Which hematological markers have predictive value as early indicators of severe COVID-19 cases in the emergency department?

A retrospective cohort study on a novel marker to predict the severity and prognosis of acute cerebral venous thrombosis: D-dimer to fibrinogen ratio

BACKGROUND AND AIM

The D-dimer to fibrinogen ratio (DFR) represents an emerging and significant clinical biomarker. However, its correlation with cerebral venous thrombosis (CVT) remains underexplored. This retrospective cohort study aims to elucidate the association between DFR values and the severity and prognosis of CVT.

METHODS

Severe CVT was defined as the presence of at least 1 of the following risk factors: mental status disorder, coma state, intracranial cerebral hemorrhage, or thrombosis of the deep cerebral venous system. The modified Rankin Scale was utilized to assess functional outcomes. DFR measurements were obtained within 24 h of hospital admission. Logistic regression analysis was employed to determine the prognostic significance of DFR. After Bonferroni correction, a two-tailed P value < 0.017 (0.05/3) was considered statistically significant.

RESULT

A total of 196 patients were included in the study, among whom 85 patients were diagnosed with severe CVT, and 35 and 14 patients experienced short-term and long-term adverse outcomes, respectively. Receiver operating characteristic curve analysis demonstrated that DFR has predictive value for severe CVT, poor short-term and long-term outcomes, with area under the curve values of 0.690 [95% CI: 0.617-0.764, P < .001], 0.773 [95% CI: 0.701-0.845, P < .001], and 0.754 [95% CI: 0.619-0.886, P = .002], respectively. DFR ≥ 0.253 was identified as a significant predictor of severe CVT [adjusted odds ratio (aOR) (95% CI): 2.03 (1.10-3.75), P = .024]. Additionally, DFR ≥ 0.322 and DFR ≥ 0.754 were significantly associated with poor short-term outcomes at discharge [aOR (95% CI): 2.63 (1.43-4.76), P = .002] and poor long-term outcomes at 12 months [aOR (95% CI): 2.86 (1.32-6.25), P = .008], respectively.

CONCLUSION

Elevated DFR is associated with increased severity of CVT. Additionally, higher DFR levels can predict poorer clinical outcomes in CVT.

Predictive ability of complete blood count, mean platelet ratio, mean platelet volume, and neutrophil/lymphocyte ratio for severe pneumonia among RT-PCR or radiologically proven COVID-19 patients

Background

Immuno-inflammatory markers related to white blood cells, and platelets are shown to be associated with COVID-19 infection, and considered to be independent markers for clinical outcomes and mortality. The present study aimed to study the predictive value of these hematologic parameters in progression of COVID-19 to severe pneumonia.

Methods

This was an analytical cross-sectional study conducted among RT-PCR or radiologically proven COVID-19 patients in a tertiary care hospital in Rajasthan. Semi-structured questionnaire was used to collect the epidemiological information of the patients with COVID-19. Complete blood count and other laboratory parameters were also studied among the patients.

Results

Mean age of participants in the study was 52 years, with about 70% being males. Cough and breathlessness were the most common symptoms among the patients. It was found that the parameters related to white blood cells were significantly different between patients with COVID-19 infection and severe pneumonia (except absolute monocyte count). NLR was significantly higher among those with severe pneumonia. In the univariate analysis, age (OR - 1.02), NLR (OR - 1.16), and albumin (OR - 0.45) were found to be significant predictors of progression to severe pneumonia. In the final model, adjusted for confounders, only NLR and albumin levels significantly predicted progression to severe pneumonia among COVID-19 patients.

Conclusion

The study consolidates the predictive ability of NLR for severe pneumonia. It is an important finding, as health facilities with limited access to laboratory investigations can rely on simple markers in routine practice to predict the progression of COVID-19 infection to severe pneumonia.

The association between mean platelet volume and poor outcome in patients with COVID-19: Systematic review, meta-analysis, and meta-regression

Introduction

This study aims to assess the association between mean platelet volume (MPV) and poor outcome in patients with COVID-19.

Methods

We performed a comprehensive literature search using the PubMed, Embase and Scopus databases with keywords "2019-nCoV" OR "SARS-CoV-2" OR "COVID-19" AND "mean platelet volume" OR "MPV" on 8 July 2021. The primary outcome was composite poor outcome, defined as severe COVID-19 or mortality. The pooled effect estimate was reported as mean differences in terms of MPV between the group with and without outcome.

