Can Hematological Ratios Predict Outcome of COVID-19 Patients? A Multicentric Study

The predictive role of inflammation indices derived from complete blood count in severe COVID-19 patients: a study from the United Arab Emirates

Purpose

To investigate the predictive effect and determine the cut-off values of complete blood count parameters in severe COVID-19 hospitalized patients in the United Arab Emirates.

Methods

A retrospective observational analytical study analyzed data from 738 medical records of COVID-19 hospitalized patients across several healthcare centers in the United Arab Emirates between 29 January 2020 and 14 October 2021. Complete blood count ratios and indices on admission were evaluated for COVID-19 severity using receiver operating characteristic curves, sensitivity, and specificity.

Results

Main complete blood count-based ratios and indices significantly predicting severe COVID-19 were elevated ratios index (optimal cut-off point ≥3; AOR = 2.8, 95% CI: 1.77-4.42), systemic immune-inflammation index (≥1259.95; AOR = 2.4, 95% CI: 1.53-3.87), systemic inflammation response index (≥3.96; AOR = 2.9, 95% CI: 1.79-4.72), aggregate index of systemic inflammation (≥949.02; AOR = 2.3, 95% CI: 1.43-3.77), platelet-to-lymphocyte ratio (≥188.91; AOR = 2.2, 95% CI: 1.39-3.53), derived neutrophil-to-lymphocyte ratio (≥2.91; AOR = 3.0, 95% CI: 1.84-4.87), and neutrophil-to-lymphocyte ratio (≥6.01; AOR = 3.2, 95% CI: 1.98-5.12).

Conclusion

Identifying hematological markers' predictive effects and their cut-off values can aid healthcare providers in risk classification and the development of tailored treatment plans. It can also provide cheap, quick, and easy guidance for surveillance systems to lessen the impact of any future outbreaks.

Hematological ratios and cytokine profiles in heterozygous beta-thalassemia

INTRODUCTION

β-Thalassemia is defined by a reduced or complete absence of β-globin chain synthesis in hemoglobin, leading to hemolytic anemia. Heterozygous β-thalassemia, also known as β-thalassemia trait (hBTh), the mildest form of this anemia, typically does not cause symptoms in carriers. However, it may lead to changes in the immune system, including an increase in total leukocyte, neutrophil, and lymphocyte counts.

OBJECTIVE

This study aimed to evaluate various immune and inflammation markers, including neutrophil/lymphocyte, derived neutrophil/lymphocyte, lymphocyte/monocyte, platelet/lymphocyte, neutrophil/platelet ratios, systemic immune-inflammation index, systemic inflammation response index, neutrophil/natural killer cell ratio (NNKR), and inflammatory cytokines in β-thalassemia trait carriers.

METHOD

A retrospective observational study was conducted, including 50 β-thalassemia trait individuals and 100 healthy controls.

RESULTS

Leukocyte, neutrophil and reticulocyte counts, and interleukin 6 levels were higher in carriers compared to controls. Notably, the β-thalassemia trait group had increased neutrophil/platelet, neutrophil/lymphocyte and derived neutrophil/lymphocyte ratios, and the systemic immune-inflammation and systemic inflammation response indexes were higher compared to the controls.

CONCLUSIONS

β-thalassemia trait shows a more pronounced inflammatory profile as indicated by hematological ratios. These ratios, therefore are potentially cost-effective and easily applicable markers for monitoring patients with the β-thalassemia trait.

Machine learning algorithm approach to complete blood count can be used as early predictor of COVID-19 outcome

Although the SARS-CoV-2 infection has established risk groups, identifying biomarkers for disease outcomes is still crucial to stratify patient risk and enhance clinical management. Optimal efficacy of COVID-19 antiviral medications relies on early administration within the initial 5 d of symptoms, assisting high-risk patients in avoiding hospitalization and improving survival chances. The complete blood count (CBC) can be an efficient and affordable option to find biomarkers that predict the COVID-19 prognosis due to infection-induced alterations in various blood parameters. This study aimed to associate hematological parameters with different COVID-19 clinical forms and utilizes them as disease outcome predictors. We performed a CBC in blood samples from 297 individuals with COVID-19 from Belo Horizonte, Brazil. Statistical analysis, as well as ROC Curves and machine learning Decision Tree algorithms were used to identify correlations, and their accuracy, between blood parameters and disease severity. In the initial 4 d of infection, traditional hematological COVID-19 alterations, such as lymphopenia, were not yet apparent. However, the monocyte percentage and granulocyte-to-lymphocyte ratio (GLR) proved to be reliable predictors for hospitalization, even in cases where patients exhibited mild symptoms that later progressed to hospitalization. Thus, our findings demonstrate that COVID-19 patients with monocyte percentages lower than 7.7% and a GLR higher than 8.75 are assigned to the hospitalized group with a precision of 86%. This suggests that these variables can serve as important biomarkers in predicting disease outcomes and could be used to differentiate patients at hospital admission for managing therapeutic interventions, including early antiviral administration. Moreover, they are simple parameters that can be useful in minimally equipped health care units.

