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

The Evaluation of Laboratory Parameters as Predictors of Disease Severity and Mortality in COVID-19 Patients: A Retrospective Study From a Tertiary Care Hospital in India

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects and alters various laboratory parameters that are predictors of disease severity and mortality, and hence, their prompt identification can aid in patient triaging and resource allocation. Objectives A retrospective study was conducted on 7416 admitted coronavirus disease 2019 (COVID-19) patients from 20 March 2020 to 9 August 2021 to identify crucial laboratory biomarkers as predictors of disease severity and outcome; also, their optimal cutoffs were also calculated. A comparison of laboratory markers between both COVID-19 waves was also performed. Results The majority of patients had mild disease (4295/7416, 57.92%), whereas 1262/7416 (17.02%) had severe disease. The overall fatal outcome was reported in 461 (6.22%) patients. Predictors for mortality were age (>52 years), albumin/globulin (A/G) ratio (≤1.47), chloride (≤101 mmol/L), ferritin (>483.89 ng/mL), lactate dehydrogenase (LDH) (>393 U/L), procalcitonin (>0.10 ng/mL), interleukin-6 (IL-6) (>8.8 pg/mL), fibrinogen (>403 mg/dL), international normalized ratio (INR) (>1.18), and D-dimer (>268 ng/mL). Disease severity predictors were neutrophils (>81%), lymphocyte (≤25.4%), absolute lymphocyte count (ALC) (≤1.38×10³/µL), absolute eosinophil count (AEC) (≤0.03×10³/µL), total bilirubin (TBIL) (≥0.51 mg/dL), A/G ratio (≤1.49), albumin (≤4.2 g/dL), ferritin (≥445.4 mg/dL), LDH (≥479 U/L), IL-6 (≥28.6 pg/mL), C-reactive protein/albumin (CRP/ALB) ratio (≥1.78), D-dimer (≥237 ng/mL), and fibrinogen (≥425 mg/dL). The majority of patients admitted in the second wave were older and had severe disease, increased fatality, and significantly deranged laboratory parameters than first wave patients. Conclusion Our findings suggested that several biomarkers are crucial for both severe disease and mortality in COVID-19 patients. Ferritin, LDH, IL-6, A/G ratio, fibrinogen, and D-dimer are important biomarkers for both severity and mortality, and when combined, they provide valuable information for patient monitoring and triaging. In addition to these, older age, INR, chloride, and procalcitonin are also significant risk factors for mortality. For severe COVID-19, TBIL, CRP/ALB, albumin, neutrophil percentage, lymphocyte percentage, ALC, and AEC are also important biomarkers. According to the study, the majority of the baseline laboratory parameters associated with COVID-19 mortality and severe disease were significantly higher during the second wave, which could be one of the possible causes for the high mortality rate in India during the second wave. So, the combination of all these parameters can be a powerful tool in emergency settings to improve the efficacy of treatment and prevent mortality, and the planning of subsequent waves should be done accordingly.

Hematological findings in COVID-19 and their correlation with severity of Disease

Objective

To evaluate the efficacy of hematological parameters to predict severity of COVID-19 patients.

Method

This was a cross-sectional comparative study conducted at Central Park Teaching Hospital, Lahore in COVID ward and COVID ICU between April 23, 2021 to June 23, 2021. Patients of all ages and both genders with positive PCR admitted in the COVID ward and ICU during this time span of two months were included in the study. Data was collected retrospectively.

Results

This study included 50 patients with male to female ratio of 1.38:1. Though males are more affected by COVID-19 but the difference is not statistically significant. The mean age of the study population was 56.21 and the patients in the severe disease group have higher age. It was observed that in severe/critical group the mean values of total leukocyte count 21.76×103 μI (p-value= 0.002), absolute neutrophil count 71.37% (p-value=0.045), neutrophil lymphocyte ratio (NLR) 12.80 (p-value=0.00) and PT 11.9 seconds (p-value=0.034) and the difference was statistically significant. While in severe/critical group, the mean values of hemoglobin 12.03g/dl (p-value=0.075), lymphocyte count 28.41% (p-value=0.8), platelet count 226×103 μI (p-value=0.67) and APTT 30.7 (p-value=0.081) and the difference was not significantly different between groups.

