Early Predictors of Clinical Deterioration in a Cohort of 239 Patients Hospitalized for Covid-19 Infection in Lombardy, Italy

A Vulnerability Index to Assess the Risk of SARS-CoV-2-Related Hospitalization/Death: Urgent Need for an Update after Diffusion of Anti-COVID Vaccines

Background: There are algorithms to predict the risk of SARS-CoV-2-related complications. Given the spread of anti-COVID vaccination, which sensibly modified the burden of risk of the infection, these tools need to be re-calibrated. Therefore, we updated our vulnerability index, namely, the Health Search (HS)-CoVulnerabiltyIndex (VI)d (HS-CoVId), to predict the risk of SARS-CoV-2-related hospitalization/death in the primary care setting. Methods: We formed a cohort of individuals aged ≥15 years and diagnosed with COVID-19 between 1 January and 31 December 2021 in the HSD. The date of COVID-19 diagnosis was the study index date. These patients were eligible if they had received an anti-COVID vaccine at least 15 days before the index date. Patients were followed up from the index date until one of the following events, whichever came first: COVID-19-related hospitalization/death (event date), end of registration with their GPs, and end of the study period (31 December 2022). To calculate the incidence rate of COVID-19-related hospitalization/death, a patient-specific score was derived through linear combination of the coefficients stemming from a multivariate Cox regression model. Its prediction performance was evaluated by obtaining explained variation, discrimination, and calibration measures. Results: We identified 2192 patients who had received an anti-COVID vaccine from 1 January to 31 December 2021. With this cohort, we re-calibrated the HS-CoVId by calculating optimism-corrected pseudo-R2, AUC, and calibration slope. The final model reported a good predictive performance by explaining 58% (95% CI: 48-71%) of variation in the occurrence of hospitalizations/deaths, the AUC was 83 (95% CI: 77-93%), and the calibration slope did not reject the equivalence hypothesis (p-value = 0.904). Conclusions: Two versions of HS-CoVId need to be differentially adopted to assess the risk of COVID-19-related complications among vaccinated and unvaccinated subjects. Therefore, this functionality should be operationalized in related patient- and population-based informatic tools intended for general practitioners.

Retrospective cohort study to evaluate the continuous use of anticholesterolemics and diuretics in patients with COVID-19

Purpose

The purpose of this study is to evaluate the interference of the continuous use of drug classes in the expression of biomarkers during the first week of hospitalization and in the prognosis of patients with COVID-19.

Methods

The patients diagnosed with COVID-19 and confirmed with SARS-CoV-2 by RT-qPCR assay underwent the collection of fasting whole blood samples for further analysis. Other data also extracted for this study included age, sex, clinical symptoms, related comorbidities, smoking status, and classes of continuous use. Routine serum biochemical parameters, including alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase, C-reactive protein, N-terminal fragment of B-type natriuretic peptide, and cardiac troponin, were measured.

Results

In this cross-sectional study, a total of 176 patients with COVID-19 hospitalizations were included. Among them, 155 patients were discharged (88.5%), and 21 patients died (12%). Among the drug classes evaluated, we verified that the continuous use of diuretic 4.800 (1.853-11.67) (p = 0.0007) and antihypercholesterolemic 3.188 (1.215-7.997) (p = 0.0171) drug classes presented a significant relative risk of death as an outcome when compared to the group of patients who were discharged. We evaluated biomarkers in patients who used continuous antihypercholesterolemic and diuretic drug classes in the first week of hospitalization. We observed significant positive correlations between the levels of CRP with cardiac troponin (r = 0.714), IL-6 (r = 0.600), and IL-10 (r = 0.900) in patients who used continuous anticholesterolemic and diuretic drug classes and were deceased. In these patients, we also evaluated the possible correlations between the biomarkers AST, NT-ProBNP, cardiac troponin, IL-6, IL-8, and IL-10. We observed a significantly negative correlations in AST levels with NT-ProBNP (r = -0.500), cardiac troponin (r = -1.00), IL-6 (r = -1.00), and IL-10 (r = -1.00) and a positive correlation with IL-8 (r = 0.500). We also observed significant negative correlation in the levels of NT-ProBNP with IL-10 (r = -0.800) and a positive correlation with cardiac troponin (r = 0.800). IL-6 levels exhibited positive correlations with cardiac troponin (r = 0.800) and IL-10 (r = 0.700).

Conclusion

In this study, we observed that hospitalized COVID-19 patients who continued using anticholesterolemic and diuretic medications showed a higher number of correlations between biomarkers, indicating a poorer clinical prognosis. These correlations suggest an imbalanced immune response to injuries caused by SARS-CoV-2.

Fully independent validation of eleven prognostic scores predicting progression to critically ill condition in hospitalized patients with COVID-19

INTRODUCTION

COVID-19 remains an important threat to global health and maintains the challenge of COVID-19 hospital care. To assist decision making regarding COVID-19 hospital care many instruments to predict COVID-19 progression to critical condition were developed and validated.

OBJECTIVE

To validate eleven COVID-19 progression prediction scores for critically ill hospitalized patients in a Brazilian population.

METHODOLOGY

Observational study with retrospective follow-up, including 301 adults confirmed for COVID-19 sequentially. Participants were admitted to non-critical units for treatment of the disease, between January and April 2021 and between September 2021 and February 2022. Eleven prognostic scores were applied using demographic, clinical, laboratory and imaging data collected in the first 48 of the hospital admission. The outcomes of greatest interest were as originally defined for each score. The analysis plan was to apply the instruments, estimate the outcome probability reproducing the original development/validation of each score, then to estimate performance measures (discrimination and calibration) and decision thresholds for risk classification.

RESULTS

The overall outcome prevalence was 41.8 % on 301 participants. There was a greater risk of the occurrence of the outcomes in older and male patients, and a linear trend with increasing comorbidities. Most of the patients studied were not immunized against COVID-19. Presence of concomitant bacterial infection and consolidation on imaging increased the risk of outcomes. College of London COVID-19 severity score and the 4C Mortality Score were the only with reasonable discrimination (ROC AUC 0.647 and 0.798 respectively) and calibration. The risk groups (low, intermediate and high) for 4C score were updated with the following thresholds: 0.239 and 0.318 (https://pedrobrasil.shinyapps.io/INDWELL/).

CONCLUSION

The 4C score showed the best discrimination and calibration performance among the tested instruments. We suggest different limits for risk groups. 4C score use could improve decision making and early therapeutic management at hospital care.

Hematological and Biochemical Laboratory Parameters in COVID-19 Patients: A Retrospective Modeling Study of Severity and Mortality Predictors

Background

It is well known that laboratory markers could help in identifying risk factors of severe illness and predicting outcomes of diseases. Here, we performed a retrospective modeling study of severity and mortality predictors of hematological and biochemical laboratory parameters in Iranian COVID-19 patients.

Methods

Data were obtained retrospectively from medical records of 564 confirmed Iranian COVID-19 cases. According to the disease severity, the patients were categorized into two groups (severe or nonsevere), and based on the outcome of the disease, patients were divided into two groups (recovered or deceased). Demographic and laboratory data were compared between groups, and statistical analyses were performed to define predictors of disease severity and mortality in the patients.

Results

The study identified a panel of hematological and biochemical markers associated with the severe outcome of COVID-19 and constructed different predictive models for severity and mortality. The disease severity and mortality rate were significantly higher in elderly inpatients, whereas gender was not a determining factor of the clinical outcome. Age-adjusted white blood cells (WBC), platelet cells (PLT), neutrophil-to-lymphocyte ratio (NLR), red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), erythrocyte sedimentation rate (ESR), mean corpuscular hemoglobin (MCHC), blood urea nitrogen (BUN), and creatinine (Cr) also showed high accuracy in predicting severe cases at the time of hospitalization, and logistic regression analysis suggested grouped hematological parameters (age, WBC, NLR, PLT, HGB, and international normalized ratio (INR)) and biochemical markers (age, BUN, and lactate dehydrogenase (LDH)) as the best models of combined laboratory predictors for severity and mortality.

Conclusion

The findings suggest that a panel of several routine laboratory parameters recorded on admission could be helpful for clinicians to predict and evaluate the risk of disease severity and mortality in COVID-19 patients.

Antimicrobial Stewardship in COVID-19 Patients: Those Who Sow Will Reap Even through Hard Times

BACKGROUND

Since the SARS-CoV-2 pandemic emerged, antimicrobial stewardship (AS) activities need to be diverted into COVID-19 management.

METHODS

In order to assess the impact of COVID-19 on AS activities, we analyzed changes in antibiotic consumption in moderate-to-severe COVID-19 patients admitted to four units in a tertiary-care hospital across three COVID-19 waves. The AS program was introduced at the hospital in 2018. During the first wave, COVID-19 forced the complete withdrawal of hospital AS activities. In the second wave, antibiotic guidance calibration for COVID-19 patients was implemented in all units, with enhanced stewardship activities in Units 1, 2, and 3 (intervention units). In a controlled before and after study, antimicrobial usage during the three waves of the COVID-19 pandemic was compared to the 12-month prepandemic unit (Unit 4 acted as the control). Antibiotic consumption data were analyzed as the overall consumption, stratified by the World Health Organization AWaRe classification, and expressed as defined-daily-dose (DDD) and days-of-therapy (DOT) per 1000 patient-day (PD).

