Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients

Acute kidney injury and COVID-19: the predictive power of BUN/albumin ratio for renal replacement therapy requirement

OBJECTIVE

To investigate the predictive power of the BUN/albumin ratio (BAR) measured in the emergency department (ED) for the requirement of renal replacement therapy (RRT) in patients admitted to the intensive care unit (ICU) with severe COVID-19 pneumonia and acute kidney injury (AKI).

MATERIALS AND METHODS

The study included 117 patients with AKI who were admitted to the ICU and had COVID-19 pneumonia detected on chest computed tomography (CT) taken in the ED's pandemic area between November 1, 2020, and June 1, 2021. The predictive power of laboratory values measured at the time of ED admission for the requirement of RRT was analyzed.

RESULTS

Of the patients, 59.8% (n = 70) were male, with an average age of 71.7 ± 14.8 years. The mortality rate of the study was 35% (n = 41). During follow-up, 23.9% (n = 28) of the patients required RRT. Laboratory parameters measured at the time of ED admission showed that patients who required RRT had significantly higher BAR, BUN, and creatinine levels, and significantly lower albumin levels (all p < 0.001). ROC analysis to determine the predictive characteristics for RRT requirement revealed that the BAR had the highest AUC value (AUC, 0.885; 95% CI 0.825-0.945; p < 0.001). According to the study data, for BAR, a cut-off value of 1.7 resulted in a sensitivity of 96.4% and a specificity of 71.9%.

CONCLUSION

In patients with severe pneumonia who develop acute kidney injury, the BUN/albumin ratio may guide clinicians early in predicting the need for renal replacement therapy.

D-dimer for efficacy prediction in COVID-19 patients treated with paxlovid

BACKGROUND

Paxlovid is one of the most effective antiviral therapies for COVID-19 patients, but no studies have explored the efficacy predictors of this drug.

METHODS

To investigate whether D-dimer could be used as a predictor of paxlovid response. Our study included 394 patients diagnosed with COVID-19 who were treated with paxlovid at Xiangya Hospital from Dec 5, 2022, to Jan 31, 2023. We analyzed the composite outcome and all-cause mortality and compared the clinical and demographic data of patients with normal and abnormal D-dimer levels.

RESULTS

We found that 324 patients (82.2%) with D-dimer levels were regularly compared with 70 patients (17.8%). Compared with patients with normal D-dimer levels, those with elevated D-dimer levels exhibited significantly reduced albumin levels, along with elevated levels of white blood cells, platelets, neutrophils, blood urea nitrogen, and procalcitonin. Kaplan-Meier survival curves showed that patients displaying increased D-dimer levels demonstrated a significantly higher incidence of composite disease progression within 28 days (p = 0.002) and all-cause death (p < 0.001). The multivariable adjusted Cox proportional hazard regression model also achieved consistent results in composite outcome (hazard ratio [HR] 2.21, 95% confidence interval [CI], 1.21-4.02, p = 0.009) and all-cause death (HR 8.06, 95% CI 2.74-23.71, p < 0.001).

CONCLUSION

Our findings suggested that the reduced efficacy of paxlovid could be predicted by elevated D-dimer levels in COVID-19 patients.

Significance of Initial Chest CT Severity Score (CTSS) and Patient Characteristics in Predicting Outcomes in Hospitalized COVID-19 Patients: A Single Center Study

The aim of this study is to examine the prognostic role of initial chest computed tomography severity score index (CTSS) and its association with demographic, socio-epidemiological, and clinical parameters in COVID-19 hospitalized patients. A retrospective study included patients who were hospitalized in the COVID Hospital of the Clinical Hospital Center Kosovska Mitrovica from July 2020 to March 2022. We compared patient characteristics and outcome of their hospital stay with values of CT severity score (mild, moderate, and severe form of the disease). Patients with severe disease were statistically significantly older, they treated more days, and they presented statistically significant highest mortality rate compared to mild and moderate forms. Smokers and obese were significantly more frequent among patients with higher CT, while vaccinated patients were more common among those with a mild form. Biochemical parameters at admission also showed statistical significance between the examined groups. We can conclude that by employing the initial CT severity score as the strongest predictor of mortality, it is possible to predict the outcome in hospitalized patients. A comprehensive examination of the patient upon admission, including determining the extent of inflammatory changes in the lungs using computed tomography, the levels of oxygen saturation, and other laboratory parameters, can assist doctors in making an adequate clinical evaluation and apply appropriate therapeutic protocols in the treatment of COVID-19.

Pulmonary fibrosis followed by severe pneumonia in patients with COVID-19 infection requiring mechanical ventilation: a prospective multicentre study

BACKGROUNDS

The management of lung complications, especially fibrosis, after COVID-19 pneumonia, is an important issue in the COVID-19 post-pandemic era. We aimed to investigate risk factors for pulmonary fibrosis development in patients with severe COVID-19 pneumonia.

METHODS

Clinical and radiological data were prospectively collected from 64 patients who required mechanical ventilation due to COVID-19 pneumonia and were enrolled from eight hospitals in South Korea. Fibrotic changes on chest CT were evaluated by visual assessment, and extent of fibrosis (mixed disease score) was measured using automatic quantification system.

RESULTS

64 patients were enrolled, and their mean age was 58.2 years (64.1% were males). On chest CT (median interval: 60 days [IQR; 41-78 days] from enrolment), 35 (54.7%) patients showed ≥3 fibrotic lesions. The most frequent fibrotic change was traction bronchiectasis (47 patients, 73.4 %). Median extent of fibrosis measured by automatic quantification was 10.6% (IQR, 3.8-40.7%). In a multivariable Cox proportional hazard model, which included nine variables with a p value of <0.10 in an unadjusted analysis as well as age, sex and Body Mass Index, male sex (HR, 3.01; 95% CI, 1.27 to 7.11) and higher initial Sequential Organ Failure Assessment (SOFA) score (HR, 1.18; 95% CI, 1.02 to 1.37) were independently associated with pulmonary fibrosis (≥3 fibrotic lesions).

CONCLUSION

Our data suggests that male gender and higher SOFA score at intensive care unit admission were associated with pulmonary fibrosis in patients with severe COVID-19 pneumonia requiring mechanical ventilation.

Utility of Hematological and Biochemical Parameters as a Screening Tool for Assessing Coronavirus Disease 2019 Infection and its Severity

Background

The rapidly evolving pandemic of Coronavirus disease 2019 (COVID-19) has presented with clinical severity, which varies from asymptomatic cases to being fatal in others. The need of the hour is to find meaningful and cost-effective COVID-19 biomarkers out of conventional hematological and biochemical parameters, which will help in the early identification of patients with a poor prognosis, leading to timely intervention.

Aim

The aim was to analyze different biochemical and hematological parameters in COVID-19 patients and also to study the association of these parameters with disease severity.

Materials and Methods

Cross-sectional observational study was carried out on 100 COVID-19 patients from a hospital from July to October 2020. Based on saturation of oxygen (SpO2), admitted patients were grouped into mild-moderate (SpO2 ≥90%) and severe groups (SpO2 <90%). Hematological and biochemical parameters were studied in both groups, and association with disease severity was analyzed.

Results

Out of 100 patients, 57 patients were seen in the mild-moderate group (SpO2 ≥90%), while 43 patients (SpO2 <90%) belonged to the severe category. Males were predominant in both mild-moderate and severe groups. Among the hematological parameters, statistically significant higher values of absolute neutrophil count (P = 0.046) and significantly lower absolute lymphocyte count (P = 0.003) values were observed. With regard to biochemical parameters, increased urea and decreased total protein were found in the severe category and this association was statistically significant.

Conclusion

To conclude, early identification and monitoring of hematological and biochemical parameters, especially those associated with higher disease severity, may contribute toward improving disease outcomes.

Prediction Model of In-Hospital Death for Acute Exacerbation of Chronic Obstructive Pulmonary Disease Patients Admitted to Intensive Care Unit: The PD-ICU Score

INTRODUCTION

Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) admitted to intensive care unit (ICU) are exposed to poor clinical outcomes, and no specific prognostic models are available among this population. We aimed to develop and validate a risk score for prognosis prediction for these patients.

METHODS

This was a multicenter observation study. AECOPD patients admitted to ICU were included for model derivation from a prospective, multicenter cohort study. Logistic regression analysis was applied to identify independent predictors for in-hospital death and establish the prognostic risk score. The risk score was further validated and compared with DECAF, BAP-65, CURB-65, and APACHE II score in another multicenter cohort.

RESULTS

Five variables were identified as independent predictors for in-hospital death in APCOPD patients admitted to ICU, and a corresponding risk score (PD-ICU score) was established, which was composed of procalcitonin >0.5 μg/L, diastolic blood pressure <60 mm Hg, need for invasive mechanical ventilation, disturbance of consciousness, and blood urea nitrogen >7.2 mmol/L. Patients were classified into three risk categories according to the PD-ICU score. The in-hospital mortality of low-risk, intermediate-risk, and high-risk patients was 0.3%, 7.3%, and 27.9%, respectively. PD-ICU score displayed excellent discrimination ability with an area under the receiver-operating characteristic curve (AUC) of 0.815 in the derivation cohort and 0.754 in the validation cohort which outperformed other prognostic models.