Conclusion

It can be concluded from the study that total leucocyte count, absolute neutrophil count and neutrophil lymphocyte ratio can predict in-hospital mortality and morbidity in COVID-19 patients.

COVID-19 Biomarkers at the Crossroad between Patient Stratification and Targeted Therapy: The Role of Validated and Proposed Parameters

Clinical knowledge about SARS-CoV-2 infection mechanisms and COVID-19 pathophysiology have enormously increased during the pandemic. Nevertheless, because of the great heterogeneity of disease manifestations, a precise patient stratification at admission is still difficult, thus rendering a rational allocation of limited medical resources as well as a tailored therapeutic approach challenging. To date, many hematologic biomarkers have been validated to support the early triage of SARS-CoV-2-positive patients and to monitor their disease progression. Among them, some indices have proven to be not only predictive parameters, but also direct or indirect pharmacological targets, thus allowing for a more tailored approach to single-patient symptoms, especially in those with severe progressive disease. While many blood test-derived parameters quickly entered routine clinical practice, other circulating biomarkers have been proposed by several researchers who have investigated their reliability in specific patient cohorts. Despite their usefulness in specific contexts as well as their potential interest as therapeutic targets, such experimental markers have not been implemented in routine clinical practice, mainly due to their higher costs and low availability in general hospital settings. This narrative review will present an overview of the most commonly adopted biomarkers in clinical practice and of the most promising ones emerging from specific population studies. Considering that each of the validated markers reflects a specific aspect of COVID-19 evolution, embedding new highly informative markers into routine clinical testing could help not only in early patient stratification, but also in guiding a timely and tailored method of therapeutic intervention.

Baseline Plasma Osteopontin Protein Elevation Predicts Adverse Outcomes in Hospitalized COVID-19 Patients

More than three years have passed since the first case, and COVID-19 is still a health concern, with several open issues such as the lack of reliable predictors of a patient’s outcome. Osteopontin (OPN) is involved in inflammatory response to infection and in thrombosis driven by chronic inflammation, thus being a potential biomarker for COVID-19. The aim of the study was to evaluate OPN for predicting negative (death or need of ICU admission) or positive (discharge and/or clinical resolution within the first 14 days of hospitalization) outcome. We enrolled 133 hospitalized, moderate-to-severe COVID-19 patients in a prospective observational study between January and May 2021. Circulating OPN levels were measured by ELISA at admission and at day 7. The results showed a significant correlation between higher plasma concentrations of OPN at hospital admission and a worsening clinical condition. At multivariate analysis, after correction for demographic (age and gender) and variables of disease severity (NEWS2 and PiO2/FiO2), OPN measured at baseline predicted an adverse prognosis with an odds ratio of 1.01 (C.I. 1.0–1.01). At ROC curve analysis, baseline OPN levels higher than 437 ng/mL predicted a severe disease evolution with 53% sensitivity and 83% specificity (area under the curve 0.649, p = 0.011, likelihood ratio of 1.76, (95% confidence interval (CI): 1.35–2.28)). Our data show that OPN levels determined at the admission to hospital wards might represent a promising biomarker for early stratification of patients’ COVID-19 severity. Taken together, these results highlight the involvement of OPN in COVID-19 evolution, especially in dysregulated immune response conditions, and the possible use of OPN measurements as a prognostic tool in COVID-19.

Active regression model for clinical grading of COVID-19

Background

In the therapeutic process of COVID-19, the majority of indicators that physicians have for assisting treatment have come from clinical tests represented by proteins, metabolites, and immune levels in patients' blood. Therefore, this study constructs an individualized treatment model based on deep learning methods, aiming to realize timely intervention based on clinical test indicator data of COVID-19 patients and provide an important theoretical basis for optimizing medical resource allocation.

Methods

This study collected clinical data from a total of 1,799 individuals, including 560 controls for non-respiratory infectious diseases (Negative), 681 controls for other respiratory virus infections (Other), and 558 coronavirus infections (Positive) for COVID-19. We first used the Student T-test to screen for statistically significant differences (Pvalue<0.05); we then used the Adaptive-Lasso method stepwise regression to screen the characteristic variables and filter the features with low importance; we then used analysis of covariance to calculate the correlation between variables and filter the highly correlated features; and finally, we analyzed the feature contribution and screened the best combination of features.

