Risk Factors Associated with Severity and Death from COVID-19 in Iran: A Systematic Review and Meta-Analysis Study.

Objectives: This study aims to investigate the risk factors associated with severity and death from COVID-19 through a systematic review and meta-analysis of the published documents in Iran. Methods: A systematic search was performed based on all articles indexed in Scopus, Embase, Web of Science (WOS), PubMed, and Google Scholar in English and Scientific Information Database (SID) and Iranian Research Institute for Information Science and Technology (IRA)NDOC indexes in Persian. To assess quality, we used the Newcastle Ottawa Scale. Publication bias was assessed using Egger's tests. Forest plots were used for a graphical description of the results. We used HRs, and ORs reported for the association between risk factors and COVID-19 severity and death. Results: Sixty-nine studies were included in the meta-analysis, of which 62 and 13 had assessed risk factors for death and severity, respectively. The results showed a significant association between death from COVID-19 and age, male gender, diabetes, hypertension, cardiovascular disease (CVD), cerebrovascular disease, chronic kidney disease (CKD), Headache, and Dyspnea. We observed a significant association between increased white blood cell (WBC), decreased Lymphocyte, increased blood urea nitrogen (BUN), increased creatinine, vitamin D deficiency, and death from COVID-19. There was only a significant relationship between CVD and disease severity. Conclusion: It is recommended that the predictive risk factors of COVID-19 severity and death mentioned in this study to be used for therapeutic and health interventions, to update clinical guidelines and determine patients' prognoses.

Epidemiologic Parameters for COVID-19: A Systematic Review and Meta-Analysis.

Background: The World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) outbreak to be a public health emergency and international concern and recognized it as a pandemic. This study aimed to estimate the epidemiologic parameters of the COVID-19 pandemic for clinical and epidemiological help. Methods: In this systematic review and meta-analysis study, 4 electronic databases, including Web of Science, PubMed, Scopus, and Google Scholar were searched for the literature published from early December 2019 up to 23 March 2020. After screening, we selected 76 articles based on epidemiological parameters, including basic reproduction number, serial interval, incubation period, doubling time, growth rate, case-fatality rate, and the onset of symptom to hospitalization as eligibility criteria. For the estimation of overall pooled epidemiologic parameters, fixed and random effect models with 95% CI were used based on the value of between-study heterogeneity (I2). Results: A total of 76 observational studies were included in the analysis. The pooled estimate for R0 was 2.99 (95% CI, 2.71-3.27) for COVID-19. The overall R0 was 3.23, 1.19, 3.6, and 2.35 for China, Singapore, Iran, and Japan, respectively. The overall serial interval, doubling time, and incubation period were 4.45 (95% CI, 4.03-4.87), 4.14 (95% CI, 2.67-5.62), and 4.24 (95% CI, 3.03-5.44) days for COVID-19. In addition, the overall estimation for the growth rate and the case fatality rate for COVID-19 was 0.38% and 3.29%, respectively. Conclusion: The epidemiological characteristics of COVID-19 as an emerging disease may be revealed by computing the pooled estimate of the epidemiological parameters, opening the door for health policymakers to consider additional control measures.

Comparing the Characteristics of Mucormycosis Between Cases with and without COVID-19; a Cross-sectional Study.

