The Impact of Serum Albumin Levels on COVID-19 Mortality

Do dynamic changes in haematological and biochemical parameters predict mortality in critically ill COVID-19 patients?

BACKGROUND

Critically ill COVID-19 patients are usually subjected to clinical, laboratory, and radiological diagnostic procedures resulting in numerous findings. Utilizing these findings as indicators for disease progression or outcome prediction is particularly intriguing.

OBJECTIVES

Exploring the significance of dynamic changes in haematological and biochemical parameters in predicting the mortality of critically ill COVID-19 patients.

METHODS

The present study was a prospective and observational study involving mechanically ventilated 75 critically ill adult COVID-19 patients with hypoxemic respiratory failure. The collected data included baseline patient characteristics, treatment options, outcome, and laboratory findings at admission and 7 days after. The dynamics of the obtained findings were compared between survivors and non-survivors.

RESULTS

The 28-day survival rate was 61.3%. In the group of non-survivors significant dynamic changes were found for C-reactive protein (p= 0.001), interleukin-6 (p< 0.001), lymphocyte (p= 0.003), neutrophil-lymphocyte ratio (p= 0.003), platelets (p< 0.001), haemoglobin (p< 0.001), iron (p= 0.012), and total iron-binding capacity (p< 0.001). Statistically significant changes over time were found for ferritin (p= 0.010), D-dimer (p< 0.001), hs-troponin T (p< 0.002), lactate dehydrogenase (p= 0.001), glucose (p= 0.023), unsaturated iron-binding capacity (p= 0.008), and vitamin D (p< 0.001).

CONCLUSION

The dynamic changes in inflammatory, haematological and biochemical parameters can predict disease severity, and outcome.

Multi-Modal Machine Learning Approach for COVID-19 Detection Using Biomarkers and X-Ray Imaging

Background: Accurate and rapid detection of COVID-19 remains critical for clinical management, especially in resource-limited settings. Current diagnostic methods face challenges in terms of speed and reliability, creating a need for complementary AI-based models that integrate diverse data sources. Objectives: This study aimed to develop and evaluate a multi-modal machine learning model that combines clinical biomarkers and chest X-ray images to enhance diagnostic accuracy and provide interpretable insights. Methods: We used a dataset of 250 patients (180 COVID-19 positive and 70 negative cases) collected from clinical settings. Biomarkers such as CRP, ferritin, NLR, and albumin were included alongside chest X-ray images. Random Forest and Gradient Boosting models were used for biomarkers, and ResNet and VGG CNN architectures were applied to imaging data. A late-fusion strategy integrated predictions from these modalities. Stratified k-fold cross-validation ensured robust evaluation while preventing data leakage. Model performance was assessed using AUC-ROC, F1-score, Specificity, Negative Predictive Value (NPV), and Matthews Correlation Coefficient (MCC), with confidence intervals calculated via bootstrap resampling. Results: The Gradient Boosting + VGG fusion model achieved the highest performance, with an AUC-ROC of 0.94, F1-score of 0.93, Specificity of 93%, NPV of 96%, and MCC of 0.91. SHAP and LIME interpretability analyses identified CRP, ferritin, and specific lung regions as key contributors to predictions. Discussion: The proposed multi-modal approach significantly enhances diagnostic accuracy compared to single-modality models. Its interpretability aligns with clinical understanding, supporting its potential for real-world application.

Low visceral fat volume and hypoalbuminemia as prognostic markers in hospitalized patients with coronavirus disease 2019 during the omicron variant epidemic

BACKGROUND & AIMS

The rate of severe cases of coronavirus disease 2019 (COVID-19) has decreased since the Omicron variant became epidemic. Visceral fat volume was a risk factor for COVID-19 severity with prior prevalent variants, but whether visceral fat volume remains a risk factor for the Omicron variant is unclear. We investigated the associations of clinical factors including visceral fat volume with severity and mortality among hospitalized patients with COVID-19 during the Omicron variant epidemic.

METHODS

This was a single-center retrospective cohort study conducted at the Teikyo University Hospital in Japan. We included hospitalized patients with COVID-19 during the Omicron variant epidemic who underwent computed tomography of the abdomen. Clinical data were obtained from the medical records and visceral fat area (VFA) was measured using a 3-dimensional image analysis system volume analyzer. Severity was determined by the presence or absence of oxygen supplementation.

RESULTS

Among the 226 patients, 66 patients showed moderate severity and 29 patients were non-survivors. Hypoalbuminemia was associated with severity (odds ratio [OR] 3.93, 95 % confidence interval [CI] 1.91-8.07; p = 0.0002), and hypoalbuminemia (OR 8.38, 95%CI 2.37-29.58; p = 0.0010) and low VFA (OR 3.40, 95%CI 1.15-10.06; p = 0.027) were associated with mortality. Decision tree analysis showed that mortality rate in the hypoalbuminemia and low-VFA group (37.3 %) was significantly higher than in other groups (p ≤ 0.01).