Results

Feature engineering reduced the feature set to 13 feature combinations. The correlation coefficient between the projected results of the artificial intelligence-based individualized diagnostic model and the fitted curve of the actual values in the test group was 0.9449 which could be applied to the clinical prognosis of COVID-19. In addition, the depletion of platelets in patients with COVID-19 is an important factor affecting their severe deterioration. With the progression of COVID-19, there is a slight decrease in the total number of platelets in the patient's body, particularly as the volume of larger platelets sharply decreases. The importance of plateletCV (count*mean platelet volume) in evaluating the severity of COVID-19 patients is higher than the count of platelets and mean platelet volume.

Conclusion

In general, we found that for patients with COVID-19, the increase in mean platelet volume was a predictor for SARS-Cov-2. The rapid decrease of platelet volume and the decrease of total platelet volume are dangerous signals for the aggravation of SARS-Cov-2 infection. The analysis and modeling results of this study provide a new perspective for individualized accurate diagnosis and treatment of clinical COVID-19 patients.

Identification of Parameters Representative of Immune Dysfunction in Patients with Severe and Fatal COVID-19 Infection: a Systematic Review and Meta-analysis

Abnormal immunological indicators associated with disease severity and mortality in patients with COVID-19 have been reported in several observational studies. However, there are marked heterogeneities in patient characteristics and research methodologies in these studies. We aimed to provide an updated synthesis of the association between immune-related indicators and COVID-19 prognosis. We conducted an electronic search of PubMed, Scopus, Ovid, Willey, Web of Science, Cochrane library, and CNKI for studies reporting immunological and/or immune-related parameters, including hematological, inflammatory, coagulation, and biochemical variables, tested on hospital admission of COVID-19 patients with different severities and outcomes. A total of 145 studies were included in the current meta-analysis, with 26 immunological, 11 hematological, 5 inflammatory, 4 coagulation, and 10 biochemical variables reported. Of them, levels of cytokines, including IL-1β, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, IFN-γ, IgA, IgG, and CD4⁺ T/CD8⁺ T cell ratio, WBC, neutrophil, platelet, ESR, CRP, ferritin, SAA, D-dimer, FIB, and LDH were significantly increased in severely ill patients or non-survivors. Moreover, non-severely ill patients or survivors presented significantly higher counts of lymphocytes, monocytes, lymphocyte/monocyte ratio, eosinophils, CD3⁺ T,CD4⁺T and CD8⁺T cells, B cells, and NK cells. The currently updated meta-analysis primarily identified a hypercytokinemia profile with the severity and mortality of COVID-19 containing IL-1β, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, and IFN-γ. Impaired innate and adaptive immune responses, reflected by decreased eosinophils, lymphocytes, monocytes, B cells, NK cells, T cells, and their subtype CD4⁺ and CD8⁺ T cells, and augmented inflammation, coagulation dysfunction, and nonpulmonary organ injury, were marked features of patients with poor prognosis. Therefore, parameters of immune response dysfunction combined with inflammatory, coagulated, or nonpulmonary organ injury indicators may be more sensitive to predict severe patients and those non-survivors.

Clinical Characteristics and Predictors of In-Hospital Mortality of Patients Hospitalized with COVID-19 Infection

Background: The identification of parameters that would serve as predictors of prognosis in COVID-19 patients is very important. In this study, we assessed independent factors of in-hospital mortality of COVID-19 patients during the second wave of the pandemic. Material and methods: The study group consisted of patients admitted to two hospitals and diagnosed with COVID-19 between October 2020 and May 2021. Clinical and demographic features, the presence of comorbidities, laboratory parameters, and radiological findings at admission were recorded. The relationship of these parameters with in-hospital mortality was evaluated. Results: A total of 1040 COVID-19 patients (553 men and 487 women) qualified for the study. The in-hospital mortality rate was 26% across all patients. In multiple logistic regression analysis, age ≥ 70 years with OR = 7.8 (95% CI 3.17−19.32), p < 0.001, saturation at admission without oxygen ≤ 87% with OR = 3.6 (95% CI 1.49−8.64), p = 0.004, the presence of typical COVID-19-related lung abnormalities visualized in chest computed tomography ≥40% with OR = 2.5 (95% CI 1.05−6.23), p = 0.037, and a concomitant diagnosis of coronary artery disease with OR = 3.5 (95% CI 1.38−9.10), p = 0.009 were evaluated as independent risk factors for in-hospital mortality. Conclusion: The relationship between clinical and laboratory markers, as well as the advancement of lung involvement by typical COVID-19-related abnormalities in computed tomography of the chest, and mortality is very important for the prognosis of these patients and the determination of treatment strategies during the COVID-19 pandemic.

