Demographic and clinical characteristics of severe Covid-19 infections: a cross-sectional study from Mashhad University of Medical Sciences, Iran

Latent class analysis of hospitalization, ICU admission, and mortality among COVID-19 patients in Iran

Identifying COVID-19 patients' clinical phenotypes based on characteristics and comorbidities, as well as their differences, helps in terms of clinical care and potential crises. Our goal is to use Latent Class Analysis (LCA) to identify COVID-19 patient profiles based on demographics, symptoms, and comorbidities, and evaluate their correlation with ICU admission, hospitalization duration, and mortality in a cross-sectional study. We included hospitalized patients with positive SARS-CoV-2 tests in two referral hospitals in the south of Iran between January 2020 and July 2021. Data from 7,968 patients were evaluated in two time-specific intervals, known as "stratum," based on the point at which the COVID-19 fourth peak and vaccinations were nearly initiated. After applying LCA, three classes identified the most at-risk individuals, including elderly women with hypertension, diabetes, and heart disease, and elderly men with heart and lung conditions, compared to two classes of healthier, younger individuals. Hospital stays were longer in the first stratum compared with the second (P-value = 0.043). ICU admission during the first stratum was significantly higher than in the second stratum (P < 0.001) and lower in classes one and two in the first stratum compared to class three (reference class). Mortality rates were not significantly different between strata (P = 0.054). Hospital stays and mortality rates significantly differed between the classes in each first and second stratum (P < 0.001). Using LCA to investigate COVID-19 patients has provided valuable insights into different patterns and outcomes of the disease. Patient variables, including age, gender, and comorbidities, were associated with greater mortality and ICU admission, which can assist in resource allocation and vaccination planning.

Clinical characteristics of COVID-19 patients treated in emergency COVID-19 hospitals in Vietnam: Experience from Phutho province, Vietnam

Background: This study aimed to evaluate the clinical characteristics, patient's management approaches, and outcomes of the COVID-19 patients in Phu Tho Province, Vietnam. Methods: A retrospective, multicenter study of 2166 COVID-19 patients in 13 hospitals in Phutho Province, Vietnam. The subjects were divided into 3 groups based on vaccination status: unvaccinated group, 1st dose of vaccine group, 2nd dose of vaccine group. The clinical characteristics, management approaches, and outcomes were collected and compared between the 3 groups. Results: The hospitalization rate of the 3 groups decreased from the unvaccinated group, the 1st dose of vaccinated group, to the 2nd dose of vaccinated group, 42.61%; 30,24% and 27,15% respectively. The 19-40 years old group had the highest hospitalization rate (38,1%) together with the group that had not accepted the full COVID 19 vaccination dose (57,64%). The 2nd dose of vaccinated group had the lowest percentages of high temperature, cough, dyspnea, chest pain and sore throat. The unvaccinated group had the highest heart rate, respiratory rate and SpO2 compared to the two other groups. The percentage needing Immunomodulation and Anticoagulant Therapy was highest (6.8% and 1.4 % respectively) in the unvaccinated group. The percentage receiving Antiviral Therapy was highest (42,5%) in those who had received the 2nd dose of vaccine. Conclusions: COVID-19 vaccination improved the symptoms of the patients and should be accepted in all ages.

The effects of opium consumption on severity of disease on hospitalized COVID-19 patients in East of Iran, a prospective cohort study

There are few theories and little empirical evidence about the bilateral impact of substance use and coronavirus disease 2019 (COVID-19), so a logical and accurate picture of this area is required. We investigated the effects of opium use on severity of disease on hospitalized COVID-19 patients in east of Iran. Demographic and clinical characteristics, vital signs, laboratory tests, mortality rate, type and duration of opium consumption in hospitalized patients who recovered from COVID-19 in the follow-up after 3 months were evaluated. In this study, 60 (20%) participants were the opium user and 251 (80%) were the non-user patients. Based on clinical symptoms, hypertension and systolic blood pressure in opium user were significantly higher than non-user patients (p < 0.05). In the laboratory tests, only the level of urea was higher in the opium positive group (37 [26.5-48.5] vs. 32 [23-43], respectively) and the percent of lymphocytes were lower in the opium positive (17 [8.2-25.8] vs. 18.7 [13.85-26.35], respectively). The initial therapies of both opium positive and negative infected patients showed not any significant changes (p > 0.05). Among the studied groups, one deceased case with COVID-19 was related to a drug user patient. Although, uses of opium reduced the levels of some risk factors, vital signs at admission and initial therapies during hospitalization in COVID-19 patients but it increased lung and heart diseases. Also, the severity of COVID-19 including hospitalization and mortality were associated with opium consumption.

