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

Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms identifies two potential cellular targets for the treatment of coronavirus disease

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread worldwide, leading coronavirus disease 2019 (COVID-19) to hit pandemic level less than 4 months after the first official cases. Hence, the search for drugs and vaccines that could prevent or treat infections by SARS-CoV-2 began, intending to reduce a possible collapse of health systems. After 2 years, efforts to find therapies to treat COVID-19 continue. However, there is still much to be understood about the virus' pathology. Tools such as transcriptomics have been used to understand the impact of SARS-CoV-2 on different cells isolated from various tissues, leaving datasets in the databases that integrate genes and differentially expressed pathways during SARS-CoV-2 infection. After retrieving transcriptome datasets from different human cells infected with SARS-CoV-2 available in the database, we performed an integrative analysis associated with deep learning algorithms to determine differentially expressed targets mainly after infection. The targets found represented a fructose transporter (GLUT5) and a component of proteasome 26s. These targets were then molecularly modeled, followed by molecular docking that identified potential inhibitors for both structures. Once the inhibition of structures that have the expression increased by the virus can represent a strategy for reducing the viral replication by selecting infected cells, associating these bioinformatics tools, therefore, can be helpful in the screening of molecules being tested for new uses, saving financial resources, time, and making a personalized screening for each infectious disease.

Survival analysis of patients with COVID-19 admitted at six hospitals in Uganda in 2021: a cohort study

Background

Assessing factors associated with mortality among COVID-19 patients could guide in developing context relevant interventions to mitigate the risk. The study aimed to describe mortality and associated factors among COVID-19 patients admitted at six health facilities in Uganda.

Methods

We reviewed medical records of patients admitted with COVID-19 between January 1st 2021 and December 31st 2021 in six hospitals in Uganda. Using Stata version 17.0, Kaplan Meier and Cox regression analyses were performed to describe the time to death and estimate associations between various exposures and time to death. Finally, accelerated failure time (AFT) models with a lognormal distribution were used to estimate corresponding survival time ratios.

Results

Out of the 1040 study participants, 234 (22.5%: 95%CI 12.9 to 36.2%) died. The mortality rate was 30.7 deaths per 1000 person days, 95% CI (26.9 to 35.0). The median survival time was 33 days, IQR (9–82). Factors associated with time to COVID-19 death included; age ≥ 60 years [adjusted hazard ratio (aHR) = 2.4, 95% CI: [1.7, 3.4]], having malaria test at admission [aHR = 2.0, 95% CI:[1.0, 3.9]], a COVID-19 severity score of severe/critical [aHR = 6.7, 95% CI:[1.5, 29.1]] and admission to a public hospital [aHR = 0.4, 95% CI:[0.3, 0.6]]. The survival time of patients aged 60 years or more is estimated to be 63% shorter than that of patients aged less than 60 years [adjusted time ratio (aTR) 0.37, 95% CI 0.24, 0.56]. The survival time of patients admitted in public hospitals was 2.5 times that of patients admitted in private hospitals [aTR 2.5 to 95%CI 1.6, 3.9]. Finally, patients with a severe or critical COVID-19 severity score had 87% shorter survival time than those with a mild score [aTR 0.13, 95% CI 0.03, 0.56].

Conclusion

In-hospital mortality among COVID-19 patients was high. Factors associated with shorter survival; age ≥ 60 years, a COVID-19 severity score of severe or critical, and having malaria at admission. We therefore recommend close monitoring of COVID-19 patients that are elderly and also screening for malaria in COVID-19 admitted patients.

Retrospective study of COVID-19 outcomes among healthcare workers in Rivers State, Nigeria

OBJECTIVE

To determine the illness severity and mortality among COVID-19-infected healthcare workers (HCWs).

DESIGN

A retrospective cohort study using population-level data. Secondary analysis was conducted on collated data from the Public Health Emergency Operations Centre (PHEOC) at the State Ministry of Health, Rivers State, Nigeria. Data were gathered from the COVID-19 patient database of the PHEOC on demographics, place of work, illness severity and outcome.

PARTICIPANTS

The cohort included all documented HCWs with confirmed COVID-19 infection (diagnosed by PCR).

PRIMARY AND SECONDARY OUTCOME MEASURES

Illness severity defined as 'hospitalisation required' and treatment outcome labelled as 'alive' or 'dead' were the outcomes of interest.

RESULTS

The mean age was 43 years and 50.5% of the cohort were female. Of the 301 HCWs infected, 187 patients were symptomatic with 32 requiring hospitalisation. Seven infected HCWs died of their COVID-19 infection, resulting in a case fatality ratio (CFR) of 2.3%. Population proportions for age groups, case presentation and mortality, would be significantly greater than those seen in the study population. Health professionals made up 79.7% (240) of the study cohort, with 68.8% (165) of them working at the teaching hospitals; the association between HCWs and health facilities they worked in, was significant. Symptomatic cases were more inclined to progress to severe illness ([Formula: see text] adjusted OR (aOR) 10.658, 95% CI 2.494 to 45.552); patients also had greater odds of dying from COVID-19 ([Formula: see text] aOR 1.079, 95% CI 1.02 to 1.141) per year increase in age adjusted for sex, case class and illness severity.

CONCLUSIONS

Frontl-ine HCWs are at an increased risk of exposure to COVID-19 infections. In Nigeria, there is a higher risk of experiencing severe illness if symptomatic while infected with COVID-19. Preventive strategies, proper education and awareness must be put in place to protect HCWs.

OBJECTIVE

To determine the illness severity and mortality among COVID-19-infected HCWs.

Multidimensional characteristics, prognostic role, and preoperative prediction of peritoneal sarcomatosis in retroperitoneal sarcoma

Background

Peritoneal sarcomatosis (PS) could occur in patients with retroperitoneal sarcomas (RPS). This study aimed to expand the understanding of PS on its characteristics and prognostic role, and develop a nomogram to predict its occurrence preoperatively.

Methods

Data of 211 consecutive patients with RPS who underwent surgical treatment between 2011 and 2019 was retrospectively reviewed. First, the clinicopathological characteristics of PS were summarized and analyzed. Second, the disease-specific survival (DSS) and recurrence-free survival (RFS) of patients were analyzed to evaluate the prognostic role of PS. Third, preoperative imaging, nearly the only way to detect PS preoperatively, was combined with other screened risk factors to develop a nomogram. The performance of the nomogram was assessed.

Results

Among the 211 patients, 49 (23.2%) patients had PS with an incidence of 13.0% in the primary patients and 35.4% in the recurrent patients. The highest incidence of PS occurred in dedifferentiated liposarcoma (25.3%) and undifferentiated pleomorphic sarcoma (25.0%). The diagnostic sensitivity of the preoperative imaging was 71.4% and its specificity was 92.6%. The maximum standardized uptake value (SUVmax) was elevated in patients with PS (P<0.001). IHC staining for liposarcoma revealed that the expression of VEGFR-2 was significantly higher in the PS group than that in the non-PS group (P = 0.008). Survival analysis (n =196) showed significantly worse DSS in the PS group than in non-PS group (median: 16.0 months vs. not reached, P < 0.001). In addition, PS was proven as one of the most significant prognostic predictors of both DSS and RFS by random survival forest algorithm. A nomogram to predict PS status was developed based on preoperative imaging combined with four risk factors including the presentation status (primary vs. recurrent), ascites, SUVmax, and tumor size. The nomogram significantly improved the diagnostic sensitivity compared to preoperative imaging alone (44/49, 89.8% vs. 35/49, 71.4%). The C-statistics of the nomogram was 0.932, and similar C-statistics (0.886) was achieved at internal cross-validation.

Conclusion

PS is a significant prognostic indicator for RPS, and it occurs more often in recurrent RPS and in RPS with higher malignant tendency. The proposed nomogram is effective to predict PS preoperatively.

Factors which impact the length of hospitalisation and death rate of COVID-19 patients based on initial triage using capillary blood gas tests: a single centre study

An important element in the effective treatment of patients infected with the SARS-CoV-2 virus during the pandemic is an effective early triage to determine patient allocation and in-patient therapy. This paper assesses the prognostic value of capillary blood gas tests in predicting extended hospitalisation and death due to COVID-19. This retrospective statistical research is based on a group of 200 patients, hospitalised from 15 October 2020 to 08 March 2021. The study utilised the treatment documentation of these patients hospitalised due to COVID-19 at the Pulmonology and Thoracic Surgery Centre in Bystra (Southern Poland) during this period. The hospital has 50 beds with access to oxygen for COVID-19 patients and a five-bed intensive care unit. On the basis of the obtained results, conclusions were drawn that the need for early oxygen therapy with an oxygen mask and low pH values in capillary blood are significant risk factors for prolonging hospitalisation due to COVID-19. Age, the need for early oxygen mask therapy and low oxygen saturation are important risk factors for death from COVID-19. Capillary blood gas analysis is a simple and effective method of early in-patient segregation of COVID-19 patients.

Is there an association between hospital staffing levels and inpatient-COVID-19 mortality rates?

Objective

This study aims to investigate the relationship between RNs and hospital-based medical specialties staffing levels with inpatient COVID-19 mortality rates.

Methods

We relied on data from AHA Annual Survey Database, Area Health Resource File, and UnitedHealth Group Clinical Discovery Database. In phase 1 of the analysis, we estimated the risk-standardized event rates (RSERs) based on 95,915 patients in the UnitedHealth Group Database 1,398 hospitals. We then used beta regression to analyze the association between hospital- and county- level factors with risk-standardized inpatient COVID-19 mortality rates from March 1, 2020, through December 31, 2020.

