Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China

Patients' treatment limitations as predictive factor for mortality in COVID-19: results from hospitalized patients of a hotspot region for SARS-CoV-2 infections

Background

In hospitalized patients with SARS-CoV-2 infection, outcomes markedly differ between locations, regions and countries. One possible cause for these variations in outcomes could be differences in patient treatment limitations (PTL) in different locations. We thus studied their role as predictor for mortality in a population of hospitalized patients with COVID-19.

Methods

In a region with high incidence of SARS-CoV-2 infection, adult hospitalized patients with PCR-confirmed SARS-CoV-2 infection were prospectively registered and characterized regarding sex, age, vital signs, symptoms, comorbidities (including Charlson comorbidity index (CCI)), transcutaneous pulse oximetry (SpO₂) and laboratory values upon admission, as well as ICU-stay including respiratory support, discharge, transfer to another hospital and death. PTL assessed by routine clinical procedures comprised the acceptance of ICU-therapy, orotracheal intubation and/or cardiopulmonary resuscitation.

Results

Among 526 patients included (median [quartiles] age 73 [57; 82] years, 47% female), 226 (43%) had at least one treatment limitation. Each limitation was associated with age, dementia and eGFR (p < 0.05 each), that regarding resuscitation additionally with Charlson comorbidity index (CCI) and cardiac disease. Overall mortality was 27% and lower (p < 0.001) in patients without treatment limitation (12%) compared to those with any limitation (47%). In univariate analyses, age and comorbidities (diabetes, cardiac, cerebrovascular, renal, hepatic, malignant disease, dementia), SpO₂, hemoglobin, leucocyte numbers, estimated glomerular filtration rate (eGFR), C-reactive protein (CRP), Interleukin-6 and LDH were predictive for death (p < 0.05 each). In multivariate analyses, the presence of any treatment limitation was an independent predictor of death (OR 4.34, 95%-CI 2.10–12.30; p = 0.001), in addition to CCI, eGFR < 55 ml/min, neutrophil number > 5 G/l, CRP > 7 mg/l and SpO₂ < 93% (p < 0.05 each).

Conclusion

In hospitalized patients with SARS-CoV-2, the percentage of patients with treatment limitations was high. PTL were linked to age, comorbidities and eGFR assessed upon admission and strong, independent risk factors for mortality. These findings might be useful for further understanding of COVID-19 mortality and its regional variations.

Clinical trial registration ClinicalTrials.gov Identifier: NCT04344171

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01756-2.

A predictive score for progression of COVID-19 in hospitalized persons: a cohort study

Accurate prediction of the risk of progression of coronavirus disease (COVID-19) is needed at the time of hospitalization. Logistic regression analyses are used to interrogate clinical and laboratory co-variates from every hospital admission from an area of 2 million people with sporadic cases. From a total of 98 subjects, 3 were severe COVID-19 on admission. From the remaining subjects, 24 developed severe/critical symptoms. The predictive model includes four co-variates: age (>60 years; odds ratio [OR] = 12 [2.3, 62]); blood oxygen saturation (<97%; OR = 10.4 [2.04, 53]); C-reactive protein (>5.75 mg/L; OR = 9.3 [1.5, 58]); and prothrombin time (>12.3 s; OR = 6.7 [1.1, 41]). Cutoff value is two factors, and the sensitivity and specificity are 96% and 78% respectively. The area under the receiver-operator characteristic curve is 0.937. This model is suitable in predicting which unselected newly hospitalized persons are at-risk to develop severe/critical COVID-19.

Epidemiological characteristics and mortality risk factors among COVID-19 patients in Ardabil, Northwest of Iran

BACKGROUND

Coronavirus disease highly contagious, is prevalent in all age and sex groups infecting the respiratory system. The present study seeks to investigate the epidemiology and effective factors in mortality of patients with COVID-19 in Ardabil province, northwestern Iran.

METHODS

In a retrospective study, the hospitalized patients with laboratory-diagnosed COVID-19 between February to August 2020 were enrolled. The data registration portal was designated according to Iranian Ministry of Health and Medical Education guidelines. In this portal, demographic information, clinical presentation, laboratory and imaging data were registered for patients in all hospitals in the same format. The Hosmer-Lemeshow strategy was used for variable selection in a multiple model.

RESULTS

Of the patients involved 2812(50.3%) were male and 150 (2.7%) had contact with a confirmed case of COVID-19 in the last 14 days. Pre-existing comorbidity was reported in 1310 (23.4%) patients. Of all patients, 477(8.5%) died due to COVID-19. the result of the multiple logistic regression model indicated that after adjusting for other factors, higher age (OR = 3.11), fever or chills (OR = 1.61), shortness of breath (OR = 1.82), fatigue (OR = 0.71), headache (OR = 0.64), runny nose (OR = 1.54), Skeletal muscle pain (OR = 1.53), hospitalization (OR = 5.66), and hospitalization in ICU (OR = 5.12) were associated with death.

CONCLUSIONS

Hospitalization had the strongest effect on mortality followed by hospitalization in ICU, and higher age. This study showed that having some extra-pulmonary symptoms in contrast with pulmonary symptoms can predict as good prognostic factors.

COVID-19 and stroke: A review

COVID-19 patients have presented with a wide range of neurological disorders, among which stroke is the most devastating. We have reviewed current studies, case series, and case reports with a focus on COVID-19 patients complicated with stroke, and presented the current understanding of stroke in this patient population. As evidenced by increased D-dimer, fibrinogen, factor VIII and von Willebrand factor, SARS-CoV-2 infection induces coagulopathy, disrupts endothelial function, and promotes hypercoagulative state. Collectively, it predisposes patients to cerebrovascular events. Additionally, due to the unprecedented strain on the healthcare system, stroke care has been inevitably compromised. The underlying mechanism between COVID-19 and stroke warrants further study, so does the development of an effective therapeutic or preventive intervention.

Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients

The COVID-19 pandemic has prompted an international effort to develop and repurpose medications and procedures to effectively combat the disease. Several groups have focused on the potential treatment utility of angiotensin-converting-enzyme inhibitors (ACEIs) and angiotensin-receptor blockers (ARBs) for hypertensive COVID-19 patients, with inconclusive evidence thus far. We couple electronic medical record (EMR) and registry data of 3,643 patients from Spain, Italy, Germany, Ecuador, and the US with a machine learning framework to personalize the prescription of ACEIs and ARBs to hypertensive COVID-19 patients. Our approach leverages clinical and demographic information to identify hospitalized individuals whose probability of mortality or morbidity can decrease by prescribing this class of drugs. In particular, the algorithm proposes increasing ACEI/ARBs prescriptions for patients with cardiovascular disease and decreasing prescriptions for those with low oxygen saturation at admission. We show that personalized recommendations can improve patient outcomes by 1.0% compared to the standard of care when applied to external populations. We develop an interactive interface for our algorithm, providing physicians with an actionable tool to easily assess treatment alternatives and inform clinical decisions. This work offers the first personalized recommendation system to accurately evaluate the efficacy and risks of prescribing ACEIs and ARBs to hypertensive COVID-19 patients.

Glycemic control before admission is an important determinant of prognosis in patients with coronavirus disease 2019

AIMS/INTRODUCTION

This study aimed to explore the association between glycemic control before admission with severity and mortality of coronavirus disease 2019, and tried to reveal the mechanism.

MATERIALS AND METHODS

A total of 77 inpatients were grouped into sufficient control group (glycated hemoglobin [HbA1c] <6.5%, n = 49) and insufficient control group (HbA1c ≥6.5%, n = 28). Regression models were used to analyze the clinical data.

RESULTS

Compared with patients with HbA1c <6.5, patients with HbA1c ≥6.5 showed higher heart rate (101 vs 89 b.p.m., P = 0.012), lower percutaneous oxygen saturation (93 vs 97%, P = 0.001), higher levels of multiple indicators of inflammation, such as white blood cell count (7.9 vs 5.9 × 109 /L, P = 0.019), neutrophil count (6.5 vs 4.1 × 109 /L, P = 0.001), high-sensitivity C-reactive protein (52 vs 30 mg/L, P = 0.025) and serum ferritin (1,287 vs 716 μg/L, P = 0.023), as well as lower levels of lymphocyte count (0.7 vs 0.8 × 109 /L, P = 0.049) at hospital admission. Thus, patients with HbA1c ≥6.5 were more likely to develop secondary respiratory infections (25 [89%] vs 33 [67%], P = 0.032) and acute respiratory distress syndrome (17 [61%] vs 14 [29%], P = 0.006) than patients with HbA1c <6.5, resulting in a higher proportion of critically ill patients (19 [68%] vs 18 [37%], P = 0.009) and non-survivors (13 [46%] vs 11 [22%], P = 0.029). After adjustment for potential risk factors, HbA1c was independently associated with in-hospital death.

CONCLUSION

HbA1c was an independent risk factor for poor outcomes in coronavirus disease 2019 patients. Severe pulmonary infection and consequent acute respiratory distress syndrome might be the primary causes of death in insufficient glycemic control patients.

Discharge Practices for COVID-19 Patients: Rapid Review of Published Guidance and Synthesis of Documents and Practices at 22 US Academic Medical Centers

BACKGROUND

There are currently no evidence-based guidelines that provide standardized criteria for the discharge of COVID-19 patients from the hospital.

OBJECTIVE

To address this gap in practice guidance, we reviewed published guidance and collected discharge protocols and procedures to identify and synthesize common practices.