CONCLUSIONS

Low visceral fat volume and hypoalbuminemia were associated with mortality in hospitalized patients with COVID-19 during the Omicron variant epidemic. Classification by VFA and serum albumin may allow simple prediction of mortality risk among hospitalized patients with COVID-19.

Association between COVID-19 severity with liver abnormalities: A retrospective study in a referral hospital in Indonesia

Coronavirus disease 2019 (COVID-19) is characterized by an acute respiratory infection with multisystem involvement and the association of its severity to liver function abnormalities is not well characterized. The aim of this study was to assess the association between the severity of COVID-19 patients and liver function abnormalities. This retrospective study included adult patients with confirmed COVID-19, which were classified as non-severe or severe according to World Health Organization guidelines. Liver function test results were compared between the severity groups. A total of 339 patients were included of which 150 (44.25%) were severe cases. The male-to-female ratio was 0.9:1 and 3:2 in the non-severe and severe groups, respectively (p=0.031). Aspartate aminotransferase (AST), alanine transaminase (ALT), and total bilirubin levels and acute liver injury (ALI) incidence were significantly higher in the severe group compared to non-severe group (p<0.001, p<0.001, p=0.025, p=0.014, respectively). In contrast, albumin levels were significantly lower (p=0.001). Multivariate analysis showed that ALI was significantly associated with human immunodeficiency virus (HIV) infection (odds ratio (OR): 5.275; 95% confidence interval (CI): 1.165-23.890, p=0.031), hemoglobin level (OR: 1.214; 95%CI: 1.083-1.361, p=0.001), and hypoalbuminemia (OR: 2.627; 95%CI: 1.283-5.379, p=0.008). Pre-existing liver diseases were present in 6.5% of patients. No significant differences were observed between the groups based on COVID-19 severity and ALI presence. Liver function test abnormalities, including ALI, are more prevalent in patients with severe COVID-19 infection. HIV infection, high hemoglobin levels, and hypoalbuminemia may be potential risk factors for ALI.

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm

It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.

Impact of Age on Predictive Capabilities of Ferritin, Ferritin-Hemoglobin Ratio, IL-6, and sIL-2R for COVID-19 Severity and Mortality

The study aimed to establish the impact of age on the predictive capability of ferritin, ferritin-hemoglobin ratio (FHR), IL-6, and sIL-2R in COVID-19 patients. Compared to patients with moderate condition, patients with severe condition had higher ferritin level (441.0 [188.0-829.8] ng/mL vs 281.0 [172.0-388.0] ng/mL, p = 0.002), sIL-2R level (6.0 [4.7-9.0] pg/mL vs 5.3 [3.7-6.9] pg/mL, p = 0.020), FHR (38.4 [15.1-63.4] vs 22.0 [12.1-32.1], p = 0.002). The area under the curves (AUC) for discriminative capabilities of the following biomarkers for severe condition were assessed in patients aged <65 years and patients aged ≥65 years: ferritin (AUC = 0.585, p = 0.309 vs AUC = 0.683, p = 0.002), FHR (AUC = 0.589, p = 0.302 vs AUC = 0.688, p = 0.002), IL-6 (AUC = 0.503, p = 0.972 vs AUC = 0.647, p = 0.019), and sIL-2R (AUC = 0.549, p = 0.552 vs AUC = 0.646, p = 0.017). Also AUCs for discriminative capabilities for in-hospital mortality were compared in patients aged <65 years and ≥65 years: ferritin (AUC = 0.607, p = 0.628 vs AUC = 0.661, p = 0.105), FHR (AUC = 0.612, p = 0.621 vs AUC = 0.688, p = 0.002), IL-6 (AUC = 0.580, p = 0.724 vs AUC = 0.695, p = 0.016), and sIL-2R (AUC = 0.620, p = 0.491 vs AUC = 0.695, p = 0.029). Thus, ferritin, FHR, IL-6, and sIL-2R didn't show acceptable predictive value for severe condition and lethal outcome in patients aged <65 years but had high predictive value for lethal outcome in patients aged ≥65 years.

An optimized machine learning model for predicting hospitalization for COVID-19 infection in the maintenance dialysis population

COVID-19 has a high rate of infection in dialysis patients and poses a serious risk to human health. Currently, there are no dialysis centers in China that have analyzed the prevalence of COVID-19 infection in dialysis patients and the mortality rate. Although machine learning-based disease prediction methods have proven to be effective, redundant attributes in the data and the interpretability of the predictive models are still worth investigating. Therefore, this paper proposed a wrapper feature selection classification model to achieve the prediction of the risk of COVID-19 infection in dialysis patients. The method was used to optimize the feature set of the sample through an enhanced JAYA optimization algorithm based on the dispersed foraging strategy and the greedy levy mutation strategy. Then, the proposed method combines fuzzy K-nearest neighbor for classification prediction. IEEE CEC2014 benchmark function experiments as well as prediction experiments on the uremia dataset are used to validate the proposed model. The experimental results showed that the proposed method has a high prediction accuracy of 95.61% for the prevalence risk of COVID-19 infection in dialysis patients. Furthermore, it was shown that proalbumin, CRP, direct bilirubin, hemoglobin, albumin, and phosphorus are of great value for clinical diagnosis. Therefore, the proposed method can be considered as a promising method.

Association between laboratory markers and Covid-19 disease severity and outcome: a retrospective cohort study in Saudi Arabia

Introduction

In Saudi Arabia, limited studies have evaluated factors including epidemiologic, clinical, and laboratory findings that are associated with COVID-19 disease. The aim of this paper was to identify laboratory parameters used in King Abdulaziz University Hospital which show an association with disease severity and patient outcome in the form of mortality.

Methods

Age, gender, medical history, and laboratory parameters were all retrospectively assessed concerning disease severity and disease outcome in a total of 111 COVID-19 patients at King Abdulaziz University Hospital between July 2020 and August 2020. Patients were categorized into mild disease if they did not require ward admission, moderate if they met the Ministry of Health criteria for isolation ward admition, and severe if they were admitted to the ICU.

Results

Age but not gender was associated with the disease severity X² (4, N = 110) = 27.2, p <0.001. Of all laboratory parameters on admission, only the levels of Albumin appeared to be significantly associated X² (2, N =70) = 6.6, p <0.05 with disease severity. Age but not gender was also significantly associated with disease outcome X² (2, N = 110) = 12.8, p < 0.01. Interestingly, RBC count also showed a significant relation with disease outcome X² (2, N = 71) = 6.1, p <0.05.

Discussion

This study provides more understanding of the laboratory characteristics in our part of the world to efficiently manage the disease.

Vitamin D, Albumin, and D-Dimer as Significant Prognostic Markers in Early Hospitalization in Patients with COVID-19

SARS-CoV-2 continues to pose a major challenge to scientists and clinicians. We examined the significance of the serum concentrations of vitamin D, albumin, and D-dimer for the severity of the clinical picture and mortality in COVID-19.

MATERIALS AND METHODS

A total of 288 patients treated for COVID-19 infection participated in the research. The patients were treated in the period from May 2020 to January 2021. All patients were divided based on the need for oxygen therapy (Sat > 94%) into patients with mild or severe clinical pictures. The biochemical and radiographic parameters of the patients were analyzed. Appropriate statistical methods were used in the statistical analysis.

RESULTS

In patients with COVID-19 with confirmed severe clinical pictures, lower values of serum albumin (p < 0.0005) and vitamin D (p = 0.004) were recorded, as opposed to elevated values of D-dimer (p < 0.0005). Accordingly, the patients with fatal disease outcomes had lower levels of albumin (p < 0.0005) and vitamin D (p = 0.002), while their D-dimer (p < 0.0005) levels were elevated. An increase in the radiographic score, as a parameter for assessing the severity of the clinical picture, was accompanied by a decrease in serum albumin (p < 0.0005) and a simultaneous increase in D-dimer (p < 0.0005), without a change in the vitamin D concentration (p = 0.261). We also demonstrated the interrelations of the serum levels of vitamin D, albumin, and D-dimer in patients with COVID-19 as well as their significance as predictors of the outcome of the disease.

CONCLUSION

The significance of the predictive parameters in our study indicates the existence of an important combined role of vitamin D, albumin, and D-dimer in the early diagnosis of the most severe patients suffering from COVID-19. Reduced values of vitamin D and albumin, in combination with elevated values of D-dimer, can be timely indicators of the development of a severe clinical picture and death due to COVID-19.

