The prophylactic effect of hydroxychloroquine on the severity of COVID-19 infection in an asymptomatic population: A randomized clinical trial

Background: Laboratory and observational data suggest that hydroxychloroquine (HCQ) has biological activity against SARS-CoV-2, potentially permitting its use for disease prevention. This study aimed to evaluate the hydroxychloroquine effect as prophylaxis for SARS-CoV-2 infection. Methods: In this double-blind randomized controlled trial,  1000  healthy people without any signs and symptoms of COVID-19 were randomly assigned in a 1:1 ratio to receive either 800 mg hydroxychloroquine or placebo (four 200 mg tablets in two divided doses on day 1 of the first week, followed by 200 mg (in a single dose) weekly for the next 6 weeks). Results: Among 871 participants who remained and followed within 10 weeks 97(11.1%) became SARS-CoV-2 positive. there were statistically significant differences between infected or non-infected in the hydroxychloroquine (36 of 97 [37.1%]) and placebo (61 of 97 [62.9 %]) groups with a risk ratio of 2.1 (95% confidence interval (CI) 1.01 - 3.21; p = 0.005). The incidence of severe forms of COVID-19 ( hospitalized in the coronavirus ward or the ICU) was 2 of 97 (0.02 %) in participants who received a placebo compared to hydroxychloroquine. The proportion of non-infected people who received hydroxychloroquine prophylaxis was nearly twice higher than that of placebo users (1.87, 95% CI: 1.19 - 2.84, p = 0.05). There were no significant differences between the two groups regarding side effects (1.1% vs. 0.9%), and no severe adverse reactions were observed. Conclusion: Pre-exposure therapy with hydroxychloroquine appears to prevent moderate and severe illness caused by COVID-19 in asymptomatic persons

Association between Cytochrome P450 2 C9 and Vitamin K Epoxide Reductase Complex Subunit 1 Polymorphisms with Warfarin dose among Iranian Patients

Background: Warfarin is a common anticoagulant drug that has a narrow therapeutic index; higher dose causes excessive bleeding and lower dose leads to cerebrovascular clotting and stroke in patients. Genetic factors that have been associated with warfarin response are the genes of cytochrome P450 2C9 (CYP2C9), which metabolize the more active S-enantiomer of warfarin, and vitamin K epoxide reductase (VKOR), the target site for warfarin. The present study was conducted to investigate the association between CYP2C9*2, CYP2C9*3 and VKORC1 (-1639 G>A) polymorphisms with warfarin daily dose on &nbsp;Iranian patients under warfarin treatment. Materials and Methods: This study is comprised of 118 Iranian patients on warfarin treatment who attended the PT Clinic. Genotyping of CYP2C9*2, CYP2C9*3 and VKORC1 (-1639 G>A) was performed by PCR-RFLP method. Multiple regression model was performed for statistical analyses and P<0.05 was considered as significance level. Results: The allelic frequencies of CYP2C9*2 and CYP2C9*3 were 19% and 7%, respectively. Patients with &ge;1 CYP2C9 variant allele had a significantly lower mean warfarin daily dose compared with patients with the wild-type genotype. The allelic frequencies of VKORC1 were 14.4%, 57.6% and 27.9% for GG, GA, and AA genotypes, respectively. The mean (SD) warfarin daily dose in patients with the VKORC1 (&ndash;1639) GG genotype was significantly higher than GA and AA patients. Conclusion: CYP2C9*2, CYP2C9*3 and VKORC1 (-1639 G>A) polymorphisms had significant association with warfarin daily dose; furthermore, the daily warfarin dose was not influenced by age, height, weight and sex

Analysis of the association Hind III Polymorphism of Lipoprotein Lipase gene on the risk of coronary artery disease