Results

Higher staffing levels of RNs and emergency medicine physicians were associated with lower COVID-19 mortality rates. Moreover, larger teaching hospitals located in urban settings had higher COVID-19 mortality rates. Finally, counties with greater social vulnerability, specifically in terms of housing type and transportation, and those with high infection rates had the worst patient mortality rates.

Conclusion

Higher staffing levels are associated with lower inpatient mortality rates for COVID-19 patients. More research is needed to determine appropriate staffing levels and how staffing levels interact with other factors such as teams, leadership, and culture to impact patient care during pandemics.

Risk factors for mortality in COVID-19 patients in sub-Saharan Africa: A systematic review and meta-analysis

Aim

Mortality rates of coronavirus-2019 (COVID-19) disease continue to increase worldwide and in Africa. In this study, we aimed to summarize the available results on the association between sociodemographic, clinical, biological, and comorbidity factors and the risk of mortality due to COVID-19 in sub-Saharan Africa.

Methods

We followed the PRISMA checklist (S1 Checklist). We searched PubMed, Google Scholar, and European PMC between January 1, 2020, and September 23, 2021. We included observational studies with Subjects had to be laboratory-confirmed COVID-19 patients; had to report risk factors or predictors of mortality in COVID-19 patients, Studies had to be published in English, include multivariate analysis, and be conducted in the sub-Saharan region. Exclusion criteria included case reports, review articles, commentaries, errata, protocols, abstracts, reports, letters to the editor, and repeat studies. The methodological quality of the studies included in this meta-analysis was assessed using the methodological items for nonrandomized studies (MINORS). Pooled hazard ratios (HR) or odds ratios (OR) and 95% confidence intervals (CI) were calculated separately to identify mortality risk. In addition, publication bias and subgroup analysis were assessed.

Results and discussion

Twelve studies with a total of 43598 patients met the inclusion criteria. The outcomes of interest were mortality. The results of the analysis showed that the pooled prevalence of mortality in COVID-19 patients was 4.8%. Older people showed an increased risk of mortality from SARS-Cov-2. The pooled hazard ratio (pHR) and odds ratio (pOR) were 9.01 (95% CI; 6.30–11.71) and 1.04 (95% CI; 1.02–1.06), respectively. A significant association was found between COVID-19 mortality and men (pOR = 1.52; 95% CI 1.04–2). In addition, the risk of mortality in patients hospitalized with COVID-19 infection was strongly influenced by chronic kidney disease (CKD), hypertension, severe or critical infection on admission, cough, and dyspnea. The major limitations of the present study are that the data in the meta-analysis came mainly from studies that were published, which may lead to publication bias, and that the causal relationship between risk factors and poor outcome in patients with COVID-19 cannot be confirmed because of the inherent limitations of the observational study.

Conclusions

Advanced age, male sex, CKD, hypertension, severe or critical condition on admission, cough, and dyspnea are clinical risk factors for fatal outcomes associated with coronavirus. These findings could be used for research, control, and prevention of the disease and could help providers take appropriate measures and improve clinical outcomes in these patients.

Predicting In-Hospital Mortality in Severe COVID-19: A Systematic Review and External Validation of Clinical Prediction Rules

Multiple prediction models for risk of in-hospital mortality from COVID-19 have been developed, but not applied, to patient cohorts different to those from which they were derived. The MEDLINE, EMBASE, Scopus, and Web of Science (WOS) databases were searched. Risk of bias and applicability were assessed with PROBAST. Nomograms, whose variables were available in a well-defined cohort of 444 patients from our site, were externally validated. Overall, 71 studies, which derived a clinical prediction rule for mortality outcome from COVID-19, were identified. Predictive variables consisted of combinations of patients′ age, chronic conditions, dyspnea/taquipnea, radiographic chest alteration, and analytical values (LDH, CRP, lymphocytes, D-dimer); and markers of respiratory, renal, liver, and myocardial damage, which were mayor predictors in several nomograms. Twenty-five models could be externally validated. Areas under receiver operator curve (AUROC) in predicting mortality ranged from 0.71 to 1 in derivation cohorts; C-index values ranged from 0.823 to 0.970. Overall, 37/71 models provided very-good-to-outstanding test performance. Externally validated nomograms provided lower predictive performances for mortality in their respective derivation cohorts, with the AUROC being 0.654 to 0.806 (poor to acceptable performance). We can conclude that available nomograms were limited in predicting mortality when applied to different populations from which they were derived.

Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients

The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers’ performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.

Prediction of Length of Hospital Stay of COVID-19 Patients Using Gradient Boosting Decision Tree

The aim of this paper is to predict the patient hospitalization time with coronavirus disease 2019 (COVID-19). It uses various data mining techniques, such as random forest. Many rules were derived by applying these techniques to the dataset. The extracted rules mainly were related to people over 55 years old. The rule with the most support states that if the person is between 70 and 80 years old, has cardiovascular disease, and the gender is female; then, the person will be hospitalized for at least five days. The gradient boosting random forest technique has performed better than other techniques. As a limitation of the study, it can be pointed out that a few features were unavailable and had not been recorded. Patients with diabetes, chronic respiratory problems, and cardiovascular diseases have a relatively long hospitalization. So, the hospital manager should consider a suitable priority for these patients. Older people were also more likely to take part in the selection rules.

Assessment of Kidney Involvement in COVID-19 Patient

Background and aim: Physicians need to be aware of the difficulties that SARS-CoV-2 infection brings to other regions of the body, such as the kidneys, even though the key emphasis is on pulmonary characteristics. The most frequent kidney complication among COVID-19 hospitalized patients is considered acute kidney injury (AKI). This study aimed to describe overall different aspects of acute kidney injury (AKI) in COVID-19 patients admitted to JLNMCH during the COVID-19 pandemic and to determine the prevalence of AKI among COVID-19 hospitalized patients.

Methods and materials: All adult patients (over the age of 18 years) who screened positive for COVID-19 in a swab specimen from areas of nasopharyngeal by reverse transcriptase polymerase chain reaction and then hospitalized were included in the study. Information was gathered on the patient's demographics, general medical history, and drugs prescribed. From past medical information, associated comorbidities and home pharmaceuticals were identified. We gathered hospitalization information, such as duration of stay in ICU, details about the application of mechanical ventilation, information regarding extracorporeal membrane aeration, details of the use of vasopressor administration, and baseline results of laboratory test along with baseline clinical information during 48 hours of hospitalization.

Results: The percentage of patients with no history of AKI requiring traumatic mechanical ventilation was 79.4%, while the percentage of patients with no history of AKI not requiring traumatic mechanical ventilation was 11.5%. The difference was relevant statistically (p<0.001). The percentage of patients with AKI of any stage requiring traumatic mechanical ventilation was 22.8%, while the percentage of patients with no history of AKI not requiring traumatic mechanical ventilation was 76.8%. The difference was relevant statistically (p<0.022).

Conclusion: We discovered that AKI was a rather typical finding among hospitalized COVID-19 patients. Patients hospitalized for COVID-19 had a poor prognosis if they developed AKI.

Individual genetic variability mainly of Proinflammatory cytokines, cytokine receptors, and toll-like receptors dictates pathophysiology of COVID-19 disease

Innate and acquired immunity responses are crucial for viral infection elimination. However, genetic variations in coding genes may exacerbate the inflammation or initiate devastating cytokine storms which poses severe respiratory conditions in coronavirus disease-19 (COVID-19). Host genetic variations in particular those related to the immune responses determine the patients' susceptibility and COVID-19 severity and pathophysiology. Gene polymorphisms such as single nucleotide polymorphisms (SNPs) of interferons, TNF, IL1, IL4, IL6, IL7, IL10, and IL17 predispose patients to the severe form of COVID-19 or severe acute respiratory syndrome coronavirus-2 (SARS-COV-2). These variations mainly alter the gene expression and cause a severe response by B cells, T cells, monocytes, neutrophils, and natural killer cells participating in a cytokine storm. Moreover, cytokines and chemokines SNPs are associated with the severity of COVID-19 and clinical outcomes depending on the corresponding effect. Additionally, genetic variations in genes encoding toll-like receptors (TLRs) mainly TLR3, TLR7, and TLR9 have been related to the COVID-19 severe respiratory symptoms. The specific relation of these mutations with the novel variants of concern (VOCs) infection remains to be elucidated. Genetic variations mainly within genes encoding proinflammatory cytokines, cytokine receptors, and TLRs predispose patients to COVID-19 disease severity. Understanding host immune gene variations associated with the SARS-COV-2 infection opens insights to control the pathophysiology of emerging viral infections.

Heme oxygenase-1 as an important predictor of the severity of COVID-19

Background and objective

A cytokine storm is caused by inflammatory cells, including pro-inflammatory macrophage phenotype (M1), and play a critical role in the pathogenesis of COVID-19, in which diffuse alveolar damage occurs in the lungs due to oxidative stress exposure. Heme oxygenase (HO)-1 is a stress-induced protein produced by the anti-inflammatory / anti-oxidative macrophage phenotype (M2), which also produces soluble CD163 (sCD163). In our study, we investigated and determined that serum HO-1 can be a predictive biomarker for assessing both the severity and the outcome of COVID-19 patients.

Method

The serum concentrations of HO-1 and sCD163 of COVID-19 patients were measured on admission. The relationship between these biomarkers and other clinical parameters and outcomes were evaluated.

Results

Sixty-four COVID-19 patients (11 mild, 38 moderate, and 15 severe cases) were assessed. The serum HO-1 tended to increase (11.0 ng/mL vs. 24.3 ng/mL vs. 59.6 ng/mL with severity). Serum HO-1 correlated with serum lactate dehydrogenase (R = 0.422), C-reactive protein (R = 0.463), and the ground glass opacity (GGO) and consolidation score (R = 0.625) of chest computed tomography. The serum HO-1 showed a better area under the curve (AUC) for predicting ICU admission than the serum sCD163 (HO-1; 0.816 and sCD163; 0.743). In addition, composite parameters including serum HO-1 and the GGO and consolidation score showed a higher AUC for predicting ICU admission than the AUC of a single parameter.