CONCLUSION

We derived and validated a simple and clinician-friendly prediction model (PD-ICU score) for in-hospital mortality among AECOPD patients admitted to ICU. With good performance and clinical practicability, this model may facilitate early risk stratification and optimal decision-making among these patients.

Evaluating Lactate and D-Dimer as Mortality Predictors of Paediatric Multiple Organ Dysfunction Syndrome: A Prospective Study in a Low-Middle Income Country

Background

Multiple Organ Dysfunction Syndrome (MODS) is a complex medical condition characterised by dysfunction across multiple organs. With limited information available on mortality prediction in the paediatric population, particularly in low-middle income countries, this study evaluates the mortality predicting capabilities of lactate, D-dimer, and their combination.

Methods

This prospective study involved paediatric patients admitted to the paediatric intensive care unit (PICU) of the largest central children's hospital in the Mekong Delta region, Vietnam, from 2019 to 2021. The discriminative ability and calibration of both individual and combined tests were assessed using the receiver operating characteristic (ROC) curves and the Hosmer-Lemeshow goodness-of-fit test.

Results

Among the patients studied, 63.1% did not survive. Lactate and D-dimer concentrations were significantly higher in the non-survivor group (P < 0.001). The area under the curve (AUC) values for lactate, D-dimer and the combined lactate-D-dimer test were 0.742, 0.775 and 0.804, respectively, with the combination showing the highest AUC value, though without statistical significance. Specific thresholds for lactate, D-dimer and the combination yielded sensitivities of 75.5%, 71.7%, and 66.0%, respectively. All three tests showed no statistically significant differences between observed and predicted mortality in the Hosmer-Lemeshow test (all P-values > 0.05).

Conclusion

Lactate and D-dimer levels showed a significant association with mortality, along with good discrimination and calibration abilities. These results highlight the utility of lactate and D-dimer as effective predictors in paediatric MODS, particularly in resource-limited settings, and their role in improving patient outcomes.

Development and validation of a scoring system to predict the mortality of hospitalized patients with SARS-CoV-2 Omicron: a nationwide, multicentre study

BACKGROUND

The Omicron variant broke out in China at the end of 2022, causing a considerable number of severe cases and even deaths. The study aimed to identify risk factors for death in patients hospitalized with SARS-CoV-2 Omicron infection and to establish a scoring system for predicting mortality.

METHODS

1817 patients were enrolled at eight hospitals in China from December 2022 to May 2023, including 815 patients in the training group and 1002 patients in the validation group. Forty-six clinical and laboratory features were screened using LASSO regression and multivariable logistic regression.

RESULTS

In the training set, 730 patients were discharged and 85 patients died. In the validation set, 918 patients were discharged and 84 patients died. LASSO regression identified age, levels of interleukin (IL) -6, blood urea nitrogen (BUN), lactate dehydrogenase (LDH), and D-dimer; neutrophil count, neutrophil-to-lymphocyte ratio (NLR) as associated with mortality. Multivariable logistic regression analysis showed that older age, IL-6, BUN, LDH and D-dimer were significant independent risk factors. Based on these variables, a scoring system was developed with a sensitivity of 83.6% and a specificity of 83.5% in the training group, and a sensitivity of 79.8% and a sensitivity of 83.0% in the validation group.

CONCLUSIONS

A scoring system based on age, IL-6, BUN, LDH and D-dime can help clinicians identify patients with poor prognosis early.

Developing Deep LSTMs With Later Temporal Attention for Predicting COVID-19 Severity, Clinical Outcome, and Antibody Level by Screening Serological Indicators Over Time

OBJECTIVE

The clinical course of COVID-19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID-19 patients. The dynamic changes of the antibody against Spike protein are crucial for understanding the immune response. This work explores a temporal attention (TA) mechanism of deep learning to predict COVID-19 disease severity, clinical outcomes, and Spike antibody levels by screening serological indicators over time.

METHODS

We use feature selection techniques to filter feature subsets that are highly correlated with the target. The specific deep Long Short-Term Memory (LSTM) models are employed to capture the dynamic changes of disease severity, clinical outcome, and Spike antibody level. We also propose deep LSTMs with a TA mechanism to emphasize the later blood test records because later records often attract more attention from doctors.

RESULTS

Risk factors highly correlated with COVID-19 are revealed. LSTM achieves the highest classification accuracy for disease severity prediction. Temporal Attention Long Short-Term Memory (TA-LSTM) achieves the best performance for clinical outcome prediction. For Spike antibody level prediction, LSTM achieves the best permanence.

CONCLUSION

The experimental results demonstrate the effectiveness of the proposed models. The proposed models can provide a computer-aided medical diagnostics system by simply using time series of serological indicators.

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and multiple organ dysfunction. Although some hematological and biochemical parameters are altered during moderate and severe COVID-19, there is still a lack of tools to combine these parameters to predict the clinical outcome of a patient with COVID-19. Thus, this study aimed at employing hematological and biochemical parameters of patients diagnosed with COVID-19 in order to build machine learning algorithms for predicting COVID mortality or survival. Patients included in the study had a diagnosis of SARS-CoV-2 infection confirmed by RT-PCR and biochemical and hematological measurements were performed in three different time points upon hospital admission. Among the parameters evaluated, the ones that stand out the most are the important features of the T1 time point (urea, lymphocytes, glucose, basophils and age), which could be possible biomarkers for the severity of COVID-19 patients. This study shows that urea is the parameter that best classifies patient severity and rises over time, making it a crucial analyte to be used in machine learning algorithms to predict patient outcome. In this study optimal and medically interpretable machine learning algorithms for outcome prediction are presented for each time point. It was found that urea is the most paramount variable for outcome prediction over all three time points. However, the order of importance of other variables changes for each time point, demonstrating the importance of a dynamic approach for an effective patient's outcome prediction. All in all, the use of machine learning algorithms can be a defining tool for laboratory monitoring and clinical outcome prediction, which may bring benefits to public health in future pandemics with newly emerging and reemerging SARS-CoV-2 variants of concern.

Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality

As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.

Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables

Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MGGP), as an advanced artificial intelligence (AI) tool, was used to determine the importance of laboratory predictors in the prognosis of COVID-19 patients. The present retrospective study was conducted on 1455 patients with COVID-19 (727 males and 728 females), who were admitted to Allameh Behlool Gonabadi Hospital, Gonabad, Iran in 2020-2021. For each patient, the demographic characteristics, common laboratory tests at the time of admission, duration of hospitalization, admission to the intensive care unit (ICU), and mortality were collected through the electronic information system of the hospital. Then, the data were normalized and randomly divided into training and test data. Furthermore, mathematical prediction models were developed by MGGP for each gender. Finally, a sensitivity analysis was performed to determine the significance of input parameters on the COVID-19 prognosis. Based on the achieved results, MGGP is able to predict the mortality of COVID-19 patients with an accuracy of 60-92%, the duration of hospital stay with an accuracy of 53-65%, and admission to the ICU with an accuracy of 76-91%, using common hematological tests at the time of admission. Also, sensitivity analysis indicated that blood urea nitrogen (BUN) and aspartate aminotransferase (AST) play key roles in the prognosis of COVID-19 patients. AI techniques, such as MGGP, can be used in the triage and prognosis prediction of COVID-19 patients. In addition, due to the sensitivity of BUN and AST in the estimation models, further studies on the role of the mentioned parameters in the pathophysiology of COVID-19 are recommended.

Development and validation of a predicted nomogram for mortality of COVID-19: a multicenter retrospective cohort study of 4,711 cases in multiethnic

Background

Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly worldwide. As it quickly spreads and can cause severe disease, early detection and treatment may reduce mortality. Therefore, the study aims to construct a risk model and a nomogram for predicting the mortality of COVID-19.

Methods

The original data of this study were from the article "Neurologic Syndromes Predict Higher In-Hospital Mortality in COVID-19." The database contained 4,711 multiethnic patients. In this secondary analysis, a statistical difference test was conducted for clinical demographics, clinical characteristics, and laboratory indexes. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis were applied to determine the independent predictors for the mortality of COVID-19. A nomogram was conducted and validated according to the independent predictors. The area under the curve (AUC), the calibration curve, and the decision curve analysis (DCA) were carried out to evaluate the nomogram.

Results

The mortality of COVID-19 is 24.4%. LASSO and multivariate logistic regression analysis suggested that risk factors for age, PCT, glucose, D-dimer, CRP, troponin, BUN, LOS, MAP, AST, temperature, O2Sats, platelets, Asian, and stroke were independent predictors of CTO. Using these independent predictors, a nomogram was constructed with good discrimination (0.860 in the C index) and internal validation (0.8479 in the C index), respectively. The calibration curves and the DCA showed a high degree of reliability and precision for this clinical prediction model.

Conclusion

An early warning model based on accessible variates from routine clinical tests to predict the mortality of COVID-19 were conducted. This nomogram can be conveniently used to facilitate identifying patients who might develop severe disease at an early stage of COVID-19. Further studies are warranted to validate the prognostic ability of the nomogram.

Association between early blood urea nitrogen-to-albumin ratio and one-year post-hospital mortality in critically ill surgical patients: a propensity score-matched study

BACKGROUND

Blood urea nitrogen to albumin ratio (BAR) is increasingly recognized as an early predictor for short-term outcomes in critically ill patients, but the association of BAR with long-term outcomes in critically ill surgical patients remains underexplored.

