ABSTRACT
The aim of this study was to compare the outcomes of SARS-CoV-2 RT-PCR-positive and RT-PCR negative Patients in Iran. This cohort study performed on 81393 patients with COVID-19 in six provinces of Iran during 2020. The studied variables include demographic and clinical. To examine the associations between RT-PCR test and death or ICU admission as dependent variable the multiple Bayesian logistic regression model was used by R software. 81393 individual (44.9 % female) with a mean age of 52.98 ± 20.8 years were included to the analysis. At all, 25434 tests (31.2 %) were positive RT-PCR, including 10772 men (44.9%) and 14662 women (55.1%). The multiple Bayesian logistic regression model showed a significant positive association between RT-PCR test results and COVID-19 mortality rate (OR: 1.46; 95% Crl: 1.29- 1.64). Also, males, older age, individual with chronic disease have higher risk of COVID-19 death, however, negative association observed between history of contact and COVID-19 death. We observed a significant inverse association between RT-PCR test results and ICU admission, while, the risk of ICU admission increased significantly by 1.2 times (95% Crl for odds ratio: 1.09, 1.34) among patients with negative RT-PCR test compared to positive RT-PCR test. People with positive RT-PCR test, male gender, older age, having a history of underlying disease have a higher risk of death and hospitalization in the ICU. Therefore, paying attention to these factors will be effective in reducing the risk of death and hospitalization in ICU.
Subject(s)
COVID-19 , Chronic Disease , DeathABSTRACT
Purpose This study aimed to investigate the rate of COVID-19 breakthrough infection and adverse events in medical students.Methods Iranian medical students receiving two doses of COVID-19 vaccines were included in this retrospective cohort study. 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 3591 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 for COVID-19 infection, and 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 Iran 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.
Subject(s)
COVID-19ABSTRACT
Purpose Limited number of medications are available for the post-exposure prophylaxis of COVID-19 infection. Whether bromhexine can prevent or mitigate symptomatic infection after virus exposure is undetermined.Methods A multi-center randomized; double-blind, placebo-controlled clinical trial was conducted. The 372 adults (≥ 18 years) who had close contact within 4 days with a household member with confirmed COVID-19 were randomly assigned to receive bromhexine (n = 187) or placebo (n = 185) three times a day for two weeks. The primary outcome was the incidence of symptomatic COVID-19. Secondary outcomes included the incidence of hospitalization or death, the incidence of confirmed COVID-19 detection by PCR in symptomatic patients and presence of adverse drug reactions.Results The incidence of symptomatic COVID-19 was significantly lower in individuals who received bromhexine than in those who received the placebo (16 [8.6%] vs 34 [18.4%], relative risk = 0.47, p = 0.005). PCR-confirmation was reported in 13 (7.0%) and 26 (14.1%) of the individuals in the bromhexine and placebo groups, respectively (p = 0.025), with a relative risk reduction of 50%. The hospitalization rate, death and medication side effects did not vary significantly between the bromhexine or placebo arms.Conclusion Bromhexine is an effective, non-invasive, and affordable agent with a low side-effect profile to prevent symptomatic COVID-19. Early use of bromhexine potentially provides another layer of protection and hence it can play a prominent role in ending the pandemic especially given the emergence of new variants and the vaccination challenges faced by developing countries.
Subject(s)
COVID-19ABSTRACT
The aim of this study was to evaluate the death proportion and death risk of COVID-19 hospitalized patients over time and in different surges of COVID-19. This multi-center observational study was conducted from March 21, 2021 to October 3, 2021 which included the alpha and delta SARS-CoV-2 surges occurred in April and August in Tehran, respectively. The risk of COVID-19 death was compared in different months of admission. A total of 270624 (6.9% deaths) patients with COVID-19 were admitted to hospitals in Tehran province. Compared to patients admitted in March, a higher risk of COVID-19 death was observed among patients admitted to the hospital in July (HR: 1.28; 95% CI: 1.17, 1.40), August (HR: 1.40; 95% CI: 1.28, 1.52), September (HR: 1.37; 95% CI: 1.25, 1.50) and October (HR: 4.63; 95% CI2.77, 7.74). The ICU death proportion was 36.8% (95% CI: 35.5, 38.1) in alpha surge and increased significantly to 39.8 (95%CI: 38.6, 41.1) in delta surge. The risk of COVID-19 death was significantly higher in delta surge compared to alpha surge (HR:1.22 ; 95% CI:1.17, 1.27). Delta surge was associated with a higher risk of death compared to alpha surge. High number of hospitalizations, a shortage of hospital beds, ICU spaces and medical supplies, poor nutritional status of hospitalized patients, and lack of the intensivist physicians or specialized nurses in the ICU were factors that contributed to the high mortality rate in the delta surge in Iran.
