Prevalence and Outcomes of Patients With Acute Ischemic Stroke and Concomitant Non-ST-Elevation Myocardial Infarction (Results from the National Inpatient Sample 2016 to 2019).

Acute myocardial infarction (MI) may concomitantly occur with acute ischemic stroke. The incidence and outcomes of acute non-ST-elevation MI (NSTEMI) in acute ischemic stroke are not well studied. We examined hospitalized patients with acute ischemic stroke and a concomitant NSTEMI diagnosis who were included in the National Inpatient Sample 2016 to 2019. Acute ischemic stroke and NSTEMI were defined by using the International Classification of Diseases, Tenth Revision codes. Patients with ST-elevation MI were excluded. The outcomes were expressed as percentages. A multivariable logistic regression analysis was used to examine the association of concomitant acute ischemic stroke and NSTEMI with the primary outcome of mortality and the secondary outcomes. A subgroup analysis of patients with NSTEMI with acute ischemic stroke that underwent percutaneous coronary intervention (PCI) (angiography and angioplasty) was also performed. Of the total hospitalized patients with acute ischemic stroke (n = 1,726,265), 1.60% (n = 27,630) patients (mean age 73.5 years, 52.2% women, 67% White race) had NSTEMI diagnosed during the hospitalization. Of these, 14.1% (n = 3,890) died in the NSTEMI group and 3.4% (n = 57,670) died in the non-NSTEMI group. The most common outcomes in the NSTEMI group were Acute kidney injury 31.8%, Intracranial hemorrhage 6.6%, and sepsis 6.13%. NSTEMI in acute ischemic stroke was associated with mortality (odds ratio [OR] 3.60, 95% confidence interval [CI] 3.29 to 3.93, p ≤0.001), ICH (OR 1.46, 95% CI 1.30 to 1.63, p <0.001), and having any of the secondary outcomes (OR 2.73, 95% CI 2.57 to 2.90, p <0.001). PCI was performed in 9.14% of patients with acute ischemic stroke with NSTEMI. PCI was associated with having any of the secondary outcomes (OR 0.83, 95% CI 0.7 to 1.02, p = 0.8), mortality (OR 0.35, 95% CI 0.23 to 0.54, p <0.001), and ICH (OR 0.42, 95% CI 0.25 to 0.7, p = 0.01). In conclusion, NSTEMI in acute ischemic stroke is associated with increased mortality and other adverse events. PCI in the subgroup of patients with NSTEMI was not associated with increased mortality or intracranial bleeding.

Disparities in the management of non-ST-segment elevation myocardial infarction in the United States.

Guidelines recommend managing patients aged ≥75 with non-ST-segment elevation myocardial infarction (NSTEMI) similar to younger patients. We analyze disparities in NSTEMI management and compare those ≥80 years to those <80 years. This is a matched case-control study using the 2016 National Inpatient Sample data of adults with NSTEMI receiving percutaneous coronary intervention with drug-eluting stent (PCI-DES) - one artery or no intervention. We included the statistically significant variables in univariate analysis in exploratory multivariate logistic regression models. Total sample included 156,328 patients, out of which 43,265 were ≥ 80 years, and 113,048 were < 80 years. Patients ≥80 years were more likely to not have an intervention (73.3%) when compared to those <80 (44.1%), P < 0.0005. Regardless of age, PCI-DES-one artery improved survival compared to no intervention (Age < 80: OR 0.230, 95% CI 0.189-0.279, and ≥ 80: OR 0.265, 95% CI 0.195-0.361, P < 0.0005). Women (OR 0.785, 95% CI 0.766-0.804, P < 0.0005) and non-white race (OR 0.832, 95% CI 0.809-0.855, P < 0.0005) were less likely to receive an intervention. Non-Medicare/Medicaid insurance was associated with 40% lower likelihood of dying in <80 age group (OR 0.596, 95% CI 0.491-0.724, P < 0.0005), and 16% higher chance of intervention overall (OR 1.160, 95% CI 1.125-1.197, P < 0.0005). Patients aged ≥80 with NSTEMI were 29% less likely to receive an intervention compared to patients aged <80, even though patients >80 derived similar mortality benefits from the intervention. There were gender, payor, and race-based disparities in NSTEMI management in 2016.

SARS-COV-ATE risk assessment model for arterial thromboembolism in COVID-19.

