High Systolic Blood Pressure at Hospital Admission Is an Important Risk Factor in Models Predicting Outcome of COVID-19 Patients

The possible mechanism and research progress of ACE2 involved in cardiovascular injury caused by COVID-19: a review

ACE2 is the earliest receptor discovered to mediate the entry of SARS-CoV-2. In addition to the receptor, it also participates in complex pathological and physiological processes, including regulating the RAS system, apelin, KKS system, and immune system. In addition to affecting the respiratory system, viral infections also interact with cardiovascular diseases. SARS-CoV-2 can directly invade the cardiovascular system through ACE2; Similarly, cardiovascular diseases such as hypertension and coronary heart disease can affect ACE2 levels and exacerbate the disease, and ACE2 dysregulation may also be a potential mechanism for long-term acute sequelae of COVID-19. Since the SARS CoV-2 epidemic, many large population studies have tried to clarify the current focus of debate, that is, whether we should give COVID-19 patients ACEI and ARB drug treatment, but there is still no conclusive conclusion. We also discussed potential disease treatment options for ACE2 at present. Finally, we discussed the researchers' latest findings on ACE2 and their prospects for future research.

Prediction of COVID-19 Severity at Delivery after Asymptomatic or Mild COVID-19 during Pregnancy

OBJECTIVE

 This study aimed to develop a prediction model that estimates the probability that a pregnant person who has had asymptomatic or mild coronavirus disease 2019 (COVID-19) prior to delivery admission will progress in severity to moderate, severe, or critical COVID-19.

STUDY DESIGN

 This was a secondary analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive patients who delivered from March through December 2020 at hospitals across the United States. Those eligible for this analysis presented for delivery with a current or previous asymptomatic or mild SARS-CoV-2 infection. The primary outcome was moderate, severe, or critical COVID-19 during the delivery admission through 42 days postpartum. The prediction model was developed and internally validated using stratified cross-validation with stepwise backward elimination, incorporating only variables that were known on the day of hospital admission.

RESULTS

 Of the 2,818 patients included, 26 (0.9%; 95% confidence interval [CI], 0.6-1.3%) developed moderate-severe-critical COVID-19 during the study period. Variables in the prediction model were gestational age at delivery admission (adjusted odds ratio [aOR], 1.15; 95% CI, 1.08-1.22 per 1-week decrease), a hypertensive disorder in a prior pregnancy (aOR 3.05; 95% CI, 1.25-7.46), and systolic blood pressure at admission (aOR, 1.04; 95% CI, 1.02-1.05 per mm Hg increase). This model yielded an area under the receiver operating characteristic curve of 0.82 (95% CI, 0.72-0.91).

CONCLUSION

 Among individuals presenting for delivery who had asymptomatic-mild COVID-19, gestational age at delivery admission, a hypertensive disorder in a prior pregnancy, and systolic blood pressure at admission were predictive of delivering with moderate, severe, or critical COVID-19. This prediction model may be a useful tool to optimize resources for SARS-CoV-2-infected pregnant individuals admitted for delivery.

KEY POINTS

· Three factors were associated with delivery with more severe COVID-19.. · The developed model yielded an area under the receiver operating characteristic curve of 0.82 and model fit was good.. · The model may be useful tool for SARS-CoV-2 infected pregnancies admitted for delivery..

Prognostic significance of hypertension history and blood pressure on admission in Japanese patients with coronavirus disease 2019: integrative analysis from the Japan COVID-19 Task Force

The effect of preexisting hypertension on coronavirus disease 2019 (COVID-19) prognosis remains controversial. Additionally, no studies have compared the association between blood pressure (BP) indices on admission and COVID-19 outcomes using preexisting hypertension status. Therefore, this study aimed to investigate the association between preexisting hypertension and COVID-19 outcomes in Japanese patients with COVID-19 and assess the impact of BP indices on admission on clinical outcomes in patients with and without preexisting hypertension. Preexisting hypertension presence was confirmed based on the patient's clinical history. Critical outcomes were defined as high-flow oxygen use, non-invasive and invasive positive-pressure ventilation, extracorporeal membrane oxygenation, or death during hospitalization. Preexisting hypertension was observed in 64.6% of the patients. Multivariable logistic regression analysis of severe COVID-19 risk factors indicated that preexisting hypertension was independently associated with critical outcomes [adjusted odds ratio (OR): 1.35; 95% confidence interval (CI): 1.05-1.73]. Low or high BP and high pulse pressure on admission were associated with critical outcomes in patients without preexisting hypertension [OR for systolic BP < 100 mmHg: 2.13, 95% CI: 1.21-3.75; OR for high BP stage 2 (160-179 systolic and/or 100-109 mmHg diastolic BP): 2.13, 95% CI: 1.27-3.58; OR for pulse pressure ≥60 mmHg: 1.68, 95% CI: 1.14-2.48]. Preexisting hypertension is a risk factor for critical outcomes in Japanese patients with COVID-19. BP indices are useful biomarkers for predicting COVID-19 outcomes, particularly in patients without preexisting hypertension. Thus, hypertension history, systolic BP, and pulse pressure should be assessed to predict severe COVID-19 outcomes.

Home Oxygen After Hospitalization for COVID-19: Results From the Multi-Center OXFORD Study

BACKGROUND

In the first months of the pandemic, prior to the introduction of proven-effective treatments, 15-37% of patients hospitalized with COVID-19 were discharged on home oxygen. After proven-effective treatments for acute COVID-19 were established by evidence-based guidelines, little remains known about home oxygen requirements following hospitalization for COVID-19.

