Dynamic changes of D-dimer and neutrophil-lymphocyte count ratio as prognostic biomarkers in COVID-19

Emergency Myelopoiesis Distinguishes Multisystem Inflammatory Syndrome in Children From Pediatric Severe Coronavirus Disease 2019

BACKGROUND

Multisystem inflammatory syndrome in children (MIS-C) is a hyperinflammatory condition caused by recent infection with severe acute respiratory syndrome coronavirus 2, but the underlying immunological mechanisms driving this distinct syndrome are unknown.

METHODS

We utilized high-dimensional flow cytometry, cell-free (cf) DNA, and cytokine and chemokine profiling to identify mechanisms of critical illness distinguishing MIS-C from severe acute coronavirus disease 2019 (SAC).

RESULTS

Compared to SAC, MIS-C patients demonstrated profound innate immune cell death and features of emergency myelopoiesis (EM), an understudied phenomenon observed in severe inflammation. EM signatures were characterized by fewer mature myeloid cells in the periphery and decreased expression of HLA-DR and CD86 on antigen-presenting cells. Interleukin 27 (IL-27), a cytokine known to drive hematopoietic stem cells toward EM, was increased in MIS-C, and correlated with immature cell signatures in MIS-C. Upon recovery, EM signatures decreased and IL-27 plasma levels returned to normal levels. Despite profound lymphopenia, we report a lack of cfDNA released by adaptive immune cells and increased CCR7 expression on T cells indicative of egress out of peripheral blood.

CONCLUSIONS

Immune cell signatures of EM combined with elevated innate immune cell-derived cfDNA levels distinguish MIS-C from SAC in children and provide mechanistic insight into dysregulated immunity contributing toward MIS-C, offering potential diagnostic and therapeutic targets.

Effect of vaccination on COVID-19 mortality during omicron wave among highly marginalized mexican population

Background

Social determinants have played a role in COVID-19 outcomes and vaccination has improved these and impacted on inflammatory response, we therefore sought to investigate the association between vaccination and inflammatory response with COVID-19 mortality in a Mexican population with high marginalization during the Omicron wave.

Methods

Prospective, longitudinal, single-center study in a setting of high marginalization conducted during the Omicron wave, from January to November 2022. Clinical and laboratory data were collected during admission and patients were followed until discharge or death. Patients were grouped according to outcome (survival and non-survival), and by complete (2 or more doses) and incomplete vaccination status for comparison.

Results

118 patients were included, 54% (64/118) male, with a median age 63 years and 86% (102/118) with self-reported comorbidities. Mortality was 42%. 58% (68/118) had complete vaccination. There was a 64% risk reduction for all-cause in-hospital mortality of having complete vaccination, hazard ratio 0.36, (95% CI 0.18-0.71, p = 0.004) in the proportional hazards Cox regression test. Survivor group arrived earlier to medical care and had higher SpO2 on admission, and for inflammatory response, had lower levels of Neutrophil-to-lymphocyte ratio, C-reactive protein, and D-dimer at admission. In the longitudinal measurement, only D-dimer showed significant differences between groups according to survival.

Conclusion

In a highly marginalized Mexican population, complete vaccination has a protective effect against COVID-19 all-cause in-hospital mortality compared with incomplete or no vaccination. However, mortality in this population during the Omicron wave is high. Socio-economic inequalities may play an important role in COVID-19 outcomes.

Temporal trends in laboratory parameters in survivors and non‑survivors of critical COVID‑19 illness and the effect of dexamethasone treatment

Although coronavirus disease 2019 (COVID-19)-induced changes in laboratory parameters in patients upon admission have been well-documented, information on their temporal changes is limited. The present study describes the laboratory trends and the effect of dexamethasone treatment on these parameters, in patients with COVID-19 in the intensive care unit (ICU). Routine laboratory parameters, namely white blood cell (WBC), neutrophil, lymphocyte and platelet (PLT) counts, fibrinogen, C-reactive protein (CRP), lactate dehydrogenase (LDH) and albumin concentrations, were recorded upon admission to the ICU and, thereafter, on days 3, 5, 10, 15 and 21; these values were compared between survivors and non-survivors, as well as between those who were treated with dexamethasone and those who were not. Among the 733 patients in the ICU, (mean age, 65±13 years; 68% males; ICU mortality rate 45%; 76% of patients treated with dexamethasone), the WBC and neutrophil counts were persistently high in all patients, without significant differences over the first 15 days. Initially, low lymphocyte counts exhibited increasing trends, but remained higher in survivors compared to non-survivors (P=0.01). The neutrophil-to-lymphocyte ratio (NLR) was persistently elevated in all patients, although it was significantly higher in non-survivors compared to survivors (P<0.001). The PLT count was initially increased in all patients, although it was significantly decreased in non-survivors over time. The fibrinogen and LDH values remained similarly elevated in all patients. However, the increased levels of CRP, which did not differ between patients upon admission, further increased in non-survivors compared to survivors after day 10 (P=0.001). Declining trends in albumin levels over time, overall, with a significant decrease in non-survivors compared to survivors, were observed. Dexamethasone treatment significantly affected the temporal progression of fibrinogen and CRP in survivors and that of NLR in non-survivors. On the whole, the present study demonstrates that patients in the ICU with COVID-19 present persistently abnormal laboratory findings and significant differences in laboratory trends of NLR, CRP, PLT and albumin, but not in WBC and neutrophil count, and fibrinogen and LDH levels, between survivors and non-survivors. The temporal progression of fibrinogen, CRP and NLR is affected by dexamethasone treatment.

Application of the FMEA Method in Improving the Quality Management of Emergency Complete Blood Count Testing

OBJECTIVE

Failure mode and effects analysis (FMEA) was used to identify factors that contribute to quality management deficiencies in laboratory testing of emergency complete blood count (CBC).

METHODS

Improvements included instrument updates, personnel training, and laboratory information system optimization. We used operational data from January 2021 (control group) and January 2022 (FMEA group) to compare the risk priority number (RPN) of FMEA, emergency CBC laboratory turnaround time (TAT), error report rate, and specimen failure rate.

RESULTS

After the implementation of FMEA, the average RPN dropped from 36.24 ± 9.68 to 9.45 ± 2.25, (t = 20.89, P < .05). Additionally, the median TAT for emergency CBCs decreased from 23 min to 11 min as did the interquartile distance (17-34 min to 8-16 min) (P < .05). The rate of emergency CBC error reports decreased from 1.39% to 0.71% (P < .05), and the specimen failure rate decreased from 0.95% to 0.32% (P < .05). Patient satisfaction also increased from 43% to 74% (P < .05), and the technician-performed morphology assessment pass rate increased from 16.7% to 100% (P < .05).

CONCLUSION

Improving the emergency CBC testing process with FMEA can shorten emergency CBC laboratory TAT and reduce specimen failure rates and reporting error rates. The FMEA can be used to improve quality management in emergency CBC laboratories.

Advances in Biomarkers for Diagnosis and Treatment of ARDS

Acute respiratory distress syndrome (ARDS) is a common and fatal disease, characterized by lung inflammation, edema, poor oxygenation, and the need for mechanical ventilation, or even extracorporeal membrane oxygenation if the patient is unresponsive to routine treatment. In this review, we aim to explore advances in biomarkers for the diagnosis and treatment of ARDS. In viewing the distinct characteristics of each biomarker, we classified the biomarkers into the following six categories: inflammatory, alveolar epithelial injury, endothelial injury, coagulation/fibrinolysis, extracellular matrix turnover, and oxidative stress biomarkers. In addition, we discussed the potential role of machine learning in identifying and utilizing these biomarkers and reviewed its clinical application. Despite the tremendous progress in biomarker research, there remain nonnegligible gaps between biomarker discovery and clinical utility. The challenges and future directions in ARDS research concern investigators as well as clinicians, underscoring the essentiality of continued investigation to improve diagnosis and treatment.

Temporal evolution of laboratory characteristics in patients critically ill with COVID‑19 admitted to the intensive care unit (Review)

In the context of coronavirus disease 2019 (COVID-19), laboratory medicine has played a crucial role in both diagnosis and severity assessment. Although the importance of baseline laboratory findings has been extensively reported, data regarding their evolution over the clinical course are limited. The aim of the present narrative review was to provide the dynamic changes of the routine laboratory variables reported in patients with severe COVID-19 over the course of their critical illness. A search was made of the literature for articles providing data on the time-course of routine laboratory tests in patients with severe COVID-19 during their stay in the intensive care unit (ICU). White blood cell, neutrophil and lymphocyte counts, neutrophil to lymphocyte ratio, platelet counts, as well as D-dimer, fibrinogen, C-reactive protein, lactate dehydrogenase and serum albumin levels were selected as disease characteristics and routine laboratory parameters. A total of 25 research articles reporting dynamic trends in the aforementioned laboratory parameters over the clinical course of severe COVID-19 were identified. During the follow-up period provided by each study, the majority of the laboratory values remained persistently abnormal in both survivors and non-survivors. Furthermore, in the majority of studies, the temporal trends of laboratory values distinctly differentiated patients between survivors and non-survivors. In conclusion, there are distinct temporal trends in selected routine laboratory parameters between survivors and non-survivors with severe COVID-19 admitted to the ICU, indicating their importance in the prognosis of clinical outcome.