METHODS

We enrolled consecutive patients who were admitted to surgical intensive care units (ICUs) at Taichung Veterans General Hospital between 2015 and 2020, and the dates of death were retrieved from Taiwan's National Health Insurance Research Database. In addition to Cox regression, we also used propensity score matching to determine the hazard ratios (HRs) and 95% confidence intervals (CIs) for one-year post-hospital mortality of the variables.

RESULTS

A total of 8,073 eligible subjects were included for analyses. We found that age, male gender, high Charlson Comorbidity Index, high Acute Physiology and Chronic Health Evaluation II score, positive microbial culture, and leukocytosis were predictors for mortality, whereas high body mass index, scheduled surgery, and high platelet counts were protective factors against long-term mortality. The high BAR was independently associated with increased post-hospital mortality after adjustment for the aforementioned covariates (adjHR 1.258, 95% CI, 1.127-1.405). Notably, the association tended to be stronger in females and patients with fewer comorbidities and lower disease severity of critical illness. The propensity score matching, dividing subjects by BAR higher or lower than 6, showed a consistent association between week-one BAR and post-hospital mortality (adjHR 1.503, 95% CI 1.247-1.811).

CONCLUSIONS

BAR is a newly identified predictor of short-term outcome, and we identified long-term outcome-relevant factors, including BAR, and the identified factors may be useful for risk stratification of long-term outcomes in patients discharged from surgical ICUs.

Elevated BUN Upon Admission as a Predictor of in-Hospital Mortality Among Patients with Acute Exacerbation of COPD: A Secondary Analysis of Multicenter Cohort Study

Background

High blood urea nitrogen (BUN) is observed in a subset of patients with acute exacerbation of COPD (AECOPD) and may be linked to clinical outcome, but findings from previous studies have been inconsistent.

Methods

We performed a retrospective analysis of patients prospectively enrolled in the MAGNET AECOPD Registry study (ChiCTR2100044625). Receiver operating characteristic (ROC) was used to determine the level of BUN that discriminated survivors and non-survivors. Univariate and multivariate Cox proportional hazards regression analyses were performed to assess the impact of BUN on adverse outcomes.

Results

Overall, 13,431 consecutive inpatients with AECOPD were included in this study, of whom 173 died, with the mortality of 1.29%. The non-survivors had higher levels of BUN compared with the survivors [9.5 (6.8-15.3) vs 5.6 (4.3-7.5) mmol/L, P < 0.001]. ROC curve analysis showed that the optimal cutoff of BUN level was 7.30 mmol/L for in-hospital mortality (AUC: 0.782; 95% CI: 0.748-0.816; P < 0.001). After multivariate analysis, BUN level ≥7.3 mmol/L was an independent risk factor for in-hospital mortality (HR = 2.099; 95% CI: 1.378-3.197, P = 0.001), also for invasive mechanical ventilation (HR = 1.540; 95% CI: 1.199-1.977, P = 0.001) and intensive care unit admission (HR = 1.344; 95% CI: 1.117-1.617, P = 0.002). Other independent prognostic factors for in-hospital mortality including age, renal dysfunction, heart failure, diastolic blood pressure, pulse rate, PaCO2 and D-dimer.

Conclusion

BUN is an independent risk factor for in-hospital mortality in inpatients with AECOPD and may be used to identify serious (or severe) patients and guide the management of AECOPD.

Clinical Trial Registration

MAGNET AECOPD; Chinese Clinical Trail Registry NO.: ChiCTR2100044625; Registered March 2021, URL: http://www.chictr.org.cn/showproj.aspx?proj=121626.

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

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

Nutritional and physical activity issues in frailty syndrome during the COVID-19 pandemic

'Frailty' has been described as 'a state of increased vulnerability of the individual caused by an impairment of homeostasis as a result of endogenous or exogenous stress'. Frail individuals are depicted by a dramatic change in health status following an apparently minor insult and a higher risk of adverse health-related outcomes such as osteoporosis and sarcopenia, falls and disability, and fragility fractures. Frailty is a condition of increasing importance due to the global ageing of the population during the last decades. Central to the pathophysiology of frailty is a mechanism that is partially independent of ageing, but most likely evolves with ageing: the cumulative level of molecular and cellular damage in every subject. Furthermore, an uncorrected nutrition and a sedentary behaviour play a pivotal role in worsening the syndrome. In January 2020, a cluster of a genus of the family Coronaviridae was isolated as the pathogen of the new coronavirus disease (COVID-19). Since then, this infection has spread worldwide causing one of the most dramatic pandemics of the modern era, with more than 500 million confirmed cases all over the world. The clinical spectrum of SARS-CoV-2 severity ranges from asymptomatic conditions to mild symptoms, such as fever, cough, ageusia, anosmia and asthenia, up to most severe conditions, such as acute respiratory distress syndrome (ARDS) and multi-organ failure leading to death. Primary evidence revealed that the elderly frail subjects were more susceptible to the disease in its most intense form and were at greater risk of developing severe COVID-19. Factors contributing to the severity of COVID-19, and the higher mortality rate, are a poor immune system activity and long-standing inflammatory status of the frail subjects compared with the general population. Further recent research also suggested a potential role of sedentary behaviour, metabolic chronic disorders linked to it and uncorrected nutritional status. Thus, the aim of this review was to evaluate the different studies and evidence related to COVID-19 pandemic, both nutritional status and physical activity, and, also, to provide further information on the correct nutritional approach in this peculiar pathological condition.

History of COVID-19 infection is not associated with increased D-dimer levels and risk of deep-vein thrombosis in total joint arthroplasty

INTRODUCTION

In the acute phase of COVID-19, elevated D-dimer levels indicate a hypercoagulable state putting the patients at increased risk for venous thromboembolic disease (VTE). It is unclear, if prior COVID-19 disease increases the risk for VTE after total joint arthroplasty (TJA) and if D-dimer levels can be used to identify patients at risk.

MATERIALS AND METHODS

D-Dimer levels of 313 consecutive SARS-CoV-2 IgG-positive and 2,053 -negative patients undergoing TJA between 05/20 and 12/20 were evaluated. D-Dimer levels were divided into three groups: < 200 ng/ml, 200-400 ng/ml, and > 400 ng/ml D-dimer units (DDU). 277 SARS-CoV-2 IgG-positive patients underwent a Doppler ultrasound to rule out deep-vein thrombosis (DVT) 4-6 weeks after TJA.

RESULTS

D-Dimer levels did not differ significantly between SARS-CoV-2 IgG-positive and -negative patients (p value 0.53). Among SARS-CoV-2 IgG-negative patients, 1687 (82.17%) had D-dimer levels < 200 ng/ml, 256 (12.47%) between 200 and 400 ng/ml, and 110 (5.36%) > 400 ng/ml. Of the SARS-CoV-2 IgG-positive patients, 257 (83.71%) had D-dimer levels < 200 ng/ml, 34 (11.07%) between 200 and 400 ng/ml, and 16 (5.21%) > 400 ng/ml. A postoperative DVT was detected in nine patients (2.9%) in the SARS-CoV-2 IgG-positive group and a PE in one patient (0.3%). 7/229 patients with < 200 ng/ml (3.1%), 1/28 patients (3.6%) with 200-400 ng/ml and 1/9 patients (11.1%) with D-dimer levels > 400 ng/ml had a DVT or PE (p = 0.43).

CONCLUSIONS

The findings of this investigation suggest there is no difference in D-dimer levels between SARS-CoV-2 IgG-positive and -negative patients undergoing TJA. Although there is a trend for increased VTE rates with increased D-dimer levels, routine D-dimer testing is not recommended based on the current data. SARS-CoV-2 IgG-positive patients have a low risk of VTE in the current study.

Clinical profile, treatment, and outcomes of patients with COVID-19 in a tertiary referral hospital in South Sumatera, Indonesia: A retrospective single-center study

Background

Although there are fewer COVID-19 cases in Indonesia, the pandemic is still ongoing. COVID-19 has a significant death rate in Indonesia, but lack of information on the effect of different clinical and demographic factors on COVID-19-related grimness and mortality in Indonesia.

Objective

This study examined the clinical profile, treatment, and outcomes of patients with COVID-19 at Lahat Regency Hospital in South Sumatera, Indonesia, to find relevant markers that might be utilized to predict the prognosis of these patients.

Methods

This was a retrospective single-center study of all medical record files of confirmed patients with COVID-19 admitted to Lahat Hospital from September 2020 to August 2021 (n = 285). Descriptive statistics, Chi-square, Mann-Whitney, Multiple Logistic Regression, and Cox's proportional hazards model were used for data analyses.

Results

This study included 65 non-hospitalized and 220 hospitalized patients. Hospitalized patients were divided into dead and alive groups. The median age was lower in the non-hospitalized group without gender discrimination, and most hospitalized patients had comorbidities. Vital signs and clinical features were significantly different in hospitalized patients compared to non-hospitalized. The survival patients in the hospitalized group showed lower white blood cell (WBC), neutrophil percentages, and neutrophil-lymphocyte ratio (NLR) but higher lymphocyte and eosinophil. Non-survival patients had elevated alanine aminotransferase (ALT), blood urea nitrogen (BUN), creatinine, blood glucose, and potassium. The use of Favipiravir and Remdesivir was significant between the alive and dead groups. The mean hospital stay for all patients was 9.49 ± 4.77 days, while the median duration of hospital time was 10.73 ± 4.33 days in the survival group and 5.39 ± 3.78 days in the non-survival group. Multiple logistic regression analysis determined respiration rate, WBC, and BUN as predictors of survival.