DESIGN

Rapid review of existing guidance from US and non-US public health organizations and professional societies and qualitative review using content analysis of discharge documents collected from a national sample of US academic medical centers with follow-up survey of hospital leaders SETTING AND PARTICIPANTS: We reviewed 65 websites for major professional societies and public health organizations and collected documents from 22 Academic Medical Centers (AMCs) in the US participating in the HOspital MEdicine Reengineering Network (HOMERuN).

RESULTS

We synthesized data regarding common practices around 5 major domains: (1) isolation and transmission mitigation; (2) criteria for discharge to non-home settings including skilled nursing, assisted living, or homeless; (3) clinical criteria for discharge including oxygenation levels, fever, and symptom improvement; (4) social support and ability to perform activities of daily living; (5) post-discharge instructions, monitoring, and follow-up.

LIMITATIONS

We used streamlined methods for rapid review of published guidance and collected discharge documents only in a focused sample of US academic medical centers.

CONCLUSION

AMCs studied showed strong consensus on discharge practices for COVID-19 patients related to post-discharge isolation and transmission mitigation for home and non-home settings. There was high concordance among AMCs that discharge practices should address COVID-19-specific factors in clinical, functional, and post-discharge monitoring domains although definitions and details varied.

COVID-19 Management in Clinical Dental Care. Part I: Epidemiology, Public Health Implications, and Risk Assessment

Coronavirus disease 2019 (COVID-19), a viral disease declared a pandemic by the World Health Organization (WHO) in March 2020, has posed great changes to many sectors of society across the globe. Its virulence and rapid dissemination have forced the adoption of strict public health measures in most countries, which, collaterally, resulted in economic hardship. This article is the first in a series of 3 that aims to contextualise the clinical impact of COVID-19 for the dental profession. It presents the epidemiological conditions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), namely, its modes of transmission, incubation, and transmissibility period, signs and symptoms, immunity, immunological tests, and risk management in dental care. Individuals in dental care settings are exposed to 3 potential sources of contamination with COVID-19: close interpersonal contacts (<1 m), contact with saliva, and aerosol-generating dental procedures. Thus, a risk management model is propsoed for the provision of dental care depending on the epidemiological setting, the patient's characteristics, and the type of procedures performed in the office environment. Although herd immunity seems difficult to achieve, a significant number of people has been infected throughout the first 9 months of the pandemic and vaccination has been implemented, which means that there will be a growing number of presumable "immune" individuals that might not require many precautions that differ from those before COVID-19. In conclusion, dental care professionals may manage their risk by following the proposed model, which considers the recommendations by local and international health authorities, thus providing a safe environment for both professionals and patients.

SARS-CoV-2 Causes Acute Kidney Injury by Directly Infecting Renal Tubules

Acute kidney injury (AKI) is one of the most prevalent complications among hospitalized coronavirus disease 2019 (COVID-19) patients. Here, we aim to investigate the causes, risk factors, and outcomes of AKI in COVID-19 patients. We found that angiotensin-converting enzyme II (ACE2) and transmembrane protease serine 2 (TMPRSS2) were mainly expressed by different cell types in the human kidney. However, in autopsy kidney samples, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleoprotein was detected in ACE2⁺ or TMPRSS2⁺ renal tubular cells, whereas the RNAscope^® Assay targeting the SARS-CoV-2 Spike gene was positive mainly in the distal tubular cells and seldom in the proximal tubular cells. In addition, the TMPRSS2 and kidney injury marker protein levels were significantly higher in the SARS-CoV-2-infected renal distal tubular cells, indicating that SARS-CoV-2-mediated AKI mainly occurred in the renal distal tubular cells. Subsequently, a cohort analysis of 722 patients with COVID-19 demonstrated that AKI was significantly related to more serious disease stages and poor prognosis of COVID-19 patients. The progressive increase of blood urea nitrogen (BUN) level during the course of COVID-19 suggests that the patient’s condition is aggravated. These results will greatly increase the current understanding of SARS-CoV-2 infection.

The differential demographic pattern of coronavirus disease 2019 fatality outside Hubei and from six hospitals in Hubei, China: a descriptive analysis

BACKGROUND

The coronavirus disease 2019 (COVID-19) epidemic has been largely controlled in China, to the point where case fatality rate (CFR) data can be comprehensively evaluated.

METHODS

Data on confirmed patients, with a final outcome reported as of 29 March 2020, were obtained from official websites and other internet sources. The hospitalized CFR (HCFR) was estimated, epidemiological features described, and risk factors for a fatal outcome identified.

RESULTS

The overall HCFR in China was estimated to be 4.6% (95% CI 4.5-4.8%, P < 0.001). It increased with age and was higher in males than females. Although the highest HCFR observed was in male patients ≥70 years old, the relative risks for death outcome by sex varied across age groups, and the greatest HCFR risk ratio for males vs. females was shown in the age group of 50-60 years, higher than age groups of 60-70 and ≥ 70 years. Differential age/sex HCFR patterns across geographical regions were found: the age effect on HCFR was greater in other provinces outside Hubei than in Wuhan. An effect of longer interval from symptom onset to admission was only observed outside Hubei, not in Wuhan. By performing multivariate analysis and survival analysis, the higher HCFR was associated with older age (both P < 0.001), and male sex (both P < 0.001). Only in regions outside Hubei, longer interval from symptom onset to admission, were associated with higher HCFR.

CONCLUSIONS

This up-to-date and comprehensive picture of COVID-19 HCFR and its drivers will help healthcare givers target limited medical resources to patients with high risk of fatality.

Clinical Significance of Plasma D-Dimer in COVID-19 Mortality

It is not clear whether D-dimer can be an independent predictor of coronavirus disease 2019 (COVID-19) mortality, and the cut-off of D-dimer for clinical use remains to be determined. Therefore, a comprehensive analysis is still necessary to illuminate the clinical significance of plasma D-dimer in COVID-19 mortality. We searched PubMed, Embase, Cochrane Library, and Scopus databases until November 2020. STATA software was used for all the statistical analyses. The identifier of systematic review registration was PROSPERO CRD42020220927. A total of 66 studies involving 40,614 COVID-19 patients were included in our meta-analysis. Pooled data showed that patients in high D-dimer group had poor prognosis than those in low D-dimer group [OR = 4.52, 95% CI = (3.61, 5.67), P < 0.001; HR = 2.81, 95% CI = (1.85, 4.27), P < 0.001]. Sensitivity analysis, pooled data based on different effect models and the Duval and Tweedie trim-and-fill method did not change the conclusions. Subgroup analyses stratified by different countries, cutoffs, sample size, study design, and analysis of OR/HR still keep consistent conclusions. D-dimer was identified as an independent predictor for COVID-19 mortality. A series of values including 0.5 μg/ml, 1 μg/ml, and 2 μg/ml could be determined as cutoff of D-dimer for clinic use. Measurement and monitoring of D-dimer might assist clinicians to take immediate medical actions and predict the prognosis of COVID-19.

Severity of Sleep Apnea and COVID-19 Illness

Objective

To characterize the relationship between severity of sleep apnea and coronavirus disease 2019 (COVID-19) hospitalization and severe illness.

Study Design

Retrospective cohort study.

Setting

Montefiore Health System in the Bronx, New York.

Methods

The data set consisted of adult patients with an active diagnosis of obstructive sleep apnea in the past 2 years and a positive severe acute respiratory syndrome coronavirus 2 quantitative polymerase chain reaction test at our institution between March 16, 2020, and May 26, 2020. Sleep apnea severity and continuous positive airway pressure compliance data were abstracted from the electronic medical record. The International Classification of Diseases, 10th Revision was used to classify comorbidities.

Results

A total of 461 patients with sleep apnea tested positive for COVID-19, of whom 149 were excluded for missing data in the electronic medical record. Patients with moderate and severe sleep apnea had higher rates of COVID-19 hospitalization compared to those with mild sleep apnea (P = .003). This association was reduced when accounting for confounders, most notably the Charlson Comorbidity Index, a measure of comorbid illness burden. Moderate and severe sleep apnea were associated with increased Charlson Comorbidity Indices, compared to mild sleep apnea (P = .01). Sleep apnea severity was not associated with a composite outcome of mechanical ventilation, intensive care unit admission, and death.

Conclusion

Sleep apnea severity was associated with the Charlson Comorbidity Index and may be a risk factor for COVID-19 hospitalization. We found no evidence that sleep apnea severity among hospitalized patients was associated with a composite outcome of mechanical ventilation, intensive care unit admission, and death.

Simple Parameters from Complete Blood Count Predict In-Hospital Mortality in COVID-19

Introduction

The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions.

Materials and Methods

In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients (F 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded.

Results

At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) (χ² 10.4; p < 0.001), neutrophil-to-lymphocyte (NL) ratio (χ² 7.6; p = 0.006), and platelet count (χ² 5.39; p = 0.02), along with age (χ² 87.6; p < 0.001) and gender (χ² 17.3; p < 0.001), accurately predicted in-hospital mortality. Hemoglobin levels were not associated with mortality. We also identified the best cut-off for mortality prediction: a NL ratio > 4.68 was characterized by an odds ratio for in-hospital mortality (OR) = 3.40 (2.40-4.82), while the OR for a RDW > 13.7% was 4.09 (2.87-5.83); a platelet count > 166,000/μL was, conversely, protective (OR: 0.45 (0.32-0.63)).

Conclusion

Our findings arise the opportunity of stratifying COVID-19 severity according to simple lab parameters, which may drive clinical decisions about monitoring and treatment.