Changes and Rate of Change in Neutrophil-Lymphocyte Ratio (∆NLR) as an Early Prognostic Marker for the Severity of Outcomes in Patients With COVID-19 and Its Applicability in Other Viral and Bacterial Diseases

INTRODUCTION

COVID-19 is a global pandemic that has spread rapidly and resulted in numerous deaths worldwide. Many inflammatory markers such as neutrophil-lymphocyte ratio (NLR), D-dimer, serum ferritin, C-reactive protein (CRP), and interleukin-6 (IL-6) were used for the diagnosis and prognosis of COVID-19.

METHODS

We have proposed using Delta NLR (0-48 hours) (∆NLR) as an early diagnostic marker for COVID-19 and other inflammatory disorders. We have created a prediction model based on six variables: overall severity, death, shifting to the ICU, length of stay, oxygen requirement, and ventilator support. Prediction models help us prepare for future pandemics through early diagnosis and management.

RESULTS

A total of 1,865 patient records were retrieved from the database. The final sample available for analysis was 461. Change in NLR or ∆NLR was significant for all the models (except for length of stay) created by logistic regression.

CONCLUSION

An independent predictor of the poor prognosis of COVID-19 is the severity of the disease in the initial one or two days. ∆NLR is a unique marker, and its scope of use in other disorders' prognoses must be further researched. The prediction models also help us in decision-making strategies and also prepare us for future pandemics.

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.

LC-MS/MS: A sensitive and selective analytical technique to detect COVID-19 protein biomarkers in the early disease stage

INTRODUCTION

The COVID-19 outbreak has put enormous pressure on the scientific community to detect infection rapidly, identify the status of disease severity, and provide an immediate vaccine/drug for the treatment. Relying on immunoassay and a real-time reverse transcription polymerase chain reaction (rRT-PCR) led to many false-negative and false-positive reports. Therefore, detecting biomarkers is an alternative and reliable approach for determining the infection, its severity, and disease progression. Recent advances in liquid chromatography and mass spectrometry (LC-MS/MS) enable the protein biomarkers even at low concentrations, thus facilitating clinicians to monitor the treatment in hospitals.

AREAS COVERED

This review highlights the role of LC-MS/MS in identifying protein biomarkers and discusses the clinically significant protein biomarkers such as Serum amyloid A, Interleukin-6, C-Reactive Protein, Lactate dehydrogenase, D-dimer, cardiac troponin, ferritin, Alanine transaminase, Aspartate transaminase, gelsolin and galectin-3-binding protein in COVID-19, and their analysis by LC-MS/MS in the early stage.

EXPERT OPINION

Clinical doctors monitor significant biomarkers to understand, stratify, and treat patients according to disease severity. Knowledge of clinically significant COVID-19 protein biomarkers is critical not only for COVID-19 caused by the coronavirus but also to prepare us for future pandemics of other diseases in detecting by LC-MS/MS at the early stages.

Serum Lactate Dehydrogenase Level One Week after Admission Is the Strongest Predictor of Prognosis of COVID-19: A Large Observational Study Using the COVID-19 Registry Japan

Clinical features of COVID-19 are diverse, and a useful tool for predicting clinical outcomes based on clinical characteristics of COVID-19 is needed. This study examined the laboratory values and trends that influence mortality in hospitalised COVID-19 patients. Data on hospitalised patients enrolled in a registry study in Japan (COVID-19 Registry Japan) were obtained. Patients with records on basic information, outcomes, and laboratory data on the day of admission (day 1) and day 8 were included. In-hospital mortality was set as the outcome, and associated factors were identified by multivariate analysis using the stepwise method. A total of 8860 hospitalised patients were included. The group with lactate dehydrogenase (LDH) levels >222 IU/L on day 8 had a higher mortality rate compared to the group with LDH levels ≤222 IU/L. Similar results were observed in subgroups formed by age, body mass index (BMI), underlying disease, and mutation type, except for those aged <50 years. When age, sex, BMI, underlying disease, and laboratory values on days 1 and 8 were tested for factors strongly associated with in-hospital mortality, LDH on day 8 was most strongly associated with mortality. LDH level on day 8 was the strongest predictor of in-hospital mortality in hospitalised COVID-19 patients, indicating its potential usefulness in post-treatment decision-making in severe COVID-19 cases.

Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees

Background and objective

A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis.

Material and method

This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease.

Results

Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients.

Conclusions

This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.

Selected Predictors of COVID-19 Mortality in the Hospitalised Patient Population in a Single-Centre Study in Poland

Background: The correct analysis of COVID-19 predictors could substantially improve the clinical decision-making process and enable emergency department patients at higher mortality risk to be identified. Methods: We retrospectively explored the relationship between some demographic and clinical factors, such as age and sex, as well as the levels of ten selected factors, namely, CRP, D-dimer, ferritin, LDH, RDW-CV, RDW-SD, procalcitonin, blood oxygen saturation, lymphocytes, and leukocytes, and COVID-19 mortality risk in 150 adult patients diagnosed with COVID-19 at Provincial Specialist Hospital in Zgierz, Poland (this hospital was transformed, in March 2020, into a hospital admitting COVID-19 cases only). All blood samples for testing were collected in the emergency room before admission. The length of stay in the intensive care unit and length of hospitalisation were also analysed. Results: The only factor that was not significantly related to mortality was the length of stay in the intensive care unit. The odds of dying were significantly lower in males, patients with a longer hospital stay, patients with higher lymphocyte levels, and patients with higher blood oxygen saturation, while the chances of dying were significantly higher in older patients; patients with higher RDW-CV and RDW-SD levels; and patients with higher levels of leukocytes, CRP, ferritin, procalcitonin, LDH, and D-dimers. Conclusions: Six potential predictors of mortality were included in the final model: age, RDW-CV, procalcitonin, and D-dimers level; blood oxygen saturation; and length of hospitalisation. The results obtained from this study suggest that a final predictive model with high accuracy in mortality prediction (over 90%) was successfully built. The suggested model could be used for therapy prioritization.

Serial Changes in Blood-Cell-Count-Derived and CRP-Derived Inflammatory Indices of COVID-19 Patients

The aim of the study was to investigate the serial changes in inflammatory indices derived from blood cell counts and C-reactive protein (CRP) levels in COVID-19 patients with good and poor outcomes. We retrospectively analyzed the serial changes in the inflammatory indices in 169 COVID-19 patients. Comparative analyses were performed on the first and last days of a hospital stay or death and serially from day 1 to day 30 from the symptom onset. On admission, non-survivors had higher CRP to lymphocytes ratio (CLR) and multi-inflammatory index (MII) values than survivors, while at the time of discharge/death, the largest differences were found for the neutrophil to lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and MII. A significant decrease in NLR, CLR, and MII by the time of discharge was documented in the survivors, and a significant increase in NLR was documented in the non-survivors. The NLR was the only one that remained significant from days 7-30 of disease in intergroup comparisons. The correlation between the indices and the outcome was observed starting from days 13-15. The changes in the index values over time proved to be more helpful in predicting COVID-19 outcomes than those measured on admission. The values of the inflammatory indices could reliably predict the outcome no earlier than days 13-15 of the disease.

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.

Biological Markers to Predict Outcome in Mechanically Ventilated Patients with Severe COVID-19 Living at High Altitude

BACKGROUND

There is not much evidence on the prognostic utility of different biological markers in patients with severe COVID-19 living at high altitude. The objective of this study was to determine the predictive value of inflammatory and hematological markers for the risk of mortality at 28 days in patients with severe COVID-19 under invasive mechanical ventilation, living at high altitude and in a low-resource setting.

METHODS

We performed a retrospective observational study including patients with severe COVID-19, under mechanical ventilation and admitted to the intensive care unit (ICU) located at 2850 m above sea level, between 1 April 2020 and 1 August 2021. Inflammatory (interleukin-6 (IL-6), ferritin, D-dimer, lactate dehydrogenase (LDH)) and hematologic (mean platelet volume (MPV), neutrophil/lymphocyte ratio (NLR), MPV/platelet ratio) markers were evaluated at 24 h and in subsequent controls, and when available at 48 h and 72 h after admission to the ICU. The primary outcome was the association of inflammatory and hematological markers with the risk of mortality at 28 days.

RESULTS

We analyzed 223 patients (median age (1st quartile [Q1]-3rd quartile [Q3]) 51 (26-75) years and 70.4% male). Patients with severe COVID-19 and with IL-6 values at 24 h ≥ 11, NLR values at 24 h ≥ 22, and NLR values at 72 h ≥ 14 were 8.3, 3.8, and 3.8 times more likely to die at 28 days, respectively. The SOFA and APACHE-II scores were not able to independently predict mortality.

CONCLUSIONS

In mechanically ventilated patients with severe COVID-19 and living at high altitude, low-cost and immediately available blood markers such as IL-6 and NLR may predict the severity of the disease in low-resource settings.

Key Factors Associated With Pulmonary Sequelae in the Follow-Up of Critically Ill COVID-19 Patients

INTRODUCTION

Critical COVID-19 survivors have a high risk of respiratory sequelae. Therefore, we aimed to identify key factors associated with altered lung function and CT scan abnormalities at a follow-up visit in a cohort of critical COVID-19 survivors.