Conclusion

Clinically, serum HO-1, reflecting the activation of M2, could be a very useful marker for evaluating disease severity and predicting prognoses for COVID-19 patients. In addition, controlling activated M2 might be a preventative COVID-19 therapeutic target.

Lidocaine reinforces the anti-inflammatory action of dexamethasone on myeloid and epithelial cells activated by inflammatory cytokines or SARS-CoV-2 infection

Background

Severe cases of Coronavirus Disease 2019 (COVID-19) that require admission to the Intensive Care Unit (ICU) and mechanical ventilation assistance show a high mortality rate with currently few therapeutic options available. Severe COVID-19 is characterized by a systemic inflammatory condition, also called “cytokine storm”, which can lead to various multi-organ complications and ultimately death. Lidocaine, a safe local anesthetic that given intravenously is used to treat arrhythmias, has long been reported to have an anti-inflammatory and pro-homeostatic activity.

Methods

We studied the capacity of lidocaine to modulate cytokine secretion of mouse and human myeloid cell lines activated by different cytokines or Toll Like Receptor (TLR) ligands (flagellin (FliC), Lipopolysaccharide (LPS), Polyinosinic:polycytidylic acid (Poly I:C) and N-Palmitoyl-S- [2,3-bis(palmitoyloxy)-(2RS)-propyl]-(R)-cysteinyl-(S)-seryl-(S)-lysyl-(S)-lysyl-(S)-lysyl-(S)-lysine x 3HCl (Pam3Cys-SKKKK)) or by Severe acute respiratory syndromecoronavirus 2 (SARS-CoV-2) infection to epithelial cells. Reporter cell lines were used to study modulation of lidocaine of specific signaling pathways.

Results

Lidocaine used in combination with dexamethasone, had an additive effect in the modulation of cellular inflammatory response triggered by Tumoral Necrosis Factor alpha (TNFα), Interleukin 1 beta (IL-1β) as well as different TLR ligands. We also found that lidocaine in combination with dexamethasone modulates the Nuclear factor kappa B (NF-κB) pathway, inflammasome activation as well as interferon gamma receptor (IFNγR) signaling without affecting the type I interferons (Type I IFNs) pathway. Furthermore, we showed that lidocaine and dexamethasone treatment of epithelial cells infected with SARS-CoV-2 modulated the expression of chemokines that contribute to pro-inflammatory effects in severe COVID.

Conclusions

We reported for the first time in vitro anti-inflammatory capacity of lidocaine on SARS-CoV-2 triggered immune pathways. These results indicated the potential of lidocaine to treat COVID-19 patients and add tools to the therapeutic options available for these concerning cases.

Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial

BACKGROUND

We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown.

OBJECTIVES

The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS).

METHODS

We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays.

RESULTS

Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location.

CONCLUSION

An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT04570488.

Proteomic analysis of cardiometabolic biomarkers and predictive modeling of severe outcomes in patients hospitalized with COVID-19

BACKGROUND

The high heterogeneity in the symptoms and severity of COVID-19 makes it challenging to identify high-risk patients early in the disease. Cardiometabolic comorbidities have shown strong associations with COVID-19 severity in epidemiologic studies. Cardiometabolic protein biomarkers, therefore, may provide predictive insight regarding which patients are most susceptible to severe illness from COVID-19.

METHODS

In plasma samples collected from 343 patients hospitalized with COVID-19 during the first wave of the pandemic, we measured 92 circulating protein biomarkers previously implicated in cardiometabolic disease. We performed proteomic analysis and developed predictive models for severe outcomes. We then used these models to predict the outcomes of out-of-sample patients hospitalized with COVID-19 later in the surge (N = 194).

RESULTS

We identified a set of seven protein biomarkers predictive of admission to the intensive care unit and/or death (ICU/death) within 28 days of presentation to care. Two of the biomarkers, ADAMTS13 and VEGFD, were associated with a lower risk of ICU/death. The remaining biomarkers, ACE2, IL-1RA, IL6, KIM1, and CTSL1, were associated with higher risk. When used to predict the outcomes of the future, out-of-sample patients, the predictive models built with these protein biomarkers outperformed all models built from standard clinical data, including known COVID-19 risk factors.

CONCLUSIONS

These findings suggest that proteomic profiling can inform the early clinical impression of a patient's likelihood of developing severe COVID-19 outcomes and, ultimately, accelerate the recognition and treatment of high-risk patients.

Utility of hematological and inflammatory biomarkers in predicting recovery in critical Covid-19 patients: Our experience in the largest Covid-19 treating center in Lebanon

Background

COVID-19 pandemic has led to a catastrophic shortage of ICU beds. This has resulted in the need to identify patients that can be discharged early before full clinical recovery. We designed this study to determine if in changes routine tests like CBCD and CRP can be a useful complement to clinical status when deciding to discharge patients from ICU.

Methods

This retrospective study was conducted in Rafic Hariri University Hospital. Levels of biomarkers measured at admission (T1) and within 3 days of outcome (T2) were collected and ratios (T2/T1) were calculated. The Odds Ratios of association between the changes in these biomarkers and outcome were estimated. Multivariate analysis and AUC for the performance of these biomarkers were also conducted.

Results

We found on multivariate analysis that reduction in counts of lymphocyte and platelets and elevation in counts of neutrophils and level of CRP (T2/T1 ratio > 1) are strongly associated with mortality with respective ORs estimated at 6.74, 3.26, 5.65 and 4.34 [p-values < 0.001]. AUCs were found to lie in a range of 0.68 to 0.81 indicating fair to good performance. Other factors found to impact survival were AKI, AF and ACS [p-values < 0.01]. In contrast to other studies, risk factors didn’t show an association with survival when adjusted for effects of complications and changes in biomarker levels.

Conclusions

Our results confirm that inexpensive tests like lymphocyte count and CRP can be reliably used to follow COVID-19 patients in ICU and to support the decision to discharge patients.

Predictive Risk Factors at Admission and a “Burning Point” During Hospitalization Serve as Sequential Alerts for Critical Illness in Patients With COVID-19

Background

We intended to establish a novel critical illness prediction system combining baseline risk factors with dynamic laboratory tests for patients with coronavirus disease 2019 (COVID-19).

Methods

We evaluated patients with COVID-19 admitted to Wuhan West Union Hospital between 12 January and 25 February 2020. The data of patients were collected, and the illness severity was assessed.

Results

Among 1,150 enrolled patients, 296 (25.7%) patients developed into critical illness. A baseline nomogram model consists of seven variables including age [odds ratio (OR), 1.028; 95% confidence interval (CI), 1.004–1.052], sequential organ failure assessment (SOFA) score (OR, 4.367; 95% CI, 3.230–5.903), neutrophil-to-lymphocyte ratio (NLR; OR, 1.094; 95% CI, 1.024–1.168), D-dimer (OR, 1.476; 95% CI, 1.107–1.968), lactate dehydrogenase (LDH; OR, 1.004; 95% CI, 1.001–1.006), international normalised ratio (INR; OR, 1.027; 95% CI, 0.999–1.055), and pneumonia area interpreted from computed tomography (CT) images (medium vs. small [OR, 4.358; 95% CI, 2.188–8.678], and large vs. small [OR, 9.567; 95% CI, 3.982–22.986]) were established to predict the risk for critical illness at admission. The differentiating power of this nomogram scoring system was perfect with an area under the curve (AUC) of 0.960 (95% CI, 0.941–0.972) in the training set and an AUC of 0.958 (95% CI, 0.936–0.980) in the testing set. In addition, a linear mixed model (LMM) based on dynamic change of seven variables consisting of SOFA score (value, 2; increase per day [I/d], +0.49), NLR (value, 10.61; I/d, +2.07), C-reactive protein (CRP; value, 46.9 mg/L; I/d, +4.95), glucose (value, 7.83 mmol/L; I/d, +0.2), D-dimer (value, 6.08 μg/L; I/d, +0.28), LDH (value, 461 U/L; I/d, +13.95), and blood urea nitrogen (BUN value, 6.51 mmol/L; I/d, +0.55) were established to assist in predicting occurrence time of critical illness onset during hospitalization.

Conclusion

The two-checkpoint system could assist in accurately and dynamically predicting critical illness and timely adjusting the treatment regimen for patients with COVID-19.

Evaluation of the Charlson Comorbidity Index and Laboratory Parameters as Independent Early Mortality Predictors in Covid 19 Patients

Purpose

Patients and methods

Results

Conclusion

Sports Imaging of COVID-19: A Multi-Organ System Review of Indications and Imaging Findings

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in December 2019 in Wuhan, China. Although coronavirus disease-19 (COVID-19) affects every population group, the sports community and athletes require special consideration of the effects on cardiovascular, musculoskeletal, neurologic, and respiratory systems. A comprehensive understanding of imaging indications, findings, and features of COVID-19 supports appropriate imaging utilization and effective patient management and treatment.

PURPOSE

To review the spectrum of sports imaging in COVID-19 infection, organ system manifestations, vaccine effects, and complications in recreational and competitive athletes.

STUDY DESIGN

Narrative review.

LEVEL OF EVIDENCE

Levels 4 and 5.

METHODS

Based on a PubMed database search, studies describing the imaging findings of COVID-19 infection, organ system manifestations, vaccine effects, and complications in recreational and competitive athletes were included.