METHODS

This was a retrospective, multi-center cohort study of subjects hospitalized for COVID-19 between October 2020-September 2021 at 3 academic health centers. Information was abstracted from electronic health records at the index hospitalization and for 60 d after discharge. The World Health Organization COVID-19 Clinical Progression Scale score was used to identify patients with severe COVID-19.

RESULTS

Of 517 subjects (mean age 58 y, 47% female, 42% Black, 36% Hispanic, 22% with severe COVID-19), 81% were treated with systemic corticosteroids, 61% with remdesivir, and 2.5% with tocilizumab. About one quarter of subjects were discharged on home oxygen (26% [95% CI 22-29]). Older age (adjusted odds ratio [aOR] 1.02 per 5 y [95% CI 1.02-1.02]), higher body mass index (aOR 1.02 per kg/m2 [1.00-1.04]), diabetes (yes vs no, aOR 1.73 [1.46-2.02]), severe COVID-19 (vs moderate, aOR 3.19 [2.19-4.64]), and treatment with systemic corticosteroids (yes vs no, aOR 30.63 [4.51-208.17]) were associated with an increased odds of discharge on home oxygen. Comorbid hypertension (yes vs no, aOR 0.71 [0.66-0.77) was associated with a decreased odds of home oxygen. Within 60 d of hospital discharge, 50% had documentation of pulse oximetry; in this group, home oxygen was discontinued in 46%.

CONCLUSIONS

About one in 4 subjects were prescribed home oxygen after hospitalization for COVID-19, even after guidelines established proven-effective treatments for acute illness. Evidence-based strategies to reduce the requirement for home oxygen in patients hospitalized for COVID-19 are needed.

Association between biochemical and hematologic factors with COVID-19 using data mining methods

BACKGROUND AND AIM

Coronavirus disease (COVID-19) is an infectious disease that can spread very rapidly with important public health impacts. The prediction of the important factors related to the patient's infectious diseases is helpful to health care workers. The aim of this research was to select the critical feature of the relationship between demographic, biochemical, and hematological characteristics, in patients with and without COVID-19 infection.

METHOD

A total of 13,170 participants in the age range of 35-65 years were recruited. Decision Tree (DT), Logistic Regression (LR), and Bootstrap Forest (BF) techniques were fitted into data. Three models were considered in this study, in model I, the biochemical features, in model II, the hematological features, and in model II, both biochemical and homological features were studied.

RESULTS

In Model I, the BF, DT, and LR algorithms identified creatine phosphokinase (CPK), blood urea nitrogen (BUN), fasting blood glucose (FBG), total bilirubin, body mass index (BMI), sex, and age, as important predictors for COVID-19. In Model II, our BF, DT, and LR algorithms identified BMI, sex, mean platelet volume (MPV), and age as important predictors. In Model III, our BF, DT, and LR algorithms identified CPK, BMI, MPV, BUN, FBG, sex, creatinine (Cr), age, and total bilirubin as important predictors.

CONCLUSION

The proposed BF, DT, and LR models appear to be able to predict and classify infected and non-infected people based on CPK, BUN, BMI, MPV, FBG, Sex, Cr, and Age which had a high association with COVID-19.

Social networks as a protective factor for worsened self-perceived health status related to self-perceived changes in loneliness and health conditions in adults aged 50+ during the COVID-19 outbreak

The coronavirus disease 2019 (COVID-19) has emerged as a global pandemic, leading millions of people to change their lifestyles, especially older individuals who are the most at-risk population. Social isolation, the main preventive action to slow the pandemic's spread, reduced and drastically limited social connections, increasing older individuals' loneliness and stress, and worsening their health. We examined the connection between self-perceived changes in loneliness, the existence and type of social contact (face-to-face/electronic), and health conditions on self-perceived changes in health status during the outbreak, analyzing 51,778 individuals aged 50 plus from the Survey of Health, Ageing and Retirement in Europe (SHARE) database Wave 8 beta (June-August 2020). We found that the odds for worsened self-perceived health status were 249% higher among individuals who reported increased loneliness compared to the non-increase group and were lower in individuals with face-to-face contact (31%) or electronic contact (54%) during the outbreak. In addition, the odds for worsened self-perceived health status were higher for individuals with hypertension (17%), cancer (19%), chronic lung disease (25%), heart problems (27%), and other illnesses (32%). Based on the results obtained, electronic contact has shown a stronger connection as a protective factor for worsened self-perceived health since the outbreak compared to face-to-face interactions. Thus, adopting a policy that encourages the usage of electronic communications could reduce the burden on the healthcare system, particularly during pandemics, while improving patient health outcomes and minimizing pandemic-related health risks. This approach is especially important for older individuals, for whom any departure from home can cause an additional risk of exposure to the virus.

Blood pressure pattern among blood donors exposed to SARS-CoV-2 in Luanda, Angola: A retrospective study

Background and Aims

SARS-CoV-2 infection is a public health concern. Several aspects related to the pattern of infection remain unclear. This study aimed to investigate the blood pressure pattern among blood donors exposed to SARS-CoV-2 in Luanda, Angola, a sub-Saharan African country.

Methods

We performed a retrospective analysis containing 343 blood donors from December 2019 to September 2020. Parametric tests compared means while χ 2 and logistic regression checked features associated with high blood pressure and were considered significant when p < 0.05.

