The role of inflammatory indices in the outcome of COVID-19 cancer patients

Impact of comorbidities and inflammatory markers on mortality of COVID-19 patients

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes coronavirus disease 2019 (COVID-19) is a serious global health concern. The severity of the disease can be determined by serologic indicators such as C-reactive protein, lactate dehydrogenase, D-dimer, ferritin, and interleukin-6. (IL-6). Patients with preexisting conditions such as respiratory, cardiovascular, and pulmonary disease could be at risk of adverse outcomes. It is crucial to provide adequate medical care to manage these patients and increase their chances of survival.

AIM

The study examined the impact of comorbidity and inflammatory markers on the severity and mortality of hospitalised COVID-19 patients.

MATERIALS AND METHODS

This retrospective study included 101 COVID-19 patients who had comorbidities and were hospitalised from April 2021 to April 2022.

RESULTS

Patients with a severe COVID-19 infection could be anticipated to have higher levels of inflammatory markers in their blood. Patients with chronic kidney and coronary artery disease have a worse prognosis than those with other comorbidities (P value <0.001). However, tuberculosis had no statistically significant effect on mortality and showed a minimal chance of death (P value = 0.303). In addition, tocilizumab performed poorly and was ineffective against the COVID-19 treatment. However, ivermectin exhibited a statistically significant probability of survival in COVID-19 patients.

CONCLUSION

The inflammatory markers D-dimer, ferritin, and IL-6 were identified as valuable indicators of disease severity. Further, chronic kidney disease and coronary artery disease were identified as risk factors for mortality, while tuberculosis showed potential protective effects. The study showed that higher neutrophil levels were linked to mortality in tocilizumab-treated patients, while ivermectin showed promise in increasing survival rates.

Clinical and inflammation marker features of cancer patients with COVID-19: data of Istanbul, Turkey multicenter cancer patients (2020-2022)

OBJECTIVE

We aimed to identify a rapid, accurate, and accessible biomarker in the early stages of COVID-19 that can determine the prognosis of the disease in cancer patients.

METHODS

A total number of 241 patients with solid cancers who had a COVID-19 diagnosis between March 2020 and February 2022 were included in the study. Factors and ten different markers of inflammation were analyzed by year of diagnosis of COVID-19 and grouped by severity of infection.

RESULTS

Hospitalization, referral to the intensive care unit (ICU), mechanical ventilation, and death were more frequent in 2020 than in 2021 and 2022 (mortality rates, respectively, were 18.8%, 3.8%, and 2.5%). Bilateral lung involvement and chronic lung disease were independent risk factors for severe disease in 2020. In 2021-2022, only bilateral lung involvement was found as an independent risk factor for severe disease. The neutrophil-to-lymphocyte platelet ratio (NLPR) with the highest area under the curve (AUC) value in 2020 had a sensitivity of 71.4% and specificity of 73.3% in detecting severe disease (cut-off > 0.0241, Area Under the Curve (AUC) = 0.842, p <.001). In 2021-2022, the sensitivity of the C-reactive protein-to-lymphocyte ratio (CRP/L) with the highest AUC value was 70.0%, and the specificity was 73.3% (cut-off > 36.7, AUC = 0.829, p = .001).

CONCLUSIONS

This is the first study to investigate the distribution and characteristics of cancer patients, with a focus on the years of their COVID-19 diagnosis. Based on the data from our study, bilateral lung involvement is an independent factor for severe disease, and the CRP/L inflammation index appears to be the most reliable prognostic marker.

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.

Comparison of Lymphocyte-CRP Ratio to Conventional Inflammatory Markers for Predicting Clinical Outcomes in COVID-19

Background: In COVID-19 patients, lymphocyte-CRP ratio (LCR) is a promising biomarker for predicting adverse clinical outcomes. How well LCR performs compared to conventional inflammatory markers for prognosticating COVID-19 patients remains unclear, which hinders the clinical translation of this novel biomarker. Methods: In a cohort of COVID-19 inpatients, we characterised the clinical applicability of LCR by comparing its prognostic value against conventional inflammatory markers for predicting inpatient mortality and a composite of mortality, invasive/non-invasive ventilation and intensive care unit admissions. Results: Of the 413 COVID-19 patients, 100 (24%) patients suffered inpatient mortality. On Receiver Operating Characteristics analysis, LCR performed similarly to CRP for predicting mortality (AUC 0.74 vs. 0.71, p = 0.049) and the composite endpoint (AUC 0.76 vs. 0.76, p = 0.812). LCR outperformed lymphocyte counts (AUC 0.74 vs. 0.66, p = 0.002), platelet counts (AUC 0.74 vs. 0.61, p = 0.003) and white cell counts (AUC 0.74 vs. 0.54, p < 0.001) for predicting mortality. On Kaplan-Meier analysis, patients with a low LCR (below a 58 cut-off) had worse inpatient survival than patients with other LCR values (p < 0.001). Conclusion: LCR appears comparable to CRP, but outperformed other inflammatory markers, for prognosticating COVID-19 patients. Further studies are required to improve the diagnostic value of LCR to facilitate clinical translation.