RESULTS

On March 11, 2020, World Health Organization officially declared COVID-19 a global pandemic. As of May 9, 2022, more than 515 million confirmed cases of COVID-19 were reported globally. While the multisystem effects of COVID-19 are incompletely understood, the role of imaging in diagnosing, monitoring, and prognosticating active disease, long-term effects, and complications is evolving. In the respiratory system, imaging plays an important role in diagnosing, characterizing, and monitoring pulmonary COVID-19 infections, barotrauma, and COVID-19-associated chronic pulmonary opacities and fibrotic-like lung changes. Ultrasonography, computed tomography, and magnetic resonance imaging aid in the timely diagnosis of ischemic, embolic, and thrombotic peripheral and central cardiovascular events, including deep venous thrombosis, pulmonary embolism, myocarditis, and stroke. COVID-19-associated musculoskeletal and peripheral nervous system manifestations include rhabdomyolysis and myonecrosis, plexus and peripheral neuropathies, Guillain-Barré syndrome, and shoulder injury related to vaccine administration.

CONCLUSIONS

In athletes, COVID-19 infections and associated effects on cardiovascular, musculoskeletal, neurologic, and respiratory systems require special consideration. With the increasing understanding of the multisystem effects of COVID-19, the role of imaging in diagnosing, monitoring, and prognosticating active disease, long-term effects, and complications is evolving. A comprehensive understanding of imaging indications, COVID-19 imaging features, and organ system effects aids in appropriate imaging utilization and effective patient management and treatments.

Booster Vaccination Decreases 28-Day All-Cause Mortality of the Elderly Hospitalized Due to SARS-CoV-2 Delta Variant

(1) Background: SARS-CoV-2 infections are associated with an increased risk of hospital admissions especially in the elderly (age ≥ 65 years) and people with multiple comorbid conditions. (2) Methods: We investigated the effect of additional booster vaccinations following the primary vaccination series of mRNA, inactivated whole virus, or vector vaccines on infections with the SARS-CoV-2 delta variant in the total Hungarian elderly population. The infection, hospital admission, and 28-day all-cause mortality of elderly population was assessed. (3) Results: A total of 1,984,176 people fulfilled the criteria of elderly including 299,216 unvaccinated individuals, while 1,037,069 had completed primary vaccination and 587,150 had obtained an additional booster. The primary vaccination series reduced the risk of infection by 48.88%, the risk of hospital admission by 71.55%, and mortality by 79.87%. The booster vaccination had an additional benefit, as the risk of infection, hospital admission, and all-cause mortality were even lower (82.95%; 92.71%; and 94.24%, respectively). Vaccinated patients needing hospitalization suffered significantly more comorbid conditions, indicating a more vulnerable population. (4) Conclusions: Our data confirmed that the primary vaccination series and especially the booster vaccination significantly reduced the risk of the SARS-CoV-2 delta-variant-associated hospital admission and 28-day all-cause mortality in the elderly despite significantly more severe comorbid conditions.

COVID-19–related clinical outcomes among Korean hemodialysis patients

Background

Hemodialysis (HD) patients are more vulnerable to viral epidemics, experiencing higher mortality rates compared to individuals without chronic kidney disease (CKD). This retrospective cohort study sought to demonstrate clinical outcomes and associated factors among coronavirus disease 2019 (COVID-19) confirmed Korean HD patients.

Methods

From February 2020 to November 2021, the COVID-19 Task Force Team collected clinical data for HD patients with confirmed COVID-19 via a self-report survey of nephrologists. The composite outcome included in-hospital mortality, admission to the intensive care unit (ICU), and use of mechanical ventilation. Risk factors associated with clinical outcomes were analyzed among HD patients and compared to those of individuals without CKD using the COVID-19 database from the Korea Disease Control and Prevention Agency.

Results

A total of 380 HD patients from 206 facilities were diagnosed with COVID-19. Fever (49.5%) and cough (25.7%) were the two most common initial symptoms. The overall in-hospital fatality rate was 22.4% and even higher among ICU admission cases (64.7%). Non-survivors were older, more frequently developed shortness of breath, and were more likely to come from a nursing hospital. Compared to the age- and sex-matched non-CKD population, HD patients showed greater risk of in-hospital mortality (hazard ratio, 2.07; 95% confidence interval, 1.56–2.75; p < 0.001) and composite outcome (hazard ratio, 3.50; 95% confidence interval, 2.56–4.77; p < 0.001).

Conclusion

HD patients have a greater risk of in-hospital mortality and morbidity from COVID-19. Special attention should be paid to COVID-19 HD patients when they are older or present with symptoms.

Absence of negativization of nasal swab test and frailty as risk factors for mortality in elderly COVID-19 patients admitted in long-term care facilities

METHODS

A limited amount of data is now available on prognostic factors and mortality among elderly people resident in Long-Term Care facilities and in post-acute units. These populations (in particular those with underlying chronic medical conditions) seem to have higher risk of morbidity and mortality related to COVID-19 disease, but further evidence is needed. The aim of our study is to investigate the impact of some well-known prognostic factors in elderly patients (≥ 65 years) with COVID-19 admitted in the Long-Term Care setting in AUSL Ferrara, Italy. We performed binary regression logistic analysis for some variables (demographic data, clinical data including nasal swab test (NST) at discharge and frailty assessments) to find potential predictors of mortality. We subsequently tested statistically significant variables using Kaplan-Meier curves and Cox-regression models to find survival outcomes and related hazard ratio.

RESULTS

Risk factors associated with increased mortality resulted NST at discharge, infection, age and frailty. At a further secondary analysis carried out between NST at discharge, age and clinical frailty scale (CFS) < 5, we found a positive correlation between NST at discharge and CFS < 5. Kaplan-Meier curves showed a statistically significant difference regarding frailty and NST at discharge but not for age.

CONCLUSION

Our study showed that absence of negativization of NST at discharge and frailty are strong predictors for mortality in elderly COVID-19 patients admitted in Long-Term Care facilities, while age and the comorbidity burden are less important.

Role of diabetes mellitus in the clinical course and outcome of SARS-CoV-2 infected patients

PURPOSE

Our aim was to study patients with diabetes mellitus and SARS-CoV-2-infection diagnosed during the first pandemic wave in Greece.

METHODS

Cases were retrieved from the national database of SARS-CoV-2 infections.

RESULTS

We studied 2624 SARS-CoV-2 infected cases, including 157 with diabetes. Patients with diabetes more often had other comorbidities (68.8 vs. 24.1%; p-value < 0.001). Among patients with diabetes, 149 (94.9%) developed symptomatic disease (COVID-19) compared to 1817 patients (73.7%) without diabetes (p-value < 0.001). A total of 126 patients with diabetes and COVID-19 were hospitalized and 41 died (27.5% case-fatality rate compared to 7.5% among patients without diabetes; p-value < 0.001). Patients with diabetes more often were hospitalized, developed complications, were admitted to the intensive care unit (ICU), received invasive mechanical ventilation, and died compared to patients without diabetes (p-values < 0.001 to 0.002 for all comparisons). Multivariate logistic regression analyses revealed that diabetes, having other comorbidities, and older age were significantly associated with higher risk for hospitalization, ICU admission, invasive mechanical ventilation, and death, and that obesity was significantly associated with higher risk for hospitalization, ICU admission, and mechanical intubation, while female gender protected against these outcomes.

CONCLUSION

COVID-19 is associated with increased rates of serious morbidity and adverse outcome in patients with diabetes and represents a severe illness for them.

Seroprevalence of SARS-CoV-2-specific antibodies and vaccination-related adverse events in systemic lupus erythematosus and rheumatoid arthritis

Background

This study aimed to investigate the seroreactivity of Coronavirus disease 2019 (COVID-19) vaccination and its adverse events among systemic lupus erythematosus (SLE) patients, rheumatoid arthritis (RA) patients, and healthy controls (HCs).

Methods

A total of 60 SLE patients, 70 RA patients and 35 HCs, who received a complete inactivated COVID-19 vaccine (Vero cells) regimen, were recruited in the current study. Serum IgG and IgM antibodies against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) were determined by using chemiluminescent microparticle immunoassay (CMIA).

Results

There were no significant differences regarding the seroprevalences of IgG and IgM antibodies against SARS-CoV-2, and the self-reported vaccination-related adverse events among SLE patients, RA patients and HCs. The inactivated COVID-19 vaccines appeared to be well-tolerated and moderately immunogenic. In addition, case-only analysis indicated that in SLE patients, the disease manifestation of rash and anti-SSA autoantibody were associated with seroprevalence of IgG antibody against SARS-CoV-2, whereas the uses of ciclosporin and leflunomide had influence on the seroprevalence of IgM antibody against SARS-CoV-2. In RA patients, rheumatoid factor (RF) appeared to be associated with the seroprevalence of IgG antibody against SARS-CoV-2.

Conclusion

Our study reveals that the seroprevalences of IgG and IgM antibodies against SARS-CoV-2 and vaccination-related adverse effects are similar among SLE, RA and HCs, suggesting that COVID-19 vaccine is safe and effective for SLE and RA patients to prevent from the pandemic of COVID-19.