Introduction: Mucormycosis as a rare but life-threatening disease with 46-96% mortality, which challenged the healthcare system during the COVID-19 pandemic. This study aimed to compare the characteristics of mucormycosis between cases with and without COVID-19. Methods: This cross-sectional study was done in two referral hospitals, Imam Hossein and Labbafinezhad Hospitals, Tehran, Iran, between 21 March to 21 December 2021. Data related to all hospitalized adults subject with the diagnosis of mucormycosis during the study period was collected from patients' profiles and they were divided into two groups of with and without COVID-19 based on the results of real time PCR. Then demographic, clinical, and laboratory findings as well as outcomes were compared between the two groups. Results: 64 patients with the mean age of 53.40±10.32 (range: 33-74) years were studied (53.1% male). Forty-three (67.2%) out of the 64 subjects had a positive COVID-19 PCR test. The two groups had significant differences regarding some symptoms (cough (p < 0.001), shortness of breath (p = 0.006)), acute presentation (p = 0.027), using immunosuppressive (p = 0.013), using corticosteroid (p < 0.001), and outcomes (mortality (p = 0.018), need for intubation (p < 0.001)). 22 (34.3%) patients expired during hospital admission. Univariate analysis showed the association of in-hospital mortality with need for ventilation (p < 0.001), sinus involvement (p = 0.040), recent use of dexamethasone (p = 0.011), confirmed COVID-19 disease (p = 0.025), mean body mass index (BMI) (p =0.035), hemoglobin A1c (HbA1c) (p = 0.022), and median of blood urea nitrogen (BUN) (p =0.034). Based on the multivariate model, confirmed COVID-19 disease (OR = 5.01; 95% CI: 1.14-22.00; p = 0.033) and recent use of dexamethasone (OR= 4.08, 95% CI: 1.05-15.84, p = 0.042) were independent predictors of mortality in this series. Conclusion: The mucormycosis cases with concomitant COVID-19 disease had higher frequency of cough and shortness of breath, higher frequency of acute presentation, higher need for immunosuppressive, corticosteroid, and ventilator support, and higher mortality rate. The two groups were the same regarding age, gender, BMI, risk factors, underlying diseases, symptoms, and sites of involvement.

Is cardiac Troponin I Considered as A Predictor of In-hospital Mortality among COVID-19 Patients? A Retrospective Cohort Study.

OBJECTIVE: To describe the levels of troponin I in COVID-19 patients and its role in the prediction of their in-hospital mortality as a cardiac biomarker. METHODS: The current retrospective cohort study was performed on the clinical records of 649 COVID-19-related hospitalized cases with at leat one positive polymerase chain reaction (PCR) test in Tehran, Iran from February 2020 to early June 2020. The on admission troponin I level divided into two groups of ≤0.03ng/mL (normal) and >0.03ng/mL (abnormal). The adjusted COX-regression model was used to determine the relationship between the studied variables and patient's in-hospital mortality. RESULTS: In this study, the median age of subjects was 65 years (54.8% men) and 29.53% of them had abnormal troponin I levels. Besides, the in-hospital mortality rate among patients with abnormal troponin I levels was found to be 51.56%; whereas, patients with normal levels exhibited 18.82% mortality. Also, the multivariable analysis indicated that the risk of death among hospitalized COVID-19 patients displaying abnormal troponin I levels was 67% higher than those with normal troponin I levels (Hazard ratio=1.67, 95% confidence interval=1.08-2.56, p=0.019). CONCLUSION: It seems that troponin I is one of the important factors related to in-hospital mortality of COVID-19 patients. Next, due to the high prevalence of cardiac complications in these patients, it is highly suggested to monitor and control cardiac biomarkers along with other clinical factors upon the patient's arrival at the hospital.

Development of a scoring system for the prediction of in-hospital mortality among COVID-19 patients.