Results

The mean age of blood donors was 32.2 ± 8.81 years (ranging from 18 to 61 years) and 93% of the men's gender. Overall, 4.7% of the studied population had been exposed to SARS-CoV-2. High blood pressure prevalence increased from unexposed to exposed SARS-CoV-2 (6.7%-18.8%, p = 0.071). SARS-CoV-2 exposure increase systole (131 ± 12.2 mmHg to 136 ± 14.2 mmHg, p = 0.098), diastole (79.9 ± 9.53 mmHg to 84.2 ± 12.7 mmHg, p = 0.086), pulse in beats per minute (72.0 ± 11.1 to 73.7 ± 8.50, p = 0.553), and decrease donating time (6.31 ± 3.72 min to 5.48 ± 1.61 min, p = 0.371). Chances of having high blood pressure were high [OR: 3.20 (95% confidence interval [CI]: 0.85-12.1), p = 0.086] in exposed SARS-CoV-2. Donors exposed to SARS-CoV-2 with abnormal donation time increased from the donor up to 40 years to over 40 years (from 35.7% to 50%, p = 0.696). The mean systolic, diastolic, and pulse pressure were higher for non-O donors (p > 0.05). A significant link was observed, between the Rhesus factor and blood pressure status (p = 0.032).

Conclusion

We showed important variations in blood pressure indices of the Angolan population exposed to SARS-CoV-2. Older age and non-O blood groups appear to be important biological factors for SARS-CoV-2 infection, as well as the risk of developing cardiovascular disease after or during SARS-CoV-2 exposure. Further studies assessing the impact on cardiovascular functions with ongoing or long-term SARS-CoV-2 exposure in individuals from resource-limited countries should be considered.

Functional characteristics and subjective disease perception in patients with COVID-19 two months after hospital discharge

Introduction

Although early inpatient and post-hospital rehabilitation is recognized as necessary, not all COVID-19 patients have access to rehabilitation. There are no published reports in the literature that investigate the outcomes of patients who do not receive rehabilitation after COVID-19. Our aim was to evaluate possible improvements in determinate functional and psychological parameters in COVID-19 patients two months after their hospital discharge.

Methods

On both time points various motor, cognitive, and clinical measurements such as body composition, tensiomyography, blood pressure, spirometry, grip strength test, Timed Up and Go test, gait speed, 30-second chair-stand test, and Montreal Cognitive Assessment, were performed. Additionally, questionnaires such as the SARC-CalF test, Edmonton frail scale, International Physical Activity questionnaire andThe Mediterranean Lifestyle index were conducted to assess lifestyle characteristics.

Results

A total of 39 patients (87.2% male; mean age of 59.1 ± 10.3 years), who were hospitalized due to COVID-19 at the Izola General Hospital (IGH), Slovenia between December 2020 and April 2021, were included. Patients were assessed at two time points (T1 and T2): T1 was taken after receiving a negative COVID-19 test and T2 was taken two months after T1. After two months of self-rehabilitation, we have detected a BMI increase (p < .001), fat free mass increase (p < .001), better Edmonton frail scale (p < .001), SARC-CalF score (p = .014) and MoCA score (p = .014). There were no detected changes in lifestyle habits nor in physical performance tests.

Discussion

It is already known that COVID-19 has long-term negative consequences regardless of the stage of the disease. Our findings support the notion that patients cannot fully regain all their functions within a two-month period without receiving structured or supervised rehabilitation. Therefore, it is crucial to offer patients comprehensive and structured rehabilitation that incorporates clinical, cognitive, and motor exercises.

The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections

BACKGROUND

Poor sleep is associated with an increased risk of infections and all-cause mortality but the causal direction between poor sleep and respiratory infections has remained unclear. We examined if poor sleep contributes as a causal risk factor to respiratory infections.

METHODS

We used data on insomnia, influenza and upper respiratory infections (URIs) from primary care and hospital records in the UK Biobank (N ≈ 231,000) and FinnGen (N ≈ 392,000). We computed logistic regression to assess association between poor sleep and infections, disease free survival hazard ratios, and performed Mendelian randomization analyses to assess causality.

FINDINGS

Utilizing 23 years of registry data and follow-up, we discovered that insomnia diagnosis associated with increased risk for infections (FinnGen influenza Cox's proportional hazard (CPH) HR = 4.34 [3.90, 4.83], P = 4.16 × 10-159, UK Biobank influenza CPH HR = 1.54 [1.37, 1.73], P = 2.49 × 10-13). Mendelian randomization indicated that insomnia causally predisposed to influenza (inverse-variance weighted (IVW) OR = 1.65, P = 5.86 × 10-7), URI (IVW OR = 1.94, P = 8.14 × 10-31), COVID-19 infection (IVW OR = 1.08, P = 0.037) and risk of hospitalization from COVID-19 (IVW OR = 1.47, P = 4.96 × 10-5).

INTERPRETATION

Our findings indicate that chronic poor sleep is a causal risk factor for contracting respiratory infections, and in addition contributes to the severity of respiratory infections. These findings highlight the role of sleep in maintaining sufficient immune response against pathogens.

FUNDING

Instrumentarium Science Foundation, Academy of Finland, Signe and Ane Gyllenberg Foundation, National Institutes of Health.

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

A comparative study of mortality differences and associated characteristics among elderly and young adult patients hospitalised with COVID-19 in India

INTRODUCTION

Studies have shown that elderly have been disproportionately impacted by COVID pandemic. They have more comorbidities, lower pulmonary reserve, greater risk of complications, more significant resource utilization, and bias towards receiving lower-quality treatment.

OBJECTIVES

This research aims to determine the characteristics of those who died inhospital due to COVID illness, and to compare these factors between elderly and young adults.