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department

This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision-recall curve (AUCPR), Hosmer-Lemeshow (H-L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital's ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802-0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787-0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.

Changes in symptoms and characteristics of COVID-19 patients across different variants: two years study using neural network analysis

BACKGROUND

Considering the fact that COVID-19 has undergone various changes over time, its symptoms have also varied. The aim of this study is to describe and compare the changes in personal characteristics, symptoms, and underlying conditions of individuals infected with different strains of COVID-19.

METHODS

This descriptive-analytical study was conducted on 46,747 patients who underwent PCR testing during a two-year period from February 22, 2020 to February 23, 2022, in South Khorasan province, Iran. Patient characteristics and symptoms were extracted based on self-report and the information system. The data were analyzed using logistic regression and artificial neural network approaches. The R software was used for analysis and a significance level of 0.05 was considered for the tests.

RESULTS

Among the 46,747 cases analyzed, 23,239 (49.7%) were male, and the mean age was 51.48 ± 21.41 years. There was a significant difference in symptoms among different variants of the disease (p < 0.001). The factors with a significant positive association were myalgia (OR: 2.04; 95% CI, 1.76 - 2.36), cough (OR: 1.93; 95% CI, 1.68-2.22), and taste or smell disorder (OR: 2.62; 95% CI, 2.1 - 3.28). Additionally, aging was found to increase the likelihood of testing positive across the six periods.

CONCLUSION

We found that older age, myalgia, cough and taste/smell disorder are better factors compared to dyspnea or high body temperature, for identifying a COVID-19 patient. As the disease evolved, chills and diarrhea, demonstrated prognostic strength as in Omicron.

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.

Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department

Background

The study aimed to compare the prognostic accuracy of six different severity-of-illness scoring systems for predicting in-hospital mortality among patients with confirmed SARS-COV2 who presented to the emergency department (ED). The scoring systems assessed were worthing physiological score (WPS), early warning score (EWS), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA).

Materials and methods

A cohort study was conducted using data obtained from electronic medical records of 6,429 confirmed SARS-COV2 patients presenting to the ED. Logistic regression models were fitted on the original severity-of-illness scores to assess the models' performance using the Area Under the Curve for ROC (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models' performance. Bootstrap samples with multiple imputations were used for internal validation.

Results

The mean age of the patients was 64 years (IQR:50-76) and 57.5% were male. The WPS, REMS, and NEWS models had AUROC of 0.714, 0.705, and 0.701, respectively. The poorest performance was observed in the RAPS model, with an AUROC of 0.601. The BS for the NEWS, qSOFA, EWS, WPS, RAPS, and REMS was 0.18, 0.09, 0.03, 0.14, 0.15, and 0.11 respectively. Excellent calibration was obtained for the NEWS, while the other models had proper calibration.

Conclusion

The WPS, REMS, and NEWS have a fair discriminatory performance and may assist in risk stratification for SARS-COV2 patients presenting to the ED. Generally, underlying diseases and most vital signs are positively associated with mortality and were different between the survivors and non-survivors.

How to cite this article

Rahmatinejad Z, Hoseini B, Reihani H, Hanna AA, Pourmand A, Tabatabaei SM, et al. Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department. Indian J Crit Care Med 2023;27(6):416-425.

Comparing In-Hospital Mortality Prediction by Senior Emergency Resident's Judgment and Prognostic Models in the Emergency Department

Background

A comparison of emergency residents' judgments and two derivatives of the Sequential Organ Failure Assessment (SOFA), namely, the mSOFA and the qSOFA, was conducted to determine the accuracy of predicting in-hospital mortality among critically ill patients in the emergency department (ED).

Methods

A prospective cohort research was performed on patients over 18 years of age presented to the ED. We used logistic regression to develop a model for predicting in-hospital mortality by using qSOFA, mSOFA, and residents' judgment scores. We compared the accuracy of prognostic models and residents' judgment in terms of the overall accuracy of the predicted probabilities (Brier score), discrimination (area under the ROC curve), and calibration (calibration graph). Analyses were carried out using R software version R-4.2.0.