Predicting the Outcome of Patients with Severe COVID-19 with Simple Inflammatory Biomarkers: The Utility of Novel Combined Scores-Results from a European Tertiary/Referral Centre

Background: The clinical significance of combinations of inflammatory biomarkers in severe COVID-19 infection is yet to be proved. Although several studies have evaluated the prognostic value of biomarkers in patients with COVID-19, there are limited data regarding the value of the combination scores that could take full advantage of the prognostic value of several biomarkers and that could account for the heterogeneity of patients with severe COVID-19. We investigated the prognostic value of combination scores of admission values of inflammatory biomarkers in adults with severe COVID-19. Methods: Adults admitted to the Department of Respiratory Medicine of the UHL with severe COVID-19 (April-September 2021, NCT05145751) were included. Demographics, medical history, laboratory tests and outcome (high-flow nasal cannula (HFNC), admission to Intensive Care Unit (ICU) or death) were recorded. The optimal cut-off points of on admission values of C-reactive protein (CRP), CRP to lymphocyte ratio (CLR), lymphocyte to neutrophil ratio (LNR) and derived variation of neutrophil to lymphocyte ratio (dv-NLR (neutrophil/white blood count-lymphocyte)) for the predetermined outcome were defined. Based on the cut-off of CRP, LNR, dv-NLR and CLR, which were found to be predictors for HFNC, 3 scores were defined: CRP and LNR (C-CRP #1), CRP and dv-NLR (C-CRP #2), CRP and CLR (C-CRP #3). Likewise, based on the cut-off of CRP and CLR, which were found to be predictors for death, the score of CRP and CLR (C-CRP #3*) was defined. The combination scores were then classified as: 2 points (both biomarkers elevated); 1 point (one biomarker elevated) and 0 points (normal values). None of the biomarkers was predictive for the ICU admission, so no further analysis was performed. Binomial logistic regression analysis was used to establish the predictive role for each biomarker. Results: One hundred and fifteen patients (60% males, mean age 57.7 years) were included. Thirty-seven (32.2%) patients required HFNC, nine (7.8%) died and eight (7%) were admitted to ICU, respectively. As far as HFNC is concerned, the cut-off point was 3.2 for CRP, 0.231 for LNR, 0.90 for dv-NLR and 0.004 for CLR. Two points of C-CRP #1 and 2 points of C-CRP #3 predicted HFNC with a probability as high as 0.625 (p = 0.005) and 0.561 (p < 0.001), respectively. Moreover, 1 point of C-CRP #2 and 2 points of C-CRP #2 predicted HFNC with a probability of 0.333 and 0.562, respectively. For death, the optimal cut-off point for CRP was 1.11 and for CLR 3.2*1033. Two points of C-CRP #3* with an accuracy of 0.922 predicted mortality (p = 0.0038) in severe COVID-19. Conclusions: The combination scores of CRP and inflammatory biomarkers, based on admission values, are promising predictors for respiratory support using HFNC and for mortality in patients suffering from severe COVID-19 infection.

Combined systemic inflammatory indexes as reflectors of outcome in patients with COVID‑19 infection admitted to ICU

INTRODUCTION

The principal etiology of mortality in COVID-19 patients is the systemic pro-inflammatory processes which may lead to acute respiratory distress syndrome. Hematologic indices are reachable representatives of inflammation in patients with COVID-19 infection. The purpose of the current study was to evaluate the potential predictive value of these inflammatory indices in the in-hospital mortality of ICU-admitted COVID-19 patients. The studied indexes included AISI, dNLR, NLPR, NLR, SII, and SIRI.

METHOD

315 COVID-19 patients admitted to ICU managed in Imam Khomeini Hospital of Urmia, Iran, during the last 6 months of 2020 were retrospectively enrolled in the study and divided into two subgroups based on their final outcome, discharge or death.