RESULTS

In the first wave, the overall normalized DOT in units 2-4 significantly exceeded the 2019 level (2019: 587 DOT/1000 PD ± 42.6; Unit 2: 836 ± 77.1; Unit 3: 684 ± 122.3; Unit 4: 872, ± 162.6; p < 0.05). After the introduction of AS activities, consumption decreased in the intervention units to a significantly lower level when compared to 2019 (Unit 1: 498 DOT/1000 PD ± 49; Unit 2: 232 ± 95.7; Unit 3: 382 ± 96.9; p < 0.05). Antimicrobial stewardship activities resulted in a decreased amount of total antibiotic consumption over time and positively affected the watch class and piperacillin-tazobactam use in the involved units.

CONCLUSIONS

During a pandemic, the implementation of calibrated AS activities represents a sound investment in avoiding inappropriate antibiotic therapy.

Causal associations between type 1 diabetes and COVID-19 infection and prognosis: a two-sample Mendelian randomization study

INTRODUCTION

It has been suggested that type 1 diabetes was associated with increased COVID-19 morbidity and mortality. However, their causal relationship is still unclear. Herein, we performed a two-sample Mendelian randomization (MR) to investigate the causal effect of type 1 diabetes on COVID-19 infection and prognosis.

RESEARCH DESIGN AND METHODS

The summary statistics of type 1 diabetes were obtained from two published genome-wide association studies of European population, one as a discovery sample including 15 573 cases and 158 408 controls, and the other data as a replication sample consisting of 5913 cases and 8828 controls. We first performed a two-sample MR analysis to evaluate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. Then, reverse MR analysis was conducted to determine whether reverse causality exists.

RESULTS

MR analysis results showed that the genetically predicted type 1 diabetes was associated with higher risk of severe COVID-19 (OR=1.073, 95% CI: 1.034 to 1.114, p_FDR=1.15×10^-3) and COVID-19 death (OR=1.075, 95% CI: 1.033 to 1.119, p_FDR=1.15×10^-3). Analysis of replication dataset showed similar results, namely a positive association between type 1 diabetes and severe COVID-19 (OR=1.055, 95% CI: 1.029 to 1.081, p_FDR=1.59×10^-4), and a positively correlated association with COVID-19 death (OR=1.053, 95% CI: 1.026 to 1.081, p_FDR=3.50×10^-4). No causal association was observed between type 1 diabetes and COVID-19 positive, hospitalized COVID-19, the time to the end of COVID-19 symptoms in the colchicine treatment group and placebo treatment group. Reverse MR analysis showed no reverse causality.

CONCLUSIONS

Type 1 diabetes had a causal effect on severe COVID-19 and death after COVID-19 infection. Further mechanistic studies are needed to explore the relationship between type 1 diabetes and COVID-19 infection and prognosis.

Mortality, Intensive Care Unit Admission, and Intubation among Hospitalized Patients with COVID-19: A One-Year Retrospective Study in Jordan

COVID-19 is a public health crisis that has caused numerous deaths, necessitated an increased number of hospital admissions, and led to extended inpatient stays. This study aimed to identify the factors associated with COVID-19 mortality, intensive care unit admission, intubation, and length of hospital stay among Jordanian patients. This was a one-year retrospective study of 745 COVID-19 patients admitted to Jordan University Hospital. Data regarding the patients’ demographics, clinical and co-morbid conditions, imaging, laboratory parameters, mortality, intensive care unit admission (ICU), and intubation were collected from their medical records using a coding manual. The data revealed that the overall rates of COVID-19-related mortality, ICU admission, and invasive intubation were 23.0%, 28.3%, and 10.8%, respectively. Chronic kidney disease (CKD), troponin, lactate dehydrogenase (LDH), and O2 saturation <90% were significantly associated with the mortality rate. The variables that were significantly associated with ICU admission were heart failure and the use of remdesivir. However, O2 saturation <90% and gastrointestinal (GI) symptoms were the only variables associated with invasive intubation. The findings of this study suggest that study-related health outcomes can be used to predict the severity of COVID-19, and they can inform future research aiming to identify specific populations who are at a higher risk of COVID-19 complications.

Use and Prognostic Implications of Cardiac Troponin in COVID-19

Myocardial injury is common in patients with COVID-19 and is associated with an adverse prognosis. Cardiac troponin (cTn) is used to detect myocardial injury and assist with risk stratification in this population. SARS-CoV-2 infection can play a role in the pathogenesis of acute myocardial injury due to both direct and indirect damage to the cardiovascular system. Despite the initial concerns about an increased incidence of acute myocardial infarction (MI), most cTn increases are related to chronic myocardial injury due to comorbidities and/or acute nonischemic myocardial injury. This review will discuss the latest findings on this topic.

Prognostic Role of Monocyte Distribution Width, CRP, Procalcitonin and Lactate as Sepsis Biomarkers in Critically Ill COVID-19 Patients

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic and one group of patients has developed a severe form of COVID-19 pneumonia with an urgent need for hospitalization and intensive care unit (ICU) admission. The aim of our study was to evaluate the prognostic role of MDW, CRP, procalcitonin (PCT), and lactate in critically ill COVID-19 patients. The primary outcome of interest is the 28 day mortality of ICU patients with confirmed SARS-CoV-2 infection and sepsis (according to Sepsis 3 criteria with acute change in SOFA score ≥ 2 points). Patients were divided into two groups according to survival on the 28th day after admission to the ICU. Every group was divided into two subgroups (women and men). Nonparametric tests (Mann-Whitney) for variables age, PCT, lactate, and MDW were lower than alpha p < 0.05, so there was a significant difference between survived and deceased patients. The Chi-square test confirmed statistically significant higher values of MDW and lactate in the non-survivor group. We found a significant association between MDW, lactate, procalcitonin, and fatal outcome, higher values were reported in the deceased group.

Prevalence, attitude, knowledge, and risk perception towards COVID-19 in COPD patients associated to biomass exposure

Chronic obstructive pulmonary disease (COPD) patients due to biomass exposure (BE-COPD) could be more affected than COPD due to tobacco smoke (TE-COPD) by the coronavirus disease 2019 (COVID-19) pandemic. The aim of this work was to determine the prevalence of COVID-19 in BE-COPD and TE-COPD and if housing conditions, poor attitude, knowledge, and risk perception towards COVID-19, particularly in BE-COPD women, could represent a risk factor for contagion.An 11% prevalence of COVID-19 was found with no significant difference between COPD groups. The BE-COPD group showed poorer socioeconomic status. No significant differences were found to be associated with SARS-CoV-2 infection regarding housing conditions, poor knowledge, attitude, and risk perception towards COVID-19. Living in urban areas and perceiving risk in COVID-19 were significantly associated with increased adherence to sanitary measures and concern of contagion. Around 40% of all patients showed poor risk perception and adherence to sanitary measures towards COVID-19.

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.

Health trajectories in older patients hospitalized for COVID-19: Results from the GeroCovid multicenter study

BACKGROUND

COVID-19 has disproportionately affected older adults. Yet, healthcare trajectories experienced by older persons hospitalized for COVID-19 have not been investigated. This study aimed at estimating the probabilities of transitions between severity states in older adults admitted in COVID-19 acute wards and at identifying the factors associated with such dynamics.

METHODS

COVID-19 patients aged ≥60 years hospitalized between March and December 2020 were involved in the multicentre GeroCovid project-acute wards substudy. Sociodemographic and health data were obtained from medical records. Clinical states during hospitalization were categorized on a seven-category scale, ranging from hospital discharge to death. Based on the transitions between these states, first, we defined patients' clinical course as positive (only improvements), negative (only worsening), or fluctuating (both improvements and worsening). Second, we focused on the single transitions between clinical states and estimated their probability (through multistage Markov modeling) and associated factors (with proportional intensity models).

RESULTS

Of the 1024 included patients (mean age 78.1 years, 51.1% women), 637 (62.2%) had a positive, 66 (6.4%) had a fluctuating, and 321 (31.3%) had a negative clinical course. Patients with a fluctuating clinical course were younger, had better mobility and cognitive levels, fewer diseases, but a higher prevalence of cardiovascular disease and obesity. Considering the single transitions, the probability that older COVID-19 patients experienced clinical changes was higher within a 10-day timeframe, especially for milder clinical states. Older age, male sex, lower mobility level, multimorbidity, and hospitalization during the COVID-19 first wave (compared with the second one) were associated with an increased probability of progressing towards worse clinical states or with a lower recovery.

CONCLUSION

COVID-19 in older inpatients has a complex and dynamic clinical course. Identifying individuals more likely to experience a fluctuating clinical course and sudden worsening may help organize healthcare resources and clinical management across settings at different care intensity levels.