METHODS

We conducted a large retrospective study at a government run center in Rishikesh, India, from 1^st May 2020 till 31^st May 2021, and divided study population into adults (aged 18 to 60 years) and elderly (aged 60 years). We evaluated and compared our data for presenting symptoms, vitals, risk factors, comorbidities, length of stay, level of care required, and inhospital complications. Long-term mortality was determined using telephonic follow-up six months after discharge.

RESULTS

Analysis showed that elderly had 2.51 more odds of dying inhospital compared to younger adults with COVID. Presenting symptoms were different for elderly COVID patients. The utilization of ventilatory support was higher for elderly patients. Inhospital complications revealed similar profile of complications, however, kidney injury was much higher in elderly who died, while younger adults had more Acute Respiratory Distress. Regression analysis showed that model containing cough and low oxygen saturation on admission, hypertension, Hospital Acquired Pneumonia, Acute Respiratory Distress Syndrome, and shock, predicted inhospital mortality.

CONCLUSION

Our Study determined characteristics of inhospital and long-term mortality in elderly COVID patients and compared them from adults, to help better triaging and policy making in future.

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient's C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels -saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2-, the neutrophil-to-lymphocyte ratio (NLR) -to certain extent, also neutrophil and lymphocyte counts separately-, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.

Arterial Hypertension and Severe COVID-19 in Hospitalized Patients: Data from a Cohort Study

Aim. To assess the association  of hypertension  with the severe forms and fatal outcomes of Coronavirus disease 2019 (COVID-19).

Material and Methods. This retrospective  cohort study involved adult patients (≥18 years old),  admitted to the University  hospital №4 of Sechenov University (Moscow, Russia) between 08 April 2020 and 19 November 2020 with clinically diagnosed or laboratory-confirmed COVID-19. The cohort included 1637 patients. The primary outcome was all-cause in-hospital mortality. The secondary outcomes included intensive care unit admission (ICU) and invasive ventilation. Multiple logistic regression was performed to assess the independent association  between risk factors and endpoints.

Results. A total of 1637 patients were included in the study. 51.80% (n=848) of the subjects were males. The median age was 59.0 (48.0; 70.0) years and 55.90% (n=915) had pre-existing diagnosis of hypertension. Patients with hypertension  had significantly more severe lung injury based on chest CT scan findings  as well as lower oxygen saturation  (SрO2). More of them were admitted to ICU  and  placed  on invasive  ventilation.  The hypertension  group  also  had  higher  mortality.  Age,  hypertension, glucose, C-reactive protein and decreased platelet count were independently associated with mortality, hypertension  having the strongest association (OR 1.827, 95% CI 1.174-2.846, p=0.008). Age,  hypertension, neutrophil count, platelet count, glucose, and CRP were independently associated  with ICU  admission, with hypertension  having  the strongest  association  (OR  1.595, 95% CI 1.178-2.158, p=0.002). Age, hypertension, glucose, CRP and decreased platelet count were independently associated with invasive ventilation, with hypertension having the strongest association  (OR  1.703, 95% CI 1.151-2.519, p=0.008).Based on the multiple logistic regression models, odds of death, ICU admission, and invasive ventilation were higher in the hypertension  group as compared  to the group without hypertension.

Conclusion. Hypertension can be an independent predictor of severe COVID-19 and adverse outcomes, namely death, ICU admission, and invasive ventilation in hospitalized  patients.

Research of mortality risk prediction based on hospital admission data for COVID-19 patients

As COVID-19 continues to spread across the world and causes hundreds of millions of infections and millions of deaths, medical institutions around the world keep facing a crisis of medical runs and shortages of medical resources. In order to study how to effectively predict whether there are risks of death in patients, a variety of machine learning models have been used to learn and predict the clinical demographics and physiological indicators of COVID-19 patients in the United States of America. The results show that the random forest model has the best performance in predicting the risk of death in hospitalized patients with COVID-19, as the COVID-19 patients' mean arterial pressures, ages, C-reactive protein tests' values, values of blood urea nitrogen and their clinical troponin values are the most important implications for their risk of death. Healthcare organizations can use the random forest model to predict the risks of death based on data from patients admitted to a hospital due to COVID-19, or to stratify patients admitted to a hospital due to COVID-19 based on the five key factors this can optimize the diagnosis and treatment process by appropriately arranging ventilators, the intensive care unit and doctors, thus promoting the efficient use of limited medical resources during the COVID-19 pandemic. Healthcare organizations can also establish databases of patient physiological indicators and use similar strategies to deal with other pandemics that may occur in the future, as well as save more lives threatened by infectious diseases. Governments and people also need to take action to prevent possible future pandemics.

Predictive value of neutrophil-to-lymphocyte ratio for the fatality of COVID-19 patients complicated with cardiovascular diseases and/or risk factors