PK/PD modeling links accelerated resolution of COVID‐19‐related clinical symptoms to SARS‐CoV‐2 viral load reduction in patients following treatment with Bamlanivimab alone or Bamlanivimab and Etesevimab together

The relationship between severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) viral load reduction and disease symptom resolution remains largely undefined for coronavirus disease 2019 (COVID‐19). While the vaccine‐derived immunity takes time to develop, neutralizing monoclonal antibodies offer immediate, passive immunity to patients with COVID‐19. Bamlanivimab and etesevimab are two potent neutralizing monoclonal antibodies directed to the receptor binding domain of the spike protein of SARS‐CoV‐2. This study aims to describe the relationship between viral load and resolution of eight common COVID‐19‐related symptoms in patients following treatment with neutralizing monoclonal antibodies (bamlanivimab alone or bamlanivimab and etesevimab together), in a phase II clinical trial. Corresponding pharmacokinetics (PKs), viral load, and COVID‐19‐related symptom data were modeled using Nonlinear Mixed Effects Modeling to describe the time‐course of eight COVID‐19‐related symptoms in an ordered categorical manner (none, mild, moderate, and severe), following administration of bamlanivimab or bamlanivimab and etesevimab together to participants with COVID‐19. The PK/pharmacodynamic (PD) models characterized the exposure‐viral load‐symptom time course of the eight preselected COVID‐19‐related symptoms. Baseline viral load (BVL), change in viral load from baseline, and time since the onset of symptoms, demonstrated statistically significant effects on symptom score probabilities. Higher BVL generally indicated an increased probability of symptom severity. The severity of symptoms decreased over time, partially driven by the decrease in viral load. The effect of increasing time resulting in decreased severity of symptoms was over and above the effect of decreasing viral load. Administration of bamlanivimab alone or together with etesevimab results in a faster time to resolution of COVID‐19‐related symptoms compared to placebo.

Use of azurocidin (heparin binding protein) and interleukin-1ß as prognostic indicators in COVID-19 patients

Objective: Considering the published manuscripts on this subject, it is thought that IL-1B and azurocidin may be diagnostic and prognostic factors in severe COVID-19 disease. This study aimed to determine whether azurocidin and IL-1β are useful biomarkers and are associated with disease exacerbation in patients needing advanced treatment options. Material and Method: Our study was performed retrospectively. The data of a total of 291 patients followed in the hospital due to COVID-19 were collected. Among these patients, 66 randomly selected patients were included in this study. The control group consisted of 24 healthy individuals referred to the infectious disease outpatient clinic who weren’t no diagnosis of COVID-19 infection or any other infection. After the samples were taken into tubes without anticoagulant, they were kept at room temperature for 30 min. Afterwards, it was centrifuged at 1000xg for 15 min at +4° according to the instructions of the ELISA kit we used. The remaining serum was transferred to Eppendorf tubes and stored at −80° throughout this study. Results: A statistically significant strong positive correlation was determined between IL1β and azurocidin. A statistically significant weak positive correlation was found between IL1β and CRP, ferritin and neutrophil count, weak negative correlation with albumin, and moderately strong positive correlation with leukocyte count. Moreover, a statistically significant weak positive correlation was found between azurucidin and CRP, ferritin, and neutrophil counts. Conclusion: Azurocidin and IL-1β may serve as a potential therapeutic target for patients at risk of developing systemic multi-organ failure, with improved patient prognosis and prevention of death in severely ill patients. Although these results may contribute to the improvement of the follow-up and treatment of the patients and reduce mortality rates, there is a need for randomized controlled studies with a larger number of patients to be performed in the future.

Predicting progression to severe COVID-19 using the PAINT score

Objectives

One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. We aimed to develop a new score for predicting progression from mild/moderate to severe COVID-19.

Methods

A total of 239 hospitalized patients with COVID-19 from two medical centers in China between February 6 and April 6, 2020 were retrospectively included. The prognostic abilities of variables, including clinical data and laboratory findings from the electronic medical records of each hospital, were analysed using the Cox proportional hazards model and Kaplan–Meier methods. A prognostic score was developed to predict progression from mild/moderate to severe COVID-19.

Results

Among the 239 patients, 216 (90.38%) patients had mild/moderate disease, and 23 (9.62%) progressed to severe disease. After adjusting for multiple confounding factors, pulmonary disease, age > 75, IgM, CD16⁺/CD56⁺ NK cells and aspartate aminotransferase were independent predictors of progression to severe COVID-19. Based on these five factors, a new predictive score (the ‘PAINT score’) was established and showed a high predictive value (C-index = 0.91, 0.902 ± 0.021, p < 0.001). The PAINT score was validated using a nomogram, bootstrap analysis, calibration curves, decision curves and clinical impact curves, all of which confirmed its high predictive value.

Conclusions

The PAINT score for progression from mild/moderate to severe COVID-19 may be helpful in identifying patients at high risk of progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07466-4.

Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques

Motivation

Patients with novel coronavirus disease 2019 (COVID-19) worsen into critical illness suddenly is a matter of great concern. Early identification and effective triaging of patients with a high risk of developing critical illness COVID-19 upon admission can aid in improving patient care, increasing the cure rate, and mitigating the burden on the medical care system. This study proposed and extended classical least absolute shrinkage and selection operator (LASSO) logistic regression to objectively identify clinical determination and risk factors for the early identification of patients at high risk of progression to critical illness at the time of hospital admission.

Methods

In this retrospective multicenter study, data of 1,929 patients with COVID-19 were assessed. The association between laboratory characteristics measured at admission and critical illness was screened with logistic regression. LASSO logistic regression was utilized to construct predictive models for estimating the risk that a patient with COVID-19 will develop a critical illness.

Results

The development cohort consisted of 1,363 patients with COVID-19 with 133 (9.7%) patients developing the critical illness. Univariate logistic regression analysis revealed 28 variables were prognosis factors for critical illness COVID-19 (p < 0.05). Elevated CK-MB, neutrophils, PCT, α-HBDH, D-dimer, LDH, glucose, PT, APTT, RDW (SD and CV), fibrinogen, and AST were predictors for the early identification of patients at high risk of progression to critical illness. Lymphopenia, a low rate of basophils, eosinophils, thrombopenia, red blood cell, hematocrit, hemoglobin concentration, blood platelet count, and decreased levels of K, Na, albumin, albumin to globulin ratio, and uric acid were clinical determinations associated with the development of critical illness at the time of hospital admission. The risk score accurately predicted critical illness in the development cohort [area under the curve (AUC) = 0.83, 95% CI: 0.78-0.86], also in the external validation cohort (n = 566, AUC = 0.84).

Conclusion

A risk prediction model based on laboratory findings of patients with COVID-19 was developed for the early identification of patients at high risk of progression to critical illness. This cohort study identified 28 indicators associated with critical illness of patients with COVID-19. The risk model might contribute to the treatment of critical illness disease as early as possible and allow for optimized use of medical resources.

Comparative Clinical Characteristics, Laboratory Findings, and Outcomes of Hypoxemic and Non-Hypoxemic Patients Treated at a Makeshift COVID-19 Unit in Bangladesh: A Retrospective Chart Analysis

Background: Starting on 31 December 2019, from Wuhan City, China, Coronavirus disease 2019 (COVID-19) caused a global pandemic by 11 March 2020. Bangladesh detected its first case on 8 March 2020, only 66 days later the detection of the first case in China. We aimed to describe the epidemiology, clinical features, laboratory characteristics, and outcomes of Bangladeshi COVID-19 patients. Methods: This retrospective chart analysis compared Bangladeshi COVID-19 patients with hypoxemia compared to those without hypoxemia treated in a makeshift COVID-19 unit of icddr,b. Results: By March 2021, 207 remained in-patient. Nineteen patients (9.2%) died, whereas 10 (4.8%) were referred to different facilities for definitive care. Out of 207 in-patients, 88 patients required oxygen therapy. Multivariable logistic regression identified age (1.07 (1.02−1.13)), dyspnea (3.56 (1.06−11.96)), high CRP (1.13 (1.03−1.25)), and lymphopenia (6.18 (1.81−21.10)) as the independent predictors for hypoxemia in patients hospitalized for COVID 19 (for all, p < 0.05). Conclusion: Older age, dyspnea, high CRP, and lymphopenia are simple, but important, clinical and laboratory parameters. These may help clinicians to identify COVID-19 patients early who are at risk of fatal hypoxemia. Close monitoring, and prompt and aggressive treatment of these patients would curb their morbidity and mortality, especially in resource-limited settings.

Can Lung Imaging Scores and Clinical Variables Predict Severe Course and Fatal Outcome in COVID-19 Pneumonia Patients? A Single-Center Observational Study

COVID-19 prediction models mostly consist of combined clinical features, laboratory parameters, and, less often, chest X-ray (CXR) findings. Our main goal was to propose a prediction model involving imaging methods, specifically ultrasound. This was a single-center, retrospective cohort observational study of patients admitted to the University Hospital Split from November 2020 to May 2021. Imaging protocols were based on the assessment of 14 lung zones for both lung ultrasound (LUS) and computed tomography (CT), correlated to a CXR score assessing 6 lung zones. Prediction models for the necessity of mechanical ventilation (MV) or a lethal outcome were developed by combining imaging, biometric, and biochemical parameters. A total of 255 patients with COVID-19 pneumonia were included in the study. Four independent predictors were added to the regression model for the necessity of MV: LUS score, day of the illness, leukocyte count, and cardiovascular disease (χ2 = 29.16, p < 0.001). The model accurately classified 89.9% of cases. For the lethal outcome, only two independent predictors contributed to the regression model: LUS score and patient’s age (χ2 = 48.56, p < 0.001, 93.2% correctly classified). The predictive model identified four key parameters at patient admission which could predict an adverse outcome.

Causal relationship between physical activity, leisure sedentary behaviors and COVID-19 risk: a Mendelian randomization study

BACKGROUND

The 2019 coronavirus disease pandemic (COVID-19) poses an enormous threat to public health worldwide, and the ensuing management of social isolation has greatly decreased opportunities for physical activity (PA) and increased opportunities for leisure sedentary behaviors (LSB). Given that both PA and LSB have been established as major influencing factors for obesity, diabetes and cardiometabolic syndrome, whether PA/LSB in turn affects the susceptibility to COVID-19 by disrupting metabolic homeostasis remains to be explored. In this study, we aimed to systematically evaluate the causal relationship between PA/LSB and COVID-19 susceptibility, hospitalization and severity using a Mendelian randomization study.