"Day 25": a temporal indicator of stabilization of mortality risk among COVID-19 patients with high viral load

INTRODUCTION

The relationship between SARS-CoV-2 viral load and hospitalization and mortality among COVID-19 patients has been established. However, the estimation of the duration of time after which the risk of mortality of these patients stops escalating was not extensively discussed earlier. Stratifying patients according to their risk of mortality would optimize healthcare services and costs and reduce mortality.

METHODOLOGY

In this retrospective observational study, hospital records were used to collect data of 519 COVID-19 patients from May through November 2020. Data included the clinical condition of patients, their viral loads, their admission chest computed tomography results (CO-RAD scale), and the duration of their hospitalization. A Kaplan-Meier analysis was constructed to estimate mortality risk concerning viral load.

RESULTS

By the end of the study, 20.42% of patients were deceased. The cumulative mortality was: 36.1% (75/208) among patients with high viral load, 12.6% (28/222) in those with moderate viral load, and 3.4% (3/89) among those with low viral load. Predictors of mortality were: older age [adjusted hazard ratio (aHR) = 1.02, 95% CI: [1.00-1.03], (p = 0.05)], "being female" [aHR = 1.53 with 95% CI: [1.03-2.26], (p = 0.031), "high CO-RAD scale" [aHR = 1.32 (1.06-1.64), p = 0.013], "high viral load" [aHR = 4.59 (2.38-20.92), p = 0.017, ICU admission [aHR = 15.95; 95%CI:7.22-35.20, p < 0.001] and lymphocytosis [aHR = 1.89 45;95%CI:1.04-3.45, p = 0.036]. In the ICU-admitted patients, the median survival was 19 days and mortality stabilized at "day 25". For patients with high viral load, mortality rates stabilized at "day 25 post-admission" after which the risks of mortality did not change until day 40, while patients with low and moderate viral loads reached the peak and stabilized at day "20 post-admission".

CONCLUSIONS

Initial high SARS-CoV-2 viral load might be used as an indicator of a delayed stabilization of mortality risk among COVID-19 patients.

Pulmonary Embolism Severity Index Predicts Adverse Events in Hospitalized COVID-19 Patients: A Retrospective Observational Study

OBJECTIVES

Pulmonary embolism is one of the leading causes of death in patients with COVID-19. Autopsy findings showed that the incidence of thromboembolic events was higher than clinically suspected. In this study, the authors investigated the relationship between pulmonary embolism severity index (PESI) and simplified PESI (sPESI) on admission to the hospital, as well as adverse events in hospitalized COVID-19 patients without clinically documented venous and/or pulmonary embolism. The adverse events investigated were the development of acute respiratory distress syndrome, the need for intensive care unit admission, invasive or noninvasive mechanical ventilation, and in-hospital mortality.

DESIGN

A retrospective and observational study.

SETTING

Two large-volume tertiary hospitals in the same city.

PARTICIPANTS

A total of 720 hospitalized COVID-19 patients with a positive polymerase chain reaction were evaluated.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of the study population, 48.6% (350) were women, and the median age was 66 years (19-96). The overall in-hospital mortality rate was 20.5%. In the multivariate logistic regression analysis, a significant relationship was found between the whole adverse events considered and PESI, as well as sPESI (p < 0.001). According to the results, sPESI ≥2 predicts in-hospital mortality with a sensitivity of 61.4% and specificity of 83.3% (area under the curve = 0.817, 95% confidence interval 0.787-0.845, p < 0.001). Similarly, PESI classes IV and V also were found as independent risk factors for in-hospital mortality (for PESI class IV, odds ratio = 2.81, p < 0.017; for PESI class V, odds ratio = 3.94, p < 0.001).

CONCLUSIONS

PESI and sPESI scoring systems were both found to be associated with adverse events, and they can be used to predict in-hospital mortality in hospitalized COVID-19 patients without documented venous and/or pulmonary embolism.

Blood Transfusion, All-Cause Mortality and Hospitalization Period in COVID-19 Patients: Machine Learning Analysis of National Health Insurance Claims Data

This study presents the most comprehensive machine-learning analysis for the predictors of blood transfusion, all-cause mortality, and hospitalization period in COVID-19 patients. Data came from Korea National Health Insurance claims data with 7943 COVID-19 patients diagnosed during November 2019−May 2020. The dependent variables were all-cause mortality and the hospitalization period, and their 28 independent variables were considered. Random forest variable importance (GINI) was introduced for identifying the main factors of the dependent variables and evaluating their associations with these predictors, including blood transfusion. Based on the results of this study, blood transfusion had a positive association with all-cause mortality. The proportions of red blood cell, platelet, fresh frozen plasma, and cryoprecipitate transfusions were significantly higher in those with death than in those without death (p-values < 0.01). Likewise, the top ten factors of all-cause mortality based on random forest variable importance were the Charlson Comorbidity Index (53.54), age (45.68), socioeconomic status (45.65), red blood cell transfusion (27.08), dementia (19.27), antiplatelet (16.81), gender (14.60), diabetes mellitus (13.00), liver disease (11.19) and platelet transfusion (10.11). The top ten predictors of the hospitalization period were the Charlson Comorbidity Index, socioeconomic status, dementia, age, gender, hemiplegia, antiplatelet, diabetes mellitus, liver disease, and cardiovascular disease. In conclusion, comorbidity, red blood cell transfusion, and platelet transfusion were the major factors of all-cause mortality based on machine learning analysis. The effective management of these predictors is needed in COVID-19 patients.

Development and validation of a nomogram for the early prediction of acute kidney injury in hospitalized COVID-19 patients

Introduction

Acute kidney injury (AKI) is a prevalent complication of coronavirus disease 2019 (COVID-19) and is closely linked with a poorer prognosis. The aim of this study was to develop and validate an easy-to-use and accurate early prediction model for AKI in hospitalized COVID-19 patients.

Methods

Data from 480 COVID-19-positive patients (336 in the training set and 144 in the validation set) were obtained from the public database of the Cancer Imaging Archive (TCIA). The least absolute shrinkage and selection operator (LASSO) regression method and multivariate logistic regression were used to screen potential predictive factors to construct the prediction nomogram. Receiver operating curves (ROC), calibration curves, as well as decision curve analysis (DCA) were adopted to assess the effectiveness of the nomogram. The prognostic value of the nomogram was also examined.

Results

A predictive nomogram for AKI was developed based on arterial oxygen saturation, procalcitonin, C-reactive protein, glomerular filtration rate, and the history of coronary artery disease. In the training set, the nomogram produced an AUC of 0.831 (95% confidence interval [CI]: 0.774-0.889) with a sensitivity of 85.2% and a specificity of 69.9%. In the validation set, the nomogram produced an AUC of 0.810 (95% CI: 0.737-0.871) with a sensitivity of 77.4% and a specificity of 78.8%. The calibration curve shows that the nomogram exhibited excellent calibration and fit in both the training and validation sets. DCA suggested that the nomogram has promising clinical effectiveness. In addition, the median length of stay (m-LS) for patients in the high-risk group for AKI (risk score ≥ 0.122) was 14.0 days (95% CI: 11.3-16.7 days), which was significantly longer than 8.0 days (95% CI: 7.1-8.9 days) for patients in the low-risk group (risk score <0.122) (hazard ratio (HR): 1.98, 95% CI: 1.55-2.53, p < 0.001). Moreover, the mortality rate was also significantly higher in the high-risk group than that in the low-risk group (20.6 vs. 2.9%, odd ratio (OR):8.61, 95%CI: 3.45-21.52).

Conclusions

The newly constructed nomogram model could accurately identify potential COVID-19 patients who may experience AKI during hospitalization at the very beginning of their admission and may be useful for informing clinical prognosis.

Determining the SARS-CoV-2 Anti-Spike Cutoff Level Denoting Neutralizing Activity Using Two Commercial Kits

BACKGROUND

The viral neutralization assay is the gold standard to estimate the level of immunity against SARS-CoV-2. This study analyzes the correlation between the quantitative Anti-SARS-CoV-2 QuantiVac ELISA (IgG) and the NeutraLISA neutralization assay.

METHODS

650 serum samples were tested for both SARS-CoV-2 anti-spike (anti-S) immunoglobulin G (IgG) and neutralizing antibodies (nAbs) using kits by EUROIMMUN, Germany.

RESULTS

There was a significant correlation between levels of anti-S and nAbs (Spearman's rho = 0.913). Among the positive samples for anti-S, 77.0% (n = 345) were positive for nAbs. There was a substantial agreement between anti-S and nAbs (Cohen's kappa coefficient = 0.658; agreement of 83.38%). Considering NeutraLISA as a gold standard, anti-S had a sensitivity of 98.57%, specificity of 65.66%, NPV of 97.5%, and PPV of 77.0%. When the anti-S titer was greater than 18.1 RU/mL (57.9 BAU/mL), nAbs were positive, with a sensitivity of 90.0% and specificity of 91%.

CONCLUSIONS

A titer of SARS-CoV-2 anti-S IgG can be correlated with levels of nAbs.