RESULTS

Total leucocyte count (TLC), absolute neutrophil count (NLC), urea, Cr, RDW, AISI, dNLR, NLPR, NLR, SII, and SIRI were drastically elevated in the dead patients (P < 0.05). The optimal cut-off points for AISI (378.81), dNLR (5.66), NLPR (0.03), NLR (5.97), SII (1589.25), and SIRI (2.31) were obtained using ROC curves. NLR and SII had the highest sensitivity (71.4%) and specificity (73.6%), respectively. Patients with above-cut-off levels of ISI, dNLR, NLPR, NLR, and SII had lower average survival time. Age (OR = 1.057, CI95%: 1.030-1.085, p < 0.001) and dNLR (OR = 1.131, CI95%: 1.061-1.206, p < 0.001) were the independent predictors for mortality in the studied COVID-19 patients based on multivariate logistic regression.

CONCLUSION

Age and dNLR are valuable predictive factors for in-hospital death of ICU-admitted COVID-19 patients. Besides, other indices, AISI, NLPR, NLR, SII, and SIRI, may have an additional role that requires further investigation.

Predictive value of hematological indices on incidence and severity of pulmonary embolism in COVID-19 patients

BACKGROUND

Pulmonary thromboembolism (PTE) is a common complication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which raises the COVID-19 disease's fatality rate from 3% to 45%. Nevertheless, due to fairly indistinguishable clinical symptoms and a lack of validated clinical prediction models, PTE diagnosis in COVID-19 patients is challenging. This study aims to investigate the applicability of hematological indices to predict PTE incidence and its severity in SARS-CoV-2 patients.

METHODS

A retrospective cohort study was conducted on hospitalized patients with a confirmed diagnosis of SARS-CoV-2 infection who underwent CT angiography to assess probable PTE in them. The correlation between complete blood count parameters 1 day before CT angiography and CT angiography outcomes, and simplified pulmonary embolism severity index (s-PESI) was investigated.

RESULTS

We discovered that among individuals with a probable PTE, males and those with higher platelet-to-lymphocyte (PLR) and neutrophil-to-lymphocyte (NLR) ratios had a greater likelihood of PTE incidence (p < .001, .027, and .037, respectively). PLR was a significant and independent predictor of PTE with a p value of .045. Moreover, a higher neutrophil count was associated with a higher s-PESI score in COVID-19 patients developing PTE (p: .038).

CONCLUSIONS

Among hematological indices, NLR and more precisely PLR are cost-effective and simply calculable markers that can assist physicians in determining whether or not COVID-19 patients with clinically probable PTE require CT angiography and the higher neutrophil count can be employed as an indicator of PTE severity in COVID-19 patients. Further large multicenter and prospective studies are warranted to corroborate these observations.

Dynamic NLR and PLR in Predicting COVID-19 Severity: A Retrospective Cohort Study

INTRODUCTION

The hyperinflammation phase of severe SARS-CoV-2 is characterised by complete blood count alterations. In this context, the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) can be used as prognostic factors. We studied NLR and PLR trends at different timepoints and computed optimal cutoffs to predict four outcomes: use of continuous positive airways pressure (CPAP), intensive care unit (ICU) admission, invasive ventilation and death.

METHODS

We retrospectively included all adult patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia admitted from 23 January 2020 to 18 May 2021. Analyses included non-parametric tests to study the ability of NLR and PLR to distinguish the patients' outcomes at each timepoint. Receiver operating characteristic (ROC) curves were built for NLR and PLR at each timepoint (minus discharge) to identify cutoffs to distinguish severe and non-severe disease. Their statistical significance was assessed with the chi-square test. Collection of data under the SMACORE database was approved with protocol number 20200046877.

RESULTS

We included 2169 patients. NLR and PLR were higher in severe coronavirus disease 2019 (COVID-19). Both ratios were able to distinguish the outcomes at each timepoint. For NLR, the areas under the receiver operating characteristic curve (AUROC) ranged between 0.59 and 0.81, and for PLR between 0.53 and 0.67. From each ROC curve we computed an optimal cutoff value.

CONCLUSION

NLR and PLR cutoffs are able to distinguish severity grades and mortality at different timepoints during the course of disease, and, as such, they allow a tailored approach. Future prospects include validating our cutoffs in a prospective cohort and comparing their performance against other COVID-19 scores.