Results

In the study, 2,205 patients with median age of 64 (IQR: 50-77) years were included. There were no significant differences between the qSOFA (AUC 0.70; 95% CI: 0.67-0.73) and physician's judgment (AUC 0.68; 0.65-0.71). Despite this, the discrimination of mSOFA (AUC 0.74; 0.71-0.77) was significantly higher than that of the qSOFA and residents' judgments. Additionally, the AUC-PR of mSOFA, qSOFA, and emergency resident's judgments was 0.45 (0.43-0.47), 0.38 (0.36-0.40), and 0.35 (0.33-0.37), respectively. The mSOFA appears stronger in terms of overall performance: 0.13 vs. 0.14 and 0.15. All three models showed good calibration.

Conclusion

The performance of emergency residents' judgment and the qSOFA was the same in predicting in-hospital mortality. However, the mSOFA predicted better-calibrated mortality risk. Large-scale studies should be conducted to determine the utility of these models.

Intensive Care and Organ Support Related Mortality in Patients With COVID-19: A Systematic Review and Meta-Analysis

To perform a systematic review and meta-analysis to generate estimates of mortality in patients with COVID-19 that required hospitalization, ICU admission, and organ support.

DATA SOURCES

A systematic search of PubMed, Embase, and the Cochrane databases was conducted up to December 31, 2021.

STUDY SELECTION

Previously peer-reviewed observational studies that reported ICU, mechanical ventilation (MV), renal replacement therapy (RRT) or extracorporeal membrane oxygenation (ECMO)-related mortality among greater than or equal to 100 individual patients.

DATA EXTRACTION

Random-effects meta-analysis was used to generate pooled estimates of case fatality rates (CFRs) for in-hospital, ICU, MV, RRT, and ECMO-related mortality. ICU-related mortality was additionally analyzed by the study country of origin. Sensitivity analyses of CFR were assessed based on completeness of follow-up data, by year, and when only studies judged to be of high quality were included.

DATA SYNTHESIS

One hundred fifty-seven studies evaluating 948,309 patients were included. The CFR for in-hospital mortality, ICU mortality, MV, RRT, and ECMO were 25.9% (95% CI: 24.0-27.8%), 37.3% (95% CI: 34.6-40.1%), 51.6% (95% CI: 46.1-57.0%), 66.1% (95% CI: 59.7-72.2%), and 58.0% (95% CI: 46.9-68.9%), respectively. MV (52.7%, 95% CI: 47.5-58.0% vs 31.3%, 95% CI: 16.1-48.9%; p = 0.023) and RRT-related mortality (66.7%, 95% CI: 60.1-73.0% vs 50.3%, 95% CI: 42.4-58.2%; p = 0.003) decreased from 2020 to 2021.

CONCLUSIONS

We present updated estimates of CFR for patients hospitalized and requiring intensive care for the management of COVID-19. Although mortality remain high and varies considerably worldwide, we found the CFR in patients supported with MV significantly improved since 2020.