Efficacy of colchicine in patients with moderate COVID-19: A double-blinded, randomized, placebo-controlled trial

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may cause severe life-threatening diseases called acute respiratory distress syndrome (ARDS) owing to cytokine storms. The mortality rate of COVID-19-related ARDS is as high as 40% to 50%. However, effective treatment for the extensive release of acute inflammatory mediators induced by hyperactive and inappropriate immune responses is very limited. Many anti-inflammatory drugs with variable efficacies have been investigated. Colchicine inhibits interleukin 1 beta (IL-1β) and its subsequent inflammatory cascade by primarily blocking pyrin and nucleotide-binding domain leucine-rich repeat and pyrin domain containing receptor 3 (NLRP3) activation. Therefore, this cheap, widely available, oral drug might provide an added benefit in combating the cytokine storm in COVID-19. Here, we sought to determine whether adding colchicine to other standards of care could be beneficial for moderate COVID-19 pneumonia in terms of the requirement for advanced respiratory support and mortality.

METHODS AND FINDINGS

This blinded placebo-controlled drug trial was conducted at the Dhaka Medical College Hospital, Dhaka, Bangladesh. A total of 300 patients with moderate COVID-19 based on a positive RT-PCR result were enrolled based on strict selection criteria from June 2020 to November 2020. Patients were randomly assigned to either treatment group in a 1:1 ratio. Patients were administered 1.2 mg of colchicine on day 1 followed by daily treatment with 0.6 mg of colchicine for 13 days or placebo along with the standard of care. The primary outcome was the time to clinical deterioration from randomization to two or more points on a seven-category ordinal scale within the 14 days post-randomization. Clinical outcomes were also recorded on day 28. The primary endpoint was met by 9 (6.2%) patients in the placebo group and 4 (2.7%) patients in the colchicine group (P = 0.171), which corresponds to a hazard ratio (95% CI) of 0.44 (0.13-1.43). Additional analysis of the outcomes on day 28 revealed significantly lower clinical deterioration (defined as a decrease by two or more points) in the colchicine group, with a hazard ratio [95%CI] of 0.29 [0.098-0.917], (P = 0.035). Despite a 56% reduction in the need for mechanical ventilation and death with colchicine treatment on day 14, the reduction was not statistically significant. On day 28, colchicine significantly reduced clinical deterioration measured as the need for mechanical ventilation and all-cause mortality.

CONCLUSION

Colchicine was not found to have a significant beneficial effect on reducing mortality and the need for mechanical ventilation. However, a delayed beneficial effect was observed. Therefore, further studies should be conducted to evaluate the late benefits of colchicine.

CLINICAL TRIAL REGISTRATION

Clinical trial registration no: ClinicalTrials.gov Identifier: NCT04527562 https://www.google.com/search?client=firefox-b-d&q=NCT04527562.

Assessing Unique Risk Factors for COVID-19 Complications Among Cancer Patients: A Multi-ethnic Cohort Study

A myriad of organ-specific complications have been observed with COVID-19. While racial/ethnic minorities have been disproportionately burdened by this disease, our understanding of the unique risk factors for complications among a diverse population of cancer patients remains limited. This is a multi-institutional, multi-ethnic cohort study evaluating COVID-19 complications among cancer patients. Patients with an invasive cancer diagnosis and confirmed SARS-CoV-2 infection were identified from March to November 2020. Demographic and clinical data were obtained and a multivariate logistic regression was employed to evaluate the impact of demographic and clinical factors on COVID-19 complications. The study endpoints were evaluated independently and included any complication, sepsis, pulmonary complications and cardiac complications. A total of 303 patients were evaluated, of whom 48% were male, 79% had solid tumors, and 42% were Hispanic/Latinx (Hispanic). Malignant hematologic cancers were associated with a higher risk of sepsis (OR 3.93 (95% CI 1.58–9.81)). Male patients had a higher risk of sepsis (OR 4.42 (95% CI 1.63–11.96)) and cardiac complications (OR 2.02 (95% CI 1.05–3.89)). Hispanic patients had a higher odds of any complication (OR 2.31 (95% CI 1.18–4.51)) and other race was associated with a higher odds of cardiac complications (OR 2.41 (95% CI 1.01–5.73)). Clinically, fever, cough, and ≥2 co-morbidities were independently significantly associated with any complication. This analysis evaluated covariates that can significantly predict a myriad of complications among a multi-ethnic cohort of cancer patients. The conclusions drawn from this analysis elucidate a mechanistic understanding of differential illness severity from COVID-19.

Early predictors of clinical deterioration in a cohort of outpatients with COVID-19 in southern Italy: A multicenter observational study

Data regarding early predictors of clinical deterioration in patients with infection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is still scarce. The aim of the study is to identify early symptoms or signs that may be associated with severe coronavirus disease 2019 (COVID-19). We conducted a multicentre prospective cohort study on a cohort of patients with COVID-19 in home isolation from March 2020 to April 2021. We assessed longitudinal clinical data (fever, dyspnea, need for hospitalization) through video calls at three specific time points: the beginning of symptoms or the day of the first positivity of the nasopharyngeal swab for SARS-CoV-2-RNA (t₀ ), and 3 (t₃ ) and 7 (t₇ ) days after the onset of symptoms. We included 329 patients with COVID-19: 182 (55.3%) males, mean age 53.4 ± 17.4 years, median Charlson comorbidity index (CCI) of 1 (0-3). Of the 329 patients enrolled, 171 (51.98%) had a mild, 81 (24.6%) a moderate, and 77 (23.4%) a severe illness; 151 (45.9%) were hospitalized. Compared to patients with mild COVID-19, moderate and severe patients were older (p < 0.001) and had more comorbidities, especially hypertension (p < 0.001) and cardiovascular diseases (p = 0.01). At t₃ and t₇ , we found a significant higher rate of persisting fever (≥37°C) among patients with moderate (91.4% and 58.0% at t₃ and t₇ , respectively; p < 0.001) and severe outcome (75.3% and 63.6%, respectively; p < 0.001) compared to mild COVID-19 outcome (27.5% and 11.7%, respectively; p < 0.001). Factors independently associated with a more severe outcome were persisting fever at t₃ and t₇ , increasing age, and CCI above 2 points. Persisting fever at t₃ and t₇ seems to be related to a more severe COVID-19. This data may be useful to assess hospitalization criteria and optimize the use of resources in the outpatient setting.

Prehospital characteristics of COVID-19 patients transported by emergency medical service and the predictors of a prehospital sudden deterioration in Addis Ababa, Ethiopia

Background

Severally ill COVID-19 patients may require urgent transport to a specialized facility for advanced care. Prehospital transport is inherently risky; the patient’s health may deteriorate, and potentially fatal situations may arise. Hence, early detection of clinically worsening patients in a prehospital setting may enable selecting the best receiving facility, arranging for swift transportation, and providing the most accurate and timely therapies. The incidence and predictors of abrupt prehospital clinical deterioration among critically ill patients in Ethiopia are relatively limited.

Study objectives

This study was conducted to determine the incidence of sudden clinical deterioration during prehospital transportation and its predictors.

Methods

A prospective cohort study of 591 COVID-19 patients transported by a public EMS in Addis Ababa. For data entry, Epi data V4.2 and SPSS V 25 were used for analysis. To control the effect of confounders, the candidate variables for multivariable analysis were chosen using a p 0.25 inclusion threshold from the bivariate analysis. A statistically significant association was declared at adjusted relative risk (ARR) ≠ 1 with a 95 % confidence interval (CI) and a p value < 0.05 after adjusting for potential confounders.

Results

The incidence of prehospital sudden clinical deterioration in this study was 10.8%. The independent predictors of prehospital sudden clinical deterioration were total prehospital time [ARR 1.03 (95%; CI 1.00–1.06)], queuing delays [ARR 1.03 (95%; CI 1.00–1.06)], initial prehospital respiratory rate [ARR 1.07 (95% CI 1.01–1.13)], and diabetic mellitus [ARR 1.06 (95%; CI 1.01–1.11)].

Conclusion

In the current study, one in every ten COVID-19 patients experienced a clinical deterioration while an EMS provider was present. The factors that determined rapid deterioration were total prehospital time, queueing delays, the initial respiratory rate, and diabetes mellitus. Queueing delays should be managed in order to find a way to decrease overall prehospital time. According to this finding, more research on prehospital intervention and indicators of prehospital clinical deterioration in Ethiopia is warranted.

Severity predictors of COVID-19 in SARS-CoV-2 variant, delta and omicron period; single center study

Background

The outcomes of coronavirus disease 2019 (COVID-19) treatment have improved due to vaccination and the establishment of better treatment regimens. However, the emergence of variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, and the corresponding changes in the characteristics of the disease present new challenges in patient management. This study aimed to analyze predictors of COVID-19 severity caused by the delta and omicron variants of SARS-CoV-2.

Methods

We retrospectively analyzed the data of patients who were admitted for COVID-19 at Yokohama City University Hospital from August 2021 to March 2022.