Hematological ratios in coronavirus disease 2019 patients with and without invasive mechanical ventilation

Patients with the most severe form of coronavirus disease 2019 (COVID-19) often require invasive ventilation. Determining the best moment to intubate a COVID-19 patient is complex decision and can result in important consequences for the patient. Therefore, markers that could aid in clinical decision-making such as hematological indices are highly useful. These markers are easy to calculate, do not generate extra costs for the laboratory, and are readily implemented in routine practice. Thus, this study aimed to investigate differences in the ratios calculated from the hemogram between patients with and without the need for invasive mechanical ventilation (IMV) and a control group. This was an observational retrospective analysis of 212 patients with COVID-19 that were hospitalized between April 1, 2020 and March 31, 2021 who were stratified as IMV (n = 129) or did not require invasive mechanical ventilation (NIMV) (n = 83). A control group of 198 healthy individuals was also included. From the first hemogram of each patient performed after admission, the neutrophil-to-lymphocyte ratio (NLR), the derived NLR (d-NLR), the lymphocyte-to-monocyte ratio, the platelet-to-lymphocyte ratio, the neutrophil-to-platelet ratio (NPR), and the systemic immune-inflammation index (SII) were calculated. All hematological ratios exhibited significant differences between the control group and COVID-19 patients. NLR, d-NLR, SII, and NPR were higher in the IMV group than they were in the NIMV group. The hematological indices addressed in this study demonstrated high potential for use as auxiliaries in clinical decision-making regarding the need for IMV.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

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.

Platelet-to-White Blood Cell Ratio as a Predictor of Mortality in Patients with Severe COVID-19 Pneumonia: A Retrospective Cohort Study

Purpose

Complete blood count (CBC) parameters are widely used as predictors of Coronavirus disease 2019 (COVID-19) severity. However, the clinical significance of these markers in severe COVID-19 pneumonia remains unclear. This study aimed to investigate the role of CBC parameters in predicting mortality in patients with severe COVID-19 pneumonia.

Patients and Methods

We conducted a retrospective study at a tertiary care center in southern Thailand. Between January 2020 and December 2021, adult patients who had been diagnosed with severe COVID-19 pneumonia were enrolled. Demographic and clinical data, including CBC data on admission, were analyzed and compared between survivors and non-survivors.

Results

A total of 215 patients with severe COVID-19 pneumonia were enrolled. The in-hospital mortality was 29.3%. Non-survivors had a significantly lower platelet-to-white blood cell ratio (PWR) than survivors (15.8 vs 29.0, p < 0.001). PWR had the best accuracy in predicting in-hospital mortality, with an area under the curve (AUC) of the receiver operating characteristic curve of 0.801, followed by the CURB-65 of 0.789.

Conclusion

PWR appears to be a simple independent predictor of mortality in patients with severe COVID-19 pneumonia.

The role of indices in predicting disease severity and outcomes of multisystem inflammatory syndrome in children

BACKGROUND

The aim of this study was to evaluate the role of the systemic immune inflammation index (SII), C-reactive protein/albumin ratio (CAR), the monocyte/lymphocyte ratio (MLR), and the neutrophil/lymphocyte ratio (NLR) in predicting disease severity, treatment, and prognosis in multisystem inflammatory syndrome in children (MIS-C).

METHODS

This medical record review retrospectively evaluated the clinical and laboratory findings of 191 MIS-C patients followed in the Department of Pediatric Rheumatology at Ankara City Hospital, Turkey. The patients were grouped by disease severity: mild, moderate, and severe. SII, CAR, MLR, and NLR were calculated for each group.

RESULTS

All patients had fever at the time of admission; 153 (80.1%) had gastrointestinal tract involvement, 74 (38.7%) had rash, 63 (33%) had conjunctivitis, 107 (56%) had cardiac involvement, 32 (15.6%) had renal involvement, and 143 (74.9%) had hematological involvement. According to logistic regression analysis, SII, NLR, MLR, and CAR were found to be predictive indexes for disease severity, need for intensive care, need for inotropes, and anakinra treatment in MIS-C. The cut-off values of ≥1605.3 for SII, ≥9.1 for NLR, and ≥3.9 for CAR increased the risk of severe disease by 3.4, 7.1, and 5.7 times, respectively.

CONCLUSION

NLR, SII, MLR, and CAR are effective and useful for predicting the severity of MIS-C, the need for intensive care, and the need for anakinra treatment.

Outcome predictors in COVID-19: An analysis of emergent systemic inflammation indices in Mexican population

Introduction

The systemic viral disease caused by the SARS-CoV-2 called coronavirus disease 2019 (COVID-19) continues to be a public health problem worldwide.