Patients with SARS-CoV-2 infection are at an increased risk of cardiovascular and thrombotic complications conferring an extremely poor prognosis. COVID-19 infection is known to be an independent risk factor for acute ischemic stroke and myocardial infarction (MI). We developed a risk assessment model (RAM) to stratify hospitalized COVID-19 patients for arterial thromboembolism (ATE). This multicenter, retrospective study included adult COVID-19 patients admitted between 3/1/2020 and 9/5/2021. Among 3531 patients from the training cohort, 15.5% developed acute in-hospital ATE, including stroke, MI, and other ATE, compared to 13.4% in the validation cohort. The 16-item final score was named SARS-COV-ATE (Sex: male = 1, Age [40-59 = 2, > 60 = 4], Race: non-African American = 1, Smoking = 1 and Systolic blood pressure elevation = 1, Creatinine elevation = 1; Over the range: leukocytes/lactate dehydrogenase/interleukin-6, B-type natriuretic peptide = 1, Vascular disease (cardiovascular/cerebrovascular = 1), Aspartate aminotransferase = 1, Troponin-I [> 0.04 ng/mL = 1, troponin-I > 0.09 ng/mL = 3], Electrolytes derangement [magnesium/potassium = 1]). RAM had a good discrimination (training AUC 0.777, 0.756-0.797; validation AUC 0.766, 0.741-0.790). The validation cohort was stratified as low-risk (score 0-8), intermediate-risk (score 9-13), and high-risk groups (score ≥ 14), with the incidence of ATE 2.4%, 12.8%, and 33.8%, respectively. Our novel prediction model based on 16 standardized, commonly available parameters showed good performance in identifying COVID-19 patients at risk for ATE on admission.

3D-PAST: Risk Assessment Model for Predicting Venous Thromboembolism in COVID-19.

Hypercoagulability is a recognized feature in SARS-CoV-2 infection. There exists a need for a dedicated risk assessment model (RAM) that can risk-stratify hospitalized COVID-19 patients for venous thromboembolism (VTE) and guide anticoagulation. We aimed to build a simple clinical model to predict VTE in COVID-19 patients. This large-cohort, retrospective study included adult patients admitted to four hospitals with PCR-confirmed SARS-CoV-2 infection. Model training was performed on 3531 patients hospitalized between March and December 2020 and validated on 2508 patients hospitalized between January and September 2021. Diagnosis of VTE was defined as acute deep vein thrombosis (DVT) or pulmonary embolism (PE). The novel RAM was based on commonly available parameters at hospital admission. LASSO regression and logistic regression were performed, risk scores were assigned to the significant variables, and cutoffs were derived. Seven variables with assigned scores were delineated as: DVT History = 2; High D-Dimer (>500−2000 ng/mL) = 2; Very High D-Dimer (>2000 ng/mL) = 5; PE History = 2; Low Albumin (<3.5 g/dL) = 1; Systolic Blood Pressure <120 mmHg = 1, Tachycardia (heart rate >100 bpm) = 1. The model had a sensitivity of 83% and specificity of 53%. This simple, robust clinical tool can help individualize thromboprophylaxis for COVID-19 patients based on their VTE risk category.

Antiphospholipid antibodies and vitamin D deficiency in COVID-19 infection with and without venous or arterial thrombosis: A pilot case-control study.

BACKGROUND: Coronavirus disease-2019 (COVID-19) is associated with thromboembolism. Antiphospholipid antibody (APLa) formation is one of the mechanisms. Vitamin D deficiency has been associated with thrombosis in antiphospholipid antibody syndrome. OBJECTIVE: Measure APLa and vitamin D in hospitalized COVID-19 patients with and without thrombosis to evaluate if thromboembolism is associated with concomitant APLa and vitamin D deficiency. METHODS: Case-control study. Hospitalized COVID-19 patients with a thromboembolic event (ischemic stroke, myocardial infarction, deep venous thrombosis/pulmonary embolism, Cases n = 20). Controls (n = 20): Age, sex-matched without thromboembolic events. Patients with autoimmune disorders, antiphospholipid antibody syndrome, thrombophilia, anticoagulation therapy, prior thromboembolism, chronic kidney disease 3b, 4, end-stage renal disease, and malignancy were excluded. Given the limited current literature on the role of concomitant antiphospholipid antibodies and vitamin D deficiency in causing venous and/or arterial thrombosis in hospitalized COVID-19 patients, we enrolled 20 patients in each arm. Anti-cardiolipin IgG/IgM, beta-2 glycoprotein-1 IgG/IgM, lupus anticoagulant and vitamin D levels were measured in both groups. RESULTS: Cases were 5.7 times more likely to be vitamin D deficient (OR:5.7, 95% CI:1.3-25.6) and 7.4 times more likely to have any one APLa (OR:7.4, 95% CI: 1.6-49.5) while accounting for the effects of sex. Patients with both APLa and vitamin D deficiency had significantly more thrombosis compared to patients who were antibody positive without vitamin D deficiency (100% vs 47.4%; p = 0.01). CONCLUSIONS: Thrombosis in COVID-19 was associated with concomitant APLa and vitamin D deficiency. Future studies in COVID-19 should assess the role of vitamin D in reducing thrombosis.