METHODS

Multicenter ambispective observational study in 52 Spanish intensive care units. Up to 1327 PCR-confirmed critical COVID-19 patients had sociodemographic, anthropometric, comorbidity and lifestyle characteristics collected at hospital admission; clinical and biological parameters throughout hospital stay; and, lung function and CT scan at a follow-up visit.

RESULTS

The median [p₂₅-p₇₅] time from discharge to follow-up was 3.57 [2.77-4.92] months. Median age was 60 [53-67] years, 27.8% women. The mean (SD) percentage of predicted diffusing lung capacity for carbon monoxide (D_LCO) at follow-up was 72.02 (18.33)% predicted, with 66% of patients having D_LCO<80% and 24% having D_LCO<60%. CT scan showed persistent pulmonary infiltrates, fibrotic lesions, and emphysema in 33%, 25% and 6% of patients, respectively. Key variables associated with D_LCO<60% were chronic lung disease (CLD) (OR: 1.86 (1.18-2.92)), duration of invasive mechanical ventilation (IMV) (OR: 1.56 (1.37-1.77)), age (OR [per-1-SD] (95%CI): 1.39 (1.18-1.63)), urea (OR: 1.16 (0.97-1.39)) and estimated glomerular filtration rate at ICU admission (OR: 0.88 (0.73-1.06)). Bacterial pneumonia (1.62 (1.11-2.35)) and duration of ventilation (NIMV (1.23 (1.06-1.42), IMV (1.21 (1.01-1.45)) and prone positioning (1.17 (0.98-1.39)) were associated with fibrotic lesions.

CONCLUSION

Age and CLD, reflecting patients' baseline vulnerability, and markers of COVID-19 severity, such as duration of IMV and renal failure, were key factors associated with impaired D_LCO and CT abnormalities.

Value of Laboratory Indicators in Predicting Pneumonia in Symptomatic COVID-19 Patients Infected with the SARS-CoV-2 Omicron Variant

Background

The pathogenicity of Omicron is different from that of the previous strains. The value of hematological indicators in patients at high risk of Omicron infection remains unclear. We need rapid, inexpensive and widely available biomarkers to guide the early detection of people at risk of pneumonia and to provide early intervention. We aimed to assess the value of hematological indicators as risk factors for pneumonia in symptomatic COVID-19 patients infected with the SARS-CoV-2 Omicron variant.

Patients and Methods

The study enrolled 144 symptomatic COVID-19 patients with Omicron infection. We collected available clinical details, including laboratory tests and CT examinations. Univariate and multivariate logistic analyses and receiver operating characteristic (ROC) curve analyses were used to assess the value of laboratory markers in predicting the development of pneumonia.

Results

Among the 144 patients, 50 (34.7%) had pneumonia. The ROC analysis revealed that the areas under the ROC curve (AUC) for leukocytes, lymphocytes, neutrophils, and fibrinogen were 0.603 (95% confidence interval (CI): 0.501-0.704, P=0.043), 0.615 (95% CI: 0.517-0.712, P=0.024), 0.632 (95% CI: 0.534-0.730, P=0.009) and 0.635 (95% CI: 0.539-0.730, P=0.008), respectively. The AUC for neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), fibrinogen to lymphocyte ratio (FLR), and fibrinogen to D-dimer ratio (FDR) were 0.670 (95% CI: 0.580-0.760, P=0.001), 0.632 (95% CI: 0.535-0.728, P=0.009), 0.669 (95% CI: 0.575-0.763, P=0.001) and 0.615 (95% CI: 0.510-0.721, P=0.023), respectively. Univariate analysis showed that elevated levels of NLR (odds ratio (OR): 1.219, 95% CI: 1.046-1.421, P=0.011), FLR (OR: 1.170, 95% CI: 1.014-1.349, P=0.031) and FDR (OR: 1.131, 95% CI: 1.039-1.231, P=0.005) were significantly correlated with the presence of pneumonia. Multivariate analysis indicated elevated NLR (OR: 1.248, 95% CI: 1.068-1.459, P=0.005) and FDR (OR: 1.160, 95% CI: 1.054-1.276, P=0.002) levels were associated with the existence of pneumonia. The AUC for the combination of NLR and FDR was 0.701 (95% CI: 0.606-0.796, P<0.001, sensitivity 56.0%, specificity 83.0%).

Conclusion

NLR and FDR can predict the presence of pneumonia in symptomatic COVID-19 patients infected with the SARS-CoV-2 Omicron variant.

Correlation of Patient Profiles and Biomarkers with Outcomes in Covid-19 Icu Patients: A Retrospective Analysis

Background

COVID-19 is a novel disease with a highly variable and unpredictable clinical course. Various clinicodemographic factors and numerous biomarkers have been identified in studies from the West and marked as possible predictors of severe illness and mortality which may be used to triage patients for early aggressive care. This triaging becomes even more significant in resource-limited critical care settings of the Indian subcontinent.

Methods

This retrospective observational study recruited 99 cases of COVID-19 admitted to intensive care from 1 May to 1 August 2020. Demographic, clinical and baseline laboratory data were collected and analysed for association with clinical outcomes, including survival and need for mechanical ventilatory support.

Results

Male gender (p=0.044) and diabetes mellitus (p=0.042) were associated with increased mortality. Binomial logistic regression analysis revealed Interleukin-6 (IL6) (p=0.024), D-dimer (p=0.025) and CRP (p<0.001) as significant predictors of need of ventilatory support and IL6 (p=0.036), CRP (p=0.041), D-dimer (p=0.006) and PaO2FiO2 ratio (p=0.019) as significant predictors of mortality. CRP >40 mg/L predicted mortality with sensitivity of 93.3% and specificity of 88.9% (AUC 0.933) and IL6> 32.5 pg/ml with a sensitivity of 82.2% and specificity of 70.4% (AUC 0.821).

Conclusion

Our results suggest that a baseline CRP >40 mg/L, IL6 >32.5 pg/ml or D-dimer >810 ng/ml are early accurate predictors of severe illness and adverse outcomes and may be used to triage patients for early intensive care.

A Simple Non-Invasive Score Based on Baseline Parameters Can Predict Outcome in Patients with COVID-19

We evaluated the role of CRP and other laboratory parameters in predicting the worsening of clinical conditions during hospitalization, ICU admission, and fatal outcome among patients with COVID-19. Consecutive adult inpatients with SARS-CoV-2 infection and respiratory symptoms treated in three different COVID centres were enrolled, and they were tested for laboratory parameters within 48 h from admission. Three-hundred ninety patients were enrolled. Age, baseline CRP, and LDH were associated with a P/F ratio < 200 during hospitalization. Male gender and CRP > 60 mg/L were shown to be independently associated with ICU admission. Lymphocytes < 1000 cell/μL were associated with the worst P/F ratio. CRP > 60 mg/L predicted exitus. We subsequently devised an 11-points numeric ordinary scoring system based on age, sex, CRP, and LDH at admission (ASCL score). Patients with an ASCL score of 0 or 2 were shown to be protected against a P/F ratio < 200, while patients with an ASCL score of 6 to 8 were shown to be at risk for P/F ratio < 200. Patients with an ASCL score ≥ 7 had a significantly increased probability of death during hospitalization. In conclusion, patients with elevated CRP and LDH and an ASCL score > 6 at admission should be prioritized for careful respiratory function monitoring and early treatment to prevent a progression of the disease.

Exploratory analysis of interleukin-38 in hospitalized COVID-19 patients

INTRODUCTION

A major contributor to coronavirus disease 2019 (COVID-19) progression and severity is a dysregulated innate and adaptive immune response. Interleukin-38 (IL-38) is an IL-1 family member with broad anti-inflammatory properties, but thus far little is known about its role in viral infections. Recent studies have shown inconsistent results, as one study finding an increase in circulating IL-38 in COVID-19 patients in comparison to healthy controls, whereas two other studies report no differences in IL-38 concentrations.

METHODS

Here, we present an exploratory, retrospective cohort study of circulating IL-38 concentrations in hospitalized COVID-19 patients admitted to two Dutch hospitals (discovery n = 148 and validation n = 184) and age- and sex-matched healthy subjects. Plasma IL-38 concentrations were measured by enzyme-linked immunosorbent assay, disease-related proteins by proximity extension assay, and clinical data were retrieved from hospital records.

RESULTS

IL-38 concentrations were stable during hospitalization and similar to those of healthy control subjects. IL-38 was not associated with rates of intensive care unit admission or mortality. Only in men in the discovery cohort, IL-38 concentrations were positively correlated with hospitalization duration. A positive correlation between IL-38 and the inflammatory biomarker d-dimer was observed in men of the validation cohort. In women of the validation cohort, IL-38 concentrations correlated negatively with thrombocyte numbers. Furthermore, plasma IL-38 concentrations in the validation cohort correlated positively with TNF, TNFRSF9, IL-10Ra, neurotrophil 3, polymeric immunoglobulin receptor, CHL1, CD244, superoxide dismutase 2, and fatty acid binding protein 2, and negatively with SERPINA12 and cartilage oligomeric matrix protein.