Previous studies have reported that a high neutrophil-to-lymphocyte ratio (NLR) is associated with disease severity and poor prognosis in COVID-19 patients. We aimed to investigate the clinical implications of NLR in patients with COVID-19 complicated with cardiovascular diseases and/or its risk factors (CVDRF). In total, 601 patients with known NLR values were selected from the CLAVIS-COVID registry for analysis. Patients were categorized into quartiles (Q1, Q2, Q3, and Q4) according to baseline NLR values, and demographic and clinical parameters were compared between the groups. Survival analysis was conducted using the Kaplan-Meier method. The diagnostic performance of the baseline and follow-up NLR values was tested using receiver operating characteristic (ROC) curve analysis. Finally, two-dimensional mapping of patient characteristics was conducted using t-stochastic neighborhood embedding (t-SNE). In-hospital mortality significantly increased with an increase in the baseline NLR quartile (Q1 6.3%, Q2 11.0%, Q3 20.5%; and Q4, 26.6%; p < 0.001). The cumulative mortality increased as the quartile of the baseline NLR increased. The paired log-rank test revealed significant differences in survival for Q1 vs. Q3 (p = 0.017), Q1 vs. Q4 (p < 0.001), Q2 vs. Q3 (p = 0.034), and Q2 vs. Q4 (p < 0.001). However, baseline NLR was not identified as an independent prognostic factor using a multivariate Cox proportional hazards regression model. The area under the curve for predicting in-hospital death based on baseline NLR was only 0.682, whereas that of follow-up NLR was 0.893. The two-dimensional patient map with t-SNE showed a cluster characterized by high mortality with high NLR at follow-up, but these did not necessarily overlap with the population with high NLR at baseline. NLR may have prognostic implications in hospitalized COVID-19 patients with CVDRF, but its significance depends on the timing of data collection.

Sex-adjusted approach to baseline variables demonstrated some improved predictive capabilities for disease severity and survival in patients with Coronavirus Disease 19

INTRODUCTION

The study was focused on comparing crude and sex-adjusted hazard ratio calculated by the baseline variables which may have contributed to the severity of the disease course and fatal outcomes in Coronavirus Disease-19 (COVID-19) patients.

METHOD

The study enrolled 150 eligible adult patients with confirmed SARS-CoV-2 infection. There were 61 (40.7%) male patients, and 89 (59.3%) female patients. Baseline information of patients was collected from patient medical records and surveys that the patients had completed on admission to the hospital.

RESULTS

Considerable number of baseline variables stratified according to disease severity and outcomes showed different optimal cut-points (OCP) in men and women. Sex-adjusted baseline data categories such as age; BMI; systolic and diastolic blood pressure; peripheral RBC and platelet counts; haematocrit; percentage of neutrophils, lymphocytes, monocytes, and their ratio; percentage of eosinophils; titre of plasma IL-6, IL-8, IL-10, and IL-17; and CXCL10; and ratio of pro- and anti-inflammatory cytokines demonstrated significant impacts on the development of the severe stage and fatal outcomes by the mean hazard ratio in the Kaplan-Meier and Cox regression models.

CONCLUSION

This study confirmed some improved predictive capabilities of the sex-adjusted approach in the analysis of the baseline predictive variables for severity and outcome of the COVID-19.

Clinical Characteristics and Outcomes in Elderly Patients With COVID-19: A Single-Centre Retrospective Study

Background: Even with the wide base of knowledge that has been accumulated regarding coronavirus disease 2019 (COVID-19), only limited studies have tried to establish differences in outcomes of elderly patients hospitalized with COVID-19. We, thus, conducted a retrospective study on a large cohort of hospitalized patients with COVID-19 to improve the understanding of such differences and add to the evidence available regarding this age group. 

Methods: This is a single-centre retrospective study conducted at a tertiary level hospital in the state of Uttarakhand in North India to determine clinical characteristics and outcomes in elderly patients (≥ 60 years) hospitalized with COVID-19 between May 1, 2020, and May 31, 2021. Our study included a retrospective follow-up at six months to also determine rehospitalizations and post-discharge mortality.

Results: There was a statistically significant difference (p<0.05) in in-hospital mortality, various in-hospital complications, duration of stay, number of rehospitalizations at six months, and post-discharge mortality up to six months in the elderly age group hospitalized with COVID-19.

Conclusions: This retrospective study demonstrates that the clinical characteristics and outcomes in hospitalized elderly with COVID-19 differ significantly from the younger adult population and demonstrates a need for greater hospital resource utilization in this age group. These results will help policymakers be better prepared for future pandemics. 

Prognostic value of albumin-to-globulin ratio in COVID-19 patients: A systematic review and meta-analysis

Background and aims

The albumin-to-globulin ratio (AGR) has been used to predict severity and mortality in infectious diseases. The aim of this study is to evaluate the prognostic value of the AGR in COVID-19 patients.

Methods

A systematic review and meta-analysis were conducted. We included observational studies assessing the association between the AGR values upon hospital admission and severity or all-cause mortality in COVID-19 patients. In the meta-analyses we used random effect models. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). The effect measures were expressed as mean difference (MD) and their 95% confidence intervals (CI). We performed Egger's test and funnel plots to assess the publication bias.

Results

The included studies had a total of 11356 patients corresponding to 31 cohort studies. Severe COVID-19 patients had lower AGR values than non-severe COVID-19 patients (mean difference (MD), −0.27; 95% IC, −0.32 to −0.22; p < 0.001; I² = 88%). Non-survivor patients with COVID-19 had lower AGR values than survivor patients (MD, −0.29; 95% IC, −0.35 to −0.24; p < 0.001; I² = 79%). In the sensitivity analysis, we only included studies with low risk of bias, which decreased the heterogeneity for both outcomes (severity, I² = 20%; mortality, I² = 5%).

Conclusions

Low AGR values upon hospital admission were found in COVID-19 patients with a worse prognosis.

Admission vital signs as predictors of COVID-19 mortality: a retrospective cross-sectional study

Background

COVID-19 remains a major healthcare concern. Vital signs are routinely measured on admission and may provide an early, cost-effective indicator of outcome – more so in developing countries where such data is scarce. We sought to describe the association between six routinely measured admission vital signs and COVID-19 mortality, and secondarily to derive potential applications for resource-limited settings.