Results

A total of 141 patients were included in this study. Of these, 91 had moderate COVID-19, whereas 50 had severe COVID-19. There were significant differences in sex, vaccination status, dyspnea, sore throat symptoms, and body mass index (BMI) (p <0.0001, p <0.001, p <0.001, p = 0.02, p< 0.0001, respectively) between the moderate and severe COVID-19 groups. Regarding comorbidities, smoking habit and renal dysfunction were significantly different between the two groups (p = 0.007 and p = 0.01, respectively). Regarding laboratory data, only LDH level on the first day of hospitalization was significantly different between the two groups (p<0.001). Multiple logistic regression analysis revealed that time from the onset of COVID-19 to hospitalization, BMI, smoking habit, and LDH level were significantly different between the two groups (p<0.03, p = 0.039, p = 0.008, p<0.001, respectively). The cut-off value for the time from onset of COVID-19 to hospitalization was four days (sensitivity, 0.73; specificity, 0.70).

Conclusions

Time from the onset of COVID-19 to hospitalization is the most important factor in the prevention of the aggravation of COVID-19 caused by the delta and omicron SARS-CoV-2 variants. Appropriate medical management within four days after the onset of COVID-19 is essential for preventing the progression of COVID-19, especially in patients with smoking habits.

The Association between Admission Procalcitonin Level and The Severity of COVID-19 Pneumonia: A Retrospective Cohort Study

Background and Objectives: An elevated procalcitonin level has classically been linked to bacterial infections. Data on the association between elevated procalcitonin and the outcome of coronavirus disease 2019 (COVID-19) are conflicting. Some linked it to associated bacterial co-infections, while others correlated the elevation with disease severity without coexisting bacterial infections. We aimed to investigate the association between high procalcitonin and the severity of COVID-19. Materials and Methods: Hospitalized patients with confirmed COVID-19 pneumonia were divided into two groups: the normal-procalcitonin group and the high-procalcitonin group (>0.05 ng/mL). Patients with concomitant bacterial infections on admission were excluded. The primary outcomes were the need for intensive care unit (ICU) admission, progression to invasive mechanical ventilation (IMV), and in-hospital 28-day mortality. Results: We included 260 patients in the normal procalcitonin group and 397 patients in the high procalcitonin group. The mean age was 55 years and 49% were females. A higher number of patients in the elevated procalcitonin group required ICU admission (32.7% vs. 16.2%, p < 0.001) and IMV (27.2% vs. 13.5%, p < 0.001). In-hospital mortality was significantly higher in the elevated procalcitonin group (18.9% vs. 8.5%, p < 0.001). After adjusting for other covariates, procalcitonin > 0.05 ng/mL was an independent predictor of progression to IMV (OR, 1.71; 95% CI, 1.08−2.71; p = 0.022), ICU admission (OR, 1.73; 95% CI, 1.13−2.66; p = 0.011), and in-hospital mortality (OR, 1.99; 95% CI, 1.14−3.47; p = 0.015). An elevated procalcitonin level was the strongest predictor of in-hospital mortality. Conclusions: Measurement of procalcitonin can have a prognostic role among COVID-19 patients. The admission procalcitonin level can identify patients at risk of ICU admission, progression to IMV, and in-hospital mortality.

Predictors of Mortality among COVID-19 Patients Admitted to Intensive Care Units: A Single-Center Study in Tehran, Iran

BACKGROUND

Iran was one of the first countries to become an epicenter of the coronavirus disease 2019 (COVID-19) epidemic. However, there is a dearth of data on the outcomes of COVID-19 and predictors of death in intensive care units (ICUs) in Iran. We collected extensive data from patients admitted to the ICUs of the one of the tertiary referral hospitals in Tehran, Iran, to investigate the predictors of ICU mortality.

METHODS

The study population included 290 COVID-19 patients who were consecutively admitted to the ICUs of the Sina hospital from May 5, 2021, to December 6, 2021, a period that included the peak of the epidemic of the delta (δ) variant. Demographic data, history of prior chronic diseases, laboratory data (including markers of inflammation), radiologic data, and medication data were collected.

RESULTS

Of the 290 patients admitted to the ICUs, 187 (64.5%) died and 103 (35.5%) survived. One hundred forty-one (141, 48.6%) were men, and the median age (10th percentile, 90th percentile) was 60 (41, 80). Using logistic regression models, older age, history of hypertension, high levels of inflammatory markers, low oxygen saturation, substantial lung involvement in computed tomography (CT) scans, and gravity of the disease as indicated by the WHO 8-point ordinal scale were primary predictors of mortality at ICU. The use of remdesivir and imatinib was associated with a statistically non-significant reduction in mortality. The use of tocilizumab had almost no effect on mortality.

CONCLUSION

The findings are consistent with and add to the currently existing international literature. The findings may be used to predict risk of mortality from COVID-19 and provide some guidance on potential treatments.

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

OBJECTIVES

To develop a disease stratification model for COVID-19 that updates according to changes in a patient's condition while in hospital to facilitate patient management and resource allocation.

DESIGN

In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression.

SETTING

All data used in this study were obtained from a single UK teaching hospital.

PARTICIPANTS

We developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation.

RESULTS

Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88).

CONCLUSIONS

Our model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient's clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool.

TRIAL REGISTRATION

The study is registered as 'researchregistry5464' on the Research Registry (www.researchregistry.com).

Assembling care: How nurses organise care in uncharted territory and in times of pandemic

This article draws on ethnographic research to conceptualise how nurses mobilise assemblages of caring to organise and deliver COVID care; particularly so by reorganising organisational infrastructures and practices of safe and good care. Based on participatory observations, interviews and nurse diaries, all collected during the early phase of the pandemic, the research shows how the organising work of nurses unfolds at different health-care layers: in the daily care for patients and their families, in the coordination of care in and between hospitals, and at the level of the health-care system. These findings contrast with the dominant pandemic-image of nurses as 'heroes at the bedside', which fosters the classic and microlevel view of nursing and leaves the broader contribution of nurses to the pandemic unaddressed. Theoretically, the study adds to the literature on translational mobilisation and assemblage theory by focussing on the layered and often invisible organising work of nurses in health care.

On-Admission Anemia and Survival Rate in COVID-19 Patients

Anemia often worsens the severity of respiratory illnesses, and few studies have so far elucidated the impact of anemia on COVID-19 infection. This study aimed to evaluate the effect of anemia at admission on the overall survival of COVID-19 patients using accelerated failure time (AFT) models.

This registry-based, single-center retrospective cohort study was conducted in a university hospital in Ilam, the southwest of Iran, between March 2020 and September 2021. AFT models were applied to set the data of 2,441 COVID-19 patients. Performance of AFT models was assessed using Akaike’s information criterion (AIC) and Cox-Snell residual. On-admission anemia was defined as hemoglobin (Hb) concentration <120 g/l in men, <110 g/l in women, and <100 g/l in pregnant women.

The median in-hospital survival times for anemic and non-anemic patients were 27 and 31 days, respectively. Based on the AIC and Cox-Snell residual graph, the Weibull model had the lowest AIC and it was the best fitted model to the data set among AFT models. In the adjusted model, the results of the Weibull model suggested that the anemia (adjusted time ratio: 1.04; 95% CI: 1.00-1.08; p = 0.03) was the accelerated factor for progression to death in COVID-19 patients. Each unit of increase in hemoglobin in COVID-19 patients enhanced the survival rate by 4%.

Anemia is an independent risk factor associated with the risk of mortality from COVID-19 infection. Therefore, healthcare professionals should be more sensitive to the Hb level of COVID-19 patients upon admission.

SARS-CoV-2 pre-exposure prophylaxis: A potential COVID-19 preventive strategy for high-risk populations, including healthcare workers, immunodeficient individuals, and poor vaccine responders

The unprecedented worldwide spread of SARS-CoV-2 has imposed severe challenges on global health care systems. The roll-out and widespread administration of COVID-19 vaccines has been deemed a major milestone in the race to restrict the severity of the infection. Vaccines have as yet not entirely suppressed the relentless progression of the pandemic, due mainly to the emergence of new virus variants, and also secondary to the waning of protective antibody titers over time. Encouragingly, an increasing number of antiviral drugs, such as remdesivir and the newly developed drug combination, Paxlovid® (nirmatrelvir/ritonavir), as well as molnupiravir, have shown significant benefits for COVID-19 patient outcomes. Pre-exposure prophylaxis (PrEP) has been proven to be an effective preventive strategy in high-risk uninfected people exposed to HIV. Building on knowledge from what is already known about the use of PrEP for HIV disease, and from recently gleaned knowledge of antivirals used against COVID-19, we propose that SARS-CoV-2 PrEP, using specific antiviral and adjuvant drugs against SARS-CoV-2, may represent a novel preventive strategy for high-risk populations, including healthcare workers, immunodeficient individuals, and poor vaccine responders. Herein, we critically review the risk factors for severe COVID-19 and discuss PrEP strategies against SARS-CoV-2. In addition, we outline details of candidate anti-SARS-CoV-2 PrEP drugs, thus creating a framework with respect to the development of alternative and/or complementary strategies to prevent COVID-19, and contributing to the global armamentarium that has been developed to limit SARS-CoV-2 infection, severity, and transmission.