Neutrophil/Lymphocyte Ratio (NLR) and Lymphocyte/Monocyte Ratio (LMR) - Risk of Death Inflammatory Biomarkers in Patients with COVID-19

Aim

The aim of our retrospective study was search for new prognostic parameters, which can help quickly and cheaply identify patients with risk for severe course of SARS-CoV-2 infection.

Materials and Methods

The following peripheral blood combination biomarkers were calculated: NLR (neutrophil/lymphocytes ratio), LMR (lymphocyte/monocyte ratio), PLR (platelet/lymphocyte ratio), dNLR (neutrophils/(white blood cells - neutrophils)), NLPR (neutrophil/(lymphocyte × platelet ratio)) in 374 patients who were admitted to the Temporary Hospital no 2 of Clinical Hospital in Bialystok (Poland) with COVID-19. The patients were divided into four groups depending on the severity of the course of COVID-19 using MEWS classification.

Results

The NLR and dNLR were significantly increased with the severity of COVID-19, according to MEWS score. The AUC for the assessed parameters was higher in predicting death in patients with COVID-19: NLR (0.656, p=0.0018, cut-off=6.22), dNLR (0.615, p=0.02, cut-off=3.52) and LMR (0.609, p=0.03, cut-off=2.06). Multivariate COX regression analysis showed that NLR median above 5.56 (OR: 1.050, P=0.002), LMR median below 2.23 (OR: 1.021, P=0.011), and age >75 years old (OR: 1.072, P=0.000) had a significant association with high risk of death during COVID-19.

Conclusion

Our results indicate that NLR, dNLR, and LMR calculated on admission to the hospital can quickly and easy identify patients with risk of a more severe course of COVID-19. Increase NLR and decrease LMR have a significant predictive value in COVID-19 patient's mortality and might be a potential biomarker for predicting death in COVID-19 patients.

The Role of Hemogram-derived Ratios in COVID-19 Severity Stratification in a Primary Healthcare Facility

Background

Coronavirus disease 2019 (COVID-19) can cause a wide clinical spectrum, ranging from asymptomatic to severe disease with a high mortality rate. In view of the current pandemic and the increasing influx of patients into healthcare facilities, there is a need to identify simple and reliable tools for stratifying patients.

Objective

Study aimed to analyze whether hemogram-derived ratios (HDRs) can be used to identify patients with a risk of developing a severe clinical form and admission to hospital.

Methods

This cross-sectional and observational study included 500 patients with a confirmed diagnosis of COVID-19. Data on clinical features and laboratory parameters were collected from medical records and 13 HDRs were calculated and analyzed. Descriptive and inferential statistics were included in the analysis.

Results

Of the 500 patients, 43.8% had a severe form of the disease. Lymphocytopenia, monocytopenia, higher C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR) were found in severe patients (p < 0.05). Significantly higher neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), neutrophil-to-platelet ratio (NPR), neutrophil-to-lymphocyte-to-platelet ratio (NLPR) and CRP-to-lymphocyte ratio (CRP/Ly) values were found in severe patients (p < 0.001). In addition, they have statistically significant prognostic potential (p < 0.001). The area under the curve (AUC) for CRP/Ly, dNLR, NLPR, NLR, and NPR were 0.693, 0.619, 0.619, 0.616, and 0.603, respectively. The sensitivity and specificity were 65.7% and 65.6% for CRP/Ly, 51.6% and 70.8 for dNLR, 61.6% and 57.3% for NLPR, 40.6% and 80.4% for NLR, and 48.8% and 69.1% for NPR.