Clinical course and management of 73 hospitalized moderate patients with COVID-19 outside Wuhan

Moderate cases account for the majority in hospitalized patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and can also progress to severe/critical condition. Here, we investigated the clinical course and management of hospitalized moderate SARS-CoV-2 patients. The medical records and follow-up data were analyzed from the SARS-CoV-2 patients outside Wuhan. A total of 73 moderate patients (38 men, 35 women) were included, with median age of 47.0 (38.5–57.5) years. Among them, only one patient (1.4%) died using active treatment to improve symptoms. The median duration of the four main symptoms cough, fever, chest tightness, and fatigue were 11.0, 8.0, 11.0, and 7.0 days, respectively; the median duration of the positive nucleic acid test (NAT) results for SARS-CoV-2 was 16.5 days; the median hospitalization time was 25.0 days in 72 moderate survivors. The duration of cough and fever was positively correlated with the duration of the positive NAT results. On admission, 50% had lymphopenia; less than 30% had abnormal blood biochemistry findings involving hyperglycemia, liver function and myocardial enzymes. At discharge, the laboratory indexes were substantially improved. Two weeks after discharge, 5.6% survivors experienced a recurrence of the positive NAT results. Moderate SARS-CoV-2 patients have a good prognosis by the active treatment. A small proportion of the recovered moderate patients still may be virus carriers and require an additional round of viral detection.

The value of computed tomography in assessing the risk of death in COVID-19 patients presenting to the emergency room

Objective

The aims of this study were to develop a multiparametric prognostic model for death in COVID-19 patients and to assess the incremental value of CT disease extension over clinical parameters.

Methods

Consecutive patients who presented to all five of the emergency rooms of the Reggio Emilia province between February 27 and March 23, 2020, for suspected COVID-19, underwent chest CT, and had a positive swab within 10 days were included in this retrospective study. Age, sex, comorbidities, days from symptom onset, and laboratory data were retrieved from institutional information systems. CT disease extension was visually graded as < 20%, 20–39%, 40–59%, or ≥ 60%. The association between clinical and CT variables with death was estimated with univariable and multivariable Cox proportional hazards models; model performance was assessed using k-fold cross-validation for the area under the ROC curve (cvAUC).

Results

Of the 866 included patients (median age 59.8, women 39.2%), 93 (10.74%) died. Clinical variables significantly associated with death in multivariable model were age, male sex, HDL cholesterol, dementia, heart failure, vascular diseases, time from symptom onset, neutrophils, LDH, and oxygen saturation level. CT disease extension was also independently associated with death (HR = 7.56, 95% CI = 3.49; 16.38 for ≥ 60% extension). cvAUCs were 0.927 (bootstrap bias-corrected 95% CI = 0.899–0.947) for the clinical model and 0.936 (bootstrap bias-corrected 95% CI = 0.912–0.953) when adding CT extension.

Conclusions

A prognostic model based on clinical variables is highly accurate in predicting death in COVID-19 patients. Adding CT disease extension to the model scarcely improves its accuracy.

Key Points

• Early identification of COVID-19 patients at higher risk of disease progression and death is crucial; the role of CT scan in defining prognosis is unclear.

• A clinical model based on age, sex, comorbidities, days from symptom onset, and laboratory results was highly accurate in predicting death in COVID-19 patients presenting to the emergency room.

• Disease extension assessed with CT was independently associated with death when added to the model but did not produce a valuable increase in accuracy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07993-9.

Impact of Kidney Failure on the Severity of COVID-19

Background: Patients with kidney failure are at an increased risk of progression to a severe form of coronavirus disease 2019 (COVID-19) with high mortality. The current analysis was aimed to assess the impact of renal failure on the severity of COVID-19 and identify the risk factors of the fatal outcome in this population. Methods: The analysis included patients from the SARSTer database, a national real-world study evaluating treatment for COVID-19 in 30 Polish centers. Data were completed retrospectively and submitted online. Results: A total of 2322 patients were included in the analysis. Kidney failure was diagnosed in 455 individuals (19.65%), of whom 373 presented moderate stage and 82 patients, including 14 dialysis individuals, presented severe renal failure. Patients with kidney failure were significantly older and demonstrated a more severe course of COVID-19. The age, baseline SpO₂, the ordinal scale of 4 and 5, neutrophil and platelet count, estimated glomerular filtration rate, and C-reactive protein concentration as well as malignancy and arterial hypertension were the independent predictors of 28-day mortality in logistic regression analysis. Conclusions: Underlying kidney disease in patients with COVID-19 is among the leading factors associated with a higher risk of severe clinical presentation and increased mortality rate.

Ethnic Disparities in COVID-19 Among Older Adults Presenting to the Geriatric Emergency Department

Background

There is a dearth of epidemiological data on ethnic disparities among older patients with COVID-19. The objective of this study was to characterize ethnic differences in clinical presentation and outcomes from COVID-19 among older U.S. adults.

Methods

This was a retrospective cohort study within two geriatric emergency departments (GEDs) at a large academic health system. One hundred patients 65 years or older who visited a GED between March 10, 2020 and August 9, 2020 and tested positive for COVID-19 were examined. Electronic medical records were used to determine presenting COVID-19–related symptoms, comorbidities, and clinical outcomes. Descriptive statistics are reported with associated 95% confidence intervals (CIs).

Results

In the overall sample, mean age was 75.9 years; 18% were 85 years or older; 50% were male; and 46.0% were Hispanic. Relative to non-Hispanic patients with COVID-19, Hispanic patients with COVID-19 had a higher percentage of shortness of breath (78.3% vs. 51.9%; difference: 26.4%; 95% CI 7.6–42.5%), pneumonia (82.6% vs. 50.0%; difference: 32.6%; 95% CI 14.1–47.9%), acute respiratory distress syndrome (13.0% vs. 1.9%; difference: 11.1%; 95% CI 0.7–23.9%), and acute kidney failure (41.3% vs. 22.2%; difference: 19.1%; 95% CI 0.9–36.0%). Rates of other poor outcomes, including hospitalization, intensive care unit (ICU) admission, return visits to the GED within 30 days of discharge, or death, did not significantly differ between Hispanic and non-Hispanic patients with COVID-19.

Conclusions

These preliminary data show that older Hispanic patients relative to non-Hispanic patients with COVID-19 presenting to a GED did not experience worse outcomes, including hospitalization, ICU admission, 30-day return visits to the GED, or death.

Age-adjusted Charlson comorbidity index score is the best predictor for severe clinical outcome in the hospitalized patients with COVID-19 infection

Aged population with comorbidities demonstrated high mortality rate and severe clinical outcome in the patients with coronavirus disease 2019 (COVID-19). However, whether age-adjusted Charlson comorbidity index score (CCIS) predict fatal outcomes remains uncertain.This retrospective, nationwide cohort study was performed to evaluate patient mortality and clinical outcome according to CCIS among the hospitalized patients with COVID-19 infection. We included 5621 patients who had been discharged from isolation or had died from COVID-19 by April 30, 2020. The primary outcome was composites of death, admission to intensive care unit, use of mechanical ventilator or extracorporeal membrane oxygenation. The secondary outcome was mortality. Multivariate Cox proportional hazard model was used to evaluate CCIS as the independent risk factor for death.Among 5621 patients, the high CCIS (≥ 3) group showed higher proportion of elderly population and lower plasma hemoglobin and lower lymphocyte and platelet counts. The high CCIS group was an independent risk factor for composite outcome (HR 3.63, 95% CI 2.45-5.37, P < .001) and patient mortality (HR 22.96, 95% CI 7.20-73.24, P < .001). The nomogram showed that CCIS was the most important factor contributing to the prognosis followed by the presence of dyspnea (hazard ratio [HR] 2.88, 95% confidence interval [CI] 2.16-3.83), low body mass index < 18.5 kg/m2 (HR 2.36, CI 1.49-3.75), lymphopenia (<0.8 x109/L) (HR 2.15, CI 1.59-2.91), thrombocytopenia (<150.0 x109/L) (HR 1.29, CI 0.94-1.78), anemia (<12.0 g/dL) (HR 1.80, CI 1.33-2.43), and male sex (HR 1.76, CI 1.32-2.34). The nomogram demonstrated that the CCIS was the most potent predictive factor for patient mortality.The predictive nomogram using CCIS for the hospitalized patients with COVID-19 may help clinicians to triage the high-risk population and to concentrate limited resources to manage them.

Sex-specific impact of severe obesity in the outcomes of hospitalized patients with COVID-19: a large retrospective study from the Bronx, New York

It has been demonstrated that obesity is an independent risk factor for worse outcomes in patients with COVID-19. Our objectives were to investigate which classes of obesity are associated with higher in-hospital mortality and to assess the association between obesity and systemic inflammation. This was a retrospective study which included consecutive hospitalized patients with COVID-19 in a tertiary center. Three thousand five hundred thirty patients were included in this analysis (female sex: 1579, median age: 65 years). The median body mass index (BMI) was 28.8 kg/m². In the overall cohort, a J-shaped association between BMI and in-hospital mortality was depicted. In the subgroup of men, BMI 35–39.9 kg/m² and BMI ≥40 kg/m² were found to have significant association with higher in-hospital mortality, while only BMI ≥40 kg/m² was found significant in the subgroup of women. No significant association between BMI and IL-6 was noted. Obesity classes II and III in men and obesity class III in women were independently associated with higher in-hospital mortality in patients with COVID-19. The male population with severe obesity was the one that mainly drove this association. No significant association between BMI and IL-6 was noted.

Impact of cardiovascular disease and cardiovascular risk factors in hospitalised COVID-19 patients

BACKGROUND

Hospitalised COVID-19 patients with underlying cardiovascular disease (CVD) and cardiovascular risk factors appear to be at risk of poor outcome. It is unknown if these patients should be considered a vulnerable group in healthcare delivery and healthcare recommendations during the COVID-19 pandemic.

METHODS

A systematic literature search was performed to answer the following question: In which hospitalised patients with proven COVID-19 and with underlying CVD and cardiovascular risk factors should doctors be alert to a poor outcome? Relevant outcome measures were mortality and intensive care unit admission. Medline and Embase databases were searched using relevant search terms until 9 June 2020. After systematic analysis, 8 studies were included.

RESULTS

Based on the literature search, there was insufficient evidence that CVD and cardiovascular risk factors are significant predictors of mortality and poor outcome in hospitalised patients with COVID-19. Due to differences in methodology, the level of evidence of all studies was graded 'very low' according to the Grading Recommendations Assessment, Development and Evaluation methodology. It is expected that in the near future, two multinational and multicentre European registries (CAPACITY-COVID and LEOSS) will offer more insight into outcome in COVID-19 patients.

CONCLUSION

This literature review demonstrated there was insufficient evidence to identify CVD and cardiovascular risk factors as important predictors of poor outcome in hospitalised COVID-19 patients. However, patients with CVD and cardiovascular risk factors remain vulnerable to infectious disease outbreaks. As such, governmental and public health COVID-19 recommendations for vulnerable groups apply to these patients.

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.

Geographical Differences in Cardiovascular Comorbidities and Outcomes of COVID-19 Hospitalized Patients in the USA

Introduction

Cardiovascular comorbidities may predispose to adverse outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19). However, across the USA, the burden of cardiovascular comorbidities varies significantly. Whether clinical outcomes of hospitalized patients with COVID-19 differ between regions has not yet been studied systematically. Here, we report differences in underlying cardiovascular comorbidities and clinical outcomes of patients hospitalized with COVID-19 in Texas and in New York state.

Methods

We established a multicenter retrospective registry including patients hospitalized with COVID-19 between March 15 and July 12, 2020. Demographic and clinical data were manually retrieved from electronic medical records. We focused on the following outcomes: mortality, need for pharmacologic circulatory support, need for mechanical ventilation, and need for hemodialysis. Univariate and multivariate logistic regression analyses were performed.

Results

Patients in the Texas cohort (n = 296) were younger (57 vs. 63 years, p value <0.001), they had a higher BMI (30.3 kg/m² vs. 28.5 kg/m², p = 0.015), and they had higher rates of diabetes mellitus (41 vs. 30%; p = 0.014). In contrast, patients in the New York state cohort (n = 218) had higher rates of coronary artery disease (19 vs. 10%, p = 0.005) and atrial fibrillation (11 vs. 5%, p = 0.012). Pharmacologic circulatory support, mechanical ventilation, and hemodialysis were more frequent in the Texas cohort (21 vs. 13%, p = 0.020; 30 vs. 12%, p < 0.001; and 11 vs. 5%, p = 0.009, respectively). In-hospital mortality was similar between the 2 cohorts (16 vs. 18%, p = 0.469). After adjusting for differences in underlying comorbidities, only the use of mechanical ventilation remained significantly higher in the participating Texas hospitals (odds ratios [95% CI]: 3.88 [1.23, 12.24]). Median time to pharmacologic circulatory support was 8 days (interquartile range: 2, 13.8) in the Texas cohort compared to 1 day (0, 3) in the New York state cohort, while median time to in-hospital mortality was 16 days (10, 25.5) and 7 days (4, 14), respectively (both p < 0.001). In-hospital mortality was higher in the late versus the early study phase in the New York state cohort (24 vs. 14%, p = 0.050), while it was similar between the 2 phases in the Texas cohort (16 vs. 15%, p = 0.741).

Conclusions

Geographical differences, including practice pattern variations and the impact of disease burden on provision of health care, are important for the evaluation of COVID-19 outcomes. Unadjusted data may cause bias affecting future regulatory policies and proper allocation of resources.

Progress in the Clinical Features and Pathogenesis of Abnormal Liver Enzymes in Coronavirus Disease 2019

With the rapid development of research on coronavirus disease 2019 (COVID-19), more and more attention has been drawn to its damage to extrapulmonary organs. There are increasing lines of evidence showing that liver injury is closely related to the severity of COVID-19, which may have an adverse impact on the progression and prognosis of the patients. What is more, severe acute respiratory syndrome coronavirus-2 infection, cytokine storm, ischemia/hypoxia reperfusion injury, aggravation of the primary liver disease and drug-induced liver injury may all contribute to the hepatic damage in COVID-19 patients; although, the drug-induced liver injury, especially idiosyncratic drug-induced liver injury, requires further causality confirmation by the updated Roussel Uclaf Causality Assessment Method published in 2016. Up to now, there is no specific regimen for COVID-19, and COVID-19-related liver injury is mainly controlled by symptomatic and supportive treatment. Here, we review the clinical features of abnormal liver enzymes in COVID-19 and pathogenesis of COVID-19-related liver injury based on the current evidence, which may provide help for clinicians and researchers in exploring the pathogenesis and developing treatment strategies.

An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning

COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on a dataset made public by Yan et al. in [1] of 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high-sensitivity C-reactive protein, and age (LNLCA)-acquired at hospital admission-were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate-, and high-risk groups using LNLCA cutoff values of 10.4 and 12.65 with the death probability less than 5%, 5-50%, and above 50%, respectively. The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19

Objectives

To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19.

Methods

We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning–based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients.

Results

There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936–0.976) and 0.953 (95% CI: 0.891–0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness.

Conclusions

We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation.

Key Points

• Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources.

• We proposed a deep learning–based framework for accurate lung involvement quantification on chest CT images.

• Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07957-z.

Development and validation of an individualized nomogram for early prediction of the duration of SARS-CoV-2 shedding in COVID-19 patients with non-severe disease

With the number of cases of coronavirus disease-2019 (COVID-19) increasing rapidly, the World Health Organization (WHO) has recommended that patients with mild or moderate symptoms could be released from quarantine without nucleic acid retesting, and self-isolate in the community. This may pose a potential virus transmission risk. We aimed to develop a nomogram to predict the duration of viral shedding for individual COVID-19 patients. This retrospective multicentric study enrolled 135 patients as a training cohort and 102 patients as a validation cohort. Significant factors associated with the duration of viral shedding were identified by multivariate Cox modeling in the training cohort and combined to develop a nomogram to predict the probability of viral shedding at 9, 13, 17, and 21 d after admission. The nomogram was validated in the validation cohort and evaluated by concordance index (C-index), area under the curve (AUC), and calibration curve. A higher absolute lymphocyte count (P=0.001) and lymphocyte-to-monocyte ratio (P=0.013) were correlated with a shorter duration of viral shedding, while a longer activated partial thromboplastin time (P=0.007) prolonged the viral shedding duration. The C-indices of the nomogram were 0.732 (95% confidence interval (CI): 0.685‒0.777) in the training cohort and 0.703 (95% CI: 0.642‒0.764) in the validation cohort. The AUC showed a good discriminative ability (training cohort: 0.879, 0.762, 0.738, and 0.715 for 9, 13, 17, and 21 d; validation cohort: 0.855, 0.758, 0.728, and 0.706 for 9, 13, 17, and 21 d), and calibration curves were consistent between outcomes and predictions in both cohorts. A predictive nomogram for viral shedding duration based on three easily accessible factors was developed to help estimate appropriate self-isolation time for patients with mild or moderate symptoms, and to control virus transmission.

Admission High-Sensitive Cardiac Troponin T Level Increase Is Independently Associated with Higher Mortality in Critically Ill Patients with COVID-19: A Multicenter Study

BACKGROUND

In coronavirus disease 2019 (COVID-19) patients, increases in high-sensitive cardiac troponin T (hs-cTnT) have been reported to be associated with worse outcomes. In the critically ill, the prognostic value of hs-cTnT, however, remains to be assessed given that most previous studies have involved a case mix of non- and severely ill COVID-19 patients.

METHODS

We conducted, from March to May 2020, in three French intensive care units (ICUs), a multicenter retrospective cohort study to assess in-hospital mortality predictability of hs-cTnT levels in COVID-19 patients.

RESULTS

111 laboratory-confirmed COVID-19 patients (68% of male, median age 67 (58-75) years old) were included. At ICU admission, the median Charlson Index, Simplified Acute Physiology Score II, and PaO2/FiO2 were at 3 (2-5), 37 (27-48), and 140 (98-154), respectively, and the median hs-cTnT serum levels were at 16.0 (10.1-31.9) ng/L. Seventy-five patients (68%) were mechanically ventilated, 41 (37%) were treated with norepinephrine, and 17 (15%) underwent renal replacement therapy. In-hospital mortality was 29% (32/111) and was independently associated with lower PaO2/FiO2 and higher hs-cTnT serum levels.

CONCLUSIONS

At ICU admission, besides PaO2/FiO2, hs-cTnT levels may allow early risk stratification and triage in critically ill COVID-19 patients.

Association of smoking history with severe and critical outcomes in COVID-19 patients: A systemic review and meta-analysis

INTRODUCTION

The highly infectious coronavirus disease 2019 (COVID-19) has now rapidly spread around the world. This meta-analysis was strictly focused on the influence of smoking history on the severe and critical outcomes on people with COVID-19 pneumonia.

METHODS

A systematic literature search was conducted in eight online databases before 1 February 2021. All studies meeting our selection criteria were included and evaluated. Stata 14.0 software was used to analyze the data.

RESULTS

A total of 109 articles involving 517,020 patients were included in this meta-analysis. A statistically significant association was discovered between smoking history and COVID-19 severity, the pooled OR was 1.55 (95%CI: 1.41-1.71). Smoking was significantly associated with the risk of admission to intensive care unit (ICU) (OR=1.73, 95%CI: 1.36-2.19), increased mortality (OR=1.58, 95%CI: 1.38-1.81), and critical diseases composite endpoints (OR=1.61, 95%CI: 1.35-1.93), whereas there was no relationship with mechanical ventilation. The pooled prevalence of smoking using the random effects model (REM) was 15% (95%CI: 14%-16%). Meta-regression analysis showed that age (P=0.004), hypertension (P=0.007), diabetes (P=0.029), chronic obstructive pulmonary disease (COPD) (P=0.001) were covariates that affect the association.

CONCLUSIONS

Smoking was associated with severe or critical outcomes and increased the risk of admission to ICU and mortality in COVID-19 patients, but not associated with mechanical ventilation. This association was more significant for former smokers than in current smokers. Current smokers also had a higher risk of developing severe COVID-19 compared with non-smokers. More detailed data, which are representative of more countries, are needed to confirm these preliminary findings.

Community- and Hospital-Acquired Acute Kidney Injury in COVID-19: Different Phenotypes and Dismal Prognosis

INTRODUCTION

Acute kidney injury (AKI) is common in coronavirus disease 2019 (COVID-19). It is unknown if hospital-acquired AKI (HA-AKI) and community-acquired AKI (CA-AKI) convey a distinct prognosis.

METHODS

The study aim was to evaluate the incidence and risk factors associated with both CA-AKI and HA-AKI. Consecutive patients hospitalized at a reference center for COVID-19 were included in this prospective cohort study.

RESULTS

We registered 349 (30%) AKI episodes in 1,170 hospitalized patients, 224 (19%) corresponded to CA-AKI, and 125 (11%) to HA-AKI. Compared to patients with HA-AKI, subjects with CA-AKI were older (61 years [IQR 49-70] vs. 50 years [IQR 43-61]), had more comorbidities (hypertension [44 vs. 26%], CKD [10 vs. 3%]), higher Charlson Comorbidity Index (2 points [IQR 1-4] vs. 1 point [IQR 0-2]), and presented to the emergency department with more severe disease. Mortality rates were not different between CA-AKI and HA-AKI (119 [53%] vs. 63 [50%], p = 0.66). In multivariate analysis, CA-AKI was strongly associated to a history of CKD (OR 4.17, 95% CI 1.53-11.3), hypertension (OR 1.55, 95% CI 1.01-2.36), Charlson Comorbidity Index (OR 1.16, 95% CI 1.02-1.32), and SOFA score (OR 2.19, 95% CI 1.87-2.57). HA-AKI was associated with the requirement for mechanical ventilation (OR 68.2, 95% CI 37.1-126), elevated troponin I (OR 1.95, 95% CI 1.01-3.83), and glucose levels at admission (OR 1.05, 95% CI 1.02-1.08).

DISCUSSION/CONCLUSIONS

CA-AKI and HA-AKI portend an adverse prognosis in CO-VID-19. Nevertheless, CA-AKI was associated with a higher comorbidity burden (including CKD and hypertension), while HA-AKI occurred in younger patients by the time severe multiorgan disease developed.

Nomogram for prediction of fatal outcome in patients with severe COVID-19: a multicenter study

BACKGROUND

To develop an effective model of predicting fatal outcomes in the severe coronavirus disease 2019 (COVID-19) patients.

METHODS

Between February 20, 2020 and April 4, 2020, consecutive confirmed 2541 COVID-19 patients from three designated hospitals were enrolled in this study. All patients received chest computed tomography (CT) and serological examinations at admission. Laboratory tests included routine blood tests, liver function, renal function, coagulation profile, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and arterial blood gas. The SaO2 was measured using pulse oxygen saturation in room air at resting status. Independent high-risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients.

RESULTS

There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years (HR = 1.184, 95% CI 1.061-1.321), panting (breathing rate ≥ 30/min) (HR = 3.300, 95% CI 2.509-6.286), lymphocyte count < 1.0 × 109/L (HR = 2.283, 95% CI 1.779-3.267), and interleukin-6 (IL-6) >  10 pg/ml (HR = 3.029, 95% CI 1.567-7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC = 0.900, 95% CI 0.841-0.960, sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC = 0.811, 95% CI 0.763-0.961, sensitivity 77.3%, specificity 73.5%); in validation cohort 2 (AUC = 0.862, 95% CI 0.698-0.924, sensitivity 92.9%, specificity 64.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. The prognosis of severe COVID-19 patients with high levels of IL-6 receiving tocilizumab were better than that of those patients without tocilizumab both in the training and validation cohorts, but without difference (P = 0.105 for training cohort, P = 0.133 for validation cohort 1, and P = 0.210 for validation cohort 2).

CONCLUSIONS

This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Tocilizumab may improve the prognosis of severe COVID-19 patients with high levels of IL-6.

Clinical Course and Outcome of COVID-19 Acute Respiratory Distress Syndrome: Data From a National Repository

BACKGROUND

Mortality attributable to coronavirus disease-19 (COVID-19) 2 infection occurs mainly through the development of viral pneumonia-induced acute respiratory distress syndrome (ARDS).

RESEARCH QUESTION

The objective of the study is to delineate the clinical profile, predictors of disease progression, and 30-day mortality from ARDS using the Veterans Affairs Corporate Data Warehouse.

STUDY DESIGN AND METHODS

Analysis of a historical cohort of 7,816 hospitalized patients with confirmed COVID-19 infection between January 1, 2020, and August 1, 2020. Main outcomes were progression to ARDS and 30-day mortality from ARDS, respectively.

RESULTS

The cohort was comprised predominantly of men (94.5%) with a median age of 69 years (interquartile range [IQR] 60-74 years). 2,184 (28%) were admitted to the intensive care unit and 643 (29.4%) were diagnosed with ARDS. The median Charlson Index was 3 (IQR 1-5). Independent predictors of progression to ARDS were body mass index (BMI) ≥40 kg/m², diabetes, lymphocyte counts <700 × 109/L, LDH >450 U/L, ferritin >862 ng/ml, C-reactive protein >11 mg/dL, and D-dimer >1.5 ug/ml. In contrast, the use of an anticoagulant lowered the risk of developing ARDS (OR 0.66 [95% CI 0.49-0.89]. Crude 30-day mortality rate from ARDS was 41% (95% CI 38%-45%). Risk of death from ARDS was significantly higher in those who developed acute renal failure and septic shock. Use of an anticoagulant was associated with 2-fold reduction in mortality. Survival benefit was observed in patients who received corticosteroids and/or remdesivir but there was no advantage of combination therapy over either agent alone.

CONCLUSIONS

Among those hospitalized for COVID-19, nearly 1 in 10 progressed to ARDS. Septic shock, and acute renal failure are the leading causes of death in these patients. Treatment with either remdesivir and corticosteroids reduced the risk of mortality from ARDS. All hospitalized patients with COVID-19 should be placed at a minimum on prophylactic doses of anticoagulation.

Association between antecedent statin use and severe disease outcomes in COVID-19: A retrospective study with propensity score matching

Background

Statins have been associated with a reduction in inflammatory markers and improved endothelial function. Whether statins offer any benefit in COVID-19 needs to be elucidated.

Objective

To determine the association between antecedent statin use and severe disease outcomes among COVID-19 patients.

Methods

A retrospective cohort study on 1014 patients with confirmed COVID-19 diagnosis. Outcomes were mortality, need for mechanical ventilation, and intensive care admission. Patients were classified into statin-users vs statin non-users based on antecedent use of statins. Multivariable regression analysis was performed adjusting for confounders such as age, sex, race, BMI, smoking, insurance, and comorbidities. Propensity score matching was performed to achieve a 1:1 balanced cohort.

Results

A total of 1014 patients (Median age 65 (IQR 53–73); 530 (52.3%) males; 753 (74.3%) African Americans; median BMI 29.4 (IQR 25.1–35.9); 615 (60.7%) with Medicare insurance) were included in the study. About 454 patients (44.77%) were using statins as home medication. Antecedent statin use was associated with significant decrease in mortality in the total cohort (OR, 0.66; 95% CI, 0.46 – 0.95; p = 0.03). Among the propensity score matched (PSM) cohort of 466 patients (233 statin users and 233 statin non-users), all the baseline characteristics had similar distribution among the two groups. Statin users had significant reduction in mortality in the PSM cohort as well (OR, 0.56; 95% CI, 0.37 – 0.83; p = 0.004).

Conclusions

Statin use was associated with significant reduction in mortality among COVID-19 patients. These findings support the pursuit of randomized clinical trials to explore the possible benefits of statins in COVID-19.