CONCLUSIONS

These data indicate that IL-38 is not associated with disease outcomes in hospitalized COVID-19 patients. However, moderate correlations between IL-38 concentrations and biomarkers of disease were identified in one of two cohorts. While we demonstrate that IL-38 concentrations are not indicative of COVID-19 severity, its anti-inflammatory effects may reduce COVID-19 severity and should be experimentally investigated.

D-Dimer as a Sensitive Biomarker of Survival Rate in Patients with COVID-19

OBJECTIVE

The global case fatality rate of coronavirus disease 2019 is 2.16% as announced by the World Health Organization. In Indonesia, according to the Ministry of Health, the number is even higher, reaching a 2.8% case fatality rate. D-dimer levels were found to affect coronavirus disease 2019 patient's survival in several studies. The study aimed to determine whether the amount of D-dimer predicted survival in coronavirus disease 2019 patients.

MATERIALS AND METHODS

This research was performed in a retrospective cohort design and used survival analysis. From March 1, 2020, to August 31, 2020, the samples were collected from polymerase chain reaction-confirmed coronavirus disease 2019 patients at Mohammad Hoesin General Hospital in Palembang, South Sumatera, Indonesia. We used electronic medical records to obtain demographic (age and gender), coexisting condition, laboratory (coagulation and hematologic test), and outcome (non-survivors or survivors) data. The chi-square and Mann-Whitney tests were used to evaluate the results. The Kaplan-Meier method and the Mantel-Haenszel log-rank test were used to examine D-dimer levels and patient outcomes. Youden index was calculated to determine the optimal cut-off value of D-dimer.

RESULTS

There were 52 non-survivors and 235 survivors among the 287 patients who met the inclusion criterion. Non-survivors had D-dimer levels of more than 1.49 mg/L in 82.69%of cases. Males had lower cut-off compared to females (>1.49 mg/L vs. >2.2 mg/L). The researchers discovered a highly significant correlation between D-dimer levels and coronavirus disease 2019 mortality (P=.001). The c-index analysis showed that D-dimer (0.79, 95% CI: 0.73-0.83) ability for mortality prediction was the second-best compared with other laboratory markers.

CONCLUSION

D-dimer can be used as a predictor of coronavirus disease 2019 in-hospital mortality for early identification of coagulopathy.

Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19

Background

The COVID-19 pandemic has created pressure on healthcare systems worldwide. Tools that can stratify individuals according to prognosis could allow for more efficient allocation of healthcare resources and thus improved patient outcomes. It is currently unclear if blood gene expression signatures derived from patients at the point of admission to hospital could provide useful prognostic information.

Methods

Gene expression of whole blood obtained at the point of admission from a cohort of 78 patients hospitalised with COVID-19 during the first wave was measured by high resolution RNA sequencing. Gene signatures predictive of admission to Intensive Care Unit were identified and tested using machine learning and topological data analysis, TopMD.

Results

The best gene expression signature predictive of ICU admission was defined using topological data analysis with an accuracy: 0.72 and ROC AUC: 0.76. The gene signature was primarily based on differentially activated pathways controlling epidermal growth factor receptor (EGFR) presentation, Peroxisome proliferator-activated receptor alpha (PPAR-α) signalling and Transforming growth factor beta (TGF-β) signalling.

Conclusions

Gene expression signatures from blood taken at the point of admission to hospital predicted ICU admission of treatment naïve patients with COVID-19.

A systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio and COVID-19 progression and mortality

BACKGROUND AND AIMS

Severe manifestations of coronavirus disease 2019 (COVID-19) are associated with alterations in blood cells that regulate immunity, inflammation, and hemostasis. We conducted an updated systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), and COVID-19 progression and mortality.

METHODS

A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published between January 2020 and June 2022.

RESULTS

In 71 studies reporting the investigated parameters within 48 hours of admission, higher NLR (HR 1.21, 95% CI 1.16 to 1.27, p < 0.0001), relative neutrophilia (HR 1.62, 95% CI 1.46 to 1.80, p < 0.0001), relative lymphopenia (HR 1.62, 95% CI 1.27 to 2.08, p < 0.001), and relative thrombocytopenia (HR 1.74, 95% CI 1.36 to 2.22, p < 0.001), but not PLR (p = 0.11), were significantly associated with disease progression and mortality. Between-study heterogeneity was large-to-extreme. The magnitude and direction of the effect size were not modified in sensitivity analysis.

CONCLUSIONS

NLR and neutrophil, lymphocyte, and platelet count significantly discriminate COVID-19 patients with different progression and survival outcomes. (PROSPERO registration number: CRD42021267875).

The Role of Neutrophil-to-Lymphocyte Ratio in Risk Stratification and Prognostication of COVID-19: A Systematic Review and Meta-Analysis

Several studies have proposed that the neutrophil−lymphocyte ratio (NLR) is one of the various biomarkers that can be useful in assessing COVID-19 disease-related outcomes. Our systematic review analyzes the relationship between on-admission NLR values and COVID-19 severity and mortality. Six different severity criteria were used. A search of the literature in various databases was conducted from 1 January 2020 to 1 May 2021. We calculated the pooled standardized mean difference (SMD) for the collected NLR values. A meta-regression analysis was performed, looking at the length of hospitalization and other probable confounders, such as age, gender, and comorbidities. A total of sixty-four studies were considered, which included a total of 15,683 patients. The meta-analysis showed an SMD of 3.12 (95% CI: 2.64−3.59) in NLR values between severe and non-severe patients. A difference of 3.93 (95% CI: 2.35−5.50) was found between survivors and non-survivors of the disease. Upon summary receiver operating characteristics analysis, NLR showed 80.2% (95% CI: 74.0−85.2%) sensitivity and 75.8% (95% CI: 71.3−79.9%) specificity for the prediction of severity and 78.8% (95% CI: 73.5−83.2%) sensitivity and 73.0% (95% CI: 68.4−77.1%) specificity for mortality, and was not influenced by age, gender, or co-morbid conditions. Conclusion: On admission, NLR predicts both severity and mortality in COVID-19 patients, and an NLR > 6.5 is associated with significantly greater the odds of mortality.

Comparative analysis of neutrophil to lymphocyte ratio and derived neutrophil to lymphocyte ratio with respect to outcomes of in-hospital coronavirus disease 2019 patients: A retrospective study

Introduction and objectives

In patients with coronavirus disease 2019 (COVID-19), several abnormal hematological biomarkers have been reported. The current study aimed to find out the association of neutrophil to lymphocyte ratio (NLR) and derived NLR (dNLR) with COVID-19. The objective was to compare the accuracy of both of these markers in predicting the severity of the disease.

Materials and methods

The study was conducted in a single-center having patients with COVID-19 with a considerable hospital stay. NLR is easily calculated by dividing the absolute neutrophil count (ANC) with the absolute lymphocyte count (ALC) {ANC/ALC}, while dNLR is calculated by ANC divided by total leukocyte count minus ANC {ANC/(WBC-ANC)}. Medians and interquartile ranges (IQR) were represented by box plots. Multivariable logistic regression was performed obtaining an odds ratio (OR), 95% CI, and further adjusted to discover the independent predictors and risk factors associated with elevated NLR and dNLR.

Results

A total of 1,000 patients with COVID-19 were included. The baseline NLR and dNLR were 5.00 (2.91–10.46) and 4.00 (2.33–6.14), respectively. A cut-off value of 4.23 for NLR and 2.63 for dNLR were set by receiver operating characteristic (ROC) analysis. Significant associations of NLR were obtained by binary logistic regression for dependent outcome variables as ICU stay (p < 0.001), death (p < 0.001), and invasive ventilation (p < 0.001) while that of dNLR with ICU stay (p = 0.002), death (p < 0.001), and invasive ventilation (p = 0.002) on multivariate analysis when adjusted for age, gender, and a wave of pandemics. Moreover, the indices were found correlating with other inflammatory markers such as C-reactive protein (CRP), D-dimer, and procalcitonin (PCT).

Conclusion

Both markers are equally reliable and sensitive for predicting in-hospital outcomes of patients with COVID-19. Early detection and predictive analysis of these markers can allow physicians to risk assessment and prompt management of these patients.

Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study

Background

The clinical heterogeneity of COVID-19 suggests the existence of different phenotypes with prognostic implications. We aimed to analyze comorbidity patterns in critically ill COVID-19 patients and assess their impact on in-hospital outcomes, response to treatment and sequelae.

Methods

Multicenter prospective/retrospective observational study in intensive care units of 55 Spanish hospitals. 5866 PCR-confirmed COVID-19 patients had comorbidities recorded at hospital admission; clinical and biological parameters, in-hospital procedures and complications throughout the stay; and, clinical complications, persistent symptoms and sequelae at 3 and 6 months.