Results

There were 17 studies which consist of 4549 patients with COVID-19 were included in this study. The incidence of poor outcome was 25% (20%-30%). Mean MPV was found to be higher in the poor outcome group in compare to no poor outcome group (10.3 ± 1.9 fL vs 9.9 ± 1.7 fL). The mean MPV difference between both group was 0.47 fL [95% CI 0.27, 0.67], p < 0.001; I2: 62.91%, p < 0.001). In the sub-group analysis, patients with severe COVID-19 had higher MPV (mean difference 0.54 fL [95% CI 0.28, 0.80], p < 0.001; I2: 54.84%, p = 0.014). Furthermore, MPV was also higher in the mortality group (mean difference 0.54 fL [95% CI 0.29, 0.80], p = 0.020; I2: 71.11%, p = 0.004). Meta-regression analysis showed that the association between MPV and poor outcome was not affected by age (p = 0.789), gender (p = 0.167), platelets (p = 0.056), white blood cells (p = 0.639), and lymphocytes (p = 0.733).

Conclusion

This meta-analysis indicated that increased MPV was associated with severity and mortality in patients with COVID-19. Further research is needed to determine the optimum cut-off point.

Systemic inflammation index, disease severity, and mortality in patients with COVID-19: a systematic review and meta-analysis

Introduction

An excessive systemic pro-inflammatory state increases the risk of severe disease and mortality in patients with coronavirus disease 2019 (COVID-19). However, there is uncertainty regarding whether specific biomarkers of inflammation can enhance risk stratification in this group. We conducted a systematic review and meta-analysis to investigate an emerging biomarker of systemic inflammation derived from routine hematological parameters, the systemic inflammation index (SII), in COVID-19 patients with different disease severity and survival status.

Methods

A systematic literature search was conducted in PubMed, Web of Science, and Scopus, between the 1st of December 2019 and the 15th of March 2023. Risk of bias and certainty of evidence were assessed using the Joanna Briggs Institute Critical Appraisal Checklist and the Grades of Recommendation, Assessment, Development and Evaluation, respectively (PROSPERO registration number: CRD42023420517).

Results

In 39 studies, patients with a severe disease or non-survivor status had significantly higher SII values on admission compared to patients with a non-severe disease or survivor status (standard mean difference (SMD)=0.91, 95% CI 0.75 to 1.06, p<0.001; moderate certainty of evidence). The SII was also significantly associated with the risk of severe disease or death in 10 studies reporting odds ratios (1.007, 95% CI 1.001 to 1.014, p=0.032; very low certainty of evidence) and in six studies reporting hazard ratios (1.99, 95% CI 1.01 to 3.92, p=0.047; very low certainty of evidence). Pooled sensitivity, specificity, and area under the curve for severe disease or mortality were 0.71 (95% CI 0.67 to 0.75), 0.71 (95% CI 0.64 to 0.77), and 0.77 (95% CI 0.73 to 0.80), respectively. In meta-regression, significant correlations were observed between the SMD and albumin, lactate dehydrogenase, creatinine, and D-dimer.

Discussion

Our systematic review and meta-analysis has shown that the SII on admission is significantly associated with severe disease and mortality in patients with COVID-19. Therefore, this inflammatory biomarker derived from routine haematological parameters can be helpful for early risk stratification in this group.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420517.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

The Relationship Between Prognosis and Red Cell Distribution Width (RDW) and RDW-Albumin Ratio (RAR) in Patients with Severe COVID-19 Disease

Purpose

The present study aimed to investigate the relationship between prognosis and the red cell distribution width (RDW) and the RDW-albumin ratio (RAR) in patients with coronavirus diseases 2019 (COVID-19), since serum albumin and RDW levels may reflect inflammatory conditions.

Patients and Methods

A total of 289 patients who had been diagnosed with severe COVID-19 in the emergency department were retrospectively analyzed. The RAR levels were calculated by dividing RDW-CV by albumin. Patient groups (survivors, dying patients, those who received mechanical ventilation (MV) support or not, and those who needed vasopressors or not) were compared with regard to RDW-SD, RDW-CV and the RAR levels.

Results

RDW-SD, RDW-CV and the RAR levels were found to be statistically significantly higher in patients who died, and who received MV and vasopressor support, compared to those who survived and did not receive support (p<0.001 for all). In addition, while the cut-off value of RAR was >5.43, the sensitivity was 91.6%, the specificity was 93.7%, NPV was 93.1% and the AUC was 0.965 in predicting mortality (p<0.001). Logistic regression analysis showed that RDW-SD and RAR were independent risk factors for mortality in patients with severe COVID-19.

Conclusion

Elevated RDW and RAR levels at the time of admission may independently predict mortality and the need for vasopressor or MV support.