Long-Term Survival among Patients Hospitalized for COVID-19 during the First Three Epidemic Waves: An Observational Study in a Northern Italy Hospital

The mortality rate of hospitalized COVID-19 patients differed strongly between the first three pandemic waves. Nevertheless, their long-term survival has been poorly assessed. The aim of this study was to compare the clinical characteristics and mortality rates of 825 patients with coronavirus disease 2019 (COVID-19) infection who were hospitalized at the Alessandria hub hospital, in Northern Italy, during the first fifty days of the first three pandemic waves. Each subject was followed in terms of vital status for six months from the date of hospital admission or until deceased. Patients admitted during the three waves differed in age (p = 0.03), disease severity (p < 0.0001), Charlson comorbidity index (p = 0.0002), oxygen therapy (p = 0.002), and invasive mechanical ventilation (p < 0.0001). By the end of follow-up, 309 deaths (38.7%) were observed, of which 186 occurred during hub hospitalization (22.5%). Deaths were distributed differently among the waves (p < 0.0001), resulting in being higher amongst those subjects admitted during the first wave. The COVID-19 infection was reported as the main cause of death and patients with a higher mortality risk were those aged ≥65 years [adjusted HR = 3.40 (95% CI 2.20-5.24)], with a higher disease severity [adjusted HR = 1.87 (95%CI 1.43-2.45)], and those requiring oxygen therapy [adjusted HR = 2.30 (95%CI 1.61-3.30)]. In conclusion, COVID-19 patients admitted to our hub hospital during the second and the third waves had a lower risk of long-term mortality than those admitted during the first. Older age, more severe disease, and the need for oxygen therapy were among the strongest risk factors for poor prognosis.

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.

Prognosing the risk of COVID-19 death through a machine learning-based routine blood panel: A retrospective study in Brazil

Background:

Despite an extensive network of primary care availability, Brazil has suffered profoundly during the COVID-19 pandemic, experiencing the greatest sanitary collapse in its history. Thus, it is important to understand phenotype risk factors for SARS-CoV-2 infection severity in the Brazilian population in order to provide novel insights into the pathogenesis of the disease.

Objective:

This study proposes to predict the risk of COVID-19 death through machine learning, using blood biomarkers data from patients admitted to two large hospitals in Brazil.

Methods:

We retrospectively collected blood biomarkers data in a 24-h time window from 6,979 patients with COVID-19 confirmed by positive RT-PCR admitted to two large hospitals in Brazil, of whom 291 (4.2%) died and 6,688 (95.8%) were discharged. We then developed a large-scale exploration of risk models to predict the probability of COVID-19 severity, finally choosing the best performing model regarding the average AUROC. To improve generalizability, for each model five different testing scenarios were conducted, including two external validations.

Results:

We developed a machine learning-based panel composed of parameters extracted from the complete blood count (lymphocytes, MCV, platelets and RDW), in addition to C-Reactive Protein, which yielded an average AUROC of 0.91 ± 0.01 to predict death by COVID-19 confirmed by positive RT-PCR within a 24-h window.

Conclusion:

Our study suggests that routine laboratory variables could be useful to identify COVID-19 patients under higher risk of death using machine learning. Further studies are needed for validating the model in other populations and contexts, since the natural history of SARS-CoV-2 infection and its consequences on the hematopoietic system and other organs is still quite recent.