Conclusions

Age and comorbidities are significant elements impacting the seriousness of COVID-19. Abnormal signs in laboratory markers can be used as early warning and prognostic signs to prevent severity and death. Potential biomarkers at various degrees in patients with COVID-19 may also aid healthcare professionals in providing precision medicine and nursing.

Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome

Our knowledge of the role of the gut microbiome in acute coronavirus disease 2019 (COVID-19) and post-acute COVID-19 is rapidly increasing, whereas little is known regarding the contribution of multi-kingdom microbiota and host-microbial interactions to COVID-19 severity and consequences. Herein, we perform an integrated analysis using 296 fecal metagenomes, 79 fecal metabolomics, viral load in 1378 respiratory tract samples, and clinical features of 133 COVID-19 patients prospectively followed for up to 6 months. Metagenomic-based clustering identifies two robust ecological clusters (hereafter referred to as Clusters 1 and 2), of which Cluster 1 is significantly associated with severe COVID-19 and the development of post-acute COVID-19 syndrome. Significant differences between clusters could be explained by both multi-kingdom ecological drivers (bacteria, fungi, and viruses) and host factors with a good predictive value and an area under the curve (AUC) of 0.98. A model combining host and microbial factors could predict the duration of respiratory viral shedding with 82.1% accuracy (error ± 3 days). These results highlight the potential utility of host phenotype and multi-kingdom microbiota profiling as a prognostic tool for patients with COVID-19.

Association Between Blood Urea Nitrogen Levels and Length of Stay in Patients with Pneumonic Chronic Obstructive Pulmonary Disease Exacerbation: A Secondary Analysis Based on a Multicentre, Retrospective Cohort Study

Purpose

High blood urea nitrogen (BUN) is associated with an elevated risk of mortality in various diseases, such as heart failure and pneumonia. Heart failure and pneumonia are common comorbidities of chronic obstructive pulmonary disease (COPD) exacerbation. However, data on the relationship of BUN levels with length of stay (LOS) in patients with pneumonic COPD exacerbation are sparse. The purpose of this study was to evaluate the correlation between BUN levels and LOS in a cohort of patients with pneumonic COPD exacerbation.

Patients and Methods

The present study was a multicentre, retrospective cohort study. A total of 1226 patients with pneumonic COPD exacerbation were included through a validated algorithm derived from the 10th revision of the International Classification of Diseases and Related Health Problems (ICD-10). It should be noted that the entire study was completed by Shiroshita et al, who uploaded the data to the DATADRYAD website. The author only used these data for secondary analysis.

Results

After adjusting for potential confounders (age, gender), a nonlinear relationship was detected between BUN levels less than 40 mg/dl and LOS. The effect sizes and the confidence intervals on the left and right sides of the inflection point were 0.27 (0.16, 0.39) and −0.17 (−0.34, 0.01), respectively.

Conclusion

High levels of BUN in the hospital may be associated with increased LOS. BUN was positively related to LOS when BUN was less than 40 mg/dl.

An early novel prognostic model for predicting 80-day survival of patients with COVID-19

The outbreak of the novel coronavirus disease 2019 (COVID-19) has had an unprecedented impact worldwide, and it is of great significance to predict the prognosis of patients for guiding clinical management. This study aimed to construct a nomogram to predict the prognosis of COVID-19 patients. Clinical records and laboratory results were retrospectively reviewed for 331 patients with laboratory-confirmed COVID-19 from Huangshi Hospital of Traditional Chinese Medicine (TCM) (Infectious Disease Hospital) and Third Affiliated Hospital of Sun Yat-sen University. All COVID-19 patients were followed up for 80 days, and the primary outcome was defined as patient death. Cases were randomly divided into training (n=199) and validation (n=132) groups. Based on baseline data, we used statistically significant prognostic factors to construct a nomogram and assessed its performance. The patients were divided into Death (n=23) and Survival (n=308) groups. Analysis of clinical characteristics showed that these patients presented with fever (n=271, 81.9%), diarrhea (n=20, 6.0%) and had comorbidities (n=89, 26.9.0%). Multivariate Cox regression analysis showed that age, UREA and LDH were independent risk factors for predicting 80-day survival of COVID-19 patients. We constructed a qualitative nomogram with high C-indexes (0.933 and 0.894 in the training and validation groups, respectively). The calibration curve for 80-day survival showed optimal agreement between the predicted and actual outcomes. Decision curve analysis revealed the high clinical net benefit of the nomogram. Overall, our nomogram could effectively predict the 80-day survival of COVID-19 patients and hence assist in providing optimal treatment and decreasing mortality rates.

Estimation of biochemical factors affecting survival in intensive care COVID-19 patients undergoing chest CT scoring: A retrospective cross-sectional study

Coronavirus disease 2019 (COVID-19) is a rapidly spreading deadly respiratory disease that emerged in the city of Wuhan in December 2019. As a result of its rapid and widespread transmission, the WHO declared a pandemic on March 11, 2020 and studies evaluating mortality and prognosis in COVID-19 gained importance. The aim of this study was to determine the factors affecting the survival of COVID-19 patients followed up in a tertiary intensive care unit (ICU) and undergoing chest computed tomography (CT) scoring. This retrospective cross-sectional study was conducted with the approval of Uşak University Medical Faculty Ethics Committee between July and September 2020. It included 187 symptomatic patients (67 females, 120 males) with suspected COVID-19 who underwent chest CT scans in the ICU. Demographics, acute physiology and chronic health evaluation (APACHE II), chest CT scores, COVID-19 real-time polymerase chain reaction (RT PCR) results, and laboratory parameters were recorded. SPSS 15.0 for Windows was used for the data analysis. The ages of the patients ranged from 18 to 94 and the mean age was 68.0 ± 13.9 years. The COVID-19 RT PCR test was positive in 86 (46.0%) patients and 110 patients (58.8%) died during the follow-up. ICU stay (P = .024) and total invasive mechanical ventilation time (P < .001) were longer and blood urea nitrogen (BUN) was higher (P < .001) in the nonsurvivors. Patients with an APACHE II score of 23 and above had a 1.12-fold higher mortality rate (95% CI 0.061-0.263). There was no significant difference in total chest CT score between the survivors and nonsurvivors (P = .210). Chest CT score was not significantly associated with mortality in COVID-19 patients. Our idea that COVID-19 will cause greater mortality in patients with severe chest CT findings has changed. More studies on COVID-19 are needed to reveal the markers that affect prognosis and mortality in this period when new variants are affecting the world.

Prognostic significance of the blood urea nitrogen to creatinine ratio in in-hospital cardiac arrest after targeted temperature management

BACKGROUND

Targeted temperature management (TTM) has been reported to improve outcomes in in-hospital cardiac arrest (IHCA) patients but little has been investigated into the relationship between prognoses and the blood urea nitrogen to creatinine ratio (BCR).

METHODS

A retrospective analysis of data from IHCA survivors treated with TTM between 2011 and 2018 was conducted based on the Research Patient Database Registry of the Partners HealthCare system in Boston. Serum laboratory data were measured during IHCA and within 24 hours after TTM completion. Intra-arrest and post-TTM BCRs were calculated, respectively. The primary outcome was neurologic status at discharge. The secondary outcome was in-hospital mortality.

RESULTS

The study included 84 patients; 63 (75%) were discharged with a poor neurologic status and 40 (47.6%) died. Regarding poor neurological outcome at discharge, multivariate analysis revealed that post-TTM BCR was a significant predictor (adjusted OR, 1.081; 95% CI, 1.002-1.165; p = 0.043) and intra-arrest BCR was a marginal predictor (adjusted OR, 1.067; 95% CI, 1.000-1.138; p = 0.050). Post-TTM BCR had an acceptably predictive ability to discriminate neurological status at discharge, with an area under the receiver-operating characteristic curve of 0.644 (95% CI, 0.516-0.773) and a post-TTM BCR cutoff value of 16.7 had a sensitivity of 61.9% and a specificity of 70.0%.

CONCLUSION

Post-TTM BCR was a significant predictor of the neurologic outcome at discharge among IHCA patients receiving TTM. IHCA patients with elevated intra-arrest BCR also had a borderline poor neurological prognosis at discharge.

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.

Prognostic Value of Routine Blood Parameters in Intensive Care Unit COVID-19 Patients

INTRODUCTION

Laboratory medicine has an important role in the management of COVID-19. The aim of this study was to analyze routinely available blood parameters in intensive care unit COVID-19 patients and to evaluate their prognostic value.

PATIENTS AND METHODS

This is a retrospective, observational, single-center study including consecutive severe COVID-19 patients who were admitted into the intensive care unit of Ben Arous Regional Hospital in Tunisia from 28 September 2020 to 31 May 2021. The end point of the study was either hospital discharge or in-hospital death. We defined two groups based on the outcome: survivors (Group 1) and non-survivors (Group 2). Demographical, clinical, and laboratory data on admission were collected and compared between the two groups. Univariate and multivariate logistic regression analysis were performed to determine the predictive factors for COVID-19 disease mortality.