Electrolyte Imbalance Among Bangladeshi Patients With COVID-19

Introduction Infection with SARS-CoV-2 begins in the lower respiratory tract, but COVID-19 often involves the renal system, resulting in serum electrolyte imbalance. Monitoring serum electrolyte levels and parameters of liver and kidney function is essential to understand disease prognosis. Objectives This study aimed to determine the effect of imbalances in serum electrolytes and other parameters on COVID-19 severity. Material and method This retrospective study comprised 241 patients, ages 14 years and older, including 186 patients who were moderately affected and 55 who were categorized as severely affected by COVID-19. Serum electrolytes (sodium (Na⁺), potassium (K⁺), and chloride (Cl^-)) and biomarkers of kidney and liver function (creatinine and alanine aminotransferase (ALT)) were measured and correlated with disease severity. This research was conducted among admitted patients of Holy Family Red Crescent Medical College Hospital designated into two groups based on retrospective hospital records. Individuals with moderate illness had evidence of lower respiratory tract infection (cough, cold, breathless, etc.) during clinical assessment or imaging (chest X-ray and computed tomography (CT) scan of the lungs) and have an oxygen saturation by pulse oximetry (SpO₂) ≥ 94% on room air at sea level. The severely ill group involved individuals with SpO₂ ≤94% on room air at sea level and respiratory rate ≥ 30 breaths/minute, and critically ill patients are those who needed mechanical ventilation or required intensive care unit (ICU) care. This categorization was based on the Coronavirus Disease 2019 (COVID-19) Treatment Guidelines (https://www.covid19treatmentguidelines.nih.gov/about-the-guidelines/whats-new/). Results Average Na⁺ and creatinine increased by 2.30 parts (95% confidence interval (CI) = 0.20, 4.81, P = 0.041) and 0.35 units (95% CI = 0.03, 0.68, P = 0.043) in severe cases compared with moderate cases. Older participants had relatively Na⁺ lowered to -0.06 parts (95% CI = -0.12, -0.001, P = 0.045), significant Cl^- reduction by 0.09 units (95% CI = -0.14, -0.04, P = 0.001), and ALT by 0.47 units (95% CI = -0.88, -0.06, P = 0.024), whereas serum creatinine was increased by 0.01 parts (95% CI = 0.001, 0.02, P = 0.024). The creatinine and ALT of COVID-19 participants were significantly higher in males by 0.34 units and 23.2 units, respectively, compared with females. In severe COVID-19 cases compared with moderate cases, the risks of hypernatremia, elevated chloride levels, and elevated serum creatinine levels were increased by 2.83-fold (95% CI = 1.26, 6.36, P = 0.012), 5.37-fold (95% CI = 1.90, 15.3, P = 0.002), and 2.00-fold (95% CI = 1.08, 4.31, P = 0.039), respectively. Conclusion Serum electrolyte and biomarker levels can serve as good indicators of the condition and disease prognosis of patients with COVID-19. Our purpose in this study was to determine the association between serum electrolyte imbalance and disease severity. We collected data from ex post facto hospital records and did not intend to assess the mortality rate. Consequently, this study expects that the prompt diagnosis of electrolyte disparity or disturbance possibly minimizes COVID-19-related morbidity and mortality.

Priority setting in cardiovascular research in Iran using standard indigenous methods

Background

Determining cardiovascular disease (CVD) research priorities is essential given the high burden of these diseases, limited financial resources, and competing priorities. This study aimed to determine the research priorities in CVD field in Iran using standard indigenous methods.

Materials and Methods

An extensive search was done in relevant international and national studies. Then, an indigenous standard multistage approach based on multicriteria decision analysis steps was adapted to local situation and implemented. This process included forming a working group of experts in priority setting methodology, identifying the context and prioritization framework, discussing the methodology with the National Network of CVD Research (NCVDR) members who ultimately determined the priority research topics, weighted topics criteria, ranked topics, and reviewed all determined research priorities for final report.

Results

Thirteen cardiovascular research priorities were determined by the NCVDR members. The first five priorities based on their scores include studies in hypertension, prevention and control of ischemic heart disease (IHD) and its risk factors, burden of IHD, Registration of CVDs, and COVID-19 and CVDs.

Conclusion

Cardiovascular research priorities were determined using a standard indigenous approach by national experts who are the NCVDR members. These priorities can be used by researchers and health decision makers.

Combined association of triglyceride-glucose index and systolic blood pressure with all-cause and cardiovascular mortality among the general population

Background

The combined association of triglyceride-glucose (TyG) index and different systolic blood pressure (SBP) levels with all-cause and cardiovascular mortality among the general population remains unclear.

Methods

In this study, 6245 individuals were from the National Health and Nutrition Examination Survey (1999–2002). The study endpoints were all-cause and cardiovascular mortality. Multivariate Cox proportional hazards regression models were used to explore the combined association of TyG index and different SBP levels with all-cause and cardiovascular mortality.

Results

During a mean follow-up period of 66.8 months, a total of 284 all-cause deaths (331/100000 person-years) and 61 cardiovascular deaths (66/100000 person-years) were recorded. Multivariate Cox regression analysis revealed that the combination of low TyG index and low SBP (< 120 mmHg and < 130 mmHg) was associated with a reduced risk of all-cause and cardiovascular mortality than others. However, survival benefit was not observed in the combined group with the low TyG index and SBP < 140 mmHg. Furthermore, the mortality rate in the combined group of low TyG index and low SBP gradually increased with the elevation of SBP level.