Venous thromboembolism in COVID-19 patients and prediction model: a multicenter cohort study.

BACKGROUND: Patients with COVID-19 infection are commonly reported to have an increased risk of venous thrombosis. The choice of anti-thrombotic agents and doses are currently being studied in randomized controlled trials and retrospective studies. There exists a need for individualized risk stratification of venous thromboembolism (VTE) to assist clinicians in decision-making on anticoagulation. We sought to identify the risk factors of VTE in COVID-19 patients, which could help physicians in the prevention, early identification, and management of VTE in hospitalized COVID-19 patients and improve clinical outcomes in these patients. METHOD: This is a multicenter, retrospective database of four main health systems in Southeast Michigan, United States. We compiled comprehensive data for adult COVID-19 patients who were admitted between 1st March 2020 and 31st December 2020. Four models, including the random forest, multiple logistic regression, multilinear regression, and decision trees, were built on the primary outcome of in-hospital acute deep vein thrombosis (DVT) and pulmonary embolism (PE) and tested for performance. The study also reported hospital length of stay (LOS) and intensive care unit (ICU) LOS in the VTE and the non-VTE patients. Four models were assessed using the area under the receiver operating characteristic curve and confusion matrix. RESULTS: The cohort included 3531 admissions, 3526 had discharge diagnoses, and 6.68% of patients developed acute VTE (N = 236). VTE group had a longer hospital and ICU LOS than the non-VTE group (hospital LOS 12.2 days vs. 8.8 days, p < 0.001; ICU LOS 3.8 days vs. 1.9 days, p < 0.001). 9.8% of patients in the VTE group required more advanced oxygen support, compared to 2.7% of patients in the non-VTE group (p < 0.001). Among all four models, the random forest model had the best performance. The model suggested that blood pressure, electrolytes, renal function, hepatic enzymes, and inflammatory markers were predictors for in-hospital VTE in COVID-19 patients. CONCLUSIONS: Patients with COVID-19 have a high risk for VTE, and patients who developed VTE had a prolonged hospital and ICU stay. This random forest prediction model for VTE in COVID-19 patients identifies predictors which could aid physicians in making a clinical judgment on empirical dosages of anticoagulation.

Household COVID-19 secondary attack rate and associated determinants in Pakistan; A retrospective cohort study.

BACKGROUND: COVID-19 household transmissibility remains unclear in Pakistan. To understand the dynamics of Severe Acute Respiratory Syndrome Coronavirus disease epidemiology, this study estimated Secondary Attack Rate (SAR) among household and close contacts of index cases in Pakistan using a statistical transmission model. METHODOLOGY: A retrospective cohort study was conducted using an inclusive contact tracing dataset from the provinces of Punjab and Khyber-Pakhtunkhwa to estimate SAR. We considered the probability of an infected person transmitting the infection to close contacts regardless of residential addresses. This means that close contacts were identified irrespective of their relationship with the index case. We assessed demographic determinants of COVID-19 infectivity and transmissibility. For this purpose based on evolving evidence, and as CDC recommends fully vaccinated people get tested 5-7 days after close contact with a person with suspected or confirmed COVID-19. Therefore we followed the same procedure in the close contacts for secondary infection. FINDINGS: During the study period from 15th May 2020 to 15th Jan 2021, a total of 339 (33.9%) index cases were studied from 1000 cases initially notified. Among close contact groups (n = 739), households were identified with an assumed mean incubation period of 8.2+4.3 days and a maximum incubation period of 15 days. SAR estimated here is among the household contacts. 117 secondary cases from 739 household contacts, with SAR 11.1% (95% CI 9.0-13.6). All together (240) SAR achieved was 32.48% (95% CI; 29.12-37.87) for symptomatic and confirmed cases. The potential risk factors for SAR identified here included; old age group (>45 years of age), male (gender), household members >5, and residency in urban areas and for index cases high age group. Overall local reproductive number (R) based on the observed household contact frequencies for index/primary cases was 0.9 (95% CI 0.47-1.21) in Khyber Pakhtunkhwa and 1.3 (95% CI 0.73-1.56) in Punjab. CONCLUSIONS: SAR estimated here was high especially in the second phase of the COVID-19 pandemic in Pakistan. The results highlight the need to adopt rigorous preventive measures to cut the chain of viral transmission and prevent another wave of COVID-19.