RESULTS

A total of 150 patients were enrolled. Eighty patients (53.3%) died and 70 (46.7%) survived during the study period. Based on statistical analysis, median age, Simplified Acute Physiology Score (SAPS II) with the serum levels of urea, creatinine, total lactate dehydrogenase (LDH), creatine kinase, procalcitonin and hs-troponin I were significantly higher in non-survivors compared to survivors. On multivariate analysis, LDH activity ≥ 484 U/L (OR=17.979; 95%CI [1.119-2.040]; p = 0.09) and hs-troponin I ≥ 6.55 ng/L (OR=12.492; 95%CI [1.691-92.268]; p = 0.013) independently predicted COVID-19 related mortality.

CONCLUSION

Total LDH and hs-troponin I were independent predictors of death. However, further clinical investigations with even larger number of patients are needed for the evaluation of other laboratory biomarkers which could aid in assessing the prediction of mortality.

Independent predictors of in-hospital mortality and the need for intensive care in hospitalized non-critical COVID-19 patients: a prospective cohort study

One of the most helpful strategies to deal with ongoing coronavirus pandemics is to use some prudence when treating patients infected with SARS-CoV-2. We aimed to evaluate the clinical, demographic, and laboratory parameters that might have predictive value for in-hospital mortality and the need for intensive care and build a model based on them. This study was a prospective, observational, single-center study including non-critical patients admitted to COVID-19 wards. Besides classical clinic-demographic features, basic laboratory parameters obtained on admission were tested, and then new models for each outcome were developed built on the most significant variables. Receiver operating characteristics (ROC) analyses were performed by calculating each model's probability. A total of 368 non-critical hospitalized patients were recruited, the need for ICU care was observed in 70 patients (19%). The total number of patients who died in either ICU or wards was 39 (10.6%). The first two models (based on clinical features and demographics) were developed to predict ICU and death, respectively; older age, male sex, active cancer, and low baseline saturation were noted to be independent predictors. The area under the curve values of the first two models were noted 0.878 and 0.882 (p < .001; confidence interval [CI] 95% [0.837-0.919], p < .001; CI 95% [0.844-0.922]). Following two models, the third and fourth were based on laboratory parameters with clinic-demographic features. Initial lower sodium and lower albumin levels were determined as independent factors in predicting the need for ICU care; higher blood urea nitrogen and lower albumin were independent factors in predicting in-hospital mortality. The area under the curve values of the third and fourth model was noted 0.938 and 0.929, respectively (p < .001; CI 95% [0.912-0.965], p < .001; CI 95% [0.895-962]). By integrating the widely available blood tests results with simple clinic demographic data, non-critical patients can be stratified according to their risk level. Such stratification is essential to filter the patients' non-critical underlying diseases and conditions that can obfuscate the physician's predictive capacity.

Predictors of mortality among hospitalized patients with COVID-19: A single-centre retrospective analysis

BACKGROUND

The severity of disease and mortality due to coronavirus disease (COVID-19) was found to be high among patients with concurrent medical illnesses. Serum biomarkers can be used to predict the course of COVID-19 pneumonia. Data from India are very scarce about predictors of mortality among COVID-19 patients.

METHODOLOGY

In the present retrospective study of 65 RT-PCR confirmed COVID-19 patients, we retrieved data regarding clinical symptoms, laboratory parameters, and radiological grading of severity. Further, we also collected data about their hospital course, duration of stay, treatment, and outcome. Data analysis was done to compare the patient characteristics between survivor and non-survivor groups and to assess the predictors of mortality.

RESULTS

The mean age of the study population was 56.23 years (SD, 12.91) and most of them were males (63%); 81.5% of patients survived and were discharged, whereas 18.5% of patients succumbed to the disease. Univariate analysis across both groups showed that older age, diabetes mellitus, higher computed tomogram (CT) severity score, and raised levels of laboratory parameters viz, D-dimer, CPK-MB (creatine kinase), and lactate dehydrogenase (LDH) were associated with increased mortality among hospitalized patients. On multivariate analysis, elevated levels of serum D-dimer (odds ratio, 95% CI: 10.98, 1.13-106.62, p = 0.04) and LDH (odds ratio, 95% CI: 19.15, 3.28-111.87, p = 0.001) were independently associated with mortality.

CONCLUSION

Older patients, diabetics, and patients with high CT severity scores at admission are at increased risk of death from COVID-19. Serum biomarkers such as D-dimer and LDH help in predicting mortality in COVID-19 patients.

Association Between Pre-operative BUN and Post-operative 30-Day Mortality in Patients Undergoing Craniotomy for Tumors: Data From the ACS NSQIP Database

Objective

There is limited evidence to clarify the specific relationship between pre-operative blood urea nitrogen (BUN) and post-operative 30-day mortality in patients undergoing craniotomy for tumors. Therefore, we aimed to investigate this relationship in detail.

Methods

Electronic medical records of 18,642 patients undergoing craniotomy for tumors in the ACS NSQIP from 2012 to 2015 were subjected to secondary retrospective analysis. The principal exposure was pre-operative BUN. Outcome measures were post-operative 30-day mortality. We used binary logistic regression modeling to evaluate the association between them and conducted a generalized additive model and smooth curve fitting (penalized spline method) to explore the potential relationship and its explicit curve shape. We also conducted sensitivity analyses to ensure the robustness of the results and performed subgroup analyses.

Results

A total of 16,876 patients were included in this analysis. Of these, 47.48% of patients were men. The post-operative 30-day mortality of the included cases was 2.49% (420/16,876), and the mean BUN was 16.874 ± 6.648 mg/dl. After adjusting covariates, the results showed that pre-operative BUN was positively associated with post-operative 30-day mortality (OR = 1.020, 95% CI: 1.004, 1.036). There was also a non-linear relationship between BUN and post-operative 30-day mortality, and the inflection point of the BUN was 9.804. For patients with BUN < 9.804 mg/dl, a 1 unit decrease in BUN was related to a 16.8% increase in the risk of post-operative 30-day mortality (OR = 0.832, 95% CI: 0.737, 0.941); for patients with BUN > 9.804 mg/dl, a 1 unit increase in BUN was related to a 2.8% increase in the risk of post-operative 30-day mortality (OR = 1.028, 95% CI: 1.011, 1.045). The sensitivity analysis proved that the results were robust. The subgroup analysis revealed that all listed subgroups did not affect the relationship between pre-operative BUN and post-operative 30-day mortality (P > 0.05).

Conclusion

Our study demonstrated that pre-operative BUN (mg/dl) has specific linear and non-linear relationships with post-operative 30-day mortality in patients over 18 years of age who underwent craniotomy for tumors. Proper pre-operative management of BUN and maintenance of BUN near the inflection point (9.804 mg/dl) could reduce the risk of post-operative 30-day mortality in these cases.

Development of novel spectroscopic and machine learning methods for the measurement of periodic changes in COVID-19 antibody level

In this research, blood samples of 47 patients infected by COVID were analyzed. The samples were taken on the 1st, 3rd and 6th month after the detection of COVID infection. Total antibody levels were measured against the SARS-CoV-2 N antigen and surrogate virus neutralization by serological methods. To differentiate COVID patients with different antibody levels, Fourier Transform InfraRed (FTIR) and Raman spectroscopy methods were used. The spectroscopy data were analyzed by multivariate analysis, machine learning and neural network methods. It was shown, that analysis of serum using the above-mentioned spectroscopy methods allows to differentiate antibody levels between 1 and 6 months via spectral biomarkers of amides II and I. Moreover, multivariate analysis showed, that using Raman spectroscopy in the range between 1317 cm-1 and 1432 cm-1, 2840 cm-1 and 2956 cm-1 it is possible to distinguish patients after 1, 3, and 6 months from COVID with a sensitivity close to 100%.

Orally delivered perilla (Perilla frutescens) leaf extract effectively inhibits SARS-CoV-2 infection in a Syrian hamster model

On analyzing the results of cell-based assays, we have previously shown that perilla (Perilla frutescens) leaf extract (PLE), a food supplement and orally deliverable traditional Chinese medicine approved by the Taiwan Food and Drug Administration, effectively inhibits SARS-CoV-2 by directly targeting virions. PLE was also found to modulate virus-induced cytokine expression levels. In this study, we explored the anti-SARS-CoV-2 activity of PLE in a hamster model by examining viral loads and virus-induced immunopathology in lung tissues. Experimental animals were intranasally challenged with different SARS-CoV-2 doses. Jugular blood samples and lung tissue specimens were obtained in the acute disease stage (3-4 post-infection days). As expected, SARS-CoV-2 induced lung inflammation and hemorrhagic effusions in the alveoli and perivascular areas; additionally, it increased the expression of several immune markers of lung injury - including lung Ki67-positive cells, Iba-1-positive macrophages, and myeloperoxidase-positive neutrophils. Virus-induced lung alterations were significantly attenuated by orally administered PLE. In addition, pretreatment of hamsters with PLE significantly reduced viral loads and immune marker expression. A purified active fraction of PLE was found to confer higher antiviral protection. Notably, PLE prevented SARS-CoV-2-induced increase in serum markers of liver and kidney function as well as the decrease in serum high-density lipoprotein and total cholesterol levels in a dose-dependent fashion. Differently from lung pathology, monitoring of serum biomarkers in Syrian hamsters may allow a more humane assessment of the novel drugs with potential anti-SARS-CoV-2 activity. Our results expand prior research by confirming that PLE may exert an in vivo therapeutic activity against SARS-CoV-2 by attenuating viral loads and lung tissue inflammation, which may pave the way for future clinical applications.