BACKGROUND: The aim of this study is to develop and validate a scoring system as a tool for predicting the in-hospital mortality in COVID-19 patients in early stage of disease. METHODS: This retrospective cohort study, conducted on 893 COVID-19 patients in Tehran from February 18 to July 20, 2020. Potential factors were chosen via stepwise selection and multivariable logistic regression model. Cross-validation method was employed to assess the predictive performance of the model as well as the scoring system such as discrimination, calibration, and validity indices. RESULTS: The COVID-19 patients' median age was 63 yrs (54.98% male) and 233 (26.09%) patients expired during the study. The scoring system was developed based on 8 selected variables: age ≥55 yrs (OR = 5.67, 95% CI: 3.25-9.91), males (OR = 1.51, 95% CI: 1.007-2.29), ICU need (OR = 16.32, 95% CI 10.13-26.28), pulse rate >90 (OR = 1.89, 95% CI: 1.26-2.83), lymphocytes <17% (OR = 2.33, 95%CI: 1.54-3.50), RBC ≤4, 10 6/L (OR = 2.10, 95% CI: 1.35-3.26), LDH >700 U/L (OR = 1.68, 95%CI: 1.13-2.51) and troponin I level >0.03 ng/mL (OR = 1.75, 95%CI: 1.17-2.62). The AUC and the accuracy of scoring system after cross-validation were 79.4% and 79.89%, respectively. CONCLUSION: This study showed that developed scoring system has a good performance and can use to help physicians for identifying high-risk patients in early stage of disease .

Effect of Underlying Cardiovascular Disease on the Prognosis of COVID-19 Patients; a Sex and Age-Dependent Analysis.

INTRODUCTION: Adults with underlying medical disorders are at increased risk for severe illness from the virus that causes COVID-19. This study aimed to compare the effect of underlying diseases on the mortality of male and female patients as a primary objective. We also evaluated the effect of drugs previously used by COVID-19 patients on their outcome. METHODS: This retrospective cohort study was carried out on confirmed cases of COVID-19 who were admitted to a teaching hospital in Tehran, Iran. Data was gathered from patients' files. Log binomial model was used for investigating the association of underlying diseases and in-hospital mortality of these patients. RESULTS: A total of 991 patients (mean age 61.62±17.02; 54.9% male) were recruited. Hypertension (41.1%), diabetes mellitus (30.6%), and coronary artery disease (19.6%) were the most common underlying diseases. The multivariable model showed that hypertension (RR = 1.62; 95% CI: 1.22-2.14, p = 0.001) in male patients over 55 years old and coronary artery disease (RR = 2.40; 95% CI: 1.24-4.46, p = 0.009) in female patients under 65 years old were risk factors of mortality. In females over 65 years old, the history of taking Angiotensin Converting Enzyme inhibitors (ACEi) and Angiotensin Receptor Blockers (ARB) (RR = 0.272; 95% CI: 0.17-0.41, p = 0.001) was a significant protective factor for death. CONCLUSIONS: COVID-19 patients with a history of cardiovascular diseases such as hypertension and coronary artery disease, especially those in specific age and sex groups, are high-risk patients for in-hospital mortality. Additionally, a previous history of taking ACEi and ARB medications in females over 65 tears old was a protective factor against in-hospital mortality of COVID-19 patients.

Epidemiologic characteristics of cases with reinfection, recurrence, and hospital readmission due to COVID-19: A systematic review and meta-analysis.

Recent studies reported that some recovered COVID-19 patients have tested positive for virus nucleic acid again. A systematic search was performed in Web of Science, PubMed, Scopus, and Google Scholar up to March 6, 2021. The pooled estimation of reinfection, recurrence, and hospital readmission among recovered COVID-19 patients was 3, 133, and 75 per 1000 patients, respectively. The overall estimation of reinfection among males compared to females was greater. The prevalence of recurrence in females compared to males was more common. Also, hospital readmission between sex groups was the same. There is uncertainty about long-term immunity after SARS-Cov-2 infection. Thus, the possibility of reinfection and recurrence after recovery is not unexpected. In addition, there is a probability of hospital readmission due to adverse events of COVID-19 after discharge. However, with mass vaccination of people and using the principles of prevention and appropriate management of the disease, frequent occurrence of the disease can be controlled.