Conclusion

The results of the study suggest that NLR, dNLR, CRP/Ly, NPR, and NLPR can be considered as potentially useful markers for stratifying patients with a severe form of the disease. HDRs derived from routine blood tests results should be included in common laboratory practice since they are readily available, easy to calculate, and inexpensive.

A Novel Nomogram Combined the Aggregate Index of Systemic Inflammation and PIRADS Score to Predict the Risk of Clinically Significant Prostate Cancer

Background

This study is aimed at constructing a nomogram to predict the risk of clinically significant prostate cancer (csPCa) based on the aggregate index of systemic inflammation (AISI) and prostate imaging-reporting and data system version (PIRADS) score.

Methods

Clinical data on patients who had undergone initial prostate biopsy from January 2019 to December 2021 were collected. Patients were randomized in a 7 : 3 ratio to the training cohort and the validation cohort. Potential risk factors for csPCa were identified by univariable and multivariate logistic regression. Nomogram was conducted with these independent risk factors, and calibration curves, the receiver operating characteristic (ROC), and decision curve analysis (DCA) were employed to assess the nomogram's ability for prediction.

Results

A total of 1219 patients were enrolled in this study. Multivariate logistic regression identified that age, AISI, total prostatic specific-antigen (tPSA), free to total PSA (f/tPSA), prostate volume (PV), and PIRADS score were potential risk predictors of csPCa, and the nomogram was developed based on these factors. The area under the curve (AUC) of the training cohort and validation cohort was 0.884 (95% CI: 0.862-0.906) and 0.899 (95% CI: 0.867-0.931). The calibration curves showed that the apparent curves were closer to the ideal curves. The DCA results revealed that the nomogram model seemed to have clinical application value per DCA.

Conclusion

The nomogram model can efficiently predict the risk of csPCa and may assist clinicians in determining if a prostate biopsy is necessary.

The Potential Effect of Royal Jelly on Biomarkers Related to COVID-19 Infection and Severe Progression

Royal jelly is a yellowish to white gel-like substance that is known as a "superfood" and consumed by queen bees. There are certain compounds in royal jelly considered to have health-promoting properties, including 10-hydroxy-2-decenoic acid and major royal jelly proteins. Royal jelly has beneficial effects on some disorders such as cardiovascular disease, dyslipidemia, multiple sclerosis, and diabetes. Antiviral, anti-inflammatory, antibacterial, antitumor, and immunomodulatory properties have been ascribed to this substance. This chapter describes the effects of royal jelly on COVID-19 disease.

Predicting immunoglobulin resistance in Kawasaki disease: an assessment of neutrophil to lymphocyte platelet ratio

BACKGROUND

Kawasaki disease (KD) is an acute febrile illness of unknown etiology and predictors for intravenous immunoglobulin (IVIG) resistance have been widely explored in recent decades. Neutrophil to lymphocyte platelet ratio (NLPR) was reported to be associated with the outcomes in many diseases. However, its relationship with IVIG resistance has not be explored.

METHODS

The medical data of patients diagnosed with KD in Children's Hospital of Soochow University between January 2019 and December 2020 were retrospectively reviewed and analyzed. Patients were trisected into three groups based on NLPR. Logistics regression was used to analyze the association between NLPR and IVIG resistance. Restricted cubic spine was used to exhibit the relationship. Sensitivity analysis and subgroup analysis were also carried out.

RESULTS

A total of 803 patients were included in the present study (61.8% males; median age: 24 months). IVIG resistance occurred in 74 (9.2%) patients. Multivariable-adjusted analyses revealed higher NLPR (odds ratio [95% confidence interval]: 1.12 [1.00-1.24]) was an independent predictor of IVIG resistance, which was strengthened by sensitivity analyses. The association of NLPR and IVIG resistance was not modified by age, sex, CALs, or days of IVIG initiation ≤ 4.

CONCLUSION

NLPR may be a valuable prognostic marker in KD patients with IVIG resistance.