Development and validation of a predictive model of in-hospital mortality in COVID-19 patients

We retrospectively evaluated 2879 hospitalized COVID-19 patients from four hospitals to evaluate the ability of demographic data, medical history, and on-admission laboratory parameters to predict in-hospital mortality. Association of previously published risk factors (age, gender, arterial hypertension, diabetes mellitus, smoking habit, obesity, renal failure, cardiovascular/ pulmonary diseases, serum ferritin, lymphocyte count, APTT, PT, fibrinogen, D-dimer, and platelet count) with death was tested by a multivariate logistic regression, and a predictive model was created, with further validation in an independent sample. A total of 2070 hospitalized COVID-19 patients were finally included in the multivariable analysis. Age 61-70 years (p<0.001; OR: 7.69; 95%CI: 2.93 to 20.14), age 71-80 years (p<0.001; OR: 14.99; 95%CI: 5.88 to 38.22), age >80 years (p<0.001; OR: 36.78; 95%CI: 14.42 to 93.85), male gender (p<0.001; OR: 1.84; 95%CI: 1.31 to 2.58), D-dimer levels >2 ULN (p = 0.003; OR: 1.79; 95%CI: 1.22 to 2.62), and prolonged PT (p<0.001; OR: 2.18; 95%CI: 1.49 to 3.18) were independently associated with increased in-hospital mortality. A predictive model performed with these parameters showed an AUC of 0.81 in the development cohort (n = 1270) [sensitivity of 95.83%, specificity of 41.46%, negative predictive value of 98.01%, and positive predictive value of 24.85%]. These results were then validated in an independent data sample (n = 800). Our predictive model of in-hospital mortality of COVID-19 patients has been developed, calibrated and validated. The model (MRS-COVID) included age, male gender, and on-admission coagulopathy markers as positively correlated factors with fatal outcome.

Risk factors for poor outcomes in hospitalised COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

Understanding the risk factors for poor outcomes among COVID-19 patients could help identify vulnerable populations who would need prioritisation in prevention and treatment for COVID-19. We aimed to critically appraise and synthesise published evidence on the risk factors for poor outcomes in hospitalised COVID-19 patients.

METHODS

We searched PubMed, medRxiv and the WHO COVID-19 literature database for studies that reported characteristics of COVID-19 patients who required hospitalisation. We included studies published between January and May 2020 that reported adjusted effect size of any demographic and/or clinical factors for any of the three poor outcomes: mortality, intensive care unit (ICU) admission, and invasive mechanical ventilation. We appraised the quality of the included studies using Joanna Briggs Institute appraisal tools and quantitatively synthesised the evidence through a series of random-effect meta-analyses. To aid data interpretation, we further developed an interpretation framework that indicated strength of the evidence, informed by both quantity and quality of the evidence.

RESULTS

We included a total of 40 studies in our review. Most of the included studies (29/40, 73%) were assessed as "good quality", with assessment scores of 80 or more. We found that male sex (pooled odds ratio (OR) = 1.32 (95% confidence interval (CI) = 1.18-1.48; 20 studies), older age (OR = 1.05, 95% CI = 1.04-1.07, per one year of age increase; 10 studies), obesity (OR = 1.59, 95% CI = 1.02-2.48; 4 studies), diabetes (OR = 1.25, 95% CI = 1.11-1.40; 11 studies) and chronic kidney diseases (6 studies; OR = 1.57, 95% CI = 1.27-1.93) were associated with increased risks for mortality with the greatest strength of evidence based on our interpretation framework. We did not find increased risk of mortality for several factors including chronic obstructive pulmonary diseases (5 studies), cancer (4 studies), or current smoker (5 studies); however, this does not indicate absence of risk due to limited data on each of these factors.

CONCLUSION

Male sex, older age, obesity, diabetes and chronic kidney diseases are important risk factors of COVID-19 poor outcomes. Our review provides not only an appraisal and synthesis of evidence on the risk factors of COVID-19 poor outcomes, but also a data interpretation framework that could be adopted by relevant future research.

Impact of Ethnicity and Underlying Comorbidity on COVID-19 Inhospital Mortality: An Observational Study in Abu Dhabi, UAE

Background

The UAE reported its first cluster of COVID 2019 in a group of returned travellers from Wuhan in January 2020. Various comorbidities are associated with worse disease prognosis. Understanding the impact of ethnicity on the disease outcome is an important public health issue but data from our region is lacking.

Aim

We aim to identify comorbidities among patients hospitalized for COVID-19 that are associated with inhospital death. Also, to assess if ethnicity is correlated with increased risk of death. Patients and Method. The study is a single-centre, observational study in Shaikh Shakhbout Medical City, Abu Dhabi. Patients admitted with COVID-19, between 1^st of March and the end of May, were enrolled. Records were studied for demography, comorbidity, and ethnicity. Ethnicity was divided into Arabs (Gulf, North Africa, and the Levant), South Asia (India, Pakistan, Bangladesh, Nepal, and Afghanistan), Africans, the Philippines, and others. The study was approved by the Department of Health of Abu Dhabi.

Results

1075 patients (972 males) were enrolled. There were 24 nationalities under 5 ethnicity groups. Mean (average) age was 51 years (20–81). 101 (9.4%) died with deceased patients being significantly older. Death risk was not significantly influenced by sex. Duration of hospitalization among survivors was 6.2 days (0.2–40.4) with older patients and men staying longer (P < 0.01). Comorbidities of diabetes, hypertension, cardiovascular disease, chronic renal disease, liver disease, and malignancy were associated with higher risk of mortality univariate, but only liver disease reached statistical significance after adjustment for age. The highest percentage of death was seen in Arab Levant (21.2) followed by the Asian Afghan (18.8); however, differences among ethnicities did not reach statistical significance (P = 0.086).

Conclusion

COVID-19 outcome was worse in older people and those with comorbidities. Men and older patients required longer hospitalization. Ethnicity is not seen to impact the risk of mortality.

Angiotensin-converting enzyme 2 and COVID-19: patients, comorbidities, and therapies

On March 11, 2020, the World Health Organization declared coronavirus disease 2019 (COVID-19) a pandemic, and the reality of the situation has finally caught up to the widespread reach of the disease. The presentation of the disease is highly variable, ranging from asymptomatic carriers to critical COVID-19. The availability of angiotensin-converting enzyme 2 (ACE2) receptors may reportedly increase the susceptibility and/or disease progression of COVID-19. Comorbidities and risk factors have also been noted to increase COVID-19 susceptibility. In this paper, we hereby review the evidence pertaining to ACE2's relationship to common comorbidities, risk factors, and therapies associated with the susceptibility and severity of COVID-19. We also highlight gaps of knowledge that require further investigation. The primary comorbidities of respiratory disease, cardiovascular disease, renal disease, diabetes, obesity, and hypertension had strong evidence. The secondary risk factors of age, sex, and race/genetics had limited-to-moderate evidence. The tertiary factors of ACE inhibitors and angiotensin II receptor blockers had limited-to-moderate evidence. Ibuprofen and thiazolidinediones had limited evidence.

Impact of liver enzymes on SARS-CoV-2 infection and the severity of clinical course of COVID-19

BACKGROUND AND AIM

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the current pandemic, can have multi-organ impact. Recent studies show that liver injury could be a manifestation of the disease, and that liver disease could also be related to a worse prognosis. Our aim was to compare the characteristics of patients with severe coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 who required intubation versus stable hospitalized patients to identify the early biochemical predictive factors of a severe course of COVID-19 and subsequent requirement for intubation, specifically in Mexican.

METHODS

This was an observational case-control study nested in a cohort study. Complete medical records of patients admitted for confirmed COVID-19 at a tertiary level center in Mexico City were reviewed. Clinical and biochemical data were collected, and the characteristics of patients who required invasive mechanical ventilation (IMV) (cases) were compared with stable hospitalized patients without ventilation (controls).

RESULTS

We evaluated 166 patients with COVID-19 due to SARS-CoV-2 infection; 114 (68.7%) were men, the mean age was 50.6 ± 13.3 years, and 27 (16.3%) required IMV. The comparative analysis between cases and controls showed (respectively) significantly lower blood oxygen saturation (SpO₂) (73.5 ± 12.0% vs. 83.0 ± 6.8%, P < 0.0001) and elevated alanine aminotransferase (ALT) (128 (14-1123) IU/L vs. 33 (8-453) IU/L, P = 0.003), aspartate aminotransferase (AST) (214 (17-1247) vs. 44 (12-498) IU/L, P = 0.001), lactic dehydrogenase (LDH) (764.6 ± 401.9 IU/L vs. 461.0 ± 185.6 IU/L, P = 0.001), and D-dimer (3463 (524-34,227) ng/mL vs. 829 (152-41,923) ng/mL, P = 0.003) concentrations. Patients in the cases group were older (58.6 ± 12.7 years vs. 49.1 ± 12.8 years, P=0.001). Multivariate analysis showed that important factors at admission predicting the requirement for IMV during hospitalization for COVID-19 were AST ≥250 IU/L (odds ratio (OR) = 64.8, 95% confidence interval (CI) 7.5-560.3, P < 0.0001) and D-dimer ≥ 3500 ng/mL (OR = 4.1, 95% CI 1.2-13.7, P=0.02).

CONCLUSIONS

Our study confirms the importance of monitoring liver enzymes in hospitalized patients with COVID-19; seriously ill patients have significantly elevated AST and D-dimer concentrations, which have prognostic implications in the SARS-CoV-2 disease course.