Findings

Latent class analysis identified 3 phenotypes using training and test subcohorts: low-morbidity (n=3385; 58%), younger and with few comorbidities; high-morbidity (n=2074; 35%), with high comorbid burden; and renal-morbidity (n=407; 7%), with chronic kidney disease (CKD), high comorbidity burden and the worst oxygenation profile. Renal-morbidity and high-morbidity had more in-hospital complications and higher mortality risk than low-morbidity (adjusted HR (95% CI): 1.57 (1.34-1.84) and 1.16 (1.05-1.28), respectively). Corticosteroids, but not tocilizumab, were associated with lower mortality risk (HR (95% CI) 0.76 (0.63-0.93)), especially in renal-morbidity and high-morbidity. Renal-morbidity and high-morbidity showed the worst lung function throughout the follow-up, with renal-morbidity having the highest risk of infectious complications (6%), emergency visits (29%) or hospital readmissions (14%) at 6 months (p<0.01).

Interpretation

Comorbidity-based phenotypes were identified and associated with different expression of in-hospital complications, mortality, treatment response, and sequelae, with CKD playing a major role. This could help clinicians in day-to-day decision making including the management of post-discharge COVID-19 sequelae.

Funding

ISCIII, UNESPA, CIBERES, FEDER, ESF.

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O₂ Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

COVID-19 hastalarında koagülopati için C-reaktif protein/albumin oranının tanısal doğruluğu

Amaç: Bu çalışma, COVİD-19'da D-dimer yüksekliğini öngörmede C-reaktif proteinin (CRP) albümine oranının (CAR) tanısal faydasını değerlendirmek amaçlamıştır. Gereç ve Yöntem: Bu retrospektif kohort çalışmasında, bir üniversite hastanesinde COVID-19 olduğu doğrulanmış 145 yatan hastadan veri topladık. Hastalar D-dimer düzeylerine göre D-dimer düzeyi yüksek olanlar ve D-dimer düzeyi normal olanlar olarak iki gruba ayrıldı. Hastaların tıbbi kayıtlarından demografik veriler, komorbiditeler, klinik semptomlar, CAR ve laboratuvar sonuçları elde edildi ve gruplar arasında kıyaslamalar yapıldı. Bulgular: Hastaların yaş ortalaması 52,9±17,9 yıl olup, 76'sı erkek idi. CAR medyanı, D-dimer değeri yüksek olanlarda anlamlı olarak daha yüksekti (134,1'e karşı 20,7,). D-dimeri yüksek hastalarda CRP, prokalsitonin, lökosit, nötrofil, laktat dehidrojenaz, ferritin ve fibrinojen daha yüksekti. CAR ve D-Dimer arasında oldukça anlamlı pozitif korelasyon vardı. Lojistik regresyon analizi, CAR'ın D-dimer yüksekliği için önemli bir belirleyici olduğunu ortaya koydu. ROC eğrisi altında kalan alan (AUC) CAR için 0,741 idi. COVİD-19 hastalarında D-dimer yüksekliğini öngörmek için CAR'ın doğrulanmış eşik değeri, %58 duyarlılık ve %70 özgüllük ile 81,8 idi. Sonuç: Çalışmamız, CAR'ın D-dimer ile önemli ölçüde korele olduğunu ve COVİD-19’u olan hastalarda D-dimer yüksekliğini öngörmek için kullanılabileceğini göstermiştir.

Brixia Chest X-ray Severity Scoring System is in Relation with C-reactive Protein and D-dimer Values in Patients with COVID-19

Background

The Brixia scoring system interpreted chest X-ray changes, serves as an indicator of the extent of changes in the lung parenchyma.

Objective

To indicate the effect of D-dimer and C-reactive protein (CRP) on Brixia score in patients with positive polymerase chain reaction (PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Methods

The research had prospective, descriptive and analytical character, and included patients (n=104) with Coronavirus disease 2019 (COVID-19) diagnosis. Chest X-ray, as well as calculation of Brixia score was done on admission, in the first week of hospitalization, on discharge, and 10 days after discharge (the patient was considered a post-COVID patient. Maximum CRP and D-dimer values were taken into account, along with data about dependence of mechanical ventilation and oxygen therapy.

Results

Initial Brixia score was significantly associated with the values of CRP (r = .23, p <.05). Higher level of CRP affected the higher result on the Brixia score after the initial X-ray. High CRP and D-dimer were significantly associated with oxygen use in patients, while high D-dimer was also statistically significantly associated with comorbidity. The mean value of Brixia score (during four time points) was significantly related to the values of CRP, D-dimer, the use of mechanical ventilation and oxygen therapy, but also with the existence of comorbidities. The largest statistically significant positive correlation of Brixia scora is with the values of D-dimer (r = .45, p <.000), but also with the values of CRP (r = .36, p <.000).

Conclusion

Values of CRP have an impact on Brixia score. Investigation of clinical characteristics and outcomes of severe clinical presentation of COVID-19 along with CXR scoring system will contribute to early prediction, accurate diagnosis and treatment as well as to improve the prognosis of patients with severe illness.

Clinical characteristics and mortality associated with COVID-19 at high altitude: a cohort of 5161 patients in Bogotá, Colombia

Background

There are few data on the clinical outcomes of patients with coronavirus disease 2019 (COVID-19) in cities over 1000 m above sea level (masl).

Objectives

To describe the clinical characteristics and mortality of patients with COVID-19 treated at a high complexity hospital in Bogotá, Colombia, at 2640 masl.

Methods

This was an observational study of a cohort including 5161 patients with confirmed COVID-19 infection from 19 March 2020 to 30 April 2021. Demographic data, laboratory values, comorbidities, oxygenation indices, and clinical outcomes were collected. Data were compared between survivors and nonsurvivors. An independent predictive model was performed for mortality and invasive mechanical ventilation (IMV) using classification and regression trees (CART).

Results

The median cohort age was 66 years (interquartile range (IQR) 53–77), with 1305 patients dying (25%) and 3856 surviving (75%). The intensive care unit (ICU) received 1223 patients (24%). Of 898 patients who received IMV, 613 (68%) of them perished. The ratio of partial pressure arterial oxygen (PaO₂) to fraction inspired oxygen (FiO₂), or the P/F ratio, upon ICU admission was 105 (IQR 77–146) and 137 (IQR 91–199) in the deceased and survivors, respectively. The CART model showed that the need for IMV, age greater than 79 years, ratio of oxygen saturation (SaO₂) to FiO₂, or the S/F ratio, less than 259, and lactate dehydrogenase (LDH) greater than 617 U/L at admission were associated with a greater probability of death.

Conclusion

Among more than 5000 patients with COVID-19 treated in our hospital, mortality at hospital discharge was 25%. Older age, low S/F ratio, and high LDH at admission were predictors of mortality.

Insights into the Role of Neutrophils and Neutrophil Extracellular Traps in Causing Cardiovascular Complications in Patients with COVID-19: A Systematic Review

Background: The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus has resulted in significant mortality and burdening of healthcare resources. While initially noted as a pulmonary pathology, subsequent studies later identified cardiovascular involvement with high mortalities reported in specific cohorts of patients. While cardiovascular comorbidities were identified early on, the exact manifestation and etiopathology of the infection remained elusive. This systematic review aims to investigate the role of inflammatory pathways, highlighting several culprits including neutrophil extracellular traps (NETs) which have since been extensively investigated. Method: A search was conducted using three databases (MEDLINE; MEDLINE In-Process & Other Non-Indexed Citations and EMBASE). Data from randomized controlled trials (RCT), prospective series, meta-analyses, and unmatched observational studies were considered for the processing of the algorithm and treatment of inflammatory response during SARS-CoV-2 infection. Studies without the SARS-CoV-2 Infection period and case reports were excluded. Results: A total of 47 studies were included in this study. The role of the acute inflammatory response in the propagation of the systemic inflammatory sequelae of the disease plays a major part in determining outcomes. Some of the mechanisms of activation of these pathways have been highlighted in previous studies and are highlighted. Conclusion: NETs play a pivotal role in the pathogenesis of the inflammatory response. Despite moving into the endemic phase of the disease in most countries, COVID-19 remains an entity that has not been fully understood with long-term effects remaining uncertain and requiring ongoing monitoring and research.

COVID-19 INFECTION IN KIDNEY TRANSPLANT RECIPIENTS: A SINGLE CENTER EXPERIENCE

Background

Methods

Results

Conclusions

Regenerative therapy by using Mesenchymal Stem Cells-derived exosomes in COVID-19 treatment. The potential role and underlying mechanisms

COVID-19 disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), started in December 2019 in Wuhan, China, and quickly became the global pandemic. The high spread rate, relatively high mortality rate, and the lack of specific medicine have led researchers and clinicians worldwide to find new treatment strategies. Unfortunately, evidence shows that the virus-specific receptor Angiotensin-Converting Enzyme 2 (ACE-2) is present on the surface of most cells in the body, leading to immune system dysfunction and multi-organ failure in critically ill patients. In this context, the use of Mesenchymal Stem Cells (MSCs) and their secret has opened new therapeutic horizons for patients due to the lack of ACE2 receptor expression. MSCs exert their beneficial therapeutic actions, particularly anti-inflammatory and immunomodulatory properties, mainly through paracrine effects which are mediated by exosomes. Exosomes are bilayer nanovesicles that carry a unique cargo of proteins, lipids and functional nucleic acids based on their cell origin. This review article aims to investigate the possible role of exosomes and the underlying mechanism involved in treating COVID-19 disease based on recent findings.