The Prognostic Value of Hematologic Inflammatory Markers in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

The aggregate index of systemic inflammation (AISI), systemic inflammation response index (SIRI), and neutrophil-to-lymphocyte*platelet ratio (NLRP) are novel indices that simultaneously reflect the inflammatory and immune status. However, the role of these indices in acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) remains unclear. We aimed to elucidate the predictive value of AISI, SIRI, and NLRP in patients with ACS undergoing PCI. A total of 1558 patients with ACS undergoing PCI were consecutively enrolled from January 2016 to December 2018. The AISI, SIRI, NLRP, systemic immune-inflammatory index, derived neutrophil-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio cutoff values for predicting major adverse cardiovascular events (MACE) were calculated using receiver-operating characteristic curves, and Spearman's test was used to analyze correlations between these indices. Kaplan-Meier curves and Cox regression models were used for survival analyses, and the endpoint was a MACE, which included all-cause mortality and rehospitalization for severe heart failure during the follow-up period. The Kaplan-Meier curves showed that higher AISI, SIRI, and NLRP values were associated with a higher risk of MACE (all P < .001). The association between AISI, SIRI, and NLRP and ACS prognosis was stable in various subgroups according to sex, age, smoking, dyslipidemia, hypertension, diabetes mellitus, history of stroke, and heart failure (P for interaction > .05). Increasing tertiles of AISI, SIRI, and NLRP significantly increased the MACE risk (P for trend < .05). AISI, SIRI, and NLRP may be suitable laboratory markers for identifying high-risk patients with ACS after PCI.

Detection of abnormal lymphocytes in the peripheral blood of COVID-19 cancer patients: diagnostic and prognostic possibility

BACKGROUND

Peripheral morphological abnormalities play important roles in the early diagnosis and prognosis of the COVID-19 infection. The aim of the present study was to assess the morphological alterations in the peripheral blood (PB) cells in patients with COVID-19 infection, with special attention to a different group of atypical lymphocytes that had been observed in the PB of COVID-19 cancer and non-cancer patients.

METHODS

The PB cells were examined in 84 COVID-19 positive cancer patients, and 20 COVID-19 positive non-cancer patients, compared to 30 healthy normal controls. The data were correlated to the disease severity, patients' clinicopathological features, and outcomes.

RESULTS

There was an increased incidence of giant platelets, neutrophils shifting left, and abnormal monocytes in the COVID-19 positive cancer and non-cancer patients compared to the control group (P < .001, P < .001 and P = .014; respectively). Neutrophils with abnormal toxic granulations, Pseudo Pelger-Heut abnormality, and reactive lymphocytes were significantly increased in COVID-19 cancer patients compared to COVID-19 non-cancer patients and the control group (P = .001, P < .001, and P < .001; respectively). An abnormal form of lymphocytes' morphological changes (Covicytes) was significantly detected in COVID-19 cancer patients [60.7% (51/84)], and in COVID-19 non-cancer patients [55% (11/20)], while it was absent in the normal controls [0.0% (0/30), P < 0.001]. The presence of the Covicytes is associated significantly with a better prognosis in cancer and non-cancer COVID-19 patients.

CONCLUSION

Covicytes could be a useful marker supporting the diagnosis of SARS-COV-2 infection, and it is associated with a favorable prognosis.

The prognostic value of the preoperative inflammatory index on the survival of glioblastoma patients

OBJECTIVES

The growth and development of tumors are closely related to the initiation and amplification of the inflammatory response. Various inflammatory biomarkers had attained growing attention for nearly two decades and were discovered strongly associated with cancer patients' prognosis, indicating that systemic inflammatory response is possibly essential to cancer progression. However, little was known about the sensitive biomarkers associated with the detection, persistence, treatment, and prognosis of GBM. Hence, the retrospective research endeavored to evaluate the prognostic value of preoperative inflammatory biomarkers in patients with GBM who initially received standardized treatment.

METHODS

The 232 glioblastoma patients eligible who were admitted to Qilu Hospitals in Shandong Province from January 2014 to January 2018 were collected for this analysis. Inflammatory markers, including the systemic immune-inflammation index (SII), systemic immune response index (SIRI), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), and albumin/globulin ratio (AGR), were designed. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method, and we calculated the area under the ROC curve to determine the AUC value. Besides, we used the Cox proportional hazard model to estimate the relationship between variables and PFS and OS. The statistical differences between variables and PFS and OS were tested through the log-rank test. What is more, the LR method was used to perform Cox multiple regression analysis. The results were represented by hazard ratio (HR), 95% CI, any 2-tailed P < 0.01 was accepted as statistically different.