Methods

Retrospective analysis of consecutive patients admitted to King Edward VIII Hospital, South Africa, with COVID-19 during June–September 2020 was undertaken. The sample was subdivided into survivors and non-survivors and comparisons made in terms of demographics and admission vital signs. Univariate and multivariate analysis of predictor variables identified associations with in-hospital mortality, with the resulting multivariate regression model evaluated for its predictive ability with receiver operating characteristic (ROC) curve analysis.

Results

The 236 participants enrolled comprised 153(77.54%) survivors and 53(22.46%) non-survivors. Most participants were Black African(87.71%) and female(59.75%) with a mean age of 53.08(16.96) years. The non-survivor group demonstrated a significantly lower median/mean for admission oxygen saturation (%) [87(78–95) vs. 96(90–98)] and diastolic BP (mmHg) [70.79(14.66) vs. 76.3(12.07)], and higher median for admission respiratory rate (breaths/minute) [24(20–28) vs. 20(20–23)] and glucose (mmol/l) [10.2(6.95–16.25) vs. 7.4(5.5–9.8)]. Age, oxygen saturation, respiratory rate, glucose and diastolic BP were found to be significantly associated with mortality on univariate analysis. A log rank test revealed significantly lower survival rates in patients with an admission oxygen saturation < 90% compared with ≥90% (p = 0.001). Multivariate logistic regression revealed a significant relationship between age and oxygen saturation with in-hospital mortality (OR 1.047; 95% CI 1.016–1.080; p = 0.003 and OR 0.922; 95% CI 0.880–0.965; p = 0.001 respectively). A ROC curve analysis generated an area under the curve (AUC) of 0.778 (p < 0.001) when evaluating the predictive ability of oxygen saturation, respiratory rate, glucose and diastolic BP for in-hospital death. This improved to an AUC of 0.832 (p < 0.001) with the inclusion of age.

Conclusion

A multivariate regression model comprising admission oxygen saturation, respiratory rate, glucose and diastolic BP (with/without age) demonstrated promising predictive capacity, and may provide a cost-effective means for early prognostication of patients admitted with COVID-19 in resource-limited settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-022-00631-7.

Glycated Albumin and Glycated Albumin/HbA1c Predict the Progression of Coronavirus Disease 2019 from Mild to Severe Disease in Korean Patients with Type 2 Diabetes

Hyperglycemia is among the main risk factors for severe COVID-19. We evaluated the association of glycated albumin (GA) and GA/HbA1c ratio with progression of COVID-19 from mild to severe disease in patients with type 2 diabetes mellitus (T2DM). Our retrospective study included 129 patients aged over 18 years with COVID-19 and T2DM who did not have any need of oxygen supplement. Of these, 59 patients whose COVID-19 was aggravated and required oxygen supplementation eventually were classified as having severe disease. Clinical and laboratory data were compared between mild and severe cases. The median of GA (18.4% vs. 20.95%, p = 0.0013) and GA/HbA1c (2.55 vs. 2.68, p = 0.0145) were higher in severe disease than in mild disease and positively correlated with C-reactive protein (Kendal Tau coefficient 0.200 and 0.126, respectively; all p < 0.05). Multiple logistic regression analysis showed that GA (odds ratio (OR), 1.151; 95% confidence interval (CI), 1.024−1.294) and GA/HbA1c (OR, 8.330; 95% CI, 1.786−38.842) increased the risk of severe disease. Patients with GA 20% or higher were 4.03 times more likely to progress from mild to severe disease. GA and GA/HbA1c ratio predicted progression of COVID-19 from mild to severe disease in patients with T2DM.

Hypertension and COVID-19: Current Evidence and Perspectives

Coronavirus disease 2019 (COVID-19) outbreak, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), represents a real challenge for health-care systems worldwide. Male sex, older age and the coexistence of chronic comorbidities have been described as the most relevant conditions associated with a worse prognosis. Early reports suggested that hypertension might represent a risk factor for susceptibility to SARS-CoV-2 infection, a more severe course of COVID-19 and increased COVID-19-related deaths. Nevertheless, the independent role of hypertension remains under debate, since hypertension is often associated with the older age and other cardiovascular (CV) risk factors in the general population, which may also contribute to the SARS-Cov-2 infection and COVID-19. Moreover, the role of antihypertensive drugs, primarily angiotensin-converting inhibitors (ACEIs) and ARBs (angiotensin receptor blockers) in COVID-19 development and outcome appears controversial. Indeed, preclinical studies using these classes of drugs have suggested a potential upregulation of angiotensin-converting-enzyme 2 (ACE2) which is the key binding receptor promoting cell entry of SARS-CoV-2 in the organism. Renin–angiotensin system (RAS) blockers may potentially upregulate ACE2, hence, it has been initially hypothesized that these agents might contribute to a higher risk of SARS-CoV-2 infection and progressive course of COVID-19. However, several clinical reports do not support a detrimental role of RAS blockers in COVID-19, and an intense debate about the withdrawal or maintenance of chronic therapy with ACEi/ARB has been developed. In this review we will discuss the available evidence on the role of hypertension and antihypertensive drugs on SARS-CoV-2 infection and COVID-19 development.

The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections

Background

Poor sleep is associated with an increased risk of infections and all-cause mortality, and acute sleep loss and disruption have been linked with inflammation and poorer immune control. Previous studies, however, have been unable to evidence causality between the chronic effects of poor sleep and respiratory infection risk. In light of the ongoing COVID-19 pandemic and potential future disease outbreaks, understanding the risk factors for these infections is of great importance.