Objective

This study is aimed to evaluate the association and predictive value of indices of systemic inflammation with severity and non-survival of COVID-19 in Mexican patients.

Materials and Methods

A retrospective study was carried out on 807 subjects with a confirmed diagnosis of COVID-19. Clinical characteristics, acute respiratory distress syndrome (ARDS), severity according to PaO2/FiO2 ratio, invasive mechanical ventilation (IMV), and non-survival outcome were considered to assess the predictive value and the association of 11 systemic inflammatory indices derived from hematological parameters analyzed at the hospital admission of patients. The receiver operating characteristics curve was applied to determine the thresholds for 11 biomarkers, and their prognostic values were assessed via the Kaplan-Meier method.

Results

26% of the studied subjects showed COVID-19 severe (PaO2/FiO2 ratio ≤ 100), 82.4% required IMV, and 39.2% were non-survival. The indices NHL, NLR, RDW, dNLR, and SIRI displayed predictive values for severe COVID-19 and non-survival. NHL, SIRI, and NLR showed predictive value for IMV. The cut-off values for RDW (OR = 1.85, p < 0.001), NHL (OR = 1.67, p = 0.004) and NLR (OR = 1.56, p = 0.012) were mainly associated with severe COVID-19. NHL (OR = 3.07, p < 0.001), AISI (OR = 2.64, p < 0.001) and SIRI (OR = 2.51, p < 0.001) were associated with IMV support, while for non-survival the main indices associated were NHL (OR = 2.65, p < 0.001), NLR (OR = 2.26, p < 0.001), dNLR (OR = 1.92, p < 0.001), SIRI (OR = 1.67, p = 0.002) and SII (OR = 1.50, p = 0.010). The patients with an RDW, PLR, NLR, dNLR, MLR, SII, and NHL above the cut-off had a survival probability of COVID-19 50% lower, with an estimated mean survival time of 40 days.

Conclusion

The emergent systemic inflammation indices NHL, NLR, RDW, SII, and SIRI have a predictive power of severe COVID-19, IMV support, and low survival probability during hospitalization by COVID-19 in Mexican patients.

Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio Can Predict the Severity in COVID-19 Patients from Ethiopia: A Retrospective Study

Background

Coronaviruses are a broad family of pathogens that can cause mild to severe respiratory illnesses. Due to a strong inflammatory response and a weak immunological response, viral pneumonia inflammation, like Coronavirus Disease 2019 (COVID-19), displays an unbalanced immune response. Therefore, circulating biomarkers of inflammation and the immune system can serve as reliable predictors of a patient's prognosis for COVID-19. Hematological ratios are reliable markers of inflammation that are frequently utilized in pneumonia, primarily in viral infections with low cost in developing countries.

Purpose

To examine the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) in predicting the severity of COVID-19 patients.

Methods

An institutional-based retrospective study was done on 105 hospitalized COVID-19 patients at the University of Gondar comprehensive specialized referral hospital, Northwest Ethiopia. The laboratory evaluations that were gathered, evaluated, and reported on included the total leucocyte count (TLC), absolute neutrophil count (ANC), absolute lymphocyte count (ALC), absolute monocyte count (AMC), NLR, LMR, and PLR. The Kruskal-Wallis test and Wilcoxon matched-pairs signed test were used to see whether there were any differences between the continuous variables. Receiver operating curve (ROC) analysis was used to determine the appropriate cut-off values for NLR, PLR, and LMR. P-value <0.05 was considered a statistically significant association.

Results

ANC, NLR, and PLR were highest in the critical group (p = 0.001), while this group had the least ALC and LMR (p = 0.001). We calculated the optimal cut-off values of the hematological ratios; NLR (8.4), LMR (1.4), and PLR (18.0). NLR had the highest specificity and sensitivity, at 83.8% and 80.4%, respectively.

Conclusion

Our research showed that NLR and PLR were good indicators of severity in COVID-19. However, our findings indicate that MLR is not a reliable predictor.