METHODS

Data were obtained from a large-scale PA dataset (N = 377,000), LSB dataset (N = 422,218) and COVID-19 Host Genetics Initiative (N = 2,586,691). The causal effects were estimated with inverse variance weighted, MR-Egger, weighted median and MR-PRESSO. Sensitivity analyses were implemented with Cochran's Q test, MR-Egger intercept test, MR-PRESSO, leave-one-out analysis and the funnel plot. Risk factor analyses were further conducted to investigate the potential mediators.

RESULTS

Genetically predicted accelerometer-assessed PA decreased the risk for COVID-19 hospitalization (OR = 0.93, 95% CI 0.88-0.97; P = 0.002), while leisure television watching significantly increased the risk of COVID-19 hospitalization (OR = 1.55, 95% CI 1.29-1.88; P = 4.68 × 10-6) and disease severity (OR = 1.85, 95% CI 1.33-2.56; P = 0.0002) after Bonferroni correction. No causal effects of self-reported moderate to vigorous physical activity (MVPA), accelerometer fraction of accelerations > 425 milligravities, computer use or driving on COVID-19 progression were observed. Risk factor analyses indicated that the above causal associations might be mediated by several metabolic risk factors, including smoking, high body mass index, elevated serum triglyceride levels, insulin resistance and the occurrence of type 2 diabetes.

CONCLUSION

Our findings supported a causal effect of accelerometer-assessed PA on the reduced risk of COVID-19 hospitalization as well as television watching on the increased risk of COVID-19 hospitalization and severity, which was potentially mediated by smoking, obesity and type 2 diabetes-related phenotypes. Particular attention should be given to reducing leisure sedentary behaviors and encouraging proper exercise during isolation and quarantine for COVID-19.

Survival analysis of COVID-19 patients in Ethiopia: A hospital-based study

BACKGROUND

COVID-19 is a global public health problem causing high mortality worldwide. This study aimed to assess time to death and predictors of mortality among patients hospitalized for COVID-19 in the Arsi zone treatment center.

METHOD

We performed a retrospective observational cohort study using medical records of laboratory-confirmed COVID-19 cases hospitalized at Bokoji Hospital COVID-19 treatment center from 1st July 2020 to 5th March 2021. We extracted data on the patients' sociodemographic and clinical characteristics from medical records of hospitalized patients retrospectively. We carried out Kaplan Meier and Cox regression analysis to estimate survival probability and investigate predictors of COVID-19 death 5% level of significance. The Adjusted Hazard Ratio (aHR) with 95% Confidence Interval (CI) was estimated and interpreted for predictors of time to death in the final cox model.

RESULT

A total of 422 COVID-19 patients treated were analyzed, of these more than one tenth (11.14%) deaths, with a mortality rate of 6.35 cases per 1000 person-days. The majority (87.2%) of deaths occurred within the first 14 days of admission, with a median time-to-death of nine (IQR: 8-12) days. We found patients that age between 31 and 45 years (aHR = 2.55; 95% CI: (1.03, 6.34), older than 46 years (aHR = 2.59 (1.27, 5.30), chronic obstructive pulmonary disease (aHR = 4.60, 95%CI: (2.37, 8.91), Chronic kidney disease (aHR = 5.58, 95%CI: (1.70, 18.37), HIV/AIDS (aHR = 3.66, 95%CI: (1.20, 11.10), admission to the Intensive care unit(aHR = 7.44, 95%CI: (1.82, 30.42), and being on intranasal oxygen care (aHR = 6.27, 95%CI: (2.75, 4.30) were independent risk factors increasing risk of death from COVID-19 disease than their counterparts.

CONCLUSION

The risk of dying due to COVID-19 disease was higher among patients with HIV/AIDS, chronic obstructive pulmonary disease, and chronic kidney diseases. We also found that older people, those admitted to ICU, and patients who received intranasal oxygen care had a higher risk of dying due to COVID-19 disease. Therefore, close monitoring hospitalized patients that are old aged and those with comorbidities after hospitalization is crucial within the first ten days of admission.

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

The Multifaceted Manifestations of Multisystem Inflammatory Syndrome during the SARS-CoV-2 Pandemic

The novel coronavirus SARS-CoV-2, which has similarities to the 2002–2003 severe acute respiratory syndrome coronavirus known as SARS-CoV-1, causes the infectious disease designated COVID-19 by the World Health Organization (Coronavirus Disease 2019). Although the first reports indicated that activity of the virus is centered in the lungs, it was soon acknowledged that SARS-CoV-2 causes a multisystem disease. Indeed, this new pathogen causes a variety of syndromes, including asymptomatic disease; mild disease; moderate disease; a severe form that requires hospitalization, intensive care, and mechanical ventilation; multisystem inflammatory disease; and a condition called long COVID or postacute sequelae of SARS-CoV-2 infection. Some of these syndromes resemble previously described disorders, including those with no confirmed etiology, such as Kawasaki disease. After recognition of a distinct multisystem inflammatory syndrome in children, followed by a similar syndrome in adults, various multisystem syndromes occurring during the pandemic associated or related to SARS-CoV-2 began to be identified. A typical pattern of cytokine and chemokine dysregulation occurs in these complex syndromes; however, the disorders have distinct immunological determinants that may help to differentiate them. This review discusses the origins of the different trajectories of the inflammatory syndromes related to SARS-CoV-2 infection.

A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients

Background

Coronavirus outbreak (SARS-CoV-2) has become a serious threat to human society all around the world. Due to the rapid rate of disease outbreaks and the severe shortages of medical resources, predicting COVID-19 disease severity continues to be a challenge for healthcare systems. Accurate prediction of severe patients plays a vital role in determining treatment priorities, effective management of medical facilities, and reducing the number of deaths. Various methods have been used in the literature to predict the severity prognosis of COVID-19 patients. Despite the different appearance of the methods, they all aim to achieve generalizable results by increasing the accuracy and reducing the errors of predictions. In other words, accuracy is considered the only effective factor in the generalizability of models. In addition to accuracy, reliability and consistency of results are other critical factors that must be considered to yield generalizable medical predictions. Since the role of reliability in medical decisions is significant, upgrading reliable medical data-driven models requires more attention.

Methods

This paper presents a new modeling technique to specify and maximize the reliability of results in predicting the severity prognosis of COVID-19 patients. We use the well-known classic regression as the basic model to implement our proposed procedure on it. To assess the performance of the proposed model, it has been applied to predict the severity prognosis of COVID-19 by using a dataset including clinical information of 46 COVID-19 patients. The dataset consists of two types of patients’ outcomes including mild (discharge) and severe (ICU or death). To measure the efficiency of the proposed model, we compare the accuracy of the proposed model to the classic regression model.

Results

The proposed reliability-based regression model, by achieving 98.6% sensitivity, 88.2% specificity, and 93.10% accuracy, has better performance than classic accuracy-based regression model with 95.7% sensitivity, 85.5% specificity, and 90.3% accuracy. Also, graphical analysis of ROC curve showed AUC 0.93 (95% CI 0.88–0.98) and AUC 0.90 (95% CI 0.85–0.96) for classic regression models, respectively.

Conclusions

Maximizing reliability in the medical forecasting models can lead to more generalizable and accurate results. The competitive results indicate that the proposed reliability-based regression model has higher performance in predicting the deterioration of COVID-19 patients compared to the classic accuracy-based regression model. The proposed framework can be used as a suitable alternative for the traditional regression method to improve the decision-making and triage processes of COVID-19 patients.

Humoral immunity against SARS-CoV-2 variants including omicron in solid organ transplant recipients after three doses of a COVID-19 mRNA vaccine

Objectives

Solid organ transplant recipients (SOTR) receiving post‐transplant immunosuppression show increased COVID‐19‐related mortality. It is unclear whether an additional dose of COVID‐19 vaccines can overcome the reduced immune responsiveness against severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) variants.

Methods

We analysed humoral immune responses against SARS‐CoV‐2 and its variants in 53 SOTR receiving SARS‐CoV‐2 vaccination.

Results

Following the initial vaccination series, 60.3% of SOTR showed no measurable neutralisation and only 18.9% demonstrated neutralising activity of > 90%. More intensive immunosuppression, antimetabolites in particular, negatively impacted antiviral immunity. While absolute IgG levels were lower in SOTR than controls, antibody titres against microbial recall antigens were higher. By contrast, SOTR showed reduced vaccine‐induced IgG/IgA antibody titres against SARS‐CoV‐2 and its delta variants and fewer linear B‐cell epitopes, indicating reduced B‐cell diversity. Importantly, a third vaccine dose led to an increase in anti‐SARS‐CoV‐2 antibody titres and neutralising activity across alpha, beta and delta variants and to the induction of anti‐SARS‐CoV‐2 CD4⁺ T cells in a subgroup of patients analysed. By contrast, we observed significantly lower antibody titres after the third dose with the omicron variant compared to the ancestral SARS‐CoV‐2 and the improvement in neutralising activity was much less pronounced than for all the other variants.

Conclusion

Only a small subgroup of solid organ transplant recipients is able to generate functional antibodies after an initial vaccine series; however, an additional vaccine dose resulted in dramatically improved antibody responses against all SARS‐CoV‐2 variants except omicron where antibody responses and neutralising activity remained suboptimal.

Characteristics of mechanically ventilated COVID-19 patients in the Al-Ahsa Region of Saudi Arabia: a retrospective study with survival analysis

BACKGROUND

About 5-10% of coronavirus disease 2019 (COVID-19) infected patients require critical care hospitalization and a variety of respiratory support, including invasive mechanical ventilation. Several nationwide studies from Saudi Arabia have identified common comorbidities but none were focused on mechanically ventilated patients in the Al-Ahsa region of Saudi Arabia.