Predicting COVID-19 county-level case number trend by combining demographic characteristics and social distancing policies

Objective

Predicting daily trends in the Coronavirus Disease 2019 (COVID-19) case number is important to support individual decisions in taking preventative measures. This study aims to use COVID-19 case number history, demographic characteristics, and social distancing policies both independently/interdependently to predict the daily trend in the rise or fall of county-level cases.

Materials and Methods

We extracted 2093 features (5 from the US COVID-19 case number history, 1824 from the demographic characteristics independently/interdependently, and 264 from the social distancing policies independently/interdependently) for 3142 US counties. Using the top selected 200 features, we built 4 machine learning models: Logistic Regression, Naïve Bayes, Multi-Layer Perceptron, and Random Forest, along with 4 Ensemble methods: Average, Product, Minimum, and Maximum, and compared their performances.

Results

The Ensemble Average method had the highest area-under the receiver operator characteristic curve (AUC) of 0.692. The top ranked features were all interdependent features.

Conclusion

The findings of this study suggest the predictive power of diverse features, especially when combined, in predicting county-level trends of COVID-19 cases and can be helpful to individuals in making their daily decisions. Our results may guide future studies to consider more features interdependently from conventionally distinct data sources in county-level predictive models. Our code is available at: https://doi.org/10.5281/zenodo.6332944.

Usefulness of monocyte distribution width and presepsin for early assessment of disease severity in COVID-19 patients

Early predictors of severe coronavirus disease 2019 (COVID-19) would identify patients requiring intensive care. Recently, the monocyte distribution width (MDW) and presepsin level have been used for the early diagnosis of sepsis. Here, we assessed the utility of MDW and presepsin for the early assessment of COVID-19 severity. Eighty-seven inpatients with confirmed COVID-19 were enrolled and divided into 3 groups by the type of respiratory support: (1) mechanical ventilation or high-flow nasal cannula oxygen therapy (MVHF-OT), (2) conventional oxygen therapy, and (3) no oxygen therapy. We measured the complete blood count; MDW; erythrocyte sedimentation rate; and the levels of presepsin, C-reactive protein, procalcitonin, lactate dehydrogenase, ferritin, Krebs von den Lungen-6 (KL-6), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) neutralizing antibody. Thirteen (14.9%) patients on MVHF-OT exhibited a significantly higher mortality and a longer hospital stay than did the others. The MDW and presepsin levels were significantly elevated on admission, and correlated with COVID-19 severity (both P < .001). Notably, only the MDW correlated significantly with symptoms in the no oxygen therapy group (P < .012). In the first week after admission, the MDW fell and no longer differed among the groups. The KL-6 level did not differ by disease severity at any time. Neutralizing antibodies were detected in 74 patients (91.4%) and the level of neutralization correlated significantly with COVID-19 severity (P < .001). The MDW and presepsin are useful indicators for early assessment of disease severity in COVID-19 patients.

A tool for the management of personal protective equipment during COVID-19 pandemic: Abderrahmen Mami Hospital case study

INTRODUCTION

The COVID-19 pandemic is causing management difficulties in the Tunisian healthcare system inventory management and the supply of Personal Protective Equipment (PPE).

AIM

Calculate the number of PPE needed for MAMI Hospital Ariana (dedicated hospital to COVID patients) to avoid stock-outs.

METHODS

This study proposed a calculation method of the PPE needs for the intensive care and pneumology departments. We developed a mathematical formulation of the number of PPE needed according to the number of visits per medical and other teams, their types, the number of patients, and the validity of each type of PPE.

RESULTS

Considering as input data: the number of visits for the different intervening teams (medical, paramedical, worker or other), the capacity of the different services (number of beds), the average length of stay of patients, the validity duration of an equipment and urgent visits, the developed model generates the required number of PPE (especially surgical masks, FFP2 masks, disposables gowns and coveralls). This allows to calculate the number of personal protective equipment (PPE) needed by the Mami hospital's pharmacy in this period of COVID-19 crisis.

CONCLUSION

Our configurable application allowed us to calculate PPE requirements for the intensive care and pneumology departments and estimate their use duration.

Factors Predicting Progression to Severe COVID-19: A Competing Risk Survival Analysis of 1753 Patients in Community Isolation in Wuhan, China

Most studies of coronavirus disease 2019 (COVID-19) progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units. Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community isolation, who are either asymptomatic or suffer from only mild to moderate symptoms. Using a multivariable competing risk survival analysis, we identify several important predictors of progression to severe COVID-19-rather than to recovery-among patients in the largest community isolation center in Wuhan, China from 6 February 2020 (when the center opened) to 9 March 2020 (when it closed). All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms. We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients (n = 1753) in the isolation center. The potential predictors we investigated were the routine patient data collected upon admission to the isolation center: age, sex, respiratory symptoms, gastrointestinal symptoms, general symptoms, and computed tomography (CT) scan signs. The main outcomes were time to severe COVID-19 or recovery. The factors predicting progression to severe COVID-19 were: male sex (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.04-1.58, p = 0.018), young and old age, dyspnea (HR = 1.58, 95% CI 1.24-2.01, p < 0.001), and CT signs of ground-glass opacity (HR = 1.39, 95% CI 1.04-1.86, p = 0.024) and infiltrating shadows (HR = 1.84, 95% CI 1.22-2.78, p = 0.004). The risk of progression was found to be lower among patients with nausea or vomiting (HR = 0.53, 95% CI 0.30-0.96, p = 0.036) and headaches (HR = 0.54, 95% CI 0.29-0.99, p = 0.046). Our results suggest that several factors that can be easily measured even in resource-poor settings (dyspnea, sex, and age) can be used to identify mild COVID-19 patients who are at increased risk of disease progression. Looking for CT signs of ground-glass opacity and infiltrating shadows may be an affordable option to support triage decisions in resource-rich settings. Common and unspecific symptoms (headaches, nausea, and vomiting) are likely to have led to the identification and subsequent community isolation of COVID-19 patients who were relatively unlikely to deteriorate. Future public health and clinical guidelines should build on this evidence to improve the screening, triage, and monitoring of COVID-19 patients who are asymtomatic or suffer from mild to moderate symptoms.

Expanding the spectrum of the hyperferritinemic syndrome, from pathogenic mechanisms to clinical observations, and therapeutic implications

From the introduction of hyperferritinemic syndrome concept, a growing body of evidence has suggested the role of ferritin as a pathogenic mediator and a relevant clinical feature in the management of patients with inflammatory diseases. From a pathogenic point of view, ferritin may directly stimulate the aberrant immune response by triggering the production of pro-inflammatory mediators in inducing a vicious pathogenic loop and contributing to the occurrence of cytokine storm syndrome. The latter has been recently defined as a clinical picture characterised by elevated circulating cytokine levels, acute systemic inflammatory symptoms, and secondary organ dysfunction beyond that which could be attributed to a normal response to a pathogen It is noteworthy that the occurrence of hyperferritinemia may be correlated with the development of the cytokine storm syndrome in the context of an inflammatory disease. In addition to adult onset Still's disease, macrophage activation syndrome, catastrophic anti-phospholipids syndrome, and septic shock, recent evidence has suggested this association between ferritin and life-threatening evolution in patients with systemic lupus erythematosus, with anti-MDA5 antibodies in the context of poly-dermatomyositis, with severe COVID-19, and with multisystem inflammatory syndrome. The possible underlying common inflammatory mechanisms, associated with hyperferritinemia, may led to the similar clinical picture observed in these patients. Furthermore, similar therapeutic strategies could be suggested inhibiting pro-inflammatory cytokines and improving long-term outcomes in these disorders. Thus, it could be possible to expand the spectrum of the hyperferritinemic syndrome to those diseases burdened by a dreadful clinical picture correlated with hyperferritinemia and the occurrence of the cytokine storm syndrome. In addition, the assessment of ferritin may provide useful information to the physicians in clinical practice to manage these patients. Therefore, ferritin may be considered a relevant clinical feature to be used as biomarker in dissecting the unmet needs in the management of these disorders. Novel evidence may thus support an expansion of the spectrum of the hyperferritinemic syndrome to these diseases burdened by a life-threatening clinical picture correlated with hyperferritinemia and the occurrence of the cytokine storm syndrome.

Age-Dependent Biomarkers for Prediction of In-Hospital Mortality in COVID-19 Patients

Background: Several biomarkers and models have been proposed to predict in-hospital mortality among COVID-19 patients. However, these studies have not examined the association in sub-populations. The present study aimed to identify the association between the two most common inflammatory biomarkers in the emergency department and in-hospital mortality in subgroups of patients. Methods: A historical cohort study of adult patients who were admitted to acute-care hospital between March and December 2020 and had a diagnosis of COVID-19 infection. Data on age, sex, Charlson comorbidity index, white blood cell (WBC) count, C-reactive protein (CRP), and in-hospital mortality were collected. Discrimination ability of each biomarker was observed and the CHAID method was used to identify the association in subgroups of patients. Results: Overall, 762 patients (median age 70.9 years, 59.7% males) were included in the study. Of them, 25.1% died during hospitalization. In-hospital mortality was associated with higher CRP (median 138 mg/L vs. 85 mg/L, p < 0.001), higher WBC count (median 8.5 vs. 6.6 K/µL, p < 0.001), and higher neutrophil-to-lymphocyte ratio (NLR) (median 9.2 vs. 5.4, p < 0.001). The area under the ROC curve was similar among all biomarkers (WBC 0.643, NLR 0.677, CRP 0.646, p > 0.1 for all comparisons). The CHAID method revealed that WBC count was associated with in-hospital mortality in patients aged 43.1−66.0 years (<11 K/µL: 10.1% vs. 11+ K/µL: 27.9%), NLR in patients aged 66.1−80 years (≤8: 15.7%, >8: 43.3%), and CRP in patients aged 80.1+ years (≤47 mg/L: 18.8%, 47.1−149 mg/L: 43.1%, and 149.1+: 71.7% mortality). Conclusions: WBC, NLR, and CRP present similar discrimination abilities. However, each biomarker should be considered as a predictor for in-hospital mortality in different age groups.