Development of a scoring system for the prediction of in-hospital mortality among COVID-19 patients

<h4>Background</h4> The aim of this study is to develop and validate a scoring system as a tool for predicting the in-hospital mortality in COVID-19 patients in early stage of disease. <h4>Methods</h4> This retrospective cohort study, conducted on 893 COVID-19 patients in Tehran from February 18 to July 20, 2020. Potential factors were chosen via stepwise selection and multivariable logistic regression model. Cross-validation method was employed to assess the predictive performance of the model as well as the scoring system such as discrimination, calibration, and validity indices. <h4>Results</h4> The COVID-19 patients’ median age was 63 yrs (54.98% male) and 233 (26.09%) patients expired during the study. The scoring system was developed based on 8 selected variables: age ≥55 yrs (OR = 5.67, 95% CI: 3.25–9.91), males (OR = 1.51, 95% CI: 1.007–2.29), ICU need (OR = 16.32, 95% CI 10.13–26.28), pulse rate >90 (OR = 1.89, 95% CI: 1.26–2.83), lymphocytes <17% (OR = 2.33, 95%CI: 1.54–3.50), RBC ≤4, 10 6/L (OR = 2.10, 95% CI: 1.35–3.26), LDH >700 U/L (OR = 1.68, 95%CI: 1.13–2.51) and troponin I level >0.03 ng/mL (OR = 1.75, 95%CI: 1.17–2.62). The AUC and the accuracy of scoring system after cross-validation were 79.4% and 79.89%, respectively. <h4>Conclusion</h4> This study showed that developed scoring system has a good performance and can use to help physicians for identifying high-risk patients in early stage of disease.

Electrocardiographic Findings and In-Hospital Mortality of COVID-19 Patients; a Retrospective Cohort Study.

BACKGROUND: Although current evidence points to the possible prognostic value of electrocardiographic (ECG) findings for in-hospital mortality of COVID-19 patients, most of these studies have been performed on a small sample size. In this study, our aim was to investigate the ECG changes as prognostic indicators of in-hospital mortality. METHODS: In a retrospective cohort study, the findings of the first and the second ECGs of COVID-19 patients were extracted and changes in the ECGs were examined. Any abnormal finding in the second ECG that wasn't present in the initial ECG at the time of admission was defined as an ECG change. ECGs were interpreted by a cardiologist and the prognostic value of abnormal ECG findings for in-hospital mortality of COVID-19 patients was evaluated using multivariate analysis and the report of the relative risk (RR). RESULTS: Data of the ECGs recorded at the time of admission were extracted from the files of 893 patients; likewise, the second ECGs could be extracted from the records of 328 patients who had an initial ECG. The presence of sinus tachycardia (RR = 2.342; p <0.001), supraventricular arrhythmia (RR = 1.688; p = 0.001), ventricular arrhythmia (RR = 1.854; p = 0.011), interventricular conduction delays (RR = 1.608; p = 0.009), and abnormal R wave progression (RR = 1.766; p = 0.001) at the time of admission were independent prognostic factors for in-hospital mortality. In the second ECG, sinus tachycardia (RR = 2.222; p <0.001), supraventricular arrhythmia (RR = 1.632; p <0.001), abnormal R wave progression (RR = 2.151; p = 0.009), and abnormal T wave (RR = 1.590; p = 0.001) were also independent prognostic factors of in-hospital mortality. Moreover, by comparing the first and the second ECGs, it was found that the incidence of supraventricular arrhythmia (RR = 1.973; p = 0.005) and ST segment elevation/depression (RR = 2.296; p <0.001) during hospitalization (ECG novel changes) are two independent prognostic factors of in-hospital mortality in COVID-19 patients. CONCLUSION: Due to the fact that using electrocardiographic data is easy and accessible and it is easy to continuously monitor patients with this tool, ECGs can be useful in identifying high-risk COVID-19 patients for mortality.

Estimation of the basic reproduction number (𝑠0) of the COVID-19 epidemic in Iran.

