.0, 75.0) years, respectively. The standardized mean differences were less than 0.1 for all confounders, suggesting a good covariate balance. The use of atorvastatin was associated with decreased COVID-19 mortality (HR: 0.80; 95% CI: 0.68-0.95), whereas no relationship was found between atorvastatin and the need for ICU admission (HR: 1.21; 95% CI: 0.99-1.47). LOS was significantly higher in the atorvastatin cohort than controls (Atorvastatin vs. others: 7 [5, 11] vs. 6 [4, 10] days; p = 0.003). The survival rate was higher in combination therapy of atorvastatin plus enoxaparin than in those who received atorvastatin alone (p-value=0.001). CONCLUSION: Atorvastatin may reduce the risk of COVID-19 in-hospital mortality and could be a beneficial option for an add-on therapy. Randomized trials are warranted to confirm the results of the current observational studies.

Drivers' behavior confronting fixed and point-to-point speed enforcement camera: agent-based simulation and translation to crash relative risk change.

Utilizing a novel microsimulation approach, this study evaluates the impact of fixed and average point-to-point Speed Enforcement Cameras (SEC) on driving safety. Using the SUMO software, agent-based models for a 6-km highway without exits or obstacles were created. Telematics data from 93,160 trips were used to determine the desired free-flow speed. A total of 13,860 scenarios were simulated with 30 random seeds. The ratio of unsafe driving (RUD) is the spatial division of the total distance travelled at an unsafe speed by the total travel distance. The study compared different SEC implementations under different road traffic and community behaviours using the Power Model and calculated crash risk changes. Results showed that adding one or two fixed SECs reduced RUD by 0.20% (0.18-0.23) and 0.57% (0.54-0.59), respectively. However, average SECs significantly lowered RUD by 10.97% (10.95-10.99). Furthermore, a 1% increase in telematics enforcement decreased RUD by 0.22% (0.21-0.22). Point-to-point cameras effectively reduced crash risk in all implementation scenarios, with reductions ranging from - 3.44 to - 11.27%, pointing to their superiority as speed enforcement across various scenarios. Our cost-conscious and replicable approach can provide interim assessments of SEC effectiveness, even in low-income countries.

Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data.

BACKGROUND: Despite the globally reducing hospitalization rates and the much lower risks of Covid-19 mortality, accurate diagnosis of the infection stage and prediction of outcomes are clinically of interest. Advanced current technology can facilitate automating the process and help identifying those who are at higher risks of developing severe illness. This work explores and represents deep-learning-based schemes for predicting clinical outcomes in Covid-19 infected patients, using Visual Transformer and Convolutional Neural Networks (CNNs), fed with 3D data fusion of CT scan images and patients' clinical data. METHODS: We report on the efficiency of Video Swin Transformers and several CNN models fed with fusion datasets and CT scans only vs. a set of conventional classifiers fed with patients' clinical data only. A relatively large clinical dataset from 380 Covid-19 diagnosed patients was used to train/test the models. RESULTS: Results show that the 3D Video Swin Transformers fed with the fusion datasets of 64 sectional CT scans + 67 clinical labels outperformed all other approaches for predicting outcomes in Covid-19-infected patients amongst all techniques (i.e., TPR = 0.95, FPR = 0.40, F0.5 score = 0.82, AUC = 0.77, Kappa = 0.6). CONCLUSIONS: We demonstrate how the utility of our proposed novel 3D data fusion approach through concatenating CT scan images with patients' clinical data can remarkably improve the performance of the models in predicting Covid-19 infection outcomes. SIGNIFICANCE: Findings indicate possibilities of predicting the severity of outcome using patients' CT images and clinical data collected at the time of admission to hospital.

Effect of interferon-α on COVID-19 in-hospital mortality: a large-scale propensity score-matched study.

BACKGROUND:  Coronavirus infection can induce the production of inflammatory cytokines leading to acute respiratory distress syndrome (ARDS) and death. It is well-established that interferons (IFNs) are essential in regulating the immune response, thus their effects of IFNs on COVID-19 patients should be subject to investigation. This study aimed to investigate the effects of IFN-α alone or in combination with remdesivir in hospitalized COVID-19 patients. MATERIAL AND METHODS:  A multicentre, retrospective study was conducted on COVID-19 patients admitted to three hospitals in Tehran, Iran, from March 20, 2020, to March 18, 2021. The unadjusted and adjusted effects of IFN-α on COVID-19 outcomes were investigated through propensity score matching (PSM) to achieve a 1:1 balanced dataset. RESULTS: Among 4,782 patients, 3,764 were eligible for the study, including 1,704 patients (45.27%) receiving at least one treatment with IFN-α and 2,060 controls not receiving IFN-α. After PSM, 851 IFN-α patients and 851 controls were recruited in the PSM analysis with a median age of 60.8 (standard deviation [SD]: 16.2 and 60.9 [SD: 17.4]), respectively. The PSM results showed no significant difference between the survival curves of the IFN-α group and the control group (p=0.340). However, the unadjusted impact of IFN-α on the risk of mortality was statistically significant (p=0.043, hazard-ratio: 0.86; 95% confidence interval [CI]: 0.75-0.99). Also, the combination of IFN-α and remdesivir had no significant benefit (HR: 89, 95% CI: 0.74-1.34). CONCLUSION: Our findings indicate that subcutaneous administration of IFN-α, with or without remdesivir, does not have any significant impact on COVID-19 mortality and ICU admission. Future clinical trials considering the time, subtype, and form of IFN-α administration are warranted to investigate the potential therapeutic effects of IFN-α on COVID-19.