Data of atrial arrhythmias in hospitalized COVID-19 and influenza patients.

Atrial arrhythmias (AA) are common in hospitalized COVID-19 patients with limited data on their association with COVID-19 infection, clinical and imaging outcomes. In the related research article using retrospective research data from one quaternary care and five community hospitals, patients aged 18 years and above with positive SARS-CoV-2 polymerase chain reaction test were included. 6927 patients met the inclusion criteria. The data in this article provides demographics, home medications, in-hospital events and COVID-19 treatments, multivariable generalized linear regression regression models using a log link with a Poisson distribution (multi-parameter regression [MPR]) to determine predictors of new-onset AA and mortality in COVID-19 patients, computerized tomography chest scan findings, echocardiographic findings, and International Classification of Diseases-Tenth Revision codes. The clinical outcomes were compared to a propensity-matched cohort of influenza patients. For influenza, data is reported on baseline demographics, comorbid conditions, and in-hospital events. Generalized linear regression models were built for COVID-19 patients using demographic characteristics, comorbid conditions, and presenting labs which were significantly different between the groups, and hypoxia in the emergency room. Statistical analysis was performed using R programming language (version 4, ggplot2 package). Multivariable generalized linear regression model showed that, relative to normal sinus rhythm, history of AA (adjusted relative risk [RR]: 1.38; 95% CI: 1.11-1.71; p = 0.003) and newly-detected AA (adjusted RR: 2.02 95% CI: 1.68-2.43; p < 0.001) were independently associated with higher in-hospital mortality. Age in increments of 10 years, male sex, White race, prior history of coronary artery disease, congestive heart failure, end-stage renal disease, presenting leukocytosis, hypermagnesemia, and hypomagnesemia were found to be independent predictors of new-onset AA in the MPR model. The dataset reported is related to the research article entitled "Incidence, Mortality, and Imaging Outcomes of Atrial Arrhythmias in COVID-19" [Jehangir et al. Incidence, Mortality, and Imaging Outcomes of Atrial Arrhythmias in COVID-19, American Journal of Cardiology] [1].

Incidence, Mortality, and Imaging Outcomes of Atrial Arrhythmias in COVID-19.

Atrial arrhythmias (AAs) are common in hospitalized patients with COVID-19; however, it remains uncertain if AAs are a poor prognostic factor in SARS-CoV-2 infection. In this retrospective cohort study from 2014 to 2021, we report in-hospital mortality in patients with new-onset AA and history of AA. The incidence of new-onset congestive heart failure (CHF), hospital length of stay and readmission rate, intensive care unit admission, arterial and venous thromboembolism, and imaging outcomes were also analyzed. We further compared the clinical outcomes with a propensity-matched influenza cohort. Generalized linear regression was performed to identify the association of AA with mortality and other outcomes, relative to those without an AA diagnosis. Predictors of new-onset AA were also modeled. A total of 6,927 patients with COVID-19 were included (626 with new-onset AA, 779 with history of AA). We found that history of AA (adjusted relative risk [aRR] 1.38, confidence interval [CI], 1.11 to 1.71, p = 0.003) and new-onset AA (aRR 2.02, 95% CI 1.68 to 2.43, p <0.001) were independent predictors of in-hospital mortality. The incidence of new-onset CHF was 6.3% in history of AA (odds ratio 1.91, 95% CI 1.30 to 2.79, p <0.001) and 11.3% in new-onset AA (odds ratio 4.01, 95% CI 3.00 to 5.35, p <0.001). New-onset AA was shown to be associated with worse clinical outcomes within the propensity-matched COVID-19 and influenza cohorts. The risk of new-onset AA was higher in patients with COVID-19 than influenza (aRR 2.02, 95% CI 1.76 to 2.32, p <0.0001), but mortality associated with new-onset AA was higher in influenza (aRR 12.58, 95% CI 4.27 to 37.06, p <0.0001) than COVID-19 (aRR 1.86, 95% CI 1.55 to 2.22, p <0.0001). In a subset of the patients with COVID-19 for which echocardiographic data were captured, abnormalities were common, including valvular abnormalities (40.9%), right ventricular dilation (29.6%), and elevated pulmonary artery systolic pressure (16.5%); although there was no evidence of a difference in incidence among the 3 groups. In conclusion, new-onset AAs are associated with poor clinical outcomes in patients with COVID-19.

Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses.

BACKGROUND: There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts. METHODS: This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000. RESULTS: Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit. CONCLUSION: Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.

Evolution of prescribing practices and outcomes in the COVID-19 pandemic in metropolitan areas.

Introduction: We wanted to characterize the evolution of the COVID-19 pandemic in a typical metropolitan area. Methods: Data were extracted from the Detroit COVID-19 Consortium database for hospitalized COVID-19 patients treated in Southeast Michigan over the 12-month period from March 2020 to February 2021. Demographic and outcomes data were compared to CDC data. Results: A total of 4,775 patients were enrolled during the study period. We divided the pandemic into three phases: Phase-1 (Spring Surge); Phase-2 (Summer Lull); and Phase-3 (Fall Spike). Changes in hydroxychloroquine, remdesivir, corticosteroid, antibiotic and anticoagulant use closely followed publication of landmark studies. Mortality in critically-ill patients decreased significantly from Phase-1 to Phase-3 (60.3% vs. 47.9%, Chisq p=0.0110). Monthly mortality of all hospitalized patients ranged between 14.8% - 21.5% during Phase-1 and 9.7 to 13.4% during Phase 3 (NS). Discussion: The COVID-19 pandemic presented in three unique phases in Southeast Michigan. Medical systems rapidly modified treatment plans, often preceding CDC and NIH recommendations. Despite improved treatment regimens, intubation rates and mortality for hospitalized patients remained elevated. Conclusion: Preventive measures aimed at reducing hospitalizations for COVID-19 should be emphasized.

Characterizing Long COVID: Deep Phenotype of a Complex Condition.

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. METHODS: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. FUNDING: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. INTERPRETATION: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. FUNDING: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411.

Interpreting COVID-19 deaths among nursing home residents in the US: The changing role of facility quality over time.

A report published last year by the Centers for Medicare & Medicaid Services (CMS) highlighted that COVID-19 case counts are more likely to be high in lower quality nursing homes than in higher quality ones. Since then, multiple studies have examined this association with a handful also exploring the role of facility quality in explaining resident deaths from the virus. Despite this wide interest, no previous study has investigated how the relation between quality and COVID-19 mortality among nursing home residents may have changed, if at all, over the progression of the pandemic. This understanding is indeed lacking given that prior studies are either cross-sectional or are analyses limited to one specific state or region of the country. To address this gap, we analyzed changes in nursing home resident deaths across the US between June 1, 2020 and January 31, 2021 (n = 12,415 nursing homes X 8 months) using both descriptive and multivariable statistics. We merged publicly available data from multiple federal agencies with mortality rate (per 100,000 residents) as the outcome and CMS 5-star quality rating as the primary explanatory variable of interest. Covariates, based on the prior literature, consisted of both facility- and community-level characteristics. Findings from our secondary analysis provide robust evidence of the association between nursing home quality and resident deaths due to the virus diminishing over time. In connection, we discuss plausible reasons, especially duration of staff shortages, that over time might have played a critical role in driving the quality-mortality convergence across nursing homes in the US.

Risk Factors in Hospitalized Patients for Heparin-Induced Thrombocytopenia by Real World Database: A New Role for Primary Hypercoagulable States.