Interrelations between COVID-19 and other disorders

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a respiratory tract virus that causes Coronavirus disease (COVID-19). The virus originated in Wuhan, China, in December 2019 and has spread across the globe to-date. The disease ranges from asymptomatic carriers to symptoms such as fever, sore throat, cough, lung infections, and in severe cases, acute respiratory distress syndrome, sepsis, and death. As many as 50% of patients reported having at least one comorbidities with COVID-19 upon hospital admission. Hypertension, diabetes, chronic obstructive pulmonary disease, obesity, and cardiovascular diseases are among the most commonly reported. Comorbidities are contributing to acute disease prognosis and increased risk of severe symptoms. Around 70% of patients who require ICU care have been observed to have comorbidities. This review intends to understand how some of these comorbidities affect the disease's prognosis and how severe the outcome can be expected.

Serum β2-microglobulin levels in Coronavirus disease 2019 (Covid-19): Another prognosticator of disease severity?

β2-microglobulin (β2-m), a 11.8 kDa protein, pairs non-covalently with the α3 domain of the major histocompatibility class (MHC) I α-chain and is essential for the conformation of the MHC class I protein complex. Shed β2-m is measurable in circulation, and various disorders are accompanied by increases in β2-m levels, including several viral infections. Therefore, we explored whether β2-m levels could also be elevated in Coronavirus disease 2019 (Covid-19) and whether they predict disease severity. Serum β2-m levels were measured in a cohort of 34 patients infected with SARS-CoV-2 on admission to a tertiary care hospital in Riyadh, Saudi Arabia, as well as in an approximately age-sex matched group of 34 uninfected controls. Mean β2-m level was 3.25±1.68 mg/l (reference range 0.8-2.2 mg/l) in patients (mean age 48.2±21.6) and 1.98±0.61 mg/l in controls (mean age 48.2±21.6). 17 patients (mean age 36.9± 18.0) with mean β2-m levels of 2.27±0.64 mg/l had mild disease by WHO severity categorization, 12 patients (mean age 53.3±18.1) with mean β2-m levels of 3.57±1.39 mg/l had moderate disease, and five patients (of whom 2 died; mean age 74.4±13.8) with mean β2-m levels of 5.85±1.85 mg/l had severe disease (P < = 0.001, by ANOVA test for linear trend). In multivariate ordinal regression β2-m levels were the only significant predictor of disease severity. Our findings suggest that higher β2-m levels could be an early indicator of severity of disease and predict outcome of Covid-19. As the main limitations of the study are a single-center study, sample size and ethnicity, these results need confirmation in larger cohorts outside the Arabian Peninsula in order to delineate the value of β2-m measurements. The role of β2-m in the etiology and pathogenesis of severe Covid-19 remains to be elucidated.

COVID-19 mortality in the UK Biobank cohort: revisiting and evaluating risk factors

Most studies of severe/fatal COVID-19 risk have used routine/hospitalisation data without detailed pre-morbid characterisation. Using the community-based UK Biobank cohort, we investigate risk factors for COVID-19 mortality in comparison with non-COVID-19 mortality. We investigated demographic, social (education, income, housing, employment), lifestyle (smoking, drinking, body mass index), biological (lipids, cystatin C, vitamin D), medical (comorbidities, medications) and environmental (air pollution) data from UK Biobank (N = 473,550) in relation to 459 COVID-19 and 2626 non-COVID-19 deaths to 21 September 2020. We used univariate, multivariable and penalised regression models. Age (OR = 2.76 [2.18-3.49] per S.D. [8.1 years], p = 2.6 × 10-17), male sex (OR = 1.47 [1.26-1.73], p = 1.3 × 10-6) and Black versus White ethnicity (OR = 1.21 [1.12-1.29], p = 3.0 × 10-7) were independently associated with and jointly explanatory of (area under receiver operating characteristic curve, AUC = 0.79) increased risk of COVID-19 mortality. In multivariable regression, alongside demographic covariates, being a healthcare worker, current smoker, having cardiovascular disease, hypertension, diabetes, autoimmune disease, and oral steroid use at enrolment were independently associated with COVID-19 mortality. Penalised regression models selected income, cardiovascular disease, hypertension, diabetes, cystatin C, and oral steroid use as jointly contributing to COVID-19 mortality risk; Black ethnicity, hypertension and oral steroid use contributed to COVID-19 but not non-COVID-19 mortality. Age, male sex and Black ethnicity, as well as comorbidities and oral steroid use at enrolment were associated with increased risk of COVID-19 death. Our results suggest that previously reported associations of COVID-19 mortality with body mass index, low vitamin D, air pollutants, renin-angiotensin-aldosterone system inhibitors may be explained by the aforementioned factors.

Risk Factors and Outcomes of Acute Kidney Injury in Critically Ill Patients with Coronavirus Disease 2019

BACKGROUND

Coronavirus disease 2019 (COVID-19) has emerged as a major global health threat with a great number of deaths worldwide. Acute kidney injury (AKI) is a common complication in patients admitted to the intensive care unit. We aimed to assess the incidence, risk factors and in-hospital outcomes of AKI in COVID-19 patients admitted to the intensive care unit.

METHODS

We conducted a retrospective observational study in the intensive care unit of Tongji Hospital, which was assigned responsibility for the treatments of severe COVID-19 patients by the Wuhan government. AKI was defined and staged based on Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Mild AKI was defined as stage 1, and severe AKI was defined as stage 2 or stage 3. Logistic regression analysis was used to evaluate AKI risk factors, and Cox proportional hazards model was used to assess the association between AKI and in-hospital mortality.

RESULTS

A total of 119 patients with COVID-19 were included in our study. The median patient age was 70 years (interquartile range, 59-77) and 61.3% were male. Fifty-one (42.8%) patients developed AKI during hospitalization, corresponding to 14.3% in stage 1, 28.6% in stage 2 and 18.5% in stage 3, respectively. Compared to patients without AKI, patients with AKI had a higher proportion of mechanical ventilation mortality and higher in-hospital mortality. A total of 97.1% of patients with severe AKI received mechanical ventilation and in-hospital mortality was up to 79.4%. Severe AKI was independently associated with high in-hospital mortality (OR: 1.82; 95% CI: 1.06-3.13). Logistic regression analysis demonstrated that high serum interleukin-8 (OR: 4.21; 95% CI: 1.23-14.38), interleukin-10 (OR: 3.32; 95% CI: 1.04-10.59) and interleukin-2 receptor (OR: 4.50; 95% CI: 0.73-6.78) were risk factors for severe AKI development.

CONCLUSIONS

Severe AKI was associated with high in-hospital mortality, and inflammatory response may play a role in AKI development in critically ill patients with COVID-19.

Cardiometabolic risk factors for COVID-19 susceptibility and severity: A Mendelian randomization analysis

BACKGROUND

Epidemiological studies report associations of diverse cardiometabolic conditions including obesity with COVID-19 illness, but causality has not been established. We sought to evaluate the associations of 17 cardiometabolic traits with COVID-19 susceptibility and severity using 2-sample Mendelian randomization (MR) analyses.

METHODS AND FINDINGS

We selected genetic variants associated with each exposure, including body mass index (BMI), at p < 5 × 10-8 from genome-wide association studies (GWASs). We then calculated inverse-variance-weighted averages of variant-specific estimates using summary statistics for susceptibility and severity from the COVID-19 Host Genetics Initiative GWAS meta-analyses of population-based cohorts and hospital registries comprising individuals with self-reported or genetically inferred European ancestry. Susceptibility was defined as testing positive for COVID-19 and severity was defined as hospitalization with COVID-19 versus population controls (anyone not a case in contributing cohorts). We repeated the analysis for BMI with effect estimates from the UK Biobank and performed pairwise multivariable MR to estimate the direct effects and indirect effects of BMI through obesity-related cardiometabolic diseases. Using p < 0.05/34 tests = 0.0015 to declare statistical significance, we found a nonsignificant association of genetically higher BMI with testing positive for COVID-19 (14,134 COVID-19 cases/1,284,876 controls, p = 0.002; UK Biobank: odds ratio 1.06 [95% CI 1.02, 1.10] per kg/m2; p = 0.004]) and a statistically significant association with higher risk of COVID-19 hospitalization (6,406 hospitalized COVID-19 cases/902,088 controls, p = 4.3 × 10-5; UK Biobank: odds ratio 1.14 [95% CI 1.07, 1.21] per kg/m2, p = 2.1 × 10-5). The implied direct effect of BMI was abolished upon conditioning on the effect on type 2 diabetes, coronary artery disease, stroke, and chronic kidney disease. No other cardiometabolic exposures tested were associated with a higher risk of poorer COVID-19 outcomes. Small study samples and weak genetic instruments could have limited the detection of modest associations, and pleiotropy may have biased effect estimates away from the null.

CONCLUSIONS

In this study, we found genetic evidence to support higher BMI as a causal risk factor for COVID-19 susceptibility and severity. These results raise the possibility that obesity could amplify COVID-19 disease burden independently or through its cardiometabolic consequences and suggest that targeting obesity may be a strategy to reduce the risk of severe COVID-19 outcomes.