Conclusion

The combination of low TyG index and low SBP (< 120 mmHg and < 130 mmHg) was associated with a lower risk of all-cause and cardiovascular mortality. However, no survival benefit was observed in the combined group of low TyG index and SBP < 140 mmHg.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03678-z.

Prognosis of COVID-19 in the middle eastern population, knowns and unknowns

Since its emergence in China in 2019, the SARS-CoV-2 virus has affected all countries worldwide. The virus is easily transmitted from one person to another via infected aerosols or contaminated surfaces. Unlike its counterparts, the prognosis of COVID-19 ranges from asymptomatic to critical disease or death. Several factors play a role in determining the severity of the disease in infected patients. Among others, is the pre-existence of an underlying medical condition such as diabetes, cancer, and others. Furthermore, although children are less prone to the severe form of the COVID-19 disease, they require attention due to the report of many atypical presentations of the infection, post-asymptomatic exposure. In the Middle East, little is known about the prognosis of the SARS-CoV-2 infection in high-risk categories, notably patients with diabetes, cancer, and pregnant women. The aim of this review is to summarize the current knowledge about this group of population in the middle eastern region as well as to highlight the gap in the literature. We have found that the majority of the papers were from the Gulf countries. Although, few studies were conducted; high-risk patients appear to have an increased risk of morbidity and mortality from COVID-19 compared to their counterparts. Higher levels of inflammatory markers, C-reactive protein, erythrocyte sedimentation rate, D-dimer, and ferritin levels were also observed. Children are often asymptomatic or present with atypical presentations. More studies should be conducted to determine the clinical biomarkers of COVID-19 in high-risk categories to help in patient risk stratification and management in the middle eastern population.

Human T-lymphotropic virus type 1 and novel coronavirus disease 2019; More complex than just a simple coinfection

The recent coronavirus disease 2019 (COVID-19) significantly affected many people worldwide, especially those with underlying diseases. While some people with underlying illnesses, including cardiovascular diseases, are more vulnerable to develop severe COVID-19, other populations, including people who have autoimmune diseases, may develop severe diseases similar to the general population. The severity and outcome of COVID-19 are reviewed in individuals with underlying viral diseases, including acquired immune deficiency syndrome and hepatitis, however, some infectious diseases, including human T-lymphotropic virus type 1 (HTLV-1) diseases, is under-reported in the literature. HTLV-1 is a sexually transmitted disease that is endemic in some parts of the world. Infected patients may develop clinical symptoms of HTLV-1 associated myelopathy / tropical spastic paraparesis (HAM/TSP) and adult T cell leukemia (ATL) or may remain asymptomatic during their life. To the best of our knowledge, no clinical studies evaluate the severity and outcomes of SARS-CoV-2 infection in HTLV-1 infected patients. We aimed to review the pathogenesis of both of these viral infections and discuss their similarities in provoking immune responses. Although HTLV-1 infected patients may have had variable degrees of inflammation and immune system dysregulation, the available data is limited to conclude that HTLV-1 infected patients may be more vulnerable to developing severe COVID-19 in contrast to the general population.

Internal Validation of the Predictive Performance of Models Based on Three ED and ICU Scoring Systems to Predict Inhospital Mortality for Intensive Care Patients Referred from the Emergency Department

Background.A variety of scoring systems have been introduced for use in both the emergency department (ED) such as WPS, REMS, and MEWS and the intensive care unit (ICU) such as APACHE II, SAPS II, and SOFA for risk stratification and mortality prediction. However, the performance of these models in the ICU remains unclear and we aimed to evaluate and compare their performance in the ICU. Methods. This multicenter retrospective cohort study was conducted on severely ill patients admitted to the ICU directly from the ED in seven tertiary hospitals in Iran from August 2018 to August 2020. We evaluated all models in terms of discrimination (AUROC), the balance between positive predictive value and sensitivity (AUPRC), calibration (Hosmer-Lemeshow test and calibration plots), and overall performance using the Brier score (BS). The endpoint was considered inhospital mortality. Results. Among the 3,455 patients included in the study, 54.4% of individuals were male ( $N=\mathrm{1,879}$ ) and 26.5% deceased ( $N=916$ ). The BS for the WPS, REMS, MEWS, APACHE II, SAPS II, and SOFA were 0.178, 0.165, 0.183, 0.157, 0.170, and 0.182, respectively. The AUROC of these models were 0.728 (0.71-0.75), 0.761 (0.74-0.78), 0.682 (0.66-0.70), 0.810 (0.79-0.83), 0.767 (0.75-0.79), and 0.785 (0.77-0.80), respectively. The AUPRC was 0.517 (0.50-0.53) for WPS, 0.547 (0.53-0.56) for REMS, 0.445 (0.42-0.46) for MEWS, 0.630 (0.61-0.65) for APACHE II, 0.559 (0.54-0.58) for SAPS II, and 0.564 (0.54-0.57) for SOFA. All models except the MEWS and SOFA had good calibration. The most accurate model belonged to APACHE II with lowest BS. Conclusion. The APACHE II outperformed all the ED and ICU models and was found to be the most appropriate model in predicting inhospital mortality of patients in the ICU in terms of discrimination, calibration, and accuracy of predicted probability. Except for MEWS, the rest of the models had fair discrimination and partially good calibration. Interestingly, although the REMS is less complicated than the SAPS II, both models exhibited similar performance. Clinicians can utilize the REMS as part of a larger clinical assessment to manage patients more effectively.