Serum Ferritin Levels for the Prediction of Mortality among COVID-19 Patients in an Indonesia’s National Referral Hospital

BACKGROUND: Early identification of clinical outcomes is necessary for risk classification in COVID-19 patients. This study help in evaluating the progression of the disease and the patient’s therapy. AIM: This study aims to determine serum ferritin levels for the prediction of mortality among COVID-19 patients in an Indonesia’s National Referral Hospital. METHODS: A retrospective cohort study was conducted on 142 confirmed positive COVID-19 patients between March 2020 until March 2021 at Dr. M. Djamil General Hospital as a National Referral Hospital in Indonesia. Data obtained from medical record documents and examination of ferritin levels was carried out at the beginning of treatment. The Chi-square test and survival analysis with the log-rank test and Kaplan–Meier methods were used to analyze the data. The SPSS version 15 was used to analyze the data. RESULTS: The serum ferritin cutoff point for COVID-19 patients that can be used to predict poor outcomes was >651.02 ng/mL with sensitivity 79.3%, specificity 80.5%, and accuracy 85.0%. Age, comorbid diabetes mellitus, number of comorbidities, symptoms of trouble breathing, oxygen saturation, severity, and mortality outcome were all associated to ferritin levels >651.02 ng/mL. The Kaplan–Meier curve showed that ferritin levels >651.02 ng/mL were associated for risk of poor outcome COVID-19 patients (HR = 8.84, [95% CI 3.59–21.73]). CONCLUSION: The ferritin cutoff point for predicting poor prognosis in COVID-19 patients was 651.02 ng/mL. However, ferritin serum levels cannot be used as a single predictor in determining the poor outcome of COVID-19.

Comprehensive Review of Cardiovascular Complications of Coronavirus Disease 2019 and Beneficial Treatments

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 and was first reported in December 2019 in Wuhan, China. Since then, it caused a global pandemic with 212,324,054 confirmed cases and 4,440,840 deaths worldwide as of August 22, 2021. The disease spectrum of COVID-19 ranges from asymptomatic subclinical infection to clinical manifestations predominantly affecting the respiratory system. However, it is now evident that COVID-19 is a multiorgan disease with a broad spectrum of manifestations leading to multiple organ injuries including the cardiovascular system. We review studies that have shown that the relationship between cardiovascular diseases and COVID-19 is indeed bidirectional, implicating that preexisting cardiovascular comorbidities increase the morbidity and mortality of COVID-19, and newly emerging cardiac injuries occur in the settings of acute COVID-19 in patients with no preexisting cardiovascular disease. We present the most up-to-date literature summary to explore the incidence of new-onset cardiac complications of coronavirus and their role in predicting the severity of COVID-19. We review the association of elevated troponin with the severity of COVID-19 disease, which includes mild compared to severe disease, in nonintensive care unit compared to intensive care unit patients and in those discharged from the hospital compared to those who die. The role of serum troponin levels in predicting prognosis are compared in survivors and non-survivors. The association between COVID-19 disease and myocarditis, heart failure and coagulopathy are reviewed. Finally, an update on beneficial treatments is discussed.

Diagnostic Accuracy of the RDW for Predicting Death in COVID-19

Background and Objectives: An association between high red blood cell distribution width (RDW) and mortality has been found in several diseases, including infection and sepsis. Some studies have aimed at determining the association of elevated RDW with adverse prognosis in COVID-19, but its usefulness has not been well established. The objective of this study was to determine the accuracy of the RDW, measured at hospital admission and discharge, for predicting death in patients with COVID-19. Materials andMethods: An observational, retrospective, longitudinal, and analytical study was conducted in two different COVID-19 reference centers in the state of Guanajuato, Mexico. A total of 323 patients hospitalized by COVID-19 were included. Results: We found higher RDW levels at the time of hospital admission in the non-survivors group compared to levels in survivors (median = 13.6 vs. 13.0, p < 0.001). Final RDW levels were even higher in the deceased group when compared with those of survivors (median = 14.6 [IQR, 12.67−15.6] vs. 12.9 [IQR, 12.2−13.5], p < 0.001). For patients who died, an RDW > 14.5% was more common at the time of death than for patients who survived at the time of discharge (81 vs. 13 patients, p < 0.001; RR = 2.3, 95% CI 1.89−2.81). Conclusions: The RDW is an accessible and economical parameter that, together with other characteristics of the presentation and evolution of patients with COVID-19, can be helpful in determining the prognosis. An RDW that increases during hospitalization could be a more important mortality predictor than the RDW at hospital admission.

COVID-19 is more dangerous for older people and its severity is increasing: a case-control study

Coronavirus disease 2019 (COVID-19) triggers important changes in routine blood tests. In this retrospective case-control study, biochemical, hematological and inflammatory biomarkers between March 10, 2020, and November 30, 2020 from 3969 COVID-19 patients (3746 in the non-intensive care unit (non-ICU) group and 223 in the ICU group) were analyzed by dividing into three groups as spring, summer and autumn. In the non-ICU group, lymphocyte to monocyte ratio was lower in autumn than the other two seasons and neutrophil to lymphocyte ratio was higher in autumn than the other two seasons. Also, monocyte and platelet were higher in spring than autumn; and eosinophil, hematocrit, hemoglobin, lymphocyte, and red blood cells decreased from spring to autumn. In the non-ICU group, alanine aminotransferase and gamma-glutamyltransferase gradually increased from spring to autumn, while albumin, alkaline phosphatase, calcium, total bilirubin and total protein gradually decreased. Additionally, C-reactive protein was higher in autumn than the other seasons, erythrocyte sedimentation rate was higher in autumn than summer. The changes in routine blood biomarkers in COVID-19 varied from the emergence of the disease until now. Also, the timely changes of blood biomarkers were mostly more negative, indicating that the disease progresses severely. The study was approved by the Erzincan Binali Yildirim University Non-interventional Clinical Trials Ethic Committee (approval No. 86041) on June 21, 2021.

Can Peripheral Perfusion Index (PPI) Predict Disease Severity in COVID-19 Patients in the Emergency Department?

Background Coronavirus disease 2019 (COVID-19) causes significant mortality and morbidity in severe patients.

Objective In this study, we aimed to examine the relationship between COVID-19 disease severity and peripheral perfusion index (PPI).

Patients and Methods This prospective observational study included COVID-19 patients admitted to the tertiary hospital emergency department. Basal clinical and demographic data of the patients and PPI values at the time of admission were recorded. The patients were categorized to severe and nonsevere groups according to clinical severity. The relationship between COVID-19 severity and PPI was examined in comparison with the control group.

Results A total of 324 patients who met the inclusion criteria were analyzed. COVID-19 (+) was detected in 180 of these patients. Ninety-two of the COVID-19 (+) patients were in the severe group, and 88 of them were in the non severe group. Note that 164 COVID-19 (–) patients were in the control group. PPI average was found to be 1.44 ± 1.12 in the severe group, and 3.69 ± 2.51 in the nonsevere group. PPI average was found to be significantly lower in the severe group than the nonsevere group (p< 0.01) As for the nonsevere group and control group, PPI averages were found to be 3.69 ± 2.51 and3.54 ± 2.32, respectively, and a significant difference was determined between the two groups (p< 0.05). PPI COVID-19 severity predicting activity was calculated as area under the curve: 0.833, sensitivity:70.4%, andspecificity:71%(p = 0.025) at 2.2 cutoff value.

Conclusion The results of our study showed that PPI is an easy-to-apply and useful parameter in the emergency department in determining the severity of COVID-19 patients.

NEWS2, 4C Mortality Score, COVID-GRAM, Sequential Organ Failure Assessment Quick scales as outcomes assessment tools for severe COVID-19 (pilot retrospective cohort study)

Aim. To study the predictive ability of the NEWS2, 4C Mortality Score, COVID-GRAM and qSOFA scales in predicting clinical outcomes in patients with severe coronavirus disease 2019 (COVID-19) hospitalized in a multidisciplinary hospital.

Material and methods. The pilot retrospective cohort study used data from 90 patients (52 — intensive care unit subgroup, 38 — general unit subgroup) with a confirmed diagnosis of COVID-19 hospitalized in the O.M. Filatov City Clinical Hospital № 15 (Moscow) from January to March 2021.