Subject(s)
COVID-19ABSTRACT
Purpose: Covid-19 has affected all people especially those with chronic diseases including Parkinsons Disease (PD). Covid-19 may affect both motor and neuropsychiatric symptoms of PD patients. We intend to evaluate different aspects of Covid-19 impact on PD patients. Methods: 647 PD patients were evaluated in terms of PD related and Covid-19 related clinical presentations in addition to past medical history during the pandemic through an online questioner. They were compared with an age-matched control group consist of 673 individuals and a sample of normal population consist of 1215 individuals. Results: Prevalence of Covid-19 in PD patients was 11.28%. The mortality was 1.23% among PD patients. Prevalence of Covid-19 in PD patients undergone DBS was 18.18%. No significant association was found between duration of disease and prevalence of Covid-19. A statistically significant higher prevalence of Covid-19 in PD patient who had a direct contact with SARS-CoV-19 infected individuals was found. No statistically significant association has been found between worsening of motor symptoms and Covid-19. PD patients and the normal population may differ in the prevalence of some psychological disorders including anxiety and sleeping disorders and Covid-19 may affect the psychological status. Conclusion: PD patients possibly follow tighter preventive protocols which leads to lower prevalence and severity of Covid-19 and its consequences in these patients. Although it seems Covid-19 does not affect motor and psychological aspects of PD as much as it was expected, more accurate evaluations are suggested in order to clarify such effects.
Subject(s)
Anxiety Disorders , Severe Acute Respiratory Syndrome , Parkinson Disease , Chronic Disease , COVID-19 , Sleep Wake Disorders , Sexual Dysfunctions, PsychologicalABSTRACT
Rationale Given the expanding number of COVID-19 cases and the potential for upcoming 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 studied retrospectively 263 COVID-19 ICU patients. To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Logistic regression and random forest (RF) 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, white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patients outcomes with a sensitivity of 70% and a specificity of 75%. Conclusions 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. Complete blood count parameters were also crucial for some patients. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.
Subject(s)
COVID-19 , Respiratory Tract InfectionsABSTRACT
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%. 2440 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.org. Conclusions 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.
Subject(s)
Paresis , Paralysis , Pain , Headache , Signs and Symptoms, Digestive , Chest Pain , Consciousness Disorders , Cough , Fever , Vertigo , COVID-19 , FatigueABSTRACT
Type 1 Interferons (IFNs) have been associated with positive effects on Coronaviruses. Previous studies point towards the superior potency of IFNβ compared to IFNα against viral infections. We conducted a three-armed, individually-randomized, open-label, controlled trial of IFNβ1a and IFNβ1b, comparing them against each other and a control group. Patients were randomly assigned in a 1:1:1 ratio to IFNβ1a (subcutaneous injections of 12,000 IU on days 1, 3, 6), IFNβ1b (subcutaneous injections of 8,000,000 IU on days 1, 3, 6), or the control group. All three arms orally received Lopinavir/Ritonavir (400mg/100 mg twice a day for ten days) and a single dose of Hydroxychloroquine 400 mg on the first day. Our utilized primary outcome measure was Time To Clinical Improvement (TTCI) defined as the time from enrollment to discharge or a decline of two steps on the clinical seven-step ordinal scale, whichsoever came first. A total of 60 severely ill patients with positive RT-PCR and Chest CT scans underwent randomization (20 patients to each arm). In the Intention-To-Treat population, IFNβ1a was associated with a significant difference against the control group, in the TTCI; (HR; 2.36, 95% CI=1.10-5.17, P-value=0.031) while the IFNβ1b indicated no significant difference compared with the control; HR; 1.42, (95% CI=0•63-3•16, P-value=0•395). The median TTCI for both of the intervention groups was five days vs. seven days for the control group. The mortality was numerically lower in both of the intervention groups (20% in the IFNβ1a group and 30% in the IFNβ1b group vs. 45% in the control group). There were no significant differences between the three arms regarding the adverse events. In patients with laboratory-confirmed SARS-CoV-2 infection, as compared with the base therapeutic regiment, the benefit of a significant reduction in TTCI was observed in the IFNβ1a arm. This finding needs further confirmation in larger studies. Trial Registration Number: ClinicalTrials.gov, NCT04343768. (Submitted: 08/04/2020; First Online: 13/04/2020) (Registration Number: NCT04343768)
Subject(s)
COVID-19ABSTRACT
Background: COVID-19 has been associated with several neurological complications. One of these complications is transverse myelitis. Several cases of acute transverse myelitis are reported in association with this disease among the world. As there is lack of knowledge about the association of COVID-19 and myelitis and the clinical features of this complication are still ambiguous, we report two patients with transverse myelitis following COVID-19 infection. Patients: This study was performed in a referral center of COVID-19 in Iran(Shohada Tajrish hospital) and two patients with paraparesis and diagnosis of transverse myelitis were enrolled. Both patients had longitudinally extensive transverse myelitis that resulted in paraparesis. One of the patients had favorable outcome after treatment with plasma exchange but the other had no improvement following treatment.Conclusion: Transverse myelitis could be a complication of COVID-19 and infarction and inflammation could be suggested as probable mechanisms for this condition.