Aim

Our goal was to understand if chronic poor sleep could be identified as a causal risk factor for respiratory infections including influenza, upper respiratory infections and COVID-19.

Methods

We used population cohorts from the UK Biobank (N ≈ 231,000) and FinnGen (N ≈ 327,000) with ICD-10 based electronic health records and obtained diagnoses of insomnia, influenza and upper respiratory infections (URIs) from primary care and hospital settings. We computed logistic regression to assess association between poor sleep and infections, disease free survival hazard ratios, and used summary statistics from genome-wide association studies of insomnia, influenza, URI and COVID-19 to perform Mendelian randomization analyses and assess causality.

Findings

Utilizing 23 years of registry data and follow-up, we saw that insomnia diagnosis associated with increased risk for infections in FinnGen and in UK Biobank (FinnGen influenza HR = 5.32 [4.09, 6.92], P = 1.02×10-35, UK Biobank influenza HR = 1.54 [1.37, 1.73], P = 2.49×10-13). Mendelian randomization indicated that insomnia causally predisposed to influenza (OR = 1.59, P = 6.23×10-4), upper respiratory infections (OR = 1.71, P = 7.60×10-13), COVID-19 infection (OR = 1.08, P = 0.037) and risk of hospitalization from COVID-19 (OR = 1.47, P = 4.96×10-5).

Conclusions

Our findings indicate that chronic poor sleep is a causal risk factor for contracting respiratory infections, and in addition contributes to the severity of respiratory infections. These findings highlight the role of sleep in maintaining sufficient immune response against pathogens as suggested by earlier work. As the current COVID-19 pandemic has increased the number of people suffering from poor sleep, safe interventions such as sleep management and treating individuals with insomnia could be promoted to reduce infections and save lives.

Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues

COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people's health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects and return people's lives back to normal. The main objective of this study is to investigate the various factors in health and food access, and their spatial correlation and statistical association with COVID-19 spread. The minor aim is to explore regression models on examining COVID-19 spread with these variables. To address these objectives, we are studying the interrelation of various socio-economic factors that would help all humans to better prepare for the next pandemic. One of these critical factors is food access and food distribution as it could be high-risk population density places that are spreading the virus infections. More variables, such as income and people density, would influence the pandemic spread. In this study, we produced the spatial extent of COVID-19 cases with food outlets by using the spatial analysis method of geographic information systems. The methodology consisted of clustering techniques and overlaying the spatial extent mapping of the clusters of food outlets and the infected cases. Post-mapping, we analyzed these clusters' proximity for any spatial variability, correlations between them, and their causal relationships. The quantitative analyses of the health issues and food access areas against COVID-19 infections and deaths were performed using machine learning regression techniques to understand the multi-variate factors. The results indicate a correlation between the dependent variables and independent variables with a Pearson correlation R2-score = 0.44% for COVID-19 cases and R2 = 60% for COVID-19 deaths. The regression model with an R2-score of 0.60 would be useful to show the goodness of fit for COVID-19 deaths and the health issues and food access factors.

An analytical model of population level chronic conditions and COVID-19 related hospitalization in the United States

Background

The surge in the COVID-19 related hospitalization has been straining the US health system. COVID-19 patients with underlying chronic conditions have a disproportionately higher risk of hospitalization and intensive care unit (ICU) admission. We developed a retrospective analytical model of COVID-19 related hospitalizations and ICU admissions linked to each of the three major chronic conditions – hypertension, diabetes, and cardiovascular diseases (CVD).

Methods

Based on the differential probability of hospitalization of the COVID-19 patients with and without a chronic condition, we estimate a baseline cumulative hospitalization rate and ICU admission rate using the population level chronic condition prevalence from the 2019 Behavioral Risk Factor Surveillance System survey. Next, we estimate the hospitalization and ICU admission rates under an alternative scenario of a lower prevalence of the same chronic condition, aligned with the World Health Organization target of 25% relative reduction of prevalence by 2025. We then compare the outcomes of the baseline and the alternative scenarios.

Results

We estimate that the lower prevalence of hypertension would have lowered the cumulative hospitalization and ICU admission rates by more than 2.5%. The lower prevalence of diabetes and CVD would lower the cumulative hospitalization rate by 0.6% and 1.4% respectively. The decrease in the rates would have been relatively higher among Black and elderly (age 55+).

Conclusions

Our model, thus, provides evidence on the importance of prevention, control, and management of chronic conditions to lessen the overwhelming financial and public health burden on the health system during a pandemic like the COVID-19.

Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic

Clinical evidence suggests that some patients diagnosed with coronavirus disease 2019 (COVID-19) experience a variety of complications associated with significant morbidity, especially in severe cases during the initial spread of the pandemic. To support early interventions, we propose a machine learning system that predicts the risk of developing multiple complications. We processed data collected from 3,352 patient encounters admitted to 18 facilities between April 1 and April 30, 2020, in Abu Dhabi (AD), United Arab Emirates. Using data collected during the first 24 h of admission, we trained machine learning models to predict the risk of developing any of three complications after 24 h of admission. The complications include Secondary Bacterial Infection (SBI), Acute Kidney Injury (AKI), and Acute Respiratory Distress Syndrome (ARDS). The hospitals were grouped based on geographical proximity to assess the proposed system's learning generalizability, AD Middle region and AD Western & Eastern regions, A and B, respectively. The overall system includes a data filtering criterion, hyperparameter tuning, and model selection. In test set A, consisting of 587 patient encounters (mean age: 45.5), the system achieved a good area under the receiver operating curve (AUROC) for the prediction of SBI (0.902 AUROC), AKI (0.906 AUROC), and ARDS (0.854 AUROC). Similarly, in test set B, consisting of 225 patient encounters (mean age: 42.7), the system performed well for the prediction of SBI (0.859 AUROC), AKI (0.891 AUROC), and ARDS (0.827 AUROC). The performance results and feature importance analysis highlight the system's generalizability and interpretability. The findings illustrate how machine learning models can achieve a strong performance even when using a limited set of routine input variables. Since our proposed system is data-driven, we believe it can be easily repurposed for different outcomes considering the changes in COVID-19 variants over time.