The Associations of Iron Related Biomarkers with Risk, Clinical Severity and Mortality in SARS-CoV-2 Patients: A Meta-Analysis

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is spreading rapidly around the world and has led to millions of infections and deaths. Growing evidence indicates that iron metabolism is associated with COVID-19 progression, and iron-related biomarkers have great potential for detecting these diseases. However, the results of previous studies are conflicting, and there is not consistent numerical magnitude relationship between those biomarkers and COVID-19. Thereby, we aimed to integrate the results of current studies and to further explore their relationships through a meta-analysis. We searched peer-reviewed literature in PubMed, Scopus and Web of Science up to 31 May 2022. A random effects model was used for pooling standard mean difference (SMD) and the calculation of the corresponding 95% confidence interval (CI). I2 was used to evaluate heterogeneity among studies. A total of 72 eligible articles were included in the meta-analysis. It was found that the ferritin levels of patients increased with the severity of the disease, whereas their serum iron levels and hemoglobin levels showed opposite trends. In addition, non-survivors had higher ferritin levels (SMD (95%CI): 1.121 (0.854, 1.388); Z = 8.22 p for Z < 0.001; I2 = 95.7%, p for I2 < 0.001), lower serum iron levels (SMD (95%CI): −0.483 (−0.597, −0.368), Z = 8.27, p for Z < 0.001; I2 = 0.9%, p for I2 =0.423) and significantly lower TIBC levels (SMD (95%CI): −0.612 (−0.900, −0.324), Z = 4.16, p for Z < 0.001; I2 = 71%, p for I2 = 0.016) than survivors. This meta-analysis demonstrates that ferritin, serum iron, hemoglobin and total iron banding capacity (TIBC) levels are strongly associated with the risk, severity and mortality of COVID-19, providing strong evidence for their potential in predicting disease occurrence and progression.

Significance of hemogram-derived ratios for predicting in-hospital mortality in COVID-19: A multicenter study

Background

To address the problem of resource limitation, biomarkers having a potential for mortality prediction are urgently required. This study was designed to evaluate whether hemogram-derived ratios could predict in-hospital deaths in COVID-19 patients.

Materials and Methods

This multicenter retrospective study included hospitalized COVID-19 patients from four COVID-19 dedicated hospitals in Sylhet, Bangladesh. Data on clinical characteristics, laboratory parameters, and survival outcomes were analyzed. Logistic regression models were fitted to identify the predictors of in-hospital death.

Results

Out of 442 patients, 55 (12.44%) suffered in-hospital death. The proportion of male was higher in nonsurvivor group (61.8%). The mean age was higher in nonsurvivors (69 ± 13 vs. 59 ± 14 years, p < 0.001). Compared to survivors, nonsurvivors exhibited higher frequency of comorbidities, such as chronic kidney disease (34.5% vs. 15.2%, p ≤ 0.001), chronic obstructive pulmonary disease (23.6% vs. 10.6%, p = 0.011), ischemic heart disease (41.8% vs. 19.4%, p < 0.001), and diabetes mellitus (76.4% vs. 61.8%, p = 0.05). Leukocytosis and lymphocytopenia were more prevalent in nonsurvivors (p < 0.05). Neutrophil-to-lymphocyte ratio (NLR), derived NLR (d-NLR), and neutrophil-to-platelet ratio (NPR) were significantly higher in nonsurvivors (p < 0.05). After adjusting for potential covariates, NLR (odds ratio [OR] 1.05; 95% confidence interval [CI] 1.009-1.08), d-NLR (OR 1.08; 95% CI 1.006-1.14), and NPR (OR 1.20; 95% CI 1.09-1.32) have been found to be significant predictors of mortality in hospitalized COVID-19 patients. The optimal cut-off points for NLR, d-NLR, and NPR for prediction of in-hospital mortality for COVID-19 patients were 7.57, 5.52 and 3.87, respectively.

Conclusion

Initial assessment of NLR, d-NLR, and NPR values at hospital admission is of good prognostic value for predicting mortality of patients with COVID-19.

Real-Life Use of Tocilizumab in the Treatment of Severe COVID-19 Pneumonia

Introduction

Coronavirus disease 2019 (COVID-19) can progress to severe respiratory compromise and lead to mortality due to induction of cytokine storm. Tocilizumab (TCZ) is approved by the FDA for the treatment of cytokine release syndrome (CRS). This study aims to analyze the outcomes among patients who received TCZ in the United Arab Emirates.

Methods

A retrospective cohort study was conducted among COVID-19 patients who received TCZ in a tertiary care hospital from May 2020 to August 2021. For analysis, patients were divided into two groups based on survival and clinical improvement.

Results

Overall, 80% of patients receiving TCZ were discharged by day 28. There was a gradual improvement in oxygen requirements in our patients with a majority of them on room air by day 28. Age more than 50 years (P=0.034) and comorbidities such as cardiovascular disease (CVD) (P=0.002) and renal insufficiency (P=0.013) were significantly associated with mortality. Discussion. In our analysis, patients who were mechanically ventilated at the time of administration of TCZ had a significantly higher risk of death by day 28. In both survived and improved groups, younger patients had better outcomes than older patients. Patients who received TCZ earlier during therapy from the onset of symptoms had better survival outcomes. There was only one death among 14 patients who received vaccination. There was no significant difference in mortality among patients with comorbidities such as diabetes, hypertension, dyslipidemia, obesity, and pulmonary diseases, hypothesizing that administration of TCZ improves the outcomes in COVID-19 patients with these comorbidities.

Prognostic role of simple inflammatory biomarkers in patients with severe COVID-19: an observational study

BACKGROUND/AIM

Simple inflammatory biomarkers, such as neutrophil to lymphocyte ratio (NLR), could serve as prognosis indicators in patients with Coronavirus disease 2019 (COVID-19). The utility of on-admission inflammatory biomarkers in predicting outcomes was investigated in patients suffering from severe COVID-19 infection.

METHODS

We performed a retrospective study to assess the role of white blood count (WBC), neutrophils (N), lymphocyte (L), platelets (PLTs), C-reactive protein (CRP), reverse transcription polymerase chain reaction (RT-PCR), NLR (N/L), PLR (P/L), dv (derived variation of)-NLR (N/WBC-L), LNR (L/N), dv (derived variation of)-LNR (L/WBC-N), and CLR (CRP/L), in predicting the need for high-flow nasal cannula (HFNC) use, admission to Intensive Care Unit (ICU), and death in adult patients with severe COVID-19 admitted to the Department of Respiratory Medicine from April to September 2021.

RESULTS

One hundred and fifteen patients (60 % males) with a mean age of 57.7 ± 16.3 years were included. Thirty-seven patients (32.2 %) required escalation with HFNC, eight patients (7 %) were admitted to the ICU, and nine patients (7.8%) died. Based on univariate analysis, CRP [odds ratio (OR): 1.25, 95 % confidence interval (CI): 1.1-1.42), LNR (OR: 0.015, 95 % CI: 0.00-0.35), dv-NLR (OR: 5*106, 95 % CI: 26.7-9*109), CLR (OR: 7*1058, 95 % CI: 3*1025-2*1092), length of hospitalization (LOH; OR: 1.44, 95 % CI: 1.22-1.63), dyspnea at presentation (OR: 2.83, 95 % CI: 1.23-6.52), and ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2) on admission (OR: 0.967, 95 % CI: 0.952-0.983) were independent predictors for oxygen requirements. However, the multivariate analysis showed that LNR (OR: 1.686e0-4, 95 % CI: 6.441e00-8-0.441), PaO2/FiO2 on admission (OR: 0.965, 95 % CI: 0.941-0.989), and LOH (OR: 1.717, 95 % CI: 1.274-2.314) were the most important predictor for HFNC use. Nasal congestion at presentation (OR: 11.5, 95 % CI: 1.61-82.8) was a unique and independent predictor for ICU admission. As far as death is concerned, the univariate analysis identified elevated CRP (OR: 1.11, 95 % CI: 1.0-1.24), low RT-PCR (OR: 0.829, 95 % CI: 0.688-0.999), high CLR (OR: 3.2*1033, 95 % CI: 5.8-1.8*1066), age (OR: 1.08, 95 % CI: 1.02-1.14), body mass index (BMI) over 30 (OR: 5.25, 95 % CI: 1.26-21.96), the chronic use of angiotensin-converting enzyme inhibitors (OR: 5.72, 95 % CI: 1.35-24.09), nitrates (OR: 14.85, 95 % CI: 1.81-121.8), diuretics (OR: 8.21, 95 % CI: 1.97-34.32), PaO2/FiO2 on admission (OR: 0.983, 95 % CI: 0.970-0.998), and nasal congestion at presentation (OR: 9.81, 95 % CI: 1.40-68.68) as independent predictors. However, the multivariate analysis pinpointed that obesity (BMI >30) (OR: 10.498, 95 % CI: 1.107-99.572) remained the most important predictor for death.

CONCLUSION

LNR and PaO2/FiO2 on admission could be used to timely identify patients requiring HFNC during hospitalization, while obesity (BMI >30) could be an independent predictor of death. Nasal congestion emerges as a unique predictor for ICU admission. HIPPOKRATIA 2022, 26 (2):70-77.