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

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

Insight into prognostics, diagnostics, and management strategies for SARS CoV-2

The foremost challenge in countering infectious diseases is the shortage of effective therapeutics. The emergence of coronavirus disease (COVID-19) outbreak has posed a great menace to the public health system globally, prompting unprecedented endeavors to contain the virus. Many countries have organized research programs for therapeutics and management development. However, the longstanding process has forced authorities to implement widespread infrastructures for detailed prognostic and diagnostics study of severe acute respiratory syndrome (SARS CoV-2). This review discussed nearly all the globally developed diagnostic methodologies reported for SARS CoV-2 detection. We have highlighted in detail the approaches for evaluating COVID-19 biomarkers along with the most employed nucleic acid- and protein-based detection methodologies and the causes of their severe downfall and rejection. As the variable variants of SARS CoV-2 came into the picture, we captured the breadth of newly integrated digital sensing prototypes comprised of plasmonic and field-effect transistor-based sensors along with commercially available food and drug administration (FDA) approved detection kits. However, more efforts are required to exploit the available resources to manufacture cheap and robust diagnostic methodologies. Likewise, the visualization and characterization tools along with the current challenges associated with waste-water surveillance, food security, contact tracing, and their role during this intense period of the pandemic have also been discussed. We expect that the integrated data will be supportive and aid in the evaluation of sensing technologies not only in current but also future pandemics.

Neutrophil-to-lymphocyte ratio and D-dimer are biomarkers of death risk in severe COVID-19: A retrospective observational study

BACKGROUND AND AIMS

Over 4 million deaths from coronavirus disease (COVID)-19 have been reported in the world. Several biomarkers have been identified that predict disease severity, but there is still a need to identify biomarkers for death risk in severe COVID-19. We aim to define amongst the biomarkers already identified those which are mostly associated with increased death rate in patients with severe COVID-19.

METHODS

In this retrospective study conducted in three public hospitals linked to the Medical School of Ribeirão Preto, Brazil, patients with severe COVID-19 were evaluated regarding biomarkers (neutrophil-to-lymphocyte ratio-NLR, D-dimer, fibrinogen) of death risk, obtained before administration of corticosteroids.

RESULTS

Thirty-nine (32.8%) of the 119 patients included (104 [87.4%] on mechanical ventilation) died during hospitalization. Non-survivor group had higher median (range) NLR (12.63 [2.6-115] vs 7.43 [0.43-31.8]; P = .001), D-dimer (2.17 [0.27-20.00] vs 1.57 [0.28-20.00]; P = .03), but lower fibrinogen (631 [353-1078] vs 705 [407-1200]; P = .02). The group with NLR ≥ 10 and D-dimer ≥ 2 μg/mL had a higher death risk than the group with NLR < 10 and D-dimer < 2 μg/mL (OR: 5.39; CI 95%: 1.5-19.42; P = .01).

CONCLUSION

High NLR and D-dimer, especially when combined, are predictors of death risk for patients with severe COVID-19 and should be incorporated into their evaluation.

Prognostic and Predictive Biomarkers in Patients With Coronavirus Disease 2019 Treated With Tocilizumab in a Randomized Controlled Trial

OBJECTIVES

To explore candidate prognostic and predictive biomarkers identified in retrospective observational studies (interleukin-6, C-reactive protein, lactate dehydrogenase, ferritin, lymphocytes, monocytes, neutrophils, d-dimer, and platelets) in patients with coronavirus disease 2019 pneumonia after treatment with tocilizumab, an anti-interleukin-6 receptor antibody, using data from the COVACTA trial in patients hospitalized with severe coronavirus disease 2019 pneumonia.

DESIGN

Exploratory analysis from a multicenter, randomized, double-blind, placebo-controlled, phase 3 trial.

SETTING

Hospitals in North America and Europe.

PATIENTS

Adults hospitalized with severe coronavirus disease 2019 pneumonia receiving standard care.

INTERVENTION

Randomly assigned 2:1 to IV tocilizumab 8 mg/kg or placebo.

MEASUREMENTS AND MAIN RESULTS

Candidate biomarkers were measured in 295 patients in the tocilizumab arm and 142 patients in the placebo arm. Efficacy outcomes assessed were clinical status on a seven-category ordinal scale (1, discharge; 7, death), mortality, time to hospital discharge, and mechanical ventilation (if not receiving it at randomization) through day 28. Prognostic and predictive biomarkers were evaluated continuously with proportional odds, binomial or Fine-Gray models, and additional sensitivity analyses. Modeling in the placebo arm showed all candidate biomarkers except lactate dehydrogenase and d-dimer were strongly prognostic for day 28 clinical outcomes of mortality, mechanical ventilation, clinical status, and time to hospital discharge. Modeling in the tocilizumab arm showed a predictive value of ferritin for day 28 clinical outcomes of mortality (predictive interaction, p = 0.03), mechanical ventilation (predictive interaction, p = 0.01), and clinical status (predictive interaction, p = 0.02) compared with placebo.

CONCLUSIONS

Multiple biomarkers prognostic for clinical outcomes were confirmed in COVACTA. Ferritin was identified as a predictive biomarker for the effects of tocilizumab in the COVACTA patient population; high ferritin levels were associated with better clinical outcomes for tocilizumab compared with placebo at day 28.

Does Neutrophil-to-lymphocyte Ratio at Admission Predict Severity and Mortality in COVID-19 Patients? A Systematic Review and Meta-analysis

Background

Coronavirus disease-2019 (COVID-2019) pandemic continues to be a significant public health problem. Severe COVID-19 cases have a poor prognosis and extremely high mortality. Prognostic factor evidence can help healthcare providers understand the likely prognosis and identify subgroups likely to develop severe disease with increased mortality risk so that timely treatments can be initiated. This meta-analysis has been performed to evaluate the neutrophil-to-lymphocyte ratio (NLR) at admission as a prognostic factor to predict severe coronavirus disease and mortality.

Materials and methods

A literature search was conducted through April 30, 2021, to retrieve all published studies, including gray literature and preprints, investigating the association between NLR and severity or mortality in COVID-19 patients. Screening of studies and data extraction have been done by two authors independently. The methodological quality of the included studies was assessed by the Quality in Prognosis Studies (QUIPS) tool.

Results

Twenty-four studies involving 4,080 patients reported the prognostic value of NLR for severe COVID-19. The pooled sensitivity (SEN), specificity (SPE), and area under the curve were 0.75 (95% CI 0.69–0.80), 0.74 (95% CI 0.70–0.78), and 0.81 (95% CI 0.77–0.84). Fifteen studies involving 4,071 patients reported the prognostic value of NLR for mortality in COVID-19. The pooled sensitivity (SEN), specificity (SPE), and area under curve were 0.80 (95% CI 0.72–0.86), 0.78 (95% CI 0.69–0.85), and 0.86 (95% CI 0.83–0.89).

Conclusion

The prognostic value of NLR at admission for severity and mortality in patients with COVID-19 is good. Evaluating the NLR at admission can assist treating clinicians to identify early the cases likely to worsen. This would help to conduct early triage, identify potentially high-risk cases, and start optimal monitoring and management, thus reducing the overall mortality of COVID-19.

Trial registry

This meta-analysis was prospectively registered on PROSPERO database (Registration Number: CRD42021247801).

How to cite this article

Sarkar PG, Pant P, Kumar J, Kumar A. Does Neutrophil-to-lymphocyte Ratio at Admission Predict Severity and Mortality in COVID-19 Patients? A Systematic Review and Meta-analysis. Indian J Crit Care Med 2022;26(3):361–375.

An Unsupervised Machine Learning Clustering and Prediction of Differential Clinical Phenotypes of COVID-19 Patients Based on Blood Tests—A Hong Kong Population Study

Background

To better understand the different clinical phenotypes across the disease spectrum in patients with COVID-19 using an unsupervised machine learning clustering approach.

Materials and Methods

A population-based retrospective study was conducted utilizing demographics, clinical characteristics, comorbidities, and clinical outcomes of 7,606 COVID-19–positive patients on admission to public hospitals in Hong Kong in the year 2020. An unsupervised machine learning clustering was used to explore this large cohort.

Results

Four clusters of differing clinical phenotypes based on data at initial admission was derived in which 86.6% of the deceased cases were aggregated in one of the clusters without prior knowledge of their clinical outcomes. Other distinctive clinical characteristics of this cluster were old age and high concurrent comorbidities as well as laboratory characteristics of lower hemoglobin/hematocrit levels, higher neutrophil, C-reactive protein, lactate dehydrogenase, and creatinine levels. The clinical patterns captured by the cluster analysis was validated on other temporally distinct cohorts in 2021. The phenotypes aligned with existing literature.

Conclusion

The study demonstrated the usefulness of unsupervised machine learning techniques with the potential to uncover latent clinical phenotypes. It could serve as a more robust classification for patient triaging and patient-tailored treatment strategies.

Biomarker identification using dynamic time warping analysis: a longitudinal cohort study of patients with COVID-19 in a UK tertiary hospital

OBJECTIVES

COVID-19 is a heterogeneous disease, and many reports have described variations in demographic, biochemical and clinical features at presentation influencing overall hospital mortality. However, there is little information regarding longitudinal changes in laboratory prognostic variables in relation to disease progression in hospitalised patients with COVID-19.