Inflammation, underweight, malignancy and a marked catabolic state as predictors for worse outcomes in COVID-19 patients with moderate-to-severe disease admitted to Internal Medicine Unit

Introduction

During COVID-19 pandemic, Internal Medicine Units (IMUs) accounted for about 70% of patients hospitalized. Although a large body of data has been published regarding the so-called first wave of the pandemic, little is known about the characteristics and predictors of worse outcomes of patients managed in IMUs during the second wave.

Methods

We prospectively assessed demographics, comorbidities, treatment and outcomes, including ventilation support (VS) and death, in patients admitted to our IMU for SARS-CoV-2 between October 13th, 2020 and January 21st, 2021. Clinical evolution and biochemical testing 1, 7 and 14 days after COVID-19 diagnosis were recorded.

Results

We studied 120 patients (M/F 56/64, age 71±14.5 years) admitted to our IMU. Most of them had at least one comorbidity (80%). Patients who died were older, more frequently underweight, affected by malignant neoplasms and on statin therapy compared to patients eventually discharged. Both worse outcome groups (VS and death) presented higher neutrophils, ferritin, IL-6 and lower total proteins levels than controls. Age was significantly associated with mortality but not with VS need. The multivariate analysis showed age and gender independent association of mortality with underweight, malignancy and antibiotics use at the admission. With regard to biochemical parameters, both unfavourable outcomes were positively associated with high WBC count, neutrophils, blood urea nitrogen and low serum total proteins.

Conclusions

Our study identified inflammation, underweight, malignancy and a marked catabolic state as the main predictors for worse outcomes in COVID-19 patients admitted to IMU during the so-called second wave of the pandemic.

The role of blood urea nitrogen to serum albumin ratio in the prediction of severity and 30‐day mortality in patients with COVID‐19

Background

Considering the role of higher blood urea nitrogen and lower serum albumin (SA) levels in deceased coronavirus disease 2019 (COVID‐19) patients, an increased blood urea nitrogen to SA (B/A) ratio may help to determine those at higher risk of critical illness. This study aimed to evaluate the correlation of the B/A ratio with severity and 30‐day mortality in COVID‐19 patients.

Methods

A total of 433 adult patients with COVID‐19 were enrolled. The laboratory markers were measured on admission. Disease severity was categorized into mild disease, severe pneumonia, acute respiratory distress syndrome (ARDS), sepsis, and septic shock. The mortality was followed for 30 days after admission. χ² test, Fisher's exact test, and Mann–Whitney U test were performed, as appropriate. Also, logistic regression and the receiver operating characteristic (ROC) curve for the B/A ratio are included.

Results

Thirty‐day mortality rate was 27.25%. The frequency of mild, severe pneumonia, ARDS, sepsis, and septic shock was 30.72%, 36.95%, 24.02%, 6.00%, and 2.31%, respectively. B/A ratio and SA levels were statistically different between alive and deceased patients. The mean B/A ratio was different among classified disease severities, except for mild disease. Logistic regression revealed the B/A ratio as an independent risk factor for sepsis after adjusting for age and sex. ROC analysis showed B/A ratio had an area under the curve (AUC) of 0.733 for mortality at the cutpoint of 4.944. AUC for sepsis was 0.617 which was greater than other disease severities.

Conclusion

The results showed that B/A ratio and SA levels are associated with mortality of COVID‐19 patients. A higher B/A ratio is, additionally, associated with COVID‐19 severity, except in mild cases and it can act as an independent risk factor in sepsis. However, a greater B/A ratio is not a significant predictor of COVID‐19 severity, but it can predict mortality. Therefore, we suggest this marker for clinical assessment of patients with severe COVID‐19.

Blood Urea Nitrogen/Albumin Ratio and Mortality Risk in Patients with COVID-19

Introduction

We researched blood urea nitrogen (BUN), albumin and their ratio (BAR), and compared them with C-reactive protein (CRP), D-dimer, and computed tomography severity scores (CT-SS), to predict in-hospital mortality.

Methods

One-hundred and thirty-one coronavirus disease-2019 (COVID-19) confirmed patients brought to the emergency department (ED) were dispensed to the survivor or non-survivor group, in light of in-hospital mortality. Information on age, gender, complaints, comorbidities, laboratory parameters, and outcome were gathered from the patient's record files.

Results

The median BUN, mean total protein, mean albumin, median BAR, median creatinine, median CRP, and median D-dimer were recorded. CT-SS were utilized in categorizing the patient as mild, moderate, and severe. In-hospital mortality occurred in 42 (32.06%) patients (non-survivor group) and did not occur in 89 (67.94%) patients (survivor group). The median BUN (mg/dL) and BAR (mg/gm) values were significantly raised in the non-survivor group than in the survivor group [BUN: 23.48 (7.51–62.75) and 20.66 (4.07–74.67), respectively (p = 0.009); BAR: 8.33 mg/g (2.07–21.86) and 6.11 mg/g (1.26–23.33); (p = 0.0003)]. The mean albumin levels (gm/dL) in the non-survivor group were significantly lower than in the survivor group [2.96 ± 0.35 and 3.27 ± 0.35, respectively (p <0.0001)]. Albumin with an odd's ratio of 6.14 performed the best in predicting in-hospital mortality, followed by D-dimer (4.98). BAR and CRP had similar outcome of 3.75; BUN showed an OR of 3.13 at the selected cutoff value.

Conclusion

The BUN, albumin, and BAR were found to be dependable predictors of in-hospital mortality in COVID-19 patients, with albumin (hypoalbuminemia) performing even better.

How to cite this article

Singh S, Singh K. Blood Urea Nitrogen/Albumin Ratio and Mortality Risk in Patients with COVID-19. Indian J Crit Care Med 2022;26(5):626–631.

Blood Urea Nitrogen as a Prognostic Marker in Severe Acute Pancreatitis

Objectives

To explore independent risk factors with good and early predictive power for SAP severity and prognosis.

Methods

Patients with SAP were enrolled at Central South University Xiangya Hospital between April 2017 and May 2021 and used as the training cohort. From June 2021 to February 2022, all patients with SAP were defined as external patients for validation. Patients were grouped by survival status at a 30-day posthospital admission and then compared in terms of basic information and laboratory tests to screen the independent risk factors.

Results

A total of 249 patients with SAP were enrolled in the training cohort. The all-cause mortality rate at a 30-day postadmission was 25.8% (51/198). Blood urea nitrogen (BUN) levels were significantly higher in the mortality group (20.45 [interquartile range (IQR), 19.7] mmol/L) than in the survival group (6.685 [IQR, 6.3] mmol/L; P < 0.001). After propensity score matching (PSM), the BUN level was still higher in the mortality group than in the survival group (18.415 [IQR, 19.555] mmol/L vs. 10.63 [IQR, 6.03] mmol/L; P = 0.005). The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of BUN was 0.820 (95% confidence interval, 0.721–0.870; P < 0.001). The optimal BUN level cut-off for predicting a 30-day all-cause mortality was 10.745 mmol/L. Moreover, patients with SAP were grouped according to BUN levels and stratified according to optimal cut-off value. Patients with high BNU levels were associated with significantly higher rates of invasive mechanical ventilation (before PSM: 61.8% vs. 20.6%, P < 0.001; after PSM: 71.1% vs. 32%, P = 0.048) and a 30-day all-cause mortality (before PSM: 44.9% vs. 6.9%, P < 0.001; after PSM: 60% vs. 34.5%, P = 0.032) than those with low BNU levels before or after PSM. The effectiveness of BUN as a prognostic marker was further validated using ROC curves for the external validation set (n = 49). The AUC of BUN was 0.803 (95% CI, 0.655–0.950; P = 0.011). It showed a good ability to predict a 30-day all-cause mortality in patients with SAP. We also observed similar results regarding disease severity, including the Acute Physiology and Chronic Health Evaluation II score (before PSM: 16 [IQR, 8] vs. 8 [IQR, 6], P < 0.001; after PSM: 18 [IQR, 10] vs. 12 [IQR, 7], P < 0.001), SOFA score (before PSM: 7 [IQR, 5] vs. 3 [IQR, 3], P < 0.001; after PSM: 8 [IQR, 5] vs. 5 [IQR, 3.5], P < 0.001), and mMarshall score (before PSM: 4 [IQR, 3] vs. 3 [IQR, 1], P < 0.001; after PSM: 5 [IQR, 2.5] vs. 3 [IQR, 1], P < 0.001). There was significant increase in intensive care unit occupancy in the high BUN level group before PSM (93.3% vs. 73.1%, P < 0.001), but not after PSM (97.8% vs. 86.2%, P = 0.074).

Conclusions

Our results showed that BUN levels within 24 h after hospital admission were independent risk factors for a 30-day all-cause death in patients with SAP.