Mean Platelet Volume as a Predictor of COVID-19 Severity: A Prospective Cohort Study in the Highlands of Peru

Introduction: Although 80% of symptomatic individuals with COVID-19 develop mild forms, it is the severe (15%) and critical (5%) forms that have the greatest impact in the hospital setting. Recognizing markers that can predict severe forms is essential, especially in high-altitude populations. Methods: We conducted a prospective cohort study at 3200 masl (meters above sea level) in a city in Peru to determine if MPV (mean platelet volume) level is a predictor of COVID-19 severity. Patients with mild/moderate disease were enrolled and followed for 21 days or until the development of severe disease (primary outcome). A bivariate analysis was used to identify variables associated with severe disease. A ROC analysis determined the best MPV (mean platelet count) cut-off to predict COVID-19 severity, and then, a multiple regression analysis was performed. Results: 64 patients were enrolled. The median age was 48.5 years (IQT 39–64.5) and the proportion of women was 51.6%, the most frequent symptoms were chest pain (73%), fever (71%), and dyspnea (67%). The median time to develop a severe form from the onset of symptoms was 11 days (IQT 10.5–13). The most common radiographic phase on CT scan (computed tomography) was progressive (60.38%). We observed that an MPV of more than 10.15 fL in the first week of disease predicted severity regardless of age and sex at high altitudes. Conclusions: MPV in the first week of the disease may predict severity in patients diagnosed with COVID-19 at high altitudes; however, we need prospective studies with a larger population and at a different altitude, levels to confirm these findings.

Are platelet volume indices of clinical use in COVID-19? A systematic review

Background

The incidence of thrombotic complications is high in COVID-19 patients with severe disease. As key regulators of thrombus formation, platelets likely play a crucial role as mediators of severe acute respiratory syndrome coronavirus 2 associated pathogenesis. Studies have reported that parameters reflecting platelet size, known as platelet volume indices (PVI), are raised in patients with thrombosis and can predict poor outcomes. This systematic review evaluates the potential for PVI to be used as a predictor of COVID-19 morbidity and mortality.

Methods

English and Chinese databases were searched electronically to identify studies reporting data on mean platelet volume, platelet distribution width or platelet-large cell ratio in COVID-19 patients. Included articles underwent a quality rating and descriptive narrative analysis.

Results

Thirty-two studies were included in the systematic review. The results show a general trend for PVI to be raised in severe COVID-19 patients and non-survivors, with 14 studies reporting significant differences of baseline PVI between severe and mild disease. Nonetheless, longitudinal studies showed varying PVI trends over the course of the disease and evidence for PVI to be associated with disease progression was limited. The quality rating of 12 studies was poor, 16 were rated fair and four were good. Most studies were retrospective in design, used small study populations and did not consider confounding factors that influence platelet volume. Studies also contained technical flaws in PVI measurement, limiting the reliability of the results.

Conclusion

The evidence on the clinical usefulness of PVI is greatly limited by the lack of prospective evaluation, together with technical problems in measuring PVI. Carefully designed prospective studies are warranted.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=304305, identifier CRD42022304305.

Comparison of clinical, laboratory and radiological features in confirmed and unconfirmed COVID-19 patients

Background: We aimed to compare the clinical, laboratory and radiological findings of confirmed COVID-19 and unconfirmed patients. Methods: This was a single-center, retrospective study. Results: Overall, 620 patients (338 confirmed COVID-19 and 282 unconfirmed) were included. Confirmed COVID-19 patients had higher percentages of close contact with a confirmed or probable case. In univariate analysis, the presence of myalgia and dyspnea, decreased leukocyte, neutrophil and platelet counts were best predictors for SARS-CoV-2 RT-PCR positivity. Multivariate analyses revealed that only platelet count was an independent predictor for SARS-CoV-2 RT-PCR positivity. Conclusion: Routine complete blood count may be helpful for distinguishing COVID-19 from other respiratory illnesses at an early stage, while PCR testing is unique for the diagnosis of COVID-19.

Value of the Neutrophil-Lymphocyte Ratio in Predicting COVID-19 Severity: A Meta-analysis

Background

Coronavirus disease 2019 (COVID-19) is highly contagious and continues to spread rapidly. However, there are no simple and timely laboratory techniques to determine the severity of COVID-19. In this meta-analysis, we assessed the potential of the neutrophil-lymphocyte ratio (NLR) as an indicator of severe versus nonsevere COVID-19 cases.

Methods

A search for studies on the NLR in severe and nonsevere COVID-19 cases published from January 1, 2020, to July 1, 2021, was conducted on the PubMed, EMBASE, and Cochrane Library databases. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and area under the curve (AUC) analyses were done on Stata 14.0 and Meta-disc 1.4 to assess the performance of the NLR.

Results

Thirty studies, including 5570 patients, were analyzed. Of these, 1603 and 3967 patients had severe and nonsevere COVID-19, respectively. The overall sensitivity and specificity were 0.82 (95% confidence interval (CI), 0.77-0.87) and 0.77 (95% CI, 0.70-0.83), respectively; positive and negative correlation ratios were 3.6 (95% CI, 2.7-4.7) and 0.23 (95% CI, 0.17-0.30), respectively; DOR was 16 (95% CI, 10-24), and the AUC was 0.87 (95% CI, 0.84-0.90).

Conclusion

The NLR could accurately determine the severity of COVID-19 and can be used to identify patients with severe disease to guide clinical decision-making.