DESIGN AND SETTING

This retrospective observational report describes disease progression from symptom onset, to admission to hospital, clinical response and discharge/death among patients with COVID-19 at a tertiary centre in South East England.

PARTICIPANTS

Six hundred and fifty-one patients treated for SARS-CoV-2 between March and September 2020 were included in this analysis. Ethical approval was obtained from the HRA Specific Review Board (REC 20/HRA/2986) for waiver of informed consent.

RESULTS

The majority of patients presented within 1 week of symptom onset. The lowest risk patients had low mortality (1/45, 2%), and most were discharged within 1 week after admission (30/45, 67%). The highest risk patients, as determined by the 4C mortality score predictor, had high mortality (27/29, 93%), with most dying within 1 week after admission (22/29, 76%). Consistent with previous reports, most patients presented with high levels of C reactive protein (CRP) (67% of patients >50 mg/L), D-dimer (98%>upper limit of normal (ULN)), ferritin (65%>ULN), lactate dehydrogenase (90%>ULN) and low lymphocyte counts (81%

CONCLUSIONS

Serial measurement of routine blood tests may be a useful prognostic tool for monitoring treatment response in hospitalised patients with COVID-19. Changes in other biochemical parameters often included in a 'COVID-19 bundle' did not show significant association with outcome, suggesting there may be limited clinical benefit of serial sampling. This may have direct clinical utility in the context of escalating healthcare costs of the pandemic.

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Key Words

• COVID-19
• respiratory infections
• respiratory medicine (see thoracic medicine)

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MESH Headings

• Biomarkers
• COVID-19
• Cohort Studies
• Humans
• Longitudinal Studies
• Retrospective Studies
• SARS-CoV-2
• Tertiary Care Centers
• United Kingdom

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Grants

• 29374 Cancer Research UK
• NIHR Southampton Biomedical Research Centre (BRC) at University Hospital Southampton NHS Foundation Trust
• Clinical Informatics Research Unit, UoS
• The digital Experimental Cancer Medicine Team are supported by the AstraZeneca iDECIDE Programme (grant number no:. 119106), awarded to Manchester Cancer Research Centre and by Cancer Research UK via an Accelerator Award [Award number: A29374] through the CRUK Manchester Institute [Award number: C147/A25254]
• NIHR Southampton CRF at University Hospital Southampton NHS Foundation Trust
• the University of Southampton (UoS) charity (Office of Development and Alumni Relations - ODAR)

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Affiliation(s)

Hannah Burke Faculty of Medicine, University of Southampton, Southampton, UK
Anna Freeman Faculty of Medicine, University of Southampton, Southampton, UK
Paul O'Regan Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK
Oskar Wysocki Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK
Andre Freitas Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK
Ahilanandan Dushianthan Faculty of Medicine, University of Southampton, Southampton, UK
Michael Celinski Faculty of Medicine, University of Southampton, Southampton, UK
James Batchelor Faculty of Medicine, University of Southampton, Southampton, UK
Hang Phan Clinical Informatics Research Unit, University of Southampton Faculty of Medicine, Southampton, UK University of Southampton, Southampton, UK
Florina Borca Institute for Life Sciences, University of Southampton, Southampton, UK
Natasha Sheard University Hospital Southampton NHS Foundation Trust, Southampton, UK
Sarah Williams University Hospital Southampton NHS Foundation Trust, Southampton, UK
Alastair Watson Faculty of Medicine, University of Southampton, Southampton, UK
Paul Fitzpatrick University of Manchester, Cancer Biomarker Centre, Cancer Research UK Manchester Institute, Manchester, UK
Dónal Landers Digital Experimental Cancer Medicine Team, University of Manchester, Cancer Biomarker Centre, Cancer Research UK Manchester Institute, Alderley Edge, Cheshire, UK
Tom Wilkinson Faculty of Medicine, University of Southampton, Southampton, UK

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Mechanism for the attenuation of neutrophil and complement hyperactivity by MSC exosomes

Complements and neutrophils are two key players of the innate immune system that are widely implicated as drivers of severe COVID-19 pathogenesis, as evident by the direct correlation of respiratory failure and mortality with elevated levels of terminal complement complex C5b-9 and neutrophils. In this study, we identified a feed-forward loop between complements and neutrophils that could amplify and perpetuate the cytokine storm seen in severe SARS-CoV-2–infected patients. We observed for the first time that the terminal complement activation complex C5b-9 directly triggered neutrophil extracellular trap (NET) release and interleukin (IL)-17 production by neutrophils. This is also the first report that the production of NETs and IL-17 induced by C5b-9 assembly on neutrophils could be abrogated by mesenchymal stem cell (MSC) exosomes. Neutralizing anti-CD59 antibodies abolished this abrogation. Based on our findings, we hypothesize that MSC exosomes could alleviate the immune dysregulation in acute respiratory failure, such as that observed in severe COVID-19 patients, by inhibiting complement activation through exosomal CD59, thereby disrupting the feed-forward loop between complements and neutrophils to inhibit the amplification and perpetuation of inflammation during SARS-CoV-2 infection.

C-Reactive Protein-to-Albumin Ratio as a Prognostic Inflammatory Marker in COVID-19

Objectives  As a result of developed generalized inflammation, the main prognostic factor determining morbidity and mortality in coronavirus disease 2019 (COVID-19) patients is acute respiratory distress syndrome. The purpose of our study was to define (1) the laboratory tests that will contribute to the diagnosis and follow-up of COVID-19 patients, (2) the differences between the laboratory-confirmed (LC), unconfirmed (LUC), and control (C) groups, and (3) the variation between groups of acute-phase reactants and biomarkers that can be used as an indicator of disease severity and inflammation.

Materials and Methods  A total of 102 patients undergoing treatment with COVID-19 interim guidelines were evaluated. Reverse transcriptase-polymerase chain reaction (RT-PCR) test was positive in 56 (LC), classified as mild or severe, and negative in 46 (LUC) patients. In addition, 30 healthy subjects (C) with negative RT-PCR tests were also evaluated.

All statistical analyses were performed with the SPSS 22.0 program and the p -values for significant findings were less than 0.05. Parametric/nonparametric distribution was determined by performing the Kolmogorov–Smirnov test for all groups. Student's t -test was used for variables with parametric distribution and the Mann–Whitney U-test for variables with the nonparametric distribution. A cut-off level for biomarkers was determined using the ROC (receiver operator characteristic) curve.

Results  In the LC group, platelet, platecrit, mean platelet volume, platelet diameter width, white blood cell, lymphocyte, eosinophil, neutrophil, immature granulocyte, immature lymphocyte, immature monocyte, large immune cell, and atypical lymphocyte counts among the complete blood count parameters of mature and immature cell counts showed a significant difference according to the C and LUC groups. C-reactive protein, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and C-reactive protein-to-albumin ratio (CAR) indices were significantly elevated in LC patients and were significantly higher in patients classified as severe compared to mild. When CAR optimal cutoff was determined as 0.475, area under the curve was 0.934, sensitivity was 90.91%, specificity was 86.21%, positive predictive value was 92.59%, and negative predictive value was 83.33%. The diagnostic accuracy for CAR was 89.29%.

Conclusion  The CAR index with the highest diagnostic value and the highest predictability could be the most useful biomarker in the diagnosis and evaluation of disease severity in COVID-19 patients.

Association between plasma glycocalyx component levels and poor prognosis in severe influenza type A (H1N1)

Influenza A virus infection causes a series of diseases, but the factors associated with disease severity are not fully understood. Disruption of the endothelial glycocalyx contributes to acute lung injury in sepsis, but has not been well studied in H1N1 influenza. We aim to determine whether the plasma glycocalyx components levels are predictive of disease severity in H1N1 influenza. This prospective observational study included 53 patients with influenza A (H1N1) during the influenza season, and 30 healthy controls in our hospital. Patients were grouped by severity and survival. We collected clinical data and blood samples at admission. Inflammatory factors (tumor necrosis factor-α, interleukin-6, interleukin-10) and endothelial glycocalyx components (syndecan-1, hyaluronan, heparan sulfate) were measured. The plasma levels of syndecan-1, hyaluronan, and heparan sulfate were significantly higher in patients with severe influenza A (H1N1) than in mild cases. Syndecan-1 and hyaluronan were positively correlated with disease severity, which was indicated by the APACHE II and SOFA scores and lactate levels, and negatively correlated with albumin levels. At a cutoff point ≥ 173.9 ng/mL, syndecan-1 had a 81.3% sensitivity and 70.3% specificity for predicting of 28-day mortality. Kaplan–Meier analysis demonstrated a strong association between syndecan-1 levels and 28-day mortality (log-rank 11.04, P = 0.001). Elevated plasma levels of syndecan-1 has a potential role in systemic organ dysfunction and may be indicative of disease severity in patients with influenza A (H1N1).

Cellular Indices and Outcome in Patients with Acute Venous Thromboembolism

Background

Cellular indices provide integrative information about systemic inflammation status which is readily available from routine laboratory parameters. This study aimed to evaluate the prognostic role of three cellular indices in patients with venous thromboembolism (VTE).