Heme Scavenging and Delivery: The Role of Human Serum Albumin

Heme is the reactive center of several metal-based proteins that are involved in multiple biological processes. However, free heme, defined as the labile heme pool, has toxic properties that are derived from its hydrophobic nature and the Fe-atom. Therefore, the heme concentration must be tightly controlled to maintain cellular homeostasis and to avoid pathological conditions. Therefore, different systems have been developed to scavenge either Hb (i.e., haptoglobin (Hp)) or the free heme (i.e., high-density lipoproteins (HDL), low-density lipoproteins (LDL), hemopexin (Hx), and human serum albumin (HSA)). In the first seconds after heme appearance in the plasma, more than 80% of the heme binds to HDL and LDL, and only the remaining 20% binds to Hx and HSA. Then, HSA slowly removes most of the heme from HDL and LDL, and finally, heme transits to Hx, which releases it into hepatic parenchymal cells. The Hx:heme or HSA:heme complexes are internalized via endocytosis mediated by the CD91 and CD71 receptors, respectively. As heme constitutes a major iron source for pathogens, bacteria have evolved hemophores that can extract and uptake heme from host proteins, including HSA:heme. Here, the molecular mechanisms underlying heme scavenging and delivery from HSA are reviewed. Moreover, the relevance of HSA in disease states associated with increased heme plasma concentrations are discussed.

Phase Angle and Handgrip Strength as Predictors of Clinical Outcomes in Hospitalized COVID-19 Patients

Phase angle (PhA) and muscle strength are predictors of clinical outcomes in critically ill patients. Malnutrition may affect body composition measurements. The aim of this prospective study was to investigate the association between PhA and handgrip strength (HGS), and clinical outcomes in hospitalized COVID-19 patients. The study included a total of 102 patients. Both PhA and HGS were measured twice, within 48 h of hospital admission and on the 7th day of hospitalization. The primary outcome was the clinical status on the 28th day of hospitalization. Secondary outcomes included the hospital length of stay (LOS), the concentrations of ferritin, C-reactive protein and albumin, oxygen requirements and the severity of pneumonia. A one-way analysis of variance (ANOVA) test and Spearman rS correlation coefficient were used for statistical analysis. No differences were found for PhA [on day 1 (p = 0.769) and day 7 (p = 0.807)] and the primary outcome. A difference was found between HGS on day 1 and the primary outcome (p = 0.008), while no difference was found for HGS on day 7 (p = 0.476). Body mass index was found to be associated with the oxygen requirement on day 7 (p = 0.005). LOS was correlated neither with PhA (rs = −0.081, p = 0.422) nor with HGS (rs = 0.137, p = 0.177) on the first day. HGS could be a useful indicator of clinical outcomes in COVID-19 patients, while PhA does not seem to have a clinical impact. However, further research is needed to validate the results of our study.

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices.

Rapid Progression of COVID-19-Associated Fatal Capillary Leak Syndrome

Several cases of capillary leak syndrome (CLS) related to COVID-19 or vaccination against SARS-CoV-2 have been described in the literature. We present a case of a 42-year-old, previously healthy male, presenting with a mild form of COVID-19, who suddenly developed severe shock with hypotension and severe hemoconcentration within hours of admission to the hospital. Volume resuscitation was not effective, increasing hemoglobin (198 g/L on admission, 222 g/L 9 h later) suggested fluid leak into peripheral tissues. After cardiac arrest, the patient was resuscitated and connected to extracorporeal membrane oxygenation, but died shortly afterwards due to refractory heart failure. Retrospective investigation of blood samples confirmed diagnosis of CLS by progressive hypoalbuminemia (40 g/L on admission, 14 g/L 19 h later) and monoclonal gammopathy kappa (4.7 g/L). Patient's CLS was triggered by COVID-19, either a first attack of idiopathic CLS called Clarkson's disease or a COVID-19-induced secondary CLS.

Inadequate energy and protein intake, underweight and malnutrition are associated with in-hospital mortality among COVID-19 rehabilitation patients during the omicron outbreak in Hong Kong

Objective

Malnourished COVID-19 patients were prone to higher mortality and longer length of stay (LOS). This study aims to investigate the malnutrition risk prevalence in the COVID-19 patients and how other nutritional indicators are related to the clinical outcomes in a rehabilitation hospital.

Methods

A retrospective cross-sectional study involved 174 COVID-19 patients during the rehabilitation phase. Malnutrition risk, nutritional indicators, mortality, and LOS were compared among different risk groups. Albumin, nutrition intake, and body mass index (BMI) were investigated for their effects on the clinical outcomes.

Results

The prevalence of malnutrition risk was 94.9%; those older were higher in malnutrition risk. BMI, energy and protein intakes decreased as the malnutrition risk increased. Albumin, energy and protein intakes were lower in the death group. The high malnutrition risk group and severely underweight patients had 2.7 times and 2.2 times higher in-hospital death, respectively. For subjects ≥75 years old, the odds ratio to death was 6.2 compared to those <75 years old.

Conclusion

We observed a high malnutrition risk of 94.9% in COVID-19 patients. Patients with malnutrition risk had a lower BMI, lower nutritional intake, and a higher chance of in-hospital death. These results reinforced the importance of nutrition management in COVID-19 patients.