Novel prognostic determinants of COVID-19-related mortality: A pilot study on severely-ill patients in Russia

COVID-19 pandemic has posed a severe healthcare challenge calling for an integrated approach in determining the clues for early non-invasive diagnostics of the potentially severe cases and efficient patient stratification. Here we analyze the clinical, laboratory and CT scan characteristics associated with high risk of COVID-19-related death outcome in the cohort of severely-ill patients in Russia. The data obtained reveal that elevated dead lymphocyte counts, decreased early apoptotic lymphocytes, decreased CD14+/HLA-Dr+ monocytes, increased expression of JNK in PBMCs, elevated IL-17 and decreased PAI-1 serum levels are associated with a high risk of COVID-19-related mortality thus suggesting them to be new prognostic factors. This set of determinants could be used as early predictors of potentially severe course of COVID-19 for trials of prevention or timely treatment.

Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning

Introduction: Predicting the mortality risk of COVID-19 patients based on patient’s physiological conditions and demographic characteristics can help optimize resource consumption along with the provision of effective medical services for patients. In the current study, we aimed to develop several machine learning models to forecast the mortality risk in COVID-19 patients, evaluate their performance, and select the model with the highest predictive power.

Material and methods: We conducted a retrospective analysis of the records belonging to COVID-19 patients admitted to one of the main hospitals of Qazvin located in the northwest of Iran over 12 months period. We selected 29 variables for developing machine learning models incorporating demographic factors, physical symptoms, comorbidities, and laboratory test results. The outcome variable was mortality as a binary variable. Logistic regression analysis was conducted to identify risk factors of in-hospital death.

Results: In prediction of mortality, Ensemble demonstrated the maximum values of accuracy (0.8071, 95%CI: 0.7787, 0.8356), F1-score (0.8121 95%CI: 0.7900, 0.8341), and AUROC (0.8079, 95%CI: 0.7800, 0.8358). Including fourteen top-scored features identified by maximum relevance minimum redundancy algorithm into the subset of predictors of ensemble classifier such as BUN level, shortness of breath, seizure, disease history, fever, gender, body pain, WBC, diarrhea, sore throat, blood oxygen level, muscular pain, lack of taste and history of drug (medication) use are sufficient for this classifier to reach to its best predictive power for prediction of mortality risk of COVID-19 patients.

Conclusions: Study findings revealed that old age, lower oxygen saturation level, underlying medical conditions, shortness of breath, seizure, fever, sore throat, and body pain, besides serum BUN, WBC, and CRP levels, were significantly associated with increased mortality risk of COVID-19 patients. Machine learning algorithms can help healthcare systems by predicting and reduction of the mortality risk of COVID-19 patients.

The Development and Validation of Simplified Machine Learning Algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study

BACKGROUND

The current COVID-19 pandemic is unprecedented; under resource-constrained settings, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients; however, there are only few risk scores derived from a substantially large electronic health record (EHR) data set, using simplified predictors as input.

OBJECTIVE

The objectives of this study were to develop and validate simplified machine learning algorithms that predict COVID-19 adverse outcomes; to evaluate the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and calibration of the algorithms; and to derive clinically meaningful thresholds.

METHODS

We performed machine learning model development and validation via a cohort study using multicenter, patient-level, longitudinal EHRs from the Optum COVID-19 database that provides anonymized, longitudinal EHR from across the United States. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, intensive care unit (ICU) admission, respiratory failure, and mechanical ventilator usages at inpatient setting. Data from patients who were admitted from February 1, 2020, to September 7, 2020, were randomly sampled into development, validation, and test data sets; data collected from September 7, 2020, to November 15, 2020, were reserved as the postdevelopment prospective test data set.

RESULTS

Of the 3.7 million patients in the analysis, 585,867 patients were diagnosed or tested positive for SARS-CoV-2, and 50,703 adult patients were hospitalized with COVID-19 between February 1 and November 15, 2020. Among the study cohort (n=50,703), there were 6204 deaths, 9564 ICU admissions, 6478 mechanically ventilated or EMCO patients, and 25,169 patients developed acute respiratory distress syndrome or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC 0.89, 95% CI 0.89-0.89 on the test data set [n=10,752]), consistent prediction through the second wave of the pandemic from September to November (AUC 0.85, 95% CI 0.85-0.86) on the postdevelopment prospective test data set [n=14,863], great clinical relevance, and utility. Besides, a comprehensive set of 386 input covariates from baseline or at admission were included in the analysis; the end-to-end pipeline automates feature selection and model development. The parsimonious model with only 10 input predictors produced comparably accurate predictions; these 10 predictors (age, blood urea nitrogen, SpO₂, systolic and diastolic blood pressures, respiration rate, pulse, temperature, albumin, and major cognitive disorder excluding stroke) are commonly measured and concordant with recognized risk factors for COVID-19.

CONCLUSIONS

The systematic approach and rigorous validation demonstrate consistent model performance to predict even beyond the period of data collection, with satisfactory discriminatory power and great clinical utility. Overall, the study offers an accurate, validated, and reliable prediction model based on only 10 clinical features as a prognostic tool to stratifying patients with COVID-19 into intermediate-, high-, and very high-risk groups. This simple predictive tool is shared with a wider health care community, to enable service as an early warning system to alert physicians of possible high-risk patients, or as a resource triaging tool to optimize health care resources.

Factors associated with prolonged length of hospital stay among COVID-19 cases admitted to the largest treatment center in Eastern Ethiopia

INTRODUCTION

The hospital admissions load and how long each patient will stay in the hospital should be known to prevent the overwhelming of the health system during coronavirus disease 2019 era. Even though the length of hospital stay could vary due to different factors, the factors that affect the stay are not well characterized yet, particularly in the resource-limited settings. Knowing the time spent by the coronavirus disease 2019 patients in the hospital and its associated factors are important to prioritize mobilizing resources, such as beds, pharmacological and non-pharmacological supplies, and health personnel. Therefore, this study was intended to determine the median and identify factors associated with the length of hospital stay among coronavirus disease 2019 cases.

METHODS

A facility-based cross-sectional study design was implemented on 394 randomly selected hospitalized patients. Epidata Version 3.1 software was used for data entry, and further analysis was done using Stata version 14.2 software. Frequencies, median with interquartile range, and chi-square test were performed. A logistic regression model was used to identify the association between outcome and explanatory variables. The statistical significance was declared at p-value of less than 0.05 at 95% confidence interval.

RESULTS

The analysis was done for a total of 394 cases admitted for coronavirus disease 2019. The median age of the study participants was 40 years with interquartile range of 28-60 years. The median length of hospital stay was 12 days with the interquartile range of 8-17 days. The patients presented with shortness of breathing (AOR = 2.74, 95% confidence interval: 1.33-5.66), incident organ failure (AOR = 3.65, 95% confidence interval: 1.15-11.58), increased leukocyte count (AOR = 0.95; 95% confidence interval: 0.91-0.99), and blood urea nitrogen (AOR = 0.98, 95% confidence interval: 0.97-0.99) had a significant association with prolonged hospital stay.

CONCLUSION

This study demonstrated that the proportion of patients stayed above the median hospital stay of the total patients was 185 (46.9%) with the median length of 12 (interquartile range = 8-17) days. Patients presented with difficulty of breathing, had incident organ failure, had decreased leukocyte, and blood urea nitrogen level should be estimated to stay longer in the hospital. Hence, patients with prolonged hospital length of stay associating factors should be expected to consume more pharmacological and non-pharmacological resources during hospital care receiving.

Can blood urea Nitrogen-to-Albumin ratio predict mortality in patients with moderate-to-severe COVID-19 pneumonia hospitalized in the intensive care unit?

OBJECTIVE

Many laboratory parameters allow to follow up the course of the disease and reveal its clinical severity, particularly in patients with coronavirus disease 2019 (COVID-19) pneumonia. In this study, we aimed to investigate the role of the blood urea nitrogen-to-albumin ratio in predicting the mortality in COVID-19 patients with moderate-to-severe disease who are hospitalized in the intensive care unit.

METHODS

A total of 358 patients who were hospitalized in intensive care unit at our hospital between November 1, 2020 and May 15, 2021 were included in this study. During their course of intensive care, surviving patients were included in Group 1 and nonsurviving patients in Group 2.

RESULTS

There were no statistically significant differences between the two groups in terms of gender, smoking, and chronic obstructive pulmonary disease rates. In multivariate logistic regression analysis, advanced age (OR 1.038, 95%CI 1.014-1.064, p=0.002), neutrophil-to-lymphocyte ratio (OR 1.226, 95%CI 1.020-1.475, p=0.030), blood urea nitrogen-to-albumin ratio (OR 2.693, 95%CI 2.019-3.593, p<0.001), and chest computed tomography severity score (OR 1.163, 95%CI 1.105-1.225, p<0.001) values were determined as independent predictors for in-hospital mortality.

CONCLUSION

In this study, we showed that the blood urea nitrogen-to-albumin ratio, which was previously shown as a predictor of mortality in patients with various pneumonia, was an independent predictor of mortality in patients with COVID-19 pneumonia.

Effects of L-carnitine supplementation in patients with mild-to-moderate COVID-19 disease: a pilot study

BACKGROUND

The present single-center clinical trial was designed to evaluate the potential benefits of L-carnitine supplementation in patients with COVID-19 disease.