Gastric Cancer Risk in Association with Underweight, Overweight, and Obesity: A Systematic Review and Meta-Analysis.

This study aimed to investigate the risk of gastric cancer (GC) in abnormal body mass index (BMI) groups. A systematic search was carried out on Embase, PubMed/Medline, and Scopus from January 2000 to January 2023. The pooled risk ratio (RR) with a 95% confidence interval (CI) was assessed using a random-effect model. Thirteen studies with total of 14,020,031 participants were included in this systematic review. The pooled RR of GC was 1.124 (95% CI, 0.968-1.304, I2: 89.08%) in underweight class, 1.155 (95% CI, 1.051-1.270, I2: 95.18%) in overweight class, and in 1.218 (95% CI, 1.070-1.386, I2: 97.65%) obesity class. There is no difference between cardia and non-cardia gastric cancer, while non-Asian race and female gender have higher risk of cancer, as Meta-regression of obesity and overweight classes showed. These findings suggest that there is a positive association between excess body weight and the risk of GC, with a higher impact in women than men and in non-Asian than Asian populations. Since abnormal weight is tied to various diseases, including GC, healthcare experts, and policymakers should continue interventions aiming to achieve a normal BMI range.

On-admission and dynamic trend of laboratory profiles as prognostic biomarkers in COVID-19 inpatients.

This large-scale study aimed to investigate the trend of laboratory tests of patients with COVID-19. Hospitalized confirmed and probable COVID-19 patients in three general hospitals were examined from March 20, 2020, to June 18, 2021. The confirmed and probable COVID-19 patients with known outcomes and valid laboratory results were included. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to select admittance prognostic features. Parallel Pairwise Comparison of mortality versus survival was used to examine the trend of markers. In the final cohort, 11,944 patients were enrolled, with an in-hospital mortality rate of 21.8%, mean age of 59.4 ± 18.0, and a male-to-female ratio of 1.3. Abnormal admittance level of white blood cells, neutrophils, lymphocytes, mean cellular volume, urea, creatinine, bilirubin, creatine kinase-myoglobin binding, lactate dehydrogenase (LDH), Troponin, c-reactive protein (CRP), potassium, and creatinine phosphokinase reduced the survival of COVID-19 inpatients. Moreover, the trend analysis showed lymphocytes, platelet, urea, CRP, alanine transaminase (ALT), and LDH have a dissimilar trend in non-survivors compared to survived patients. This study proposed a novel approach to find serial laboratory markers. Serial examination of platelet count, creatinine, CRP, LDH, and ALT can guide healthcare professionals in finding patients at risk of deterioration.

Demographic, Epidemiologic, and Clinical Characteristics of Human Monkeypox Disease Pre- and Post-2022 Outbreaks: A Systematic Review and Meta-Analysis.

(1) Background: In early May 2022, an increasing number of human monkeypox (mpox) cases were reported in non-endemic disparate regions of the world, which raised concerns. Here, we provide a systematic review and meta-analysis of mpox-confirmed patients presented in peer-reviewed publications over the 10 years before and during the 2022 outbreak from demographic, epidemiological, and clinical perspectives. (2) Methods: A systematic search was performed for relevant studies published in Pubmed/Medline, Embase, Scopus, and Google Scholar from 1 January 2012 up to 15 February 2023. Pooled frequencies with 95% confidence intervals (CIs) were assessed using the random or fixed effect model due to the estimated heterogeneity of the true effect sizes. (3) Results: Out of 10,163 articles, 67 met the inclusion criteria, and 31 cross-sectional studies were included for meta-analysis. Animal-to-human transmission was dominant in pre-2022 cases (61.64%), but almost all post-2022 reported cases had a history of human contact, especially sexual contact. The pooled frequency of MSM individuals was 93.5% (95% CI 91.0-95.4, I2: 86.60%) and was reported only in post-2022 included studies. The male gender was predominant in both pre- and post-2022 outbreaks, and the mean age of confirmed cases was 29.92 years (5.77-41, SD: 9.38). The most common clinical manifestations were rash, fever, lymphadenopathy, and malaise/fatigue. Proctalgia/proctitis (16.6%, 95% CI 10.3-25.6, I2: 97.76) and anal/perianal lesions (39.8%, 95% CI 30.4-49.9, I2: 98.10) were the unprecedented clinical manifestations during the 2022 outbreak, which were not described before. Genitalia involvement was more common in post-2022 mpox patients (55.6%, 95% CI 51.7-59.4, I2: 88.11). (4) Conclusions: There are speculations about the possibility of changes in the pathogenic properties of the virus. It seems that post-2022 mpox cases experience a milder disease with fewer rashes and lower mortality rates. Moreover, the vast majority of post-2022 cases are managed on an outpatient basis. Our study could serve as a basis for ongoing investigations to identify the different aspects of previous mpox outbreaks and compare them with the current ones.