Methods

The RIETE registry database was used to determine the association between the baseline neutrophil-to-lymphocyte-ratio (NLR), platelet-to-lymphocyte-ratio (PLR) and systemic-immune-inflammation-index (SII) for 90-day adverse outcomes in patients with acute VTE.

Results

From January 2020 to April 2021, 4487 patients with acute VTE were recruited in the RIETE registry. Of these, 2683 presented with symptomatic pulmonary embolism (PE); 283 with incidental PE; 1129 with lower-limb deep vein thrombosis (DVT); 175 with upper-limb DVT; 69 with splanchnic vein thrombosis; 142 with superficial vein thrombosis and 20 with retinal vein thrombosis. Mean values were: NLR 5.9 ± 7.1, PLR 190 ± 158 and SII 1459 ± 2028. During the first 90-days, 38 patients (0.8%) developed recurrent DVT, 45 (1.0%) had recurrent PE, 152 (3.4%) suffered major bleeding, and 484 (11%) died. On multivariable analysis, patients with NLR >4.41 were at an increased risk for major bleeding and patients with NLR >4.96 were at the risk of death, while those with SII >1134.5 were at increased risk for death.

Conclusions

This study reports the results of a large cohort to date which evaluate the prognostic value of three cellular indices simultaneously in patients with acute VTE. Results support that none of the three baseline cellular indices were sufficient for prediction of VTE recurrences in acute VTE patients. The patients with higher baseline NLR values were at an increased risk of major bleeding or death, those with high SII values were only at an increased risk for mortality.

Dynamic Profile of Prognostic Hematologic Indicators in Patient Under Intensive Care for COVID-19 Disease: A One-Year Study at a Tertiary Care Centre in South India

Introduction

Viral pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS COV-2) releases cytokines which result in neutrophils migration to the bloodstream and cytotoxic effect on lymphocytes. The ongoing pathology is reflected in the derangement of blood cells and the variations and calculations based on them that help in assessing the severity of the disease and prognosis.

Aim

This study aimed to compare the differences in the dynamic changes of the blood cells among survivors and non-survivors of COVID-19 disease so that cut-offs can be arrived at to aid triage at the intensive care unit (ICU) and to predict mortality. 

Material and methods

A one-year study was conducted on patients hospitalized in the ICU. The demography and laboratory values of neutrophils and lymphocytes in percentages and absolute values, and platelet count in numbers were retrieved for eight consecutive values. Neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) was calculated from absolute counts. Statistical analysis was done using the Chi-Square test and Mann-Whitney test and a P-value of <0.05 is considered significant. The comparison was done between survivors and non-survivors.

Result

Among the 3142 patients admitted for COVID-19 disease, 7.6% required ICU care of whom 65.5% survived and 35.5% succumbed to the illness. Survivors were younger and comparable between both sexes. Though both groups had an ascending trend of neutrophils, lymphocytes, NLR, and PLR, the baseline characteristics were significantly lower in those who survived on a day-to-day basis. Neutrophilia above 80%, NLR 7.96, PLR 200 predicted the need for admission in ICU. Neutrophilia of 87% and lymphopenia of 10% were associated with adverse outcomes (mortality). Mortality can be predicted when neutrophil rises above 93% or lymphocytes fall below 5.2%. An initial NLR of 7.96 and PLR of 160 as well as peak NLR of 12.29 and peak PLR 400 predict mortality.

Conclusion

Serial blood counts are essential for hospitalized patients with COVID-19 for early triaging, and to assess severity and prognosis. The NLR of 6.7 and PLR of 160 require intensive care. The dynamic increase of NLR and PLR show worsening of the disease process and NLR of 40.95 and PLR of 400 predict mortality.

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

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

Mild COVID-19 Illness as a Risk Factor for Venous Thromboembolism

Introduction

From the beginning of the current coronavirus disease 2019 (COVID-19) pandemic, there is cumulative evidence suggesting that patients hospitalized due to this disease are at a high risk for venous thromboembolism (VTE). The association between mild non-hospitalized illness and VTE is unclear. The purpose of this research is to assess the association between VTE and mild COVID-19 infection.

Methods

A case-control study was conducted. The cases were adult patients diagnosed with VTE from March 1, 2020 to March 31, 2021. The controls were randomly chosen adult patients who required healthcare services that were equivalent to those of the cases, for any cause, during the same time period, without a VTE diagnosis. To assess the association between mild COVID and VTE, a multivariate logistic regression analysis was conducted, considering other thromboembolic risk variables, such as age, gender and active cancer, among others. A p-value <0.05 was considered statistically significant.

Results

A total of 186 cases and 475 controls were analyzed. There were 21 (11.3%) and 31 (6.5%) patients infected with mild COVID-19 in the previous three months in the groups of cases and controls, respectively. Mild COVID-19 infection was statistically significant as a risk factor for VTE both in the univariate analysis and in the multivariate analysis, OR=1.82 (95% CI 1.02-3.26) and OR=2.62 (95% CI 1.34-5.13), respectively.

Conclusion

Mild COVID-19 infection might be an independent risk factor for VTE. We conclude that the results suggest some thromboprophylaxis strategy should be considered in certain patients with COVID-19 infection in an outpatient fashion. 

Prognostic value of neutrophil-to-lymphocyte ratio in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

Neutrophil-to-lymphocyte ratio (NLR) is an accessible and widely used biomarker. NLR may be used as an early marker of poor prognosis in patients with COVID-19.

OBJECTIVE

To evaluate the prognostic value of the NLR in patients diagnosed with COVID-19.

METHODS

We conducted a systematic review and meta-analysis. Observational studies that reported the association between baseline NLR values (ie, at hospital admission) and severity or all-cause mortality in COVID-19 patients were included. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). Random effects models and inverse variance method were used for meta-analyses. The effects were expressed as odds ratios (ORs) and their 95% confidence intervals (CIs). Small study effects were assessed with the Egger's test.

RESULTS

We analysed 61 studies (n = 15 522 patients), 58 cohorts, and 3 case-control studies. An increase of one unit of NLR was associated with higher odds of severity (OR 6.22; 95%CI 4.93 to 7.84; P < .001) and higher odds of all-cause mortality (OR 12.6; 95%CI 6.88 to 23.06; P < .001). In our sensitivity analysis, we found that 41 studies with low risk of bias and moderate heterogeneity (I2  = 53% and 58%) maintained strong association between NLR values and both outcomes (severity: OR 5.36; 95% CI 4.45 to 6.45; P < .001; mortality: OR 10.42 95% CI 7.73 to 14.06; P = .005).

CONCLUSIONS

Higher values of NLR were associated with severity and all-cause mortality in hospitalised COVID-19 patients.

The Impact of Neutrophil-Lymphocyte Count Ratio in COVID-19: A Systematic Review and Meta-Analysis

Background: The neutrophil–lymphocyte count ratio (NLR) has emerged as a potential prognostic tool for different diseases. In the current coronavirus disease (COVID-19) pandemic, the NLR may be a useful tool for risk scarification and the optimal utilization of limited healthcare resources. However, there is no consensus regarding the optimal value of NLR, and the association with disease severity and mortality. Thus, this study aims to systematically analyze the current evidence of the utility of baseline NLR as a predictive tool for mortality, disease severity in COVID-19 patients. Methods: A compendious screening of electronic databases up to June 15, 2021, was done after enlisting the protocol in PROSPERO (CRD42020202659). Studies evaluating the utility of baseline NLR in COVID-19 are included for this review as per the PRISMA statement. Results: We retrieved a total of 13112 and 12986 COVID-19 patients for survivability and severity over 90 studies. The expired and critically sick patients had elevated baseline NLR on admission, in comparison to survivors and noncritical patients. (SMD = 3.82; 95% CI: 2.79-4.85; I² = 100% and SMD = 1.42; 95% CI: 1.22-1.63; I² = 95%, respectively). The summary receiver operating curve analysis for mortality (AUC = 0.87; 95% CI: 0.86-0.87; I² = 94.7%), and severity (AUC = 0.82; 95% CI: 0.80-0.84; I² = 79.7%) were also suggestive of its significant predictive value. Conclusions: The elevated NLR on admission in COVID-19 patients is associated with poor outcomes.

Complete blood count alterations in COVID-19 patients: A narrative review

Coronavirus disease 2019 (COVID-19) pandemic represents a scientific and social crisis. One of the main unmet needs for coronavirus disease 2019 is its unpredictable clinical course, which can rapidly change in an irreversible outcome. COVID-19 patients can be classified into mild, moderate, and severe. Several haematological parameters, such as platelets, white blood cell total count, lymphocytes, neutrophils, (together with neutrophil-lymphocyte and platelet-lymphocyte ratio), and haemoglobin were described to be associated with COVID-19 infection and severity. The purpose of these review is to describe the current state of the art about complete blood count alterations during COVID-19 infection, and to summarize the crucial role of some haematological parameters during the course of the disease. Decreased platelet, lymphocyte, haemoglobin, eosinophil, and basophil count, increased neutrophil count and neutrophil-lymphocyte and platelet-lymphocyte ratio have been associated with COVID-19 infection and a worse clinical outcome. Our study adds some novelty about the identification of effective biomarkers of progressive disease, and might be helpful for diagnosis, prevention of complications, and effective therapy.