Role of neutrophils, platelets, and extracellular vesicles and their interactions in COVID-19-associated thrombopathy

The COVID-19 pandemic extended all around the world causing millions of deaths. In addition to acute respiratory distress syndrome, many patients with severe COVID-19 develop thromboembolic complications associated to multiorgan failure and death. Here, we review evidence for the contribution of neutrophils, platelets, and extracellular vesicles (EVs) to the thromboinflammatory process in COVID-19. We discuss how the immune system, influenced by pro-inflammatory molecules, EVs, and neutrophil extracellular traps (NETs), can be caught out in patients with severe outcomes. We highlight how the deficient regulation of the innate immune system favors platelet activation and induces a vicious cycle amplifying an immunothrombogenic environment associated with platelet/NET interactions. In light of these considerations, we discuss potential therapeutic strategies underlining the modulation of purinergic signaling as an interesting target.

A new predictor for indicating clinical severity and prognosis in COVID-19 patients: Frontal QRS-T angle

Objective

COVID-19; It spread rapidly around the world and led to a global pandemic. Indicators of poor prognosis are important in the treatment and follow-up of COVID-19 patients and have always been a matter of interest to researchers. The aim of this study was to investigate the relationship between frontal QRS-T angle values and clinical severity and prognosis in COVID-19 patients.

Methods

This prospective case-control study was conducted with 130 COVID-19 patients whose diagnosis was confirmed by reverse transcriptase-polymerase chain reaction (RT-PCR) and 100 healthy controls. The CURB-65 score was used as the clinical severity score.

Results

A total of 130 patients and 100 healthy controls were included in the study. When the patient and control groups were compared a significant difference was found between QT (378.07 ± 33.75 vs. 368.63 ± 19.65, p < 0.001), QTc (410.79 ± 28.19 vs. 403.68 ± 11.70, p < 0.001), QRS time (95.04 ± 21.67 vs. 91.42 ± 11.08, p < 0.001) and frontal QRS-T angle (36.57 ± 22.86 vs. 22.72 ± 14.08, p < 0.001). According to clinical severity scoring, QT (370.27 ± 25.20 vs. 387.75 ± 40.19, p = 0.003), QTc (402.18 ± 19.92 vs. 421.48 ± 33.08, p < 0.001), frontal QRS-T angle (32.25 ± 18.79 vs. 41.94 ± 26.27), p = 0.0.16) parameters were found to be significantly different. Age (odds ratio [OR], 1.201; 95% confidence interval [CI], 1.111–1.298; p < 0.001) and frontal QRS-T angle ([OR], 1.045; 95% [CI], 1.015–1.075; p = 0.003) values were found to be an independent predictor for the severity of the disease. Frontal QRS-T angle ([OR], 1.101; 95% [CI], 1.030–1.176; p = 0.004), and CRP ([OR], 1.029; 95% [CI], 1.007–1.051; p = 0.01) parameters were found to be independent predictors for the mortality of the disease. As a mortality indicator; for the frontal QRS-T angle of ≥44.5°, specificity and sensitivity were 93.8% and 84.2%, respectively.

Conclusion

Frontal QRS-T angle can be used as a reproducible, convenient, inexpensive, new and powerful predictor in determining the clinical severity and prognosis of COVID-19 patients.

Hypertension, a Moving Target in COVID-19: Current Views and Perspectives

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) associates with a considerable high rate of mortality and represents currently the most important concern in global health. The risk of more severe clinical manifestation of COVID-19 is higher in males and steeply raised with age but also increased by the presence of chronic comorbidities. Among the latter, early reports suggested that arterial hypertension associates with higher susceptibility to SARS-CoV-2 infection, more severe course and increased COVID-19-related deaths. Furthermore, experimental studies suggested that key pathophysiological hypertension mechanisms, such as activation of the renin-angiotensin system (RAS), may play a role in COVID-19. In fact, ACE2 (angiotensin-converting-enzyme 2) is the pivotal receptor for SARS-CoV-2 to enter host cells and provides thus a link between COVID-19 and RAS. It was thus anticipated that drugs modulating the RAS including an upregulation of ACE2 may increase the risk for infection with SARS-CoV-2 and poorer outcomes in COVID-19. Since the use of RAS-blockers, ACE inhibitors or angiotensin receptor blockers, represents the backbone of recommended antihypertensive therapy and intense debate about their use in the COVID-19 pandemic has developed. Currently, a direct role of hypertension, independent of age and other comorbidities, as a risk factor for the SARS-COV-2 infection and COVID-19 outcome, particularly death, has not been established. Similarly, both current experimental and clinical studies do not support an unfavorable effect of RAS-blockers or other classes of first line blood pressure lowering drugs in COVID-19. Here, we review available data on the role of hypertension and its management on COVID-19. Conversely, some aspects as to how the COVID-19 affects hypertension management and impacts on future developments are also briefly discussed. COVID-19 has and continues to proof the critical importance of hypertension research to address questions that are important for global health.