Epidemiology and Histopathology of Nasopharyngeal Neoplasms in Iran.

Introduction: This study aimed to study the trend, histologic pattern, geographical distribution, and characteristics of nasopharyngeal carcinoma (NPC) and nasopharyngeal neoplasms (NPN) from 2003 to 2017 in Iran. Materials and Methods: The Ministry of Health and Medical Education collected NPN cases from the corresponding university in each province and stored them in Iran National Cancer Registry (INCR) database. The Joinpoint program calculated the average annual percent change (AAPC) and its 95% confidence interval (CI). The jump model minimized the interfering effect of INCR transformation. Results: 3653 NPN cases were reported between 2003-2010 and 2014-2017, with a mean age of 49.04 ± 18.31 years and a male-to-female ratio of 2.15. The age-standardized incidence rate (ASIR) per 100,000 person-years was 0.30 for females and 0.68 for males in 2017. Although the ASIR/100,000 of NPN raised from 0.35 to 0.49 during 2003-2017, the trend was constant with an AAPC of -2% (95% CI: -4.8% to 0.9%). The age-specific incidence rate was highest in the older than 70 population (1.56/100,000). NPC formed 77.1% of NPNs and showed a constant pattern (AAPC CI: -5.7% to 0.2%), in contrast to the significant increase of non-keratinizing squamous cell carcinoma (AAPC CI: 2.3%to 24.5%). Conclusions: Nasopharynx cancer is rare in Iran, and NPC incidence remained constant from 2003 to 2017, unlike previously reported rising trend. However, non-keratinizing squamous cell carcinoma exhibited a significant increase, and future studies are needed to examine the role of the Epstein-Barr virus on this growth rate.

Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features.

We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.

Effect of Electrolyte Imbalance on Mortality and Late Acute Kidney Injury in Hospitalized COVID-19 Patients.

INTRODUCTION: As a multisystem illness, Coronavirus disease 2019 (COVID-19) can damage different organs. This study investigated the effect of electrolyte imbalance (EI), with or without concomitant renal dysfunction, on the prognosis of COVID-19 in hospitalized patients. METHODS: We evaluated 499 hospitalized patients with confirmed COVID-19, without a history of chronic kidney disease. The patients' demographic data, laboratory values, and outcomes were retrospectively collected from the hospital information system. Serumelectrolytes including sodium, potassium, magnesium, calcium, and phosphorus abnormalities were analyzed on admission and during the hospitalization period. The outcomes of this study were the occurrence of acute kidney injury (AKI) after the first week of hospitalization and in-hospital mortality rate. Multivariate analyses were carried out to obtain the independent risk of each EI on mortality, by adjusting for age, gender, and AKI occurrence. RESULTS: Among the 499 COVID-19 patients (60.9% male), AKI occurred in 168 (33.7%) and mortality in 92 (18.4%) cases. Hypocalcemia (38%) and hyponatremia (22.6%) were the most prevalent EIs, and all EIs were more common in the AKI group than in the non-AKI group. Hyponatremia (Adjusted Odds ratio [AOR] = 2.34, 95% CI: 1.30 to 4.18), hypernatremia (AOR = 8.52, 95% CI: 1.95 to 37.32), and hyperkalemia (AOR = 4.63, 95% CI: 1.65 to 13) on admission were associated with poor prognosis. Moreover, hyponatremia (AOR = 3.02, 95% CI: 1.28 to 7.15) and hyperphosphatemia (AOR = 5.12, 95% CI: 1.24 to 21.09) on admission were associated with late AKI occurrence. CONCLUSION: This study highlights the role of hyponatremia, hypernatremia, hyperkalemia, and hyperphosphatemia in poor prognosis of COVID-19. According to the independent effect of EI on late AKI and mortality, we recommend physicians to raise awareness to closely monitor and correct EI during hospitalization.  DOI: 10.52547/ijkd.6904.