Background: The aims of the study were to identify predictors of heparin-induced thrombocytopenia (HIT) in hospitalized adults, and to find additional factors associated with higher odds of HIT in primary hypercoagulable states. Methods: A retrospective matched case-control study using discharge data from National Inpatient Sample database (2012 - 2014) was conducted. In primary outcome analysis, hospitalized patients with and without HIT were included as cases and controls, both matched for age and gender. In secondary outcome analysis, hospitalized patients with primary hypercoagulable states with and without HIT were included as cases and controls, both matched for age and gender. The statistical analyses were performed using Statistical Package for Social Sciences version 25. Results: There are several predictors of HIT in hospitalized patients, such as obesity, malignancy, diabetes, renal failure, major surgery, congestive heart failure, and autoimmune diseases. In patients with primary hypercoagulable states, the presence of renal failure (odds ratio (OR) 2.955, 95% confidence interval (CI) 1.994 - 4.380), major surgery (OR 1.735, 95% CI 1.275 - 2.361), congestive heart failure (OR 4.497, 95% CI 2.466 - 8.202), or autoimmune diseases (OR 1.712, 95% CI 1.120 - 2.618) further increases the odds of HIT. Conclusions: In hospitalized patients with primary hypercoagulable states, especially in association with renal failure, major surgery, congestive heart failure, or autoimmune diseases, unfractionated heparin should be used with caution.

Mortality risk stratification helps identify patients with COVID-19 at risk of adverse outcomes.

Mortality risk stratification can identify patients with COVID-19 who are at higher risk of mortality, discharge to skilled nursing facility, and readmission, and may benefit from focused intervention strategies.

Viral Dynamics and Real-Time RT-PCR Ct Values Correlation with Disease Severity in COVID-19.

Real-time RT-PCR is considered the gold standard confirmatory test for coronavirus disease 2019 (COVID-19). However, many scientists disagree, and it is essential to understand that several factors and variables can cause a false-negative test. In this context, cycle threshold (Ct) values are being utilized to diagnose or predict SARS-CoV-2 infection. This practice has a significant clinical utility as Ct values can be correlated with the viral load. In addition, Ct values have a strong correlation with multiple haematological and biochemical markers. However, it is essential to consider that Ct values might be affected by pre-analytic, analytic, and post-analytical variables such as collection technique, specimen type, sampling time, viral kinetics, transport and storage conditions, nucleic acid extraction, viral RNA load, primer designing, real-time PCR efficiency, and Ct value determination method. Therefore, understanding the interpretation of Ct values and other influential factors could play a crucial role in interpreting viral load and disease severity. In several clinical studies consisting of small or large sample sizes, several discrepancies exist regarding a significant positive correlation between the Ct value and disease severity in COVID-19. In this context, a revised review of the literature has been conducted to fill the knowledge gaps regarding the correlations between Ct values and severity/fatality rates of patients with COVID-19. Various databases such as PubMed, Science Direct, Medline, Scopus, and Google Scholar were searched up to April 2021 by using keywords including "RT-PCR or viral load", "SARS-CoV-2 and RT-PCR", "Ct value and viral load", "Ct value or COVID-19". Research articles were extracted and selected independently by the authors and included in the present review based on their relevance to the study. The current narrative review explores the correlation of Ct values with mortality, disease progression, severity, and infectivity. We also discuss the factors that can affect these values, such as collection technique, type of swab, sampling method, etc.

Immunopathogenesis and immunobiology of SARS-CoV-2.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), in a very short span of thirteen months has taken a considerable toll on humanity, resulting in over 3 million deaths with more than 150 million confirmed cases as on May 1, 2021. In the scarcity of a potential antiviral and protective vaccine, COVID-19 has posed high public health concerns, panic, and challenges to limit the spread of this pandemic virus. Only recently have a few vaccine candidates been developed, and vaccination programs have started in some countries. Multiple clinical presentations of COVID-19, animal spillover, cross-species jumping, zoonotic concerns, and emergence of virus variants have altogether created havoc during this ongoing pandemic. Several bodies of research are continuously working to elucidate the exact molecular mechanisms of the pathogenesis. To develop a prospective antiviral therapy/vaccine for SARSCoV-2, it is quite essential to gain insight into the immunobiology and molecular virology of SARS-CoV-2. A thorough literature search was conducted up to 28th February 2021 in the PubMed and other databases for the articles describing the immunopathology and immune response of SARS-CoV-2 infection, which were critically evaluated and used to compile this article to present an overall update. Some of the information was drawn from studies on previous MERS and SARS viruses. Innate as well as adaptive immunity responses are elicited by exposure to SARS-CoV-2. SARS-CoV-2 establishes a successful infection by escaping the host immunity as well as over activating the innate immune mechanisms that result in severe disease outcomes, including cytokine storm. This review summarizes the immunopathology and molecular immune mechanisms elicited during SARS-CoV-2 infection, and their similarities with MERS-CoV and SARS-CoV.