OBJECTIVES

Identify characteristics and risk factors for mortality in mechanically ventilated COVID-19 patients.

DESIGN

Retrospective chart review SETTING: Two general hospitals in the Al-Ahsa region of Saudi Arabia PATIENTS AND METHODS: We included mechanically ventilated COVID-19 patients (>18 years old) admitted between 1 May and 30 November 2020, in two major general hospitals in the Al-Ahsa region, Saudi Arabia. Descriptive statistics were used to characterize patients. A multivariable Cox proportional hazards (CPH) model was used exploratively to identify hazard ratios (HR) of predictors of mortality.

MAIN OUTCOME MEASURES

Patient characteristics, mortality rate, extubation rate, the need for re-intubation and clinical complications during hospitalization.

SAMPLE SIZE AND CHARACTERISTICS

154 mechanically ventilated COVID-19 patients with median (interquartile range) age of 60 (22) years; 65.6% male.

RESULTS

Common comorbidities were diabetes (72.2%), hypertension (67%), cardiovascular disease (14.9%) and chronic kidney disease (CKD) (14.3%). In the multivariable CPH model, age >60 years old (HR=1.83, 95% CI 1.2-2.7, P=.002), CKD (1.61, 95% CI 0.9-2.6, P=.062), insulin use (HR=0.65, 95% CI 0.35-.08, P<.001), and use of loop diuretics (HR=0.51, 95% CI 0.4, P=.037) were major predictors of mortality.

CONCLUSION

Common diseases in mechanically ventilated COVID-19 patients from the Al-Ahsa region were diabetes, hypertension, other cardiovascular diseases, and CKD in this exploratory analysis.

LIMITATIONS

Retrospective, weak CPH model performance.

CONFLICTS OF INTEREST

None.

Impact of diabetes on COVID-19 mortality and hospital outcomes from a global perspective: An umbrella systematic review and meta-analysis

INTRODUCTION

To date, COVID-19 has claimed 4.9 million lives. Diabetes has been identified as an independent risk factor of serious outcomes in people with COVID-19 infection. Whether that holds true across world regions uniformly has not been previously assessed.

METHODS

This study offers the first umbrella systematic review and meta-analysis to analyse the collective and geographically stratified mortality, ICU admission, ventilation requirement, illness severity and discharge rate amongst patients with diabetes. Five databases (EMBASE, MEDLINE, CAB Abstracts, PsychInfo and Web of Science) and 3 additional sources (SSRN's eLibrary, Research Square and MedRxiv) were searched from inception to 30 August 2021. Prospective and retrospective cohort studies, reporting the association between diabetes and one or more COVID-19 hospitalization outcomes, were included. This meta-analysis was registered on PROSPERO, CRD42021278579. Abbreviated MeSH terms used for search were as follows: (Diabetes) AND (2019 Novel Coronavirus Disease), adapted per database requirements. Exclusion criteria exclusion criteria were as follows: (1) none of the primary or secondary outcomes of meta-analysis reported, (2) no confirmed COVID-19 infection (laboratory or clinical) and (3) no unexposed population (solely patients with diabetes included). Quality of the included studies were assessed using the Newcastle-Ottawa Scale (NOS) whilst quality of evidence by the GRADE framework. Studies that were clinically homogeneous were pooled. Summative data and heterogeneity were generated by the Cochrane platform RevMan (V. 5.4).

RESULTS

Overall, 158 observational studies were included, with a total of 270,212 of participants, median age 59 [53-65 IQR] of who 56.5% were male. A total of 22 studies originated from EU, 90 from Far East, 16 from Middle East and 30 from America. Data were synthesized with mixed heterogeneity across outcomes. Pooled results highlighted those patients with diabetes were at a higher risk of COVID-19-related mortality, OR 1.87 [95%CI 1.61, 2.17]. ICU admissions increased across all studies for patients with diabetes, OR 1.59 [95%CI 1.15, 2.18], a result that was mainly skewed by Far East-originating studies, OR 1.94 [95%CI 1.51, 2.49]. Ventilation requirements were also increased amongst patients with diabetes worldwide, OR 1.44 [95%CI 1.20, 1.73] as well as their presentation with severe or critical condition, OR 2.88 [95%CI 2.29, 3.63]. HbA1C levels under <70 mmol and metformin use constituted protective factors in view of COVID-19 mortality, whilst the inverse was true for concurrent insulin use.

CONCLUSIONS

Whilst diabetes constitutes a poor prognosticator for various COVID-19 infection outcomes, variability across world regions is significant and may skew overall trends.

Liver Injury in COVID-19 Patients with Drugs as Causatives: A Systematic Review of 996 DILI Cases Published 2020/2021 Based on RUCAM as Causality Assessment Method

Patients with coronavirus disease 19 (COVID-19) commonly show abnormalities of liver tests (LTs) of undetermined cause. Considering drugs as tentative culprits, the current systematic review searched for published COVID-19 cases with suspected drug-induced liver injury (DILI) and established diagnosis using the diagnostic algorithm of RUCAM (Roussel Uclaf Causality Assessment Method). Data worldwide on DILI cases assessed by RUCAM in COVID-19 patients were sparse. A total of 6/200 reports with initially suspected 996 DILI cases in COVID-19 patients and using all RUCAM-based DILI cases allowed for a clear description of clinical features of RUCAM-based DILI cases among COVID-19 patients: (1) The updated RUCAM published in 2016 was equally often used as the original RUCAM of 1993, with both identifying DILI and other liver diseases as confounders; (2) RUCAM also worked well in patients treated with up to 18 drugs and provided for most DILI cases a probable or highly probable causality level for drugs; (3) DILI was preferentially caused by antiviral drugs given empirically due to their known therapeutic efficacy in other virus infections; (4) hepatocellular injury was more often reported than cholestatic or mixed injury; (5) maximum LT values were found for alanine aminotransferase (ALT) 1.541 U/L and aspartate aminotransferase (AST) 1.076 U/L; (6) the ALT/AST ratio was variable and ranged from 0.4 to 1.4; (7) the mean or median age of the COVID-19 patients with DILI ranged from 54.3 to 56 years; (8) the ratio of males to females was 1.8–3.4:1; (9) outcome was favorable for most patients, likely due to careful selection of the drugs and quick cessation of drug treatment with emerging DILI, but it was fatal in 19 patients; (10) countries reporting RUCAM-based DILI cases in COVID-19 patients included China, India, Japan, Montenegro, and Spain; (11) robust estimation of the percentage contribution of RUCAM-based DILI for the increased LTs in COVID-19 patients is outside of the current scope. In conclusion, RUCAM-based DILI with its clinical characteristics in COVID-19 patients and its classification as a confounding variable is now well defined, requiring a new correct description of COVID-19 features by removing DILI characteristics as confounders.

Discriminant models for the prediction of postponed viral shedding time and disease progression in COVID-19

Background

COVID-19 infection can cause life-threatening respiratory disease. This study aimed to fully characterize the clinical features associated with postponed viral shedding time and disease progression, then develop and validate two prognostic discriminant models.

Methods

This study included 125 hospitalized patients with COVID-19, for whom 44 parameters were recorded, including age, gender, underlying comorbidities, epidemiological features, laboratory indexes, imaging characteristics and therapeutic regimen, et al. Fisher's exact test and Mann–Whitney test were used for feature selection. All models were developed with fourfold cross-validation, and the final performances of each model were compared by the Area Under Receiving Operating Curve (AUROC). After optimizing the parameters via L₂ regularization, prognostic discriminant models were built to predict postponed viral shedding time and disease progression of COVID-19 infection. The test set was then used to detect the predictive values via assessing models’ sensitivity and specificity.

Results

Sixty-nine patients had a postponed viral shedding time (> 14 days), and 28 of 125 patients progressed into severe cases. Six and eleven demographic, clinical features and therapeutic regimen were significantly associated with postponed viral shedding time and disease progressing, respectively (p < 0.05). The optimal discriminant models are: y₁ (postponed viral shedding time) = − 0.244 + 0.2829x₁ (the interval from the onset of symptoms to antiviral treatment) + 0.2306x₄ (age) + 0.234x₂₈ (Urea) − 0.2847x₃₄ (Dual-antiviral therapy) + 0.3084x₃₈ (Treatment with antibiotics) + 0.3025x₂₁ (Treatment with Methylprednisolone); y₂ (disease progression) = − 0.348–0.099x₂ (interval from Jan 1st,2020 to individualized onset of symptoms) + 0.0945x₄ (age) + 0.1176x₅ (imaging characteristics) + 0.0398x₈ (short-term exposure to Wuhan) − 0.1646x₁₉ (lymphocyte counts) + 0.0914x₂₀ (Neutrophil counts) + 0.1254x₂₁ (Neutrphil/lymphocyte ratio) + 0.1397x₂₂ (C-Reactive Protein) + 0.0814x₂₃ (Procalcitonin) + 0.1294x₂₄ (Lactic dehydrogenase) + 0.1099x₂₉ (Creatine kinase).The output ≥ 0 predicted postponed viral shedding time or disease progressing to severe/critical state. These two models yielded the maximum AUROC and faired best in terms of prognostic performance (sensitivity of78.6%, 75%, and specificity of 66.7%, 88.9% for prediction of postponed viral shedding time and disease severity, respectively).

Conclusion

The two discriminant models could effectively predict the postponed viral shedding time and disease severity and could be used as early-warning tools for COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07338-x.

Decline in Mental Health in the Beginning of the COVID-19 Outbreak Among European Older Adults-Associations With Social Factors, Infection Rates, and Government Response

Objective

Governments across the world have deployed a wide range of non-pharmaceutical interventions (NPI) to mitigate the spread of COVID-19. Certain NPIs, like limiting social contacts or lockdowns, had negative consequences for mental health in the population. Especially elder people are prone to mental illnesses during the current pandemic. This article investigates how social factors, infections rates, and stringency of NPIs are associated with a decline in mental health in different European countries.