Epidemiology of COVID-19 in Tehran, Iran: A Cohort Study of Clinical Profile, Risk Factors, and Outcomes.

Background: The outbreak of coronavirus disease 2019 (COVID-19) dates back to December 2019 in China. Iran has been among the most prone countries to the virus. The aim of this study was to report demographics, clinical data, and their association with death and CFR. Methods: This observational cohort study was performed from 20th March 2020 to 18th March 2021 in three tertiary educational hospitals in Tehran, Iran. All patients were admitted based on the WHO, CDC, and Iran's National Guidelines. Their information was recorded in their medical files. Multivariable analysis was performed to assess demographics, clinical profile, outcomes of disease, and finding the predictors of death due to COVID-19. Results: Of all 5318 participants, the median age was 60.0 years, and 57.2% of patients were male. The most significant comorbidities were hypertension and diabetes mellitus. Cough, dyspnea, and fever were the most dominant symptoms. Results showed that ICU admission, elderly age, decreased consciousness, low BMI, HTN, IHD, CVA, dialysis, intubation, Alzheimer disease, blood injection, injection of platelets or FFP, and high number of comorbidities were associated with a higher risk of death related to COVID-19. The trend of CFR was increasing (WPC: 1.86) during weeks 25 to 51. Conclusions: Accurate detection of predictors of poor outcomes helps healthcare providers in stratifying patients, based on their risk factors and healthcare requirements to improve their survival chance.

Excess all-cause mortality and COVID-19 reported fatality in Iran (April 2013-September 2021): age and sex disaggregated time series analysis.

OBJECTIVE: The actual impact of the pandemic on COVID-19 specific mortality is still unclear due to the variability in access to diagnostic tools. This study aimed to estimate the excess all-cause mortality in Iran until September 2021 based on the national death statistics. RESULTS: The autoregressive integrated moving average was used to predict seasonal all-cause death in Iran (R-squared = 0.45). We observed a 38.8% (95% confidence interval (CI) 29.7%-40.1%) rise in the all-cause mortality from 22 June 2020 to 21 June 2021. The excess all-cause mortality per 100,000 population were 178.86 (95% CI 137.2-220.5, M:F ratio = 1.3) with 49.1% of these excess deaths due to COVID-19. Comparison of spring 2019 and spring 2021 revealed that the highest percent increase in mortality was among men aged 65-69 years old (77%) and women aged 60-64 years old (86.8%). Moreover, the excess mortality among 31 provinces of Iran ranged from 109.7 (Hormozgan) to 273.2 (East-Azerbaijan) per 100,000 population. In conclusion, there was a significant rise in all-cause mortality during the pandemic. Since COVID-19 fatality explains about half of this rise, the increase in other causes of death and underestimation in reported data should be concerned by further studies.

Correlation between Chest Computed Tomography Scan Findings and Mortality of COVID-19 Cases; a Cross sectional Study.

INTRODUCTION: Predicting the outcomes of COVID-19 cases using different clinical, laboratory, and imaging parameters is one of the most interesting fields of research in this regard. This study aimed to evaluate the correlation between chest computed tomography (CT) scan findings and outcomes of COVID-19 cases. METHODS: This cross sectional study was carried out on confirmed COVID-19 cases with clinical manifestations and chest CT scan findings based on Iran's National Guidelines for defining COVID-19. Baseline and chest CT scan characteristics of patients were investigated and their correlation with mortality was analyzed and reported using SPSS 21.0. RESULTS: 380 patients with the mean age of 53.62 ± 16.66 years were evaluated (66.1% male). The most frequent chest CT scan abnormalities were in peripheral (86.6%) and peribronchovascular interstitium (34.6%), with ground glass pattern (54.1%), and round (53.6%) or linear (46.7%) shape. There was a significant correlation between shape of abnormalities (p = 0.003), CT scan Severity Score (CTSS) (p <0.0001), and pulmonary artery CT diameter (p = 0. 01) with mortality. The mean CTSS of non-survived cases was significantly higher (13.68 ± 4.59 versus 8.72 ± 4.42; <0.0001). The area under the receiver operating characteristic (ROC) curve of CTSS in predicting the patients' mortality was 0.800 (95% CI: 0.716-0.884). The best cut off point of chest CTSS in this regard was 12 with 75.82% (95% CI: 56.07%-88.98%) sensitivity and 75.78% (95% CI: 70.88%-80.10%) specificity. The mean main pulmonary artery diameter in patients with CTSS ≥ 12 was higher than cases with CTSS < 12 (27.89 ± 3.73 vs 26.24 ± 3.14 mm; p < 0.0001). CONCLUSION: Based on the results of the present study it seems that there is a significant correlation between chest CT scan characteristics and mortality of COVID-19 cases. Patients with lower CTSS, lower pulmonary artery CT diameter, and round shape opacity had lower mortality.