Results. The probability of a positive outcome of the disease significantly negatively correlates with the patient’s age (R=-0,514; p=0,0002). The best correlation with the COVID-19 outcome had a 4C Mortality Score (R=0,836; p=0,0001). Logistic regression revealed a significant dependence of the “outcome” and “age” parameters with the greatest accuracy in the form of age subgroups according to the World Health Organization classification with odds ratio (OR) of 4,29 (p=0,0001). As a result of ROC analysis, the best predictive ability of disease outcomes was shown for the 4C Mortality Score (area under curve (AUC)=0,878; 95% confidence interval (CI): 0,782- 0,975 (p=0,00001)) and COVID-GRAM (AUC=0,807; 95% CI: 0,720- 0,895 (p=0,00001)); taking into account the division of patients into age subgroups, optimal predictive tools were obtained: in subgroups 18-44 years old and 45-59 years old  — the 4С Mortality Score (AUC=0,892, 95% CI: 0,762-0,980 (p=0,002) and AUC=0,853, 95% CI: 0,784-0,961 (p=0,0014), respectively); in the subgroup 60-74 years old — the COVID-GRAM (AUC=0,833, 95% CI: 0,682-0,990 (p=0,038)); in subgroups 75-90 years and >90 years  — NEWS2 (AUC=0,958, 95% CI: 0,807-1,0 (p=0,002) and AUC=0,818, 95% CI: 0,713-0,996 (p=0,006), respectively). ROC analysis showed that the age of 70 years is the threshold value, above which the probability of an unfavorable COVID-19 outcome increases significantly (OR=11,63; 95% CI: 9,72- 12,06 (p=0,0052)).

Conclusion. The pilot study showed the significance of predicting the hospitalization outcome of patients with severe COVID-19. The 4C Mortality Score and COVID-GRAM scales had the best predictive accuracy. The specificity and sensitivity of the scores depended on the age of a patient. The age of 70 years was the threshold value at which the risk of an adverse outcome increased significantly. Based on the data obtained, it is planned to study the problem of predicting the disease course, taking into account the severity of COVID-19.

Early procalcitonin to predict mortality in critically ill COVID-19 patients: a multicentric cohort study

BACKGROUND

High levels of procalcitonin (PCT) have been associated with a higher risk of mortality in COVID-19 patients. We explored the prognostic role of early PCT assessment in critically ill COVID-19 patients and whether PCT predictive performance would be influenced by immunosuppression.

METHODS

Retrospective multicentric analysis of prospective collected data in COVID-19 patients consecutively admitted to 36 intensive care units (ICUs) in Spain and Andorra from March to June 2020. Adult (>18 years) patients with confirmed COVID-19 and available PCT values (<72 hours from ICU admission) were included. Patients were considered as "No Immunosuppression" (NI), "Chronic Immunosuppression" (CI) and "Acute Immunosuppression" (AIT if only tocilizumab; AIS if only steroids, AITS if both). The primary outcome was the ability of PCT to predict ICU mortality.

RESULTS

Of the 1079 eligible patients, 777 patients were included in the analysis. Mortality occurred in 227 (28%) patients. In the NI group 144 (19%) patients were included, 67 (9%) in the CI group, 66 (8%) in the AIT group, 262 (34%) in the AIS group and 238 (31%) in the AITS group; PCT was significantly higher in non-survivors when compared with survivors (0.64 [0.17-1.44] vs. 0.23 [0.11-0.60] ng/mL; p<0.01); however, in the multivariable analysis, PCT values was not independently associated with ICU mortality. PCT values and ICU mortality were significantly higher in patients in the NI and CI groups.

CONCLUSIONS

PCT values are not independent predictors of ICU mortality in COVID-19 patients. Acute immunosuppression significantly reduced PCT values, although not influencing its predictive value.

Assessment of factors affecting the probability of hospitalization of COVID-19 patients with concomitant pathology and development of a prognostic model based on them

Introduction. Currently, a significant number of patients with COVID-19 require inpatient treatment. At the same time, predictors of hospitalization are still stable, including in persons with concomitant pathology. Aim. Assessment of factors affecting the probability of hospitalization of COVID-19 patients with concomitant pathology and the development of a prognostic model based on them. Materials and methods. An observational retrospective cohort study of 74 314 patients with COVID-19 with various comorbidities was carried out in the period from March to November 2020 in the Russian Federation. Results. Based on 16 factors, including age, gender, place of diagnosis, fever, rhinitis, loss of taste, shortness of breath, concomitant diseases of the cardiovascular, bronchopulmonary system, oncological, endocrine diseases in patients included in the study, a prognostic model was developed. The need for inpatient treatment of patients with COVID-19 and comorbidities was determined. Conclusion. The constructed predictive model has demonstrated sufficient efficiency to assess the likelihood of hospitalization of patients with COVID-19 by medical specialists.

Predicting the risk of severe COVID-19 outcomes in primary care: development and validation of a vulnerability index for equitable allocation of effective vaccines

BACKGROUND

General practitioners (GPs) need a valid, user-friendly tool to identify patients most vulnerable to COVID-19, especially in the hypothesis of a booster vaccine dose. The aim of this study was to develop and validate a GP-friendly prognostic index able to forecast severe COVID-19 outcomes in primary care. Indeed, no such prognostic score is as yet available in Italy.

RESEARCH DESIGN AND METHODS

In this retrospective cohort study, a representative sample of 47,868 Italian adults were followed up for 129,000 person-months. The study outcome was COVID-19-related hospitalization and/or death. Candidate predictors were chosen on the basis of systematic evidence and current recommendations. The model was calibrated by using Cox regression. Both internal and external validations were performed.

RESULTS

Age, sex and several clinical characteristics were significantly associated with severe outcomes. The final multivariable model explained 60% (95%CI 58-63%) of variance for COVID-19-related hospitalizations and/or deaths. The area under the receiver-operator curve (AUC) was 84% (95% CI: 83-85%). On applying the index to an external cohort, the AUC was 94% (95% CI: 93-95%).

CONCLUSIONS

This index is a reliable prognostic tool that can help GPs to prioritize their patients for preventive and therapeutic interventions.

Host Factor Interaction Networks Identified by Integrative Bioinformatics Analysis Reveals Therapeutic Implications in COPD Patients With COVID-19

Background: The COVID-19 pandemic poses an imminent threat to humanity, especially for those who have comorbidities. Evidence of COVID-19 and COPD comorbidities is accumulating. However, data revealing the molecular mechanism of COVID-19 and COPD comorbid diseases is limited. Methods: We got COVID-19/COPD -related genes from different databases by restricted screening conditions (top500), respectively, and then supplemented with COVID-19/COPD-associated genes (FDR<0.05, |LogFC|≥1) from clinical sample data sets. By taking the intersection, 42 co-morbid host factors for COVID-19 and COPD were finally obtained. On the basis of shared host factors, we conducted a series of bioinformatics analysis, including protein-protein interaction analysis, gene ontology and pathway enrichment analysis, transcription factor-gene interaction network analysis, gene-microRNA co-regulatory network analysis, tissue-specific enrichment analysis and candidate drug prediction. Results: We revealed the comorbidity mechanism of COVID-19 and COPD from the perspective of host factor interaction, obtained the top ten gene and 3 modules with different biological functions. Furthermore, we have obtained the signaling pathways and concluded that dexamethasone, estradiol, progesterone, and nitric oxide shows effective interventions. Conclusion: This study revealed host factor interaction networks for COVID-19 and COPD, which could confirm the potential drugs for treating the comorbidity, ultimately, enhancing the management of the respiratory disease.

Can chest radiographic findings determine disease severity in Covid-19-positive patients? A single-center study

Objectives

The purpose of this study was to describe the severity of the radiographic findings of COVID-19 over time and to assess their correlation with the duration of symptoms prior to admission, CT scores, and disease severity.

Methods

A retrospective analysis of patients with COVID-19 confirmed by reverse transcription polymerase chain reaction (RT-PCR) and CXR who were admitted at the university hospital was performed between March 25 and 30 April 2020. Baseline and serial CXRs were reviewed, along with onset and disease time courses. Correlations between CXR scores and CT scores, durations of symptoms and disease severity were evaluated; and also between regression times and disease severity.

Results

Of 208 total patients, there were 33 mild (15.9%), 103 moderate (49.5%), and 72 severe-critical (34.6%) cases. The most frequent symptoms were fever, cough, fatigue, and dyspnea. Dyspnea was more frequent in patients with severe and critical disease ( p < 0.001). The duration of symptoms experienced prior to admission was longer in patients with severe and critical disease than in moderate cases ( p < 0.05). Abnormalities on CXR were present on admission in 83.2% patients, with reticulations being the most common finding. CXR scores correlated with duration of symptoms prior to admission and CT scores ( p < 0.05 and p < 0.001, respectively). The median radiographic score of the severe-critical-type group was significantly higher than the moderate type ( p <  0.001) and regression time correlated with disease severity ( p < 0.001).

Conclusion

Our study showed that despite the limitations, CXR remains a very important tool for diagnosing and managing patients with COVID-19.