Obstructive lung diseases burden and COVID-19 in developing countries: a perspective

PURPOSE OF REVIEW

Asthma and chronic obstructive pulmonary disease (COPD) are widely prevalent disorders, and important contributors to morbidity and mortality, in both developing and developed countries. It is conjectured that these obstructive lung diseases may have had more deleterious effects in developing nations during the 2019 coronavirus disease (COVID-19) pandemic. We provide an evidence-based perspective on the relationship between asthma/COPD prevalence and COVID-19 burden, and the impact of comorbid asthma/COPD on selected COVID-19 outcomes and healthcare utilization, with special reference to developing countries.

RECENT FINDINGS

Developing countries with higher COPD (but not asthma) prevalence appear to have higher COVID-19 related mortality. Patients with asthma (but not COPD) in developing countries may be less likely to acquire COVID-19. Published literature suggests that the overall impact of comorbid asthma or COPD on adverse COVID-19 outcomes may be broadly similar between developed and developing nations.

SUMMARY

There is paucity of information on interaction between asthma/COPD and COVID-19 in developing countries. Limited data suggest minor differences between developed and developing nations. In view of inadequacies in healthcare preparedness and delivery in several developing countries, there is a need to generate quality evidence to assess impact of obstructive lung diseases and COVID-19 on each other.

Combined Blood Indexes of Systemic Inflammation as a Mirror to Admission to Intensive Care Unit in COVID-19 Patients: A Multicentric Study

Background

The Coronavirus 2019 is a pandemic that has spread worldwide, threatening human health. The main cause of death in patients with COVID-19 is a systemic pro-inflammatory mechanism that quickly progresses to acute respiratory distress syndrome. Hematological ratios as affordable indicators of inflammatory response were studied in COVID-19 patients. The study aimed to study the importance of the blood cell indexes of the systemic inflammatory response, as the Aggregate Index of Systemic Inflammation (AISI), neutrophils lymphocyte to platelet ratio (NLPR), systemic immune-inflammation index (SII) and, systemic inflammation response index (SIRI) in predicting intensive care unit (ICU) admission of COVID-19 patients.

Methods

495 COVID-19 patients managed in four tertiary centers; divided into non-ICU and ICU groups.

Results

Total leucocyte count (TLC), AISI, NLPR, SII, and SIRI were more elevated in the ICU group (P < 0.001 for all except AMC P = 0.006), while this group had less absolute lymphocyte count (ALC) (P = 0.047).

We estimated the optimal cut-off values of the hematological ratio; AISI (729), NLPR (0.0195), SII (1346), and SIRI (2.5). SII had the highest specificity (95.6%), while NLPR had the highest sensitivity (61.3%). Age, AISI, CRP, D-dimer, and oxygen aid were the independent predictors for ICU admission in COVID-19 in multivariate logistic regression.

Conclusion

AISI is a predictor for severity and ICU admission in COVID-19 patients, SII is a predictor of survival, while NLPR and SIRI have an additive role that needs further evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s44197-021-00021-5.

Mortality and Clinical Outcomes among Patients with COVID-19 and Diabetes

Background Diabetes mellitus (DM) is a decisive risk factor for severe illness in coronavirus disease 2019 (COVID-19). India is home to a large number of people with DM, and many of them were infected with COVID-19. It is critical to understand the impact of DM on mortality and other clinical outcomes of COVID-19 infection from this region. Aims The primary objective of our study was to analyze the mortality rate in people with DM infected with COVID-19. The secondary objectives were to assess the effect of various comorbidities on mortality and study the impact of DM on other clinical outcomes. Methods This is a retrospective study of COVID-19 infected patients admitted to a tertiary care hospital in north India in the early phase of the pandemic. Results Of the 1211 cases admitted, 19 were excluded because of incomplete data, and 1192 cases were finally considered for analysis. DM constituted 26.8% of total patients. The overall mortality rate was 6.1%, and the rate was 10.7% in the presence of diabetes (p < 0.01, OR 2.55). In univariate analysis, increased age, chronic kidney disease (CKD), coronary artery disease (CAD), stroke, and cancer were associated with mortality. On multiple logistic regression, the independent predictors of mortality were CAD, CKD, and cancer. Breathlessness and low SpO₂ at presentation, extensive involvement in CXR, and elevated ANC/ALC ratio were also significantly associated with mortality. Conclusions The presence of comorbidities such as DM, hypertension, CAD, CKD, and cancer strongly predict the risk of mortality in COVID-19 infection. Early triaging and aggressive therapy of patients with these comorbidities can optimize clinical outcomes.