Methods

Data stem from the eighth wave of the SHARE survey. Additional data sources were used to build macro indicators for infection rates and NPIs. Two subsamples of persons with mental health problems were selected (people who reported being depressed, n = 9.240 or nervous/anxious, n = 10.551). Decline in mental health was assessed by asking whether depressive symptoms or nervousness/anxiety have become worse since the beginning of the COVID-19 outbreak. For each outcome, logistic regression models with survey-design were used to estimate odds ratios (OR), using social factors (age, gender, education, living alone, and personal contacts) and macro indicators (stringency of NPIs and infection rates) as predictors.

Results

Higher age was associated with a lower likelihood of becoming more depressed (OR 0.87) or nervous/anxious (OR 0.88), while female gender increased the odds of a decline in mental health (OR 1.53 for being more depressed; OR 1.57 for being more nervous/anxious). Higher education was only associated with becoming more nervous/anxious (OR 1.59), while living alone or rare personal contacts were not statistically significant. People from countries with higher infection rates were more likely to become more depressed (OR 3.31) or nervous/anxious (OR 4.12), while stringency of NPIs showed inconsistent associations.

Conclusion

A majority of European older adults showed a decline in mental health since the beginning of the COVID-19 outbreak. This is especially true in countries with high prevalence rates of COVID-19. Among older European adults, age seems to be a protective factor for a decline in mental health while female gender apparently is a risk factor. Moreover, although NPIs are an essential preventative mechanism to reduce the pandemic spread, they might influence the vulnerability for elderly people suffering from mental health problems.

Propagation network of tailings dam failure risk and the identification of key hazards

The tailings dam system is complex, and the dam structure changes continuously over time, which can make it difficult to identify key hazards of failure and characterize the accident formation process. To solve the above problems, based on complex network theory, the paper uses the identified hazards and the relationship between hazards to construct the propagation network of tailings dam failure risk (PNTDFR). The traditional analysis methods of network centrality usually focus on one aspect of the information of the network, while it cannot take into account to absorb the advantages of different methods, resulting in the difference between identified key nodes and real key hazards. To find the key hazards of tailing dam failure, based on the characteristics of multi-stage propagation of failure risk, the paper proposes a multi-stage collaborative hazard remediation method (MCHRM) to determine the importance of hazard nodes by absorbing the advantages of different centrality methods under different hazard remediation (deletion) ratios. The paper applies the above methods to Feijão Dam I. It can be found that when the priority remediation range is increased to 45%, the key hazards obtained by the MCHRM will cover all the causes of accidents proposed by the Dam I failure investigation expert group. Besides, the paper compares the monitoring data, daily inspection results and safety evaluation information of key hazards with the ‘Grading standards of hazard indicators’, and obtains the formation process of the Dam I failure and 30 key hazards in trigger state.

Rhabdomyolysis and coronavirus disease‐2019 in children: A case report and review of the literature

Introduction

Coronavirus disease‐2019 (COVID‐19) presents with a variety of symptoms, but rhabdomyolysis has rarely been reported in children.

Case presentation

We report a 10‐year‐old girl who presented with fever, myalgia, and limping. The patient was tested positive for severe acute respiratory syndrome coronavirus‐2. On admission, creatine kinase (CK) level was 13 147 units per liter and the patient was diagnosed with rhabdomyolysis. She was treated with intravenous fluids, which resulted in CK levels decrease. There are currently seven case reports of children with rhabdomyolysis associated with acute COVID‐19 infection and two reports with the multisystemic inflammatory syndrome.

Conclusion

Children presenting with muscle pain and weakness in the acute phase or following COVID‐19 infection, should alert physicians of the possibility of rhabdomyolysis.

A real-time integrated framework to support clinical decision making for covid-19 patients

BACKGROUND

The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies.

METHODS

The information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented.

RESULTS

The Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level.

INTERPRETATION

The COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.

Differences in Sleep Patterns and Mental Health Problems During Different Periods of COVID-19 Outbreak Among Community-Dwelling Older Men in Hong Kong

Objectives: To determine the association of sleep with mental health among Hong Kong community-dwelling older men in the context of the COVID-19 pandemic.

Methods: This additional analysis was derived from the community-dwelling men aged &gt;60 recruited during three COVID-19 outbreaks (i.e., pre-outbreak, between the second and third wave, and during the third wave) in Hong Kong from July 2019 to September 2020. Sleep and mental health were measured by Pittsburgh Sleep Quality Index questionnaire and Hospital Anxiety and Depression Scale, respectively. Multivariate logistic regression models were performed for the associations between sleep and mental health after considering the outbreaks’ impact.

Results: Subjects enrolled between the second and third wave tended to have better sleep but worse mental health. Positive associations between poor sleep and depression (AOR = 3.27, 95% CI: 1.60–7.03) and anxiety (AOR = 2.40, 95% CI: 1.07–5.76) were observed. The period “between second and third wave” was positively associated with depression (AOR = 2.65, 95% CI: 1.22–5.83), showing an additive interaction with poor sleep.

Conclusion: The positive association between poor sleep and depression was aggravated by the period “between the second and third wave” among community-dwelling older males in Hong Kong.

Assessing sex differential in COVID-19 mortality rate by age and polymerase chain reaction test results: an Iranian multi-center study

BACKGROUND

The aim of this study is to evaluate the sex differential effect in the COVID-19 mortality by different age groups and polymerase chain reaction (PCR) test results.

RESEARCH DESIGN

In a multicenter cross-sectional study from 55 hospitals in Tehran, Iran, patients were categorized as positive, negative, and suspected cases.

RESULTS

A total of 25,481 cases (14,791 males) were included in the study with a mortality rate of 12.0%. The mortality rates in positive, negative, and suspected cases were 20.55%, 9.97%, and 7.31%, respectively. Using a Cox regression model, sex had a significant effect on the hazard of death due to COVID-19 in adult and senior male patients having positive and suspected PCR test results. However, sex was not found as significant factor for mortality in patients with a negative PCR test in different age groups.

CONCLUSIONS

Regardless of other risk factors, we found that the effect of sex on COVID-19 mortality varied significantly in different age groups. Therefore, appropriate strategies should be designed to protect adult and senior males from this deadly infectious disease. Furthermore, owing to the considerable death rate of COVID-19 patients with negative test results, new policies should be launched to increase the accuracy of diagnosis tests.

Systemic Inflammatory Predictors of In-Hospital Mortality in COVID-19 Patients: A Retrospective Study

The purpose of this study was to investigate whether routine blood tests and clinical characteristics can predict in-hospital mortality in COVID-19. Clinical data of 285 patients aged 59.7 ± 10.3 yrs. (males n = 189, females n = 96) were retrospectively collected from December 2020 to June 2021. Routine blood tests were recorded within the 1st hour of admission to hospital. The inflammatory variables, such as C-reactive protein (CRP), procalcitonin (PCT), neutrophils−lymphocyte ratio (NLR) and the systemic inflammatory index (SII), exceeded the reference values in all patients and were significantly higher in deceased patients (n = 108) compared to survivors (n = 177). The log-rank test for comparing two survival curves showed that patients aged ≥60.5 years, with PCT ≥ 0.188 ng/mL or NLR ≥ 11.57 103/µL were at a greater risk of death. NLR demonstrated a high impact on the COVID-19 mortality (HR 1.317; 95%CI 1.004−1.728; p < 0.05), whereas CRP and SII showed no effect (HR 1.000; 95%CI 1.000−1.004; p = 0.085 and HR 1.078; 95%CI 0.865−1.344; p = 0.503, respectively). In the first Polish study including COVID-19 patients, we demonstrated that age in relation to simple parameters derived from complete blood cell count has prognostic implications in the course of COVID-19 and can identify the patients at a higher risk of in-hospital mortality.

The paradox of pulmonary arterial hypertension in Italy in the COVID-19 era: is risk of disease progression around the corner?

Objective

The coronavirus disease 2019 (COVID-19) outbreak has led to significant restrictions on routine medical care. We conducted a multicentre nationwide survey of patients with pulmonary arterial hypertension (PAH) to determine the consequences of governance measures on PAH management and risk of poor outcome in patients with COVID-19.

Materials and methods

The present study, which included 25 Italian centres, considered demographic data, the number of in-person visits, 6-min walk and echocardiographic test results, brain natriuretic peptide/N-terminal pro-brain natriuretic peptide test results, World Health Organization functional class assessment, presence of elective and non-elective hospitalisation, need for treatment escalation/initiation, newly diagnosed PAH, incidence of COVID-19 and mortality rates. Data were collected, double-checked and tracked by institutional records between March 1 and May 1, 2020, to coincide with the first peak of COVID-19 and compared with the same time period in 2019.

Results

Among 1922 PAH patients, the incidences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and COVID-19 were 1.0% and 0.46%, respectively, with the latter comparable to that in the overall Italian population (0.34%) but associated with 100% mortality. Less systematic activities were converted into more effective remote interfacing between clinicians and PAH patients, resulting in lower rates of hospitalisation (1.2% versus 1.9%) and related death (0.3% versus 0.5%) compared with 2019 (p<0.001). A high level of attention is needed to avoid the potential risk of disease progression related to less aggressive escalation of treatment and the reduction in new PAH diagnoses compared with 2019.

Conclusion

A cohesive partnership between healthcare providers and regional public health officials is needed to prioritise PAH patients for remote monitoring by dedicated tools.

COVID-19 showed low incidence among PAH patients, but high mortality rates. A high level of attention is needed to avoid the potential risk of disease progression in the near future.https://bit.ly/3s1lEYM