Red blood cell distribution width: A severity indicator in patients with COVID-19

Red blood cell distribution width (RDW) was frequently assessed in COVID-19 infection and reported to be associated with adverse outcomes. However, there was no consensus regarding the optimal cutoff value for RDW. Records of 98 patients with COVID-19 from the First People's Hospital of Jingzhou were reviewed. They were divided into two groups according to the cutoff value for RDW on admission by receiver operator characteristic curve analysis: ≤11.5% (n = 50) and >11.5% (n = 48). The association of RDW with the severity and outcomes of COVID-19 was analyzed. The receiver operating characteristic curve indicated that the RDW was a good discrimination factor for identifying COVID-19 severity (area under the curve = 0.728, 95% CI: 0.626-0.830, p < 0.001). Patients with RDW > 11.5% more frequently suffered from critical COVID-19 than those with RDW ≤ 11.5% (62.5% vs. 26.0%, p < 0.001). Multivariate logistic regression analysis showed RDW to be an independent predictor for critical illness due to COVID-19 (OR = 2.40, 95% CI: 1.27-4.55, p = 0.007). A similar result was obtained when we included RDW > 11.5% into another model instead of RDW as a continuous variable (OR = 5.41, 95% CI: 1.53-19.10, p = 0.009). RDW, as an inexpensive and routinely measured parameter, showed promise as a predictor for critical illness in patients with COVID-19 infection. RDW > 11.5% could be the optimal cutoff to discriminate critical COVID-19 infection.

Prognostic Markers in Hospitalized COVID-19 Patients: The Role of IP-10 and C-Reactive Protein

Background

SARS-CoV-2 is responsible for COVID-19, a clinically heterogeneous disease, ranging from being completely asymptomatic to life-threating manifestations. An unmet clinical need is the identification at disease onset or during its course of reliable biomarkers allowing patients' stratification according to disease severity. In this observational prospective cohort study, patients' immunologic and laboratory signatures were analyzed to identify independent predictors of unfavorable (either death or intensive care unit admission need) or favorable (discharge and/or clinical resolution within the first 14 days of hospitalization) outcome.

Methods

Between January and May 2021 (third wave of the pandemic), we enrolled 139 consecutive SARS-CoV-2 positive patients hospitalized in Northern Italy to study their immunological and laboratory signatures. Multiplex cytokine, chemokine, and growth factor analysis, along with routine laboratory tests, were performed at baseline and after 7 days of hospital stay.

Results

According to their baseline characteristics, the majority of our patients experienced a moderate to severe illness. At multivariate analysis, the only independent predictors of disease evolution were the serum concentrations of IP-10 (at baseline) and of C-reactive protein (CRP) after 7 days of hospitalization. Receiver-operating characteristic (ROC) curve analysis confirmed that baseline IP − 10 > 4271 pg/mL and CRP > 2.3 mg/dL at 7 days predict a worsening in clinical conditions (87% sensitivity, 66% specificity, area under the curve (AUC) 0.772, p < 0.001 and 83% sensitivity, 73% specificity, AUC 0.826, p < 0.001, respectively).

Conclusions

According to our results, baseline IP-10 and CRP after 7 days of hospitalization could be useful in driving clinical decisions tailored to the expected disease trajectory in hospitalized COVID-19 patients.

The Prognostic Role of Metabolic and Endocrine Parameters for the Clinical Severity of COVID-19

Objective

An outbreak of coronavirus disease-19 (COVID-19) began in December 2019 and spread globally, overwhelming the entire world. COVID-19 is a public health emergency of international concern. Due to its high morbidity and mortality rate, recognition of its risk and prognostic factors is important. We aimed to understand the relationship between metabolic and endocrine parameters and the prognosis of COVID-19.

Methods and Materials

This was a cross-sectional clinical study. A total of 70 patients with severe COVID-19 were enrolled. Laboratory results at the first admission time (including complete blood count, C-reactive protein, lactate dehydrogenase, blood glucose, calcium, phosphate, albumin, creatinine, magnesium, lipid profiles, liver enzymes, thyroid hormones, cortisol, and vitamin D) and outcome data were recorded. We divided patients into (1) intensive care unit- (ICU-) admitted and non-ICU-admitted and (2) survivors and nonsurvivors for estimation of severity and prognosis. We determined the risk factors associated with critical illness and poor prognosis.

Results

Patients with higher white blood cell (WBC) count and phosphate levels had significantly higher ICU admission rates. According to univariate analysis, serum levels of T3, phosphate, and WBC as well as the duration of hospitalization were associated with mortality. Multivariate analysis revealed that only WBC and duration of hospitalization were independent predictors for mortality rate in COVID-19 patients.