A Large Retrospective Study of Epidemiological Characteristics of COVID-19 Patients in the North of Iran: Association between SARS-CoV-2 RT-PCR Ct Values with Demographic Data

OBJECTIVES

To avoid worsening from mild, moderate, and severe diseases and to reduce mortality, it is necessary to identify the subpopulation that is more vulnerable to the development of COVID-19 unfavorable consequences. This study aims to investigate the demographic information, prevalence rates of common comorbidities among negative and positive real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) patients, and the association between SARS-CoV-2 cycle threshold (Ct) at hospital admission, demographic data, and outcomes of the patients in a large population in Northern Iran.

METHODS

This large retrospective cross-sectional study was performed from 7 March to 20 December 2020. Demographic data, including gender, age, underlying diseases, clinical outcomes, and Ct values, were obtained from 8,318 cases suspected of COVID-19, who were admitted to four teaching hospitals affiliated to Babol University of Medical Sciences (MUBABOL), in the north of Iran.

RESULTS

Since 7 March 2020, the data were collected from 8,318 cases suspected of COVID-19 (48.5% female and 51.5% male) with a mean age of 53 ± 25.3 years. Among 8,318 suspected COVID-19 patients, 3,250 (39.1%) had a positive rRT-PCR result; 1,632 (50.2%) patients were male and 335 (10.3%) patients died during their hospital stay. The distribution of positive rRT-PCR revealed that most patients (464 (75.7%)) had a Ct between 21 and 30 (Group B).

CONCLUSION

Elderly patients, lower Ct, patients having at least one comorbidity, and male cases were significantly associated with increased risk for COVID-19-related mortality. Moreover, mortality was significantly higher in patients with diabetes, kidney disease, and respiratory disease.

Over 60 years of age as an independent prognostic factor of in-hospital mortality among COVID-19 patients: a cohort study in an Iranian high-incidence area

Objectives

COVID-19 continues to cause devastation throughout the world. Various factors influence the perioperative course and prognosis of COVID-19. This study aims to collate the independent prognostic factors among hospitalised COVID-19 patients in east Iran.

Study design

In this cohort study, all patients with a confirmed diagnosis of COVID-19 between 19 February 2020 and 1 August 2020 who were admitted to nine public hospitals of South Khorasan province, Iran, were enrolled.

Methods

Univariate analysis (chi-square [χ²], and Mann–Whitney U test) and multiple logistic regression were performed.

Results

This study included 1290 participants; 676 patients (52.4%) were male. A total of 1189 (92.2%) recovered, and 101 (7.8%) died. The results show that in-hospital mortality increases with advanced age (the optimal cut-off point = 62 years). The following three variables were shown to have the most significant role in in-hospital mortality: age >60 years (odds ratio [OR] = 8.01, 95% confidence interval [CI] 4.8–13.35), shortness of breath (OR = 2.65, 95% CI: 1.4–69.17) and atypical radiological manifestations in a chest X-ray on admission (OR = 2.16, 95% CI: 1.3–28.64). In the univariate analysis, associated comorbidities, such as cardiovascular diseases, influenced the in-hospital mortality rate, while the same could not be replicated in the multiple variable analysis.

Conclusions

This study revealed the potential predictors of COVID-19 and highlighted the need to be cautious with advanced age and heightened clinical symptoms at the time of admission.