Background: Estimation of the basic reproduction number of an infectious disease is an important issue for controlling the infection. Here, we aimed to estimate the basic reproduction number (𝑅0) of COVID-19 in Iran. Methods: To estimate 𝑅0 in Iran and Tehran, the capital, we used 3 different methods: exponential growth rate, maximum likelihood, and Bayesian time-dependent. Daily number of confirmed cases and serial intervals with a mean of 4.27 days and a standard deviation of 3.44 days with gamma distribution were used. Sensitivity analysis was performed to show the importance of generation time in estimating 𝑅0. Results: The epidemic was in its exponential growth 11 days after the beginning of the epidemic (Feb 19, 2020) with doubling time of 1.74 (CI: 1.58-1.93) days in Iran and 1.83 (CI: 1.39-2.71) in Tehran. Nationwide, the value of 𝑅0 from February 19 to 29 using exponential growth method, maximum likelihood, and Bayesian time-dependent methods was 4.70 (95% CI: 4.23-5.23), 3.90 (95% CI: 3.47- 4.36), and 3.23 (95% CI: 2.94-3.51), respectively. In addition, in Tehran, 𝑅0 was 5.14 (95% CI: 4.15-6.37), 4.20 (95% CI: 3.38-5.14), and 3.94 (95% CI: 3.45-4.40) for exponential growth, maximum likelihood, and Bayesian time-dependent methods, respectively. Bayesian time dependent methods usually provide less biased estimates. The results of sensitivity analyses demonstrated that changes in the mean generation time affect estimates of 𝑅0. Conclusion: The estimate of 𝑅0 for the COVID-19 ranged from 3.94 to 5.14 in Tehran and from 3.23 to 4.70 in nationwide using different methods, which were significantly larger than 1, indicating the potential of COVID-19 to cause an outbreak.

Exploring Change in Trend of Homicide Incidence Rate in Iran from 2006 to 2016: Applying Segmented Regression Model.

BACKGROUND: Murder is one of the public health problems. According to the WHO reports, murder is fourth leading cause of death among young people. The aim of this study was applying joint point regression model to study trend of homicide mortality in Iran, 2006-2016. STUDY DESIGN: A cross-sectional panel (pseudo-panel) study. METHODS: Homicide data during 2006 to 2016 were extracted from Iranian legal medicine organization. Trends of homicide incidence were summarized by annual percent change (APC) and average annual percent change (AAPC) using non-linear segmented regression model. RESULTS: Totally, 26918 homicide cases occurred during the period from 2006 to 2016. The highest and lowest frequency was related to the 15-29 yr (46.5%) and 0-4 yr (1.5%) age groups, respectively. The homicide incidence rate of the country in 2016 was 2.81 per 100,000. The four provinces of Sistan & Baluchistan, Khuzestan, Kerman and Ilam had the highest incidence rate in 2016, respectively. During the study period, the incidence rate of homicide in Iran and men have been significantly decreased (APC: -2.8% (95% CI: -3.9, -1.7) and -3.2% (95% CI: - 4.5, -1.8) respectively (P<0.001)). CONCLUSION: The pattern of homicide rate has a downward trend in the country. Moreover, the varying observed trends in some provinces can be due to the variability in mental, geographical, socio-economic and cultural conditions in each region.

Epidemiological and Clinical Aspects of COVID-19; a Narrative Review.

There are significant misconceptions and many obstacles in the way of illuminating the epidemiological and clinical aspects of COVID-19 as a new emerging epidemic. In addition, usefulness of some evidence published in the context of the recent epidemic for decision making in clinic as well as public health is questionable. However, misinterpreting or ignoring strong evidence in clinical practice and public health probably results in less effective and somehow more harmful decisions for individuals as well as subgroups in general populations of countries in the initial stages of this epidemic. Accordingly, our narrative review appraised epidemiological and clinical aspects of the disease including genetic diversity of coronavirus genus, mode of transmission, incubation period, infectivity, pathogenicity, virulence, immunogenicity, diagnosis, surveillance, clinical case management and also successful measures for preventing its spread in some communities.