Reporting of coronavirus disease 2019 prognostic models: the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis statement

Evaluation of the validity and applicability of published prognostic prediction models for coronavirus disease 2019 (COVID-19) is essential, because determining the patients’ prognosis at an early stage may reduce mortality. This study was aimed to utilize the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) to report the completeness of COVID-19-related prognostic models and appraise its effectiveness in clinical practice. A systematic search of the Web of Science and PubMed was performed for studies published until August 11, 2020. All models were assessed on model development, external validation of existing models, incremental values, and development and validation of the same model. TRIPOD was used to assess the completeness of included models, and the completeness of each item was also reported. In total, 52 publications were included, including 67 models. Age, disease history, lymphoma count, history of hypertension and cardiovascular disease, C-reactive protein, lactate dehydrogenase, white blood cell count, and platelet count were the commonly used predictors. The predicted outcome was death, development of severe or critical state, survival time, and length-of-hospital stay. The reported discrimination performance of all models ranged from 0.361 to 0.994, while few models reported calibration. Overall, the reporting completeness based on TRIPOD was between 31% and 83% [median, 67% (interquartile range: 62%, 73%)]. Blinding of the outcome to be predicted or predictors were poorly reported. Additionally, there was little description on the handling of missing data. This assessment indicated a poorly-reported COVID-19 prognostic model in existing literature. The risk of over-fitting may exist with these models. The reporting of calibration and external validation should be given more attention in future research.

Understanding Demographic Risk Factors for Adverse Outcomes in COVID-19 Patients: Explanation of a Deep Learning Model

This study was to understand the impacts of three key demographic variables, age, gender, and race, on the adverse outcome of all-cause hospitalization or all-cause mortality in patients with COVID-19, using a deep neural network (DNN) analysis. We created a cohort of Veterans who were tested positive for COVID-19, extracted data on age, gender, and race, and clinical characteristics from their electronic health records, and trained a DNN model for predicting the adverse outcome. Then, we analyzed the association of the demographic variables with the risks of the adverse outcome using the impact scores and interaction scores for explaining DNN models. The results showed that, on average, older age and African American race were associated with higher risks while female gender was associated with lower risks. However, individual-level impact scores of age showed that age was a more impactful risk factor in younger patients and in older patients with fewer comorbidities. The individual-level impact scores of gender and race variables had a wide span covering both positive and negative values. The interaction scores between the demographic variables showed that the interaction effects were minimal compared to the impact scores associated with them. In conclusion, the DNN model is able to capture the non-linear relationship between the risk factors and the adverse outcome, and the impact scores and interaction scores can help explain the complicated non-linear effects between the demographic variables and the risk of the outcome.

Impact of low molecular weight heparin administration on the clinical course of the COVID-19 disease

Background

Lymphopenia is the most important criterion of mortality and discharging feature for patients infected with coronavirus disease 2019 (COVID-19). This study aimed to investigate the clinical impact of a low molecular weight heparin (LMWH) treatment on the clinical course of COVID-19.

Materials and methods

Patients’ clinical symptoms, radiologic outcomes, hematologic, biochemical, D-dimer, and C-reactive protein (CRP) results were obtained from their medical records. Participants were separated into 2 groups: one was treated with LMWH and the other was not. Improvement in the patients was compared before and after treatment.

Results

Ninety-six patients who were diagnosed with COVID-19 between April and May 2020 were retrospectively analyzed. The multivariable analysis showed that the count of lymphocytes, D-dimer, and CRP levels were significantly improved in the LMWH group, as compared to the control group (OR, (95% CI) 0.628 (0.248–0.965), P < 0.001); OR, (95% CI) 0.356 (0.089–0.674), P < 0.001, respectively). The area under the receiver operating characteristic (ROC) curve analysis was AUC: 0.679 ± 0.055, 0.615 ± 0.058, and 0.633 ± 0.057, respectively; the β-value was found to be –1.032, –0.026, and –0.465, respectively.

Conclusion

The LMWH treatment group demonstrated better laboratory findings, including recovery in the lymphocyte count, CRP, and D-dimer results.

The Epidemiological and Mechanistic Understanding of the Neurological Manifestations of COVID-19: A Comprehensive Meta-Analysis and a Network Medicine Observation

The clinical characteristics and biological effects on the nervous system of infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain poorly understood. The aim of this study is to advance epidemiological and mechanistic understanding of the neurological manifestations of coronavirus disease 2019 (COVID-19) using stroke as a case study. In this study, we performed a meta-analysis of clinical studies reporting stroke history, intensive inflammatory response, and procoagulant state C-reactive protein (CRP), Procalcitonin (PCT), and coagulation indicator (D-dimer) in patients with COVID-19. Via network-based analysis of SARS-CoV-2 host genes and stroke-associated genes in the human protein-protein interactome, we inspected the underlying inflammatory mechanisms between COVID-19 and stroke. Finally, we further verified the network-based findings using three RNA-sequencing datasets generated from SARS-CoV-2 infected populations. We found that the overall pooled prevalence of stroke history was 2.98% (95% CI, 1.89-4.68; I 2=69.2%) in the COVID-19 population. Notably, the severe group had a higher prevalence of stroke (6.06%; 95% CI 3.80-9.52; I 2 = 42.6%) compare to the non-severe group (1.1%, 95% CI 0.72-1.71; I 2 = 0.0%). There were increased levels of CRP, PCT, and D-dimer in severe illness, and the pooled mean difference was 40.7 mg/L (95% CI, 24.3-57.1), 0.07 μg/L (95% CI, 0.04-0.10) and 0.63 mg/L (95% CI, 0.28-0.97), respectively. Vascular cell adhesion molecule 1 (VCAM-1), one of the leukocyte adhesion molecules, is suspected to play a vital role of SARS-CoV-2 mediated inflammatory responses. RNA-sequencing data analyses of the SARS-CoV-2 infected patients further revealed the relative importance of inflammatory responses in COVID-19-associated neurological manifestations. In summary, we identified an elevated vulnerability of those with a history of stroke to severe COVID-19 underlying inflammatory responses (i.e., VCAM-1) and procoagulant pathways, suggesting monotonic relationships, thus implicating causality.

Covidogram as a simple tool for predicting severe course of COVID-19: population-based study

OBJECTIVES

COVID-19 might either be entirely asymptomatic or manifest itself with a large variability of disease severity. It is beneficial to identify early patients with a high risk of severe course. The aim of the analysis was to develop a prognostic model for the prediction of the severe course of acute respiratory infection.

DESIGN

A population-based study.

SETTING

Czech Republic.

PARTICIPANTS

The first 7455 consecutive patients with COVID-19 who were identified by reverse transcription-PCR testing from 1 March 2020 to 17 May 2020.

PRIMARY OUTCOME

Severe course of COVID-19.

RESULT

Of a total 6.2% of patients developed a severe course of COVID-19. Age, male sex, chronic kidney disease, chronic obstructive pulmonary disease, recent history of cancer, chronic heart failure, acid-related disorders treated with proton-pump inhibitors and diabetes mellitus were found to be independent negative prognostic factors (Area under the ROC Curve (AUC) was 0.893). The results were visualised by risk heat maps, and we called this diagram a 'covidogram'. Acid-related disorders treated with proton-pump inhibitors might represent a negative prognostic factor.

CONCLUSION

We developed a very simple prediction model called 'covidogram', which is based on elementary independent variables (age, male sex and the presence of several chronic diseases) and represents a tool that makes it possible to identify-with a high reliability-patients who are at risk of a severe course of COVID-19. Obtained results open clinically relevant question about the role of acid-related disorders treated by proton-pump inhibitors as predictor for severe course of COVID-19.

Prognosis Score System to Predict Survival for COVID-19 Cases: a Korean Nationwide Cohort Study

BACKGROUND

As the COVID-19 pandemic continues, an initial risk-adapted allocation is crucial for managing medical resources and providing intensive care.

OBJECTIVE

In this study, we aimed to identify factors that predict the overall survival rate for COVID-19 cases and develop a COVID-19 prognosis score (COPS) system based on these factors. In addition, disease severity and the length of hospital stay for patients with COVID-19 were analyzed.

METHODS

We retrospectively analyzed a nationwide cohort of laboratory-confirmed COVID-19 cases between January and April 2020 in Korea. The cohort was split randomly into a development cohort and a validation cohort with a 2:1 ratio. In the development cohort (n=3729), we tried to identify factors associated with overall survival and develop a scoring system to predict the overall survival rate by using parameters identified by the Cox proportional hazard regression model with bootstrapping methods. In the validation cohort (n=1865), we evaluated the prediction accuracy using the area under the receiver operating characteristic curve. The score of each variable in the COPS system was rounded off following the log-scaled conversion of the adjusted hazard ratio.

RESULTS

Among the 5594 patients included in this analysis, 234 (4.2%) died after receiving a COVID-19 diagnosis. In the development cohort, six parameters were significantly related to poor overall survival: older age, dementia, chronic renal failure, dyspnea, mental disturbance, and absolute lymphocyte count <1000/mm3. The following risk groups were formed: low-risk (score 0-2), intermediate-risk (score 3), high-risk (score 4), and very high-risk (score 5-7) groups. The COPS system yielded an area under the curve value of 0.918 for predicting the 14-day survival rate and 0.896 for predicting the 28-day survival rate in the validation cohort. Using the COPS system, 28-day survival rates were discriminatively estimated at 99.8%, 95.4%, 82.3%, and 55.1% in the low-risk, intermediate-risk, high-risk, and very high-risk groups, respectively, of the total cohort (P<.001). The length of hospital stay and disease severity were directly associated with overall survival (P<.001), and the hospital stay duration was significantly longer among survivors (mean 26.1, SD 10.7 days) than among nonsurvivors (mean 15.6, SD 13.3 days).

CONCLUSIONS

The newly developed predictive COPS system may assist in making risk-adapted decisions for the allocation of medical resources, including intensive care, during the COVID-19 pandemic.