RESULTS

The multivariate Cox proportional hazard model presented that SII ≥ 659.1 was an independent risk factor affecting OS (HR = 2.238, 95% CI = 1.471-3.406, P < 0.001) and postoperative PFS (HR = 2.000, 95% CI = 1.472-2.716, P < 0.001) in GBM patients. The 1-, 3-, and 5-year OS of the SII < 659.1 group was 70.8%, 26.9%, and 14.1%, respectively, while the 1- and 3-year OS of the SII ≥ 659.1 group was 37.5% and 11.5% (P < 0.001). The 1-, 3-, and 5-year PFS of the SII < 659.1 group was 36.3%, 19.6%, and 13%, respectively, while the 1-year PFS of the SII ≥ 659.1 group was 11.3% (P < 0.001). Results of patients' clinical and pathological characteristics paraded that in comparison to the lower SII group, the higher SII group had significantly inferior Karnofsky Performance Scale (KPS) scores (P < 0.001) and more frequent cystic changes of the tumors (P < 0.001), whereas the values of SIRI, NLR, PLR, MLR, and AGR were low.

CONCLUSIONS

SII is an independent inflammatory indicator for predicting the prognosis of GBM patients after receiving initially standardized treatments.

The Role of Lymphocyte Subsets, PD-1, and FAS (CD95) in COVID-19 Cancer Patients

Lymphocytes are the main orchestrators that regulate the immune response in SARS-COV-2 infection. The exhaustion of T lymphocytes is a contributing factor to lymphopenia, which is responsible for the COVID-19 adverse outcome. However, it is still not demonstrated on a large scale, including cancer patients. Peripheral blood samples were obtained from 83 SARS-CoV2 infected cancer patients, and 29 COVID-19 infected noncancer patients compared to 28 age-matched healthy controls. Lymphocyte subsets were assessed for CD3, CD4, CD8, CD56, PD-1, and CD95 using flow cytometry. The data were correlated to the patients' clinical features, COVID-19 severity and outcomes. Lymphopenia, and decreased CD4⁺ T cells and CD8⁺ T cells were significantly observed in COVID-19 cancer and noncancer patients compared to the control group (p < 0.001, for all). There was a significantly increased expression of CD95 and PD-1 on the NK cells, CD4⁺ T cells, and CD8⁺ T cells in COVID-19 cancer and noncancer patients in comparison to the control group. The increased expression of CD95 on CD8⁺ T cells, as well as the increased expression of PD-1 on CD8⁺ T cells and NK cells are significantly associated with the severity of COVID-19 infection in cancer patients. The increased expression of CD95 and PD-1 on the CD4⁺ T cells, CD8⁺ T cells, and NK cells was observed significantly in nonsurviving patients and those who were admitted to the intensive care unit in COVID-19 cancer and noncancer patients. The increased expression of PD-1 and CD95 could be possible prognostic factors for COVID-19 severity and adverse outcomes in COVID-19 cancer and noncancer patients.

Neutrophil Counts, Neutrophil-to-Lymphocyte Ratio, and Systemic Inflammatory Response Index (SIRI) Predict Mortality after Off-Pump Coronary Artery Bypass Surgery

Background: Several perioperative inflammatory markers are postulated to be significant factors for long-term survival after off-pump coronary artery bypass surgery (OPCAB). Hematological parameters, whether single or combined as indices, provide higher predictive values. Methods: The study group comprised 538 consecutive patients (125 (23%) females and 413 (77%) males) with a mean age of 65 ± 9 years, who underwent OPCAB with a mean follow-up time of 4.7 ± 1.7 years. This single-center retrospective analysis included perioperative inflammatory markers such as the neutrophil-to-lymphocyte ratio (NLR), systemic inflammatory response index (SIRI), aggregate index of systemic inflammation (AISI), and systemic inflammatory index (SII). Results: Multivariable analysis identified levels of neutrophils above 4.3 × 109/L (HR 13.44, 95% CI 1.05−3.68, p = 0.037), values of SIRI above 5.4 (HR 0.29, 95% CI 0.09−0.92, p = 0.036) and values of NLR above 3.5 (HR 2.21, 95% CI 1.48−3.32, p < 0.001) as being significant predictors of long-term mortality. The multifactorial models revealed the possibility of strong prediction by combining preoperative factors (COPD, stroke, PAD, and preoperative PLR) and postoperative neutrophil counts (p = 0.0136) or NLR (p = 0.0136) or SIRI (p = 0.0136). Conclusions: Among the postoperative inflammatory indices, the levels of neutrophils, NLR, and SIRI are the most prominent markers for long-term survival after off-pump coronary artery bypass surgery, when combined with preoperative characteristics.