METHODS AND PATIENTS

The study was conducted on 75 patients with mild-to-moderate COVID-19 hospitalized in Shahid Beheshti Hospital-Hamadan, IRAN. The participants were randomly divided into intervention (n = 32) and control groups (n = 43). The control group received their standard hospital treatment only. In addition to standard medications, the intervention group received 3000 mg oral L-carnitine daily in three divided doses for five days. The blood samples were collected and para-clinical parameters were measured at the beginning and end of the treatment. Clinical outcomes were also recorded, and data were analyzed using χ² and t-tests.

RESULTS

Higher means of O2 saturation were observed in the intervention rather than in the control group. Mean erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) were significantly lower in the intervention group. Furthermore, mean alkaline phosphatase (ALP) activity and lactate dehydrogenase (LDH) were lower in the intervention group. Also, lower mean serum creatine phosphokinase (CPK) was observed in the intervention group. No significant differences were observed in terms of clinical symptoms; however, six patients (14%) in the control group died due to the complications of COVID-19, while all patients in the intervention group survived.

CONCLUSION

Taken together, L-carnitine can be considered as a drug supplement in patients with COVID-19.

Identification of Risk Factors for COVID-19-related Death using Machine Learning Methods

Background

Unknown cases of pneumonia appeared in late 2019 in Wuhan, China. Following the worldwide spread of the disease, the World Health Organization declared it a pandemic on March 11, 2020. The total number of infected people worldwide as of December 16, 2020, was more than 74 million, more than one million and six hundred thousand of whom died from Coronavirus Disease 2019 (COVID-19). This study aimed to identify the risk factors for the mortality of COVID-19 in Hamadan, west of Iran.

Materials and Methods

This cross-sectional study used the information of all patients with COVID-19 admitted to Shahid Beheshti and Sina hospitals in Hamadan during January 2020-November 2020. Logistic regression model, decision tree, and random forest were used to assess risk factors for death due to COVID-19.

Results

This study was conducted on 1853 people with COVID-19. Blood urea nitrogen change, SPO₂ at admission, the duration of hospitalization, age, neutrophil count, lymphocyte count, number of breaths, complete blood count, systolic blood pressure, hemoglobin, and sodium were effective predictors in both methods of decision tree and random forest.

Conclusion

The risk factors identified in the present study may serve as surrogate indicators to identify the risk of death due to COVID-19. The proper model to predict COVID-19-related mortality is random forest based on sensitivity.

Effect of hypoproteinemia on the mortality of sepsis patients in the ICU: a retrospective cohort study

The objective of the study was to evaluate the effect of hypoproteinemia on the prognosis of sepsis patients and the effectiveness of exogenous albumin supplementation. A retrospective cohort study was conducted in adult ICUs. The subjects were 1055 sepsis patients in MIMIC III database from June 2001 to October 2012. There were no interventions. A total of 1055 sepsis patients were enrolled and allocated into two groups based on the lowest in-hospital albumin level: 924 patients were in the hypoproteinemia group (the lowest in-hospital albumin ≤ 3.1 g/dL) and 131 patients were in the normal group (the lowest in-hospital albumin > 3.1 g/dL). A total of 378 patients [331 (35.8%) were in the hypoproteinemia group, and 47 (35.9%) were in the normal group] died at 28 days, and no statistically significant difference was found between the two groups (P = 0.99). The survival analysis of the 28-day mortality rate was performed using the Cox proportional risk model and it was found that the lowest in-hospital albumin level showed no significant effect on the 28-day mortality rate (P = 0.18, 95%CI). Patients in the hypoproteinemia group exhibited a longer length of stay in ICU and hospital and more complications with AKI than those in the normal group. However, multivariate regression analysis found that there was no statistical significance between the two groups. In addition, multivariate regression analysis showed that patients in the hypoproteinemia group had a shorter time without vasoactive drugs and time without mechanical ventilation than those in the normal group (P < 0.01). In the subgroup analysis, univariate analysis and multivariate regression analysis showed that there was no significant difference in the 28-day mortality rate (39.6% vs 37.5%, P = 0.80), the proportion of mechanical ventilation time (P = 0.57), and vasoactive drug time (P = 0.89) between patients with and without albumin supplementation. However, patients in the albumin supplementation group had a longer length of ICU stay and hospital stay than those in the non-supplementation group (P < 0.01). Albumin level may be an indicator of sepsis severity, but hypoproteinemia has no significant effect on the mortality of sepsis patients. Despite various physiological effects of albumin, the benefits of albumin supplementation in sepsis patients need to be evaluated with caution.

The association of blood urea nitrogen levels upon emergency admission with mortality in acute exacerbation of chronic obstructive pulmonary disease

Background and purpose

High blood urea nitrogen (BUN) is associated with an elevated risk of mortality in various diseases, such as heart failure and pneumonia. Heart failure and pneumonia are common comorbidities of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). However, data on the relationship of BUN levels with mortality in patients with AECOPD are sparse. The purpose of this study was to evaluate the correlation between BUN level and in-hospital mortality in a cohort of patients with AECOPD who presented at the emergency department (ED).

Methods

A total of 842 patients with AECOPD were enrolled in the retrospective observational study from January 2018 to September 2020. The outcome was all-cause in-hospital mortality. Receiver operating characteristic (ROC) curve analysis and logistic regression models were performed to evaluate the association of BUN levels with in-hospital mortality in patients with AECOPD. Propensity score matching was used to assemble a cohort of patients with similar baseline characteristics, and logistic regression models were also performed in the propensity score matching cohort.

Results

During hospitalization, 26 patients (3.09%) died from all causes, 142 patients (16.86%) needed invasive ventilation, and 190 patients (22.57%) were admitted to the ICU. The mean level of blood urea nitrogen was 7.5 ± 4.5 mmol/L. Patients in the hospital non-survivor group had higher BUN levels (13.48 ± 9.62 mmol/L vs. 7.35 ± 4.14 mmol/L, p < 0.001) than those in the survivor group. The area under the curve (AUC) was 0.76 (95% CI 0.73–0.79, p < 0.001), and the optimal BUN level cutoff was 7.63 mmol/L for hospital mortality. As a continuous variable, BUN level was associated with hospital mortality after adjusting respiratory rate, level of consciousness, pH, PCO_2, lactic acid, albumin, glucose, CRP, hemoglobin, platelet distribution width, D-dimer, and pro-B-type natriuretic peptide (OR 1.10, 95% CI 1.03–1.17, p=0.005). The OR of hospital mortality was significantly higher in the BUN level ≥7.63 mmol/L group than in the BUN level <7.63 mmol/L group in adjusted model (OR 3.29, 95% CI 1.05–10.29, p=0.041). Similar results were found after multiple imputation and in the propensity score matching cohort.

Conclusions

Increased BUN level at ED admission is associated with hospital mortality in patients with AECOPD who present at the ED. The level of 7.63 mmol/L can be used as a cutoff value for critical stratification.

D-dimer, disease severity, and deaths (3D-study) in patients with COVID-19: a systematic review and meta-analysis of 100 studies

Hypercoagulability and the need for prioritizing coagulation markers for prognostic abilities have been highlighted in COVID-19. We aimed to quantify the associations of D-dimer with disease progression in patients with COVID-19. This systematic review and meta-analysis was registered with PROSPERO, CRD42020186661.We included 113 studies in our systematic review, of which 100 records (n = 38,310) with D-dimer data) were considered for meta-analysis. Across 68 unadjusted (n = 26,960) and 39 adjusted studies (n = 15,653) reporting initial D-dimer, a significant association was found in patients with higher D-dimer for the risk of overall disease progression (unadjusted odds ratio (uOR) 3.15; adjusted odds ratio (aOR) 1.64). The time-to-event outcomes were pooled across 19 unadjusted (n = 9743) and 21 adjusted studies (n = 13,287); a strong association was found in patients with higher D-dimers for the risk of overall disease progression (unadjusted hazard ratio (uHR) 1.41; adjusted hazard ratio (aHR) 1.10). The prognostic use of higher D-dimer was found to be promising for predicting overall disease progression (studies 68, area under curve 0.75) in COVID-19. Our study showed that higher D-dimer levels provide prognostic information useful for clinicians to early assess COVID-19 patients at risk for disease progression and mortality outcomes. This study, recommends rapid assessment of D-dimer for predicting adverse outcomes in COVID-19.

Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression

BACKGROUND AND OBJECTIVES

Although ML has been studied for different epidemiological and clinical issues as well as for survival prediction of COVID-19, there is a noticeable shortage of literature dealing with ML usage in prediction of disease severity changes through the course of the disease. In that way, predicting disease progression from mild towards moderate, severe and critical condition, would help not only to respond in a timely manner to prevent lethal results, but also to minimize the number of patients in hospitals where this is not necessary.

METHODS

We present a methodology for the classification of patients into 4 distinct categories of the clinical condition of COVID-19 disease. Classification of patients is based on the values of blood biomarkers that were assessed by Gradient boosting regressor and which were selected as biomarkers that have the greatest influence in the classification of patients with COVID-19.

RESULTS

The results show that among several tested algorithms, XGBoost classifier achieved best results with an average accuracy of 94% and an average F1-score of 94.3%. We have also extracted 10 best features from blood analysis that are strongly associated with patient condition and based on those features we can predict the severity of the clinical condition.

CONCLUSIONS

The main advantage of our system is that it is a decision tree-based algorithm which is easier to interpret, instead of the use of black box models, which are not appealing in medical practice.