Conclusion

Our findings suggest that longer duration of hospitalization and higher WBC count are associated with poor outcomes in patients with COVID-19.

Plasma S-Adenosylmethionine Is Associated with Lung Injury in COVID-19

Objective

S-Adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) are indicators of global transmethylation and may play an important role as markers of severity of COVID-19.

Methods

The levels of plasma SAM and SAH were determined in patients admitted with COVID-19 (n = 56, mean age = 61). Lung injury was identified by computed tomography (CT) in accordance with the CT0-4 classification.

Results

SAM was found to be a potential marker of lung damage risk in COVID-19 patients (SAM > 80 nM; CT3,4 vs. CT 0-2: relative ratio (RR) was 3.0; p = 0.0029). SAM/SAH > 6.0 was also found to be a marker of lung injury (CT2-4 vs. CT0,1: RR = 3.47, p = 0.0004). There was a negative association between SAM and glutathione level (ρ = −0.343, p = 0.011). Interleukin-6 (IL-6) levels were associated with SAM (ρ = 0.44, p = 0.01) and SAH (ρ = 0.534, p = 0.001) levels.

Conclusions

A high SAM level and high methylation index are associated with the risk of lung injury in patients with COVID-19. The association of SAM with IL-6 and glutathione indicates an important role of transmethylation in the development of cytokine imbalance and oxidative stress in patients with COVID-19.

Early Prediction of Disease Progression in Patients with Severe COVID-19 Using C-Reactive Protein to Albumin Ratio

Background

Early identification of patients with severe coronavirus disease (COVID-19) at an increased risk of progression may promote more individualized treatment schemes and optimize the use of medical resources. This study is aimed at investigating the utility of the C-reactive protein to albumin (CRP/Alb) ratio for early risk stratification of patients.

Methods

We retrospectively reviewed 557 patients with COVID-19 with confirmed outcomes (discharged or deceased) admitted to the West Court of Union Hospital, Wuhan, China, between January 29, 2020 and April 8, 2020. Patients with severe COVID-19 (n = 465) were divided into stable (n = 409) and progressive (n = 56) groups according to whether they progressed to critical illness or death during hospitalization. To predict disease progression, the CRP/Alb ratio was evaluated on admission.

Results

The levels of new biomarkers, including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, CRP/Alb ratio, and systemic immune-inflammation index, were higher in patients with progressive disease than in those with stable disease. Correlation analysis showed that the CRP/Alb ratio had the strongest positive correlation with the sequential organ failure assessment score and length of hospital stay in survivors. Multivariate logistic regression analysis showed that percutaneous oxygen saturation (SpO2), D-dimer levels, and the CRP/Alb ratio were risk factors for disease progression. To predict clinical progression, the areas under the receiver operating characteristic curves of Alb, CRP, CRP/Alb ratio, SpO2, and D-dimer were 0.769, 0.838, 0.866, 0.107, and 0.748, respectively. Moreover, patients with a high CRP/Alb ratio (≥1.843) had a markedly higher rate of clinical deterioration (log − rank p < 0.001). A higher CRP/Alb ratio (≥1.843) was also closely associated with higher rates of hospital mortality, ICU admission, invasive mechanical ventilation, and a longer hospital stay.

Conclusion

The CRP/Alb ratio can predict the risk of progression to critical disease or death early, providing a promising prognostic biomarker for risk stratification and clinical management of patients with severe COVID-19.

May macroglossia in COVID-19 be related not only to angioedema?

SARS-CoV-2 infection can lead to a variety of clinical manifestations. The occurrence of tongue swelling has recently reported in severe cases of COVID-19, and angioedema has suggested as the causative mechanism. Several factors, such as genetic predisposing factor and angiotensin-converting enzyme inhibitors (ACEI) therapies, have proposed to induce angioedema, especially as concerns patients requiring ICU treatments. Nevertheless, the question is still debated and other causes not yet recognized should be considered.

Here we present a case of macroglossia occurred in a patient deceased for COVID-19 disease, who had no family history of angioedema and did not receive ACEI as antihypertensive drug. Histological and immune-histochemical analysis revealed tongue muscle atrophy with infiltrating macrophages suggesting repair mechanisms, as seen in nerve injury recovery. These new pathological findings may open new fields of study on the pathogenesis of SARS-CoV-2.