Background: Coronary artery disease (CAD) is one of the leading causes of death and disability around the world. Interaction between genetic and environmental factors determines susceptibility of an individual to develop coronary artery disease . Lipoprotein lipase (LPL) play an important role in the metabolism of HDL-C ( High Density Lipoprotein Cholesterol ), LDL-C (Low Density Lipoprotein Cholesterol ) and triglycerides (TG). Dysfunction of LPL as a result of genetic variants of lipoprotein lipase gene is associated with increased risk of CAD. The aim of the present study was to investigate the relationship between the risk of coronary artery disease and LDL-C, HDL-C and TG (triglycerides) levels by lipoprotein lipase gene Hind III polymorphism. Materials and Methods: A total of 202 subjects including 114 patients with coronary artery disease and 88 control participated in this study. The Hind III polymorphism of the lipoprotein lipase gene was determined by PCR- RFLP (Polymerase Chain Reaction-Restriction Fragment Length Polymorphism) . In the presence and absence of restriction site, the genotypes are described H+/+ , H-/- respectively. Results: In this survey, a highly significant association between the frequent H+/+ genotype and unfavorable TG levels was observed in our population . For the Hind III genotypes, within the healthy subjects (n=88), the H+/+ genotype was found in 67 individuals (58.8%), H-/+ genotype in 38 individuals (33.3%) , and 9 individuals (7.8%) carried the H-/- genotype. Within the CAD group (n=114), 47 individuals (53.4%) with H+/+ genotype, 36 (41%) with H-/+ genotype, and 5 (5.6%) carried the H-/- genotype. Conclusion: There was a significant difference between the distribution of LPL–Hind III genotypes and the healthy subjects and the patients with CAD (P<0.05, 0. 645). LPL–Hind III polymorphisms were not detected as independent risk factors for CAD in this study group, but had significant associations with TG levels (P<0.05)

Association of Polymorphisms at LDLR Locus with Coronary Artery Disease Independently from Lipid Profile

Coronary artery disease (CAD) is the leading cause of mortality in many parts of the world. Genome-wide association studies (GWAS) have identified several genetic variants associated with CAD in Low-density lipoprotein receptor (LDLR) locus. This study was evaluated the possible association of genetic markers at LDLR locus with CAD irrespective to lipid profile and as well as the association of these SNPs with severity of CAD in Iranian population. Sequencing of 2 exons in LDLR gene (Exon 2, 12) and part of intron 30 of SMARCA4 gene include rs1122608, was performed in 170 Iranian patients angiographically confirmed CAD and 104 healthy controls by direct sequencing. Sullivan's scoring system was used for determining the severity of CAD in cases. Our results showed that homozygote genotypes of rs1122608 (P<0.0001), rs4300767 (P<0.005) and rs10417578 (p<0.007) SNPs have strong protective effects on the CAD. In addition, we found that rs1122608 (GT or TT) was at higher risk of three vessel involvement compared to single vessels affecting (P=0.01)

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

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.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThe authors received no financial support for the research, authorship, and/or publication of this article.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The study was performed after approval by Iran University of Medical Sciences Ethics Committee (approval ID: IR.IUMS.REC.1399.595)All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe data that support the findings of this study are available from the corresponding authors upon request.ACE2Angiotensin-Converting Enzyme 2AIArtificial IntelligenceBUNBlood Urea NitrogenCOVID-19coronavirus disease of 2019CICclinical impact curveCrCreatinineCRPC reactive proteinDCdecision curveICUIntensive care unitINRInternational Normalized RatioIFNinterferonIL-6Interleukin 6IQRinterquartile rangeKSKolmogorov-SmirnovLRLogistics regressionLIMElocal interpretable model-agnostic explanationLIME-SPlocal interpretable model-agnostic explanation submodular-pickMLMachine learningMCHmean corpuscular hemoglobinMCVmean corpuscular volumeRFRandom forestRDWRed blood cell distribution widthROCreceiver operating characteristic curveRT-PCRreverse transcription-polymerase chain reactionWBCwhite blood cells coun

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

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

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

Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however.Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals.Results: The SPM yielded ROC-AUCs of 0.53–0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at &lt;ext-link ext-link-type="uri" xlink:href="http://www.aicovid.org/" xmlns:xlink="http://www.w3.org/1999/xlink"&gt;www.aicovid.net&lt;/ext-link&gt;.Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months