Are the Derived Indexes of Peripheral Whole Blood Cell Counts (NLR, PLR, LMR/MLR) Clinically Significant Prognostic Biomarkers in Multiple Myeloma? A Systematic Review And Meta-Analysis

Evaluation of Platelet Indices and Inflammation Markers in Preeclampsia

Background: Preeclampsia is a serious pregnancy complication known to be related to the pathophysiology of platelet dysfunction and inflammation. The aim of this study was to investigate the role of platelet indices and inflammatory markers in preeclampsia and their importance in predicting adverse neonatal outcomes. Methods: A total of 118 preeclampsia cases (84 with mild preeclampsia and 34 with severe preeclampsia) and 118 healthy pregnant women were included in the study. Blood samples obtained at the time of preeclampsia diagnosis were analyzed for platelet indices (platelet count (PC), platelet distribution width (PDW), mean platelet volume (MPV), and platelet/large cell ratio (P-LCR)) and inflammation indices (neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and mean platelet volume-to-lymphocyte ratio (MPVLR)). Results: The PC and PLR were lower in the severe preeclampsia group compared to the other groups. The PDW was higher in both mild and severe preeclampsia groups compared to the control group. A PDW value above 13.15 was identified as a significant predictor of composite adverse neonatal outcomes (area under the curve (AUC): 0.633; sensitivity: 60.9%; specificity: 58%). Conclusions: PC and PLR decrease in severe preeclampsia. This study highlights the potential of PDW as a marker for predicting adverse neonatal outcomes in preeclampsia.

The prognostic value of the platelet-to-lymphocyte ratio in multiple myeloma patients treated with a bortezomib-based regimen

Multiple myeloma (MM) is the second most common hematological malignancy. Previous studies have validated the prognostic significance of the platelet-to-lymphocyte ratio (PLR) in patients with certain solid tumors. However, the relationship between the PLR and prognosis in myeloma patients has not been clearly demonstrated. In our study, we included 122 newly diagnosed MM patients who were treated with bortezomib-based chemotherapy. These patients were divided into low-PLR and high-PLR groups based on their initial PLR values. We compared the clinical characteristics between the two groups and utilized restricted cubic splines (RCSs) in the regression model to estimate the nonlinear relationship between the initial PLR and overall survival (OS) in MM patients. The results showed that patients in the low-PLR group had significantly worse OS (P = 0.00031) and progression-free survival (PFS) (P < 0.0001) compared to those in the high-PLR group. Furthermore, within the higher-risk MM group, a low PLR was also associated with worse OS (P = 0.0037) and PFS (P = 0.0048). Therefore, a low PLR was identified as an independent predictor of poor OS in MM patients. The RCS curves further confirmed a significant nonlinear relationship between the PLR and OS in patients with MM. The PLR may serve as a significant independent prognostic indicator for MM patients undergoing bortezomib-based chemotherapy, and there exists a crucial nonlinear relationship between the PLR and OS in these patients.

Multivariate analysis of blood parameters for predicting mortality in patients with hip fractures

The present retrospective cross-sectional study aimed to evaluate the predictive value of blood parameters and ratios for predicting mortality in patients with hip fractures. In total, 758 patients with hip fractures attending the Department of Orthopedics and Traumatology, Kütahya Health Sciences University Faculty of Medicine (Kütahya, Turkey) between January 2016 and January 2023 were included in the present study. Patients were then divided into two groups, namely the mortality (n=464; 61.2%) and survivor (n=294; 38.8%) groups. Patients in the mortality group were further sub-divided into the following three subgroups: i) Those who succumbed in <1 month (n=117; 25.2%); ii) those who succumbed between 1 and 12 months (n=185; 39.9%); and iii) those who succumbed >12 months later (n=162; 34.9%). In addition, the RDW coefficient of variation, mean platelet volume (MPV), MPV/platelet ratio, neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio (PLR), mean platelet volume-to-lymphocyte ratio and monocyte-to-eosinophil ratio means were all found to be significantly higher in the mortality group (P<0.05). MPV (P<0.01), HGB (P<0.05), eosinophil, EOS (P<0.01), HRR (P<0.01), and PLR (P<0.05) were all revealed to exert significant effects on mortality. An age cut-off of 74.50 years had a sensitivity of 81.5% and specificity of 37.1%, whereas an MPV cut-off of 8.85 yielded a sensitivity of 73.5% and specificity of 36.1%. By contrast, an HGB cutoff of 11.05 had a sensitivity of 55.6% and specificity of 35.7%, an eosinophil cut-off of 0.065 had a sensitivity of 47.6% and specificity of 35.4%, whilst a HRR cut-off of 0.7587 had a sensitivity of 55.2% and specificity of 30.3%. Furthermore, a PLR cut-off of 152.620 had a sensitivity of 67.2% and specificity of 41.8% for hip fracture-associated mortality. An age cut-off of 79.50 years had a sensitivity of 70.9% and specificity of 41.5%, while an age cut-off of 83.50 years had a sensitivity of 46.2% and specificity of 64.0% for mortality occurring <1 month after hip fractures. To conclude, results from the present study suggested that HRR has potential predictive value for hip fracture-associated mortality and 30-day mortality, whereas the PLR could only predict hip fracture-associated mortality.

Can some inflammatory parameters predict the survival of patients with malignant pleural effusion?

Background

This study aimed to investigate whether there is a correlation between some serum inflammatory markers and the survival of patients with malignant pleural effusions (MPEs).

Methods

The prospective study included 125 patients (67 males, 58 females; median age: 62 years; range, 40 to 92 years) who underwent thoracentesis for pleural effusion between January 2020 and December 2021. An overall survival analysis was performed, and survival differences between the groups were investigated. The cutoff value of the inflammatory parameters associated with mortality was determined by receiver operating characteristic analysis.

Results

Median survival after detection of MPE was six months, and three- and five-year overall survivals were 16% and 4%, respectively. There was a significant correlation between the ECOG (Eastern Cooperative Oncology Group) score of the patients and the median survival. Serum C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), fluid albumin, and serum lactate dehydrogenase (LDH)-to-pleural LDH ratio and survival had a statistically significant relationship in receiver operating characteristic analysis. Threshold values were determined accordingly. Poor prognostic factors that were found to be statistically significant were high CRP (p=0.001), high NLR (p=0.001), high PLR (p=0.02), and high serum LDH-to-pleural LDH ratio (p=0.04).

Conclusion

Some serum inflammatory markers, including high CRP, high NLR, high PLR, and high serum LDH-to-pleural LDH ratio, can be a simple and inexpensive method in predicting prognosis in patients with MPE.

The potential predictive value and relationship of blood-based inflammatory markers with the clinical symptoms of Han Chinese patients with first-episode adolescent-onset schizophrenia

Background

Inflammation is associated with the pathophysiology of schizophrenia. The blood markers for systemic inflammation include neutrophil-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), lymphocyte-monocyte ratio (LMR), system inflammation response index (SIRI), and platelet-lymphocyte ratio (PLR). However, these inflammation markers and their relationships with clinical phenotypes among Han Chinese patients with first-episode adolescent-onset schizophrenia (AOS) is unclear. This investigation aimed to elucidate the impact of inflammation on Han Chinese AOS patients as well as the association of blood-based inflammation markers with clinical symptoms.

Methods

Altogether, 203 Han Chinese individuals participated in this study, 102 first-episode AOS patients and 101 healthy controls. The assessment of inflammatory indices was based on complete blood cell count. Furthermore, schizophrenia-related clinical symptoms were evaluated using the five-factor model of the Positive and Negative Syndrome Scale (PANSS).

Results

In Han Chinese first-episode AOS patients, levels of SIRI, PLR, SII, and NLR were significantly increased (p < 0.001), while LMR decreased (p < 0.001) compared to healthy controls. Furthermore, multivariate logistic regression showed that LMR, NLR, SII, and SIRI (all p < 0.05) were independently associated with AOS. Moreover, Receiver operating characteristics assessment indicated that NLR, SIRI, LMR, and SII could effectively distinguish AOS patients from healthy controls. Their areas under the curves were 0.734, 0.701, 0.715, and 0.730 (all p < 0.001). In addition, Correlation analysis revealed that LMR was negatively correlated with the PANSS total, negative, and cognitive factor scores (all p < 0.05); NLR was positively correlated with the cognitive factor score (p < 0.01); SII was negatively correlated with the positive factor score and positively with the negative and cognitive factor scores (all p < 0.05); SIRI was positively correlated with the PANSS total and cognitive factor scores (all p < 0.01).

Conclusions

This research established the involvement of peripheral blood inflammatory markers (LMR, NLR, SII, and SIRI) with the clinical manifestations and pathophysiology of schizophrenia, and these can serve as screening tools or potential indices of the inflammatory state and AOS symptoms severity.

An elevated systemic inflammation index is related to an inferior response to pomalidomide and dexamethasone treatment in patients with relapsed and refractory multiple myeloma

INTRODUCTION

The systemic inflammation index (SII) might serve as an indicator of the equilibrium between the inflammatory and immune responses. The aim of the study was to determine the clinical value and prognostic significance of SII in the cohort of multiple myeloma (MM) patients treated with a regimen of pomalidomide and dexamethasone (Pd).

MATERIAL AND METHODS

This retrospective, real-life study included patients who received a Pd regimen in our centre between November 2018 and July 2022. The systemic inflammation index was calculated from peripheral blood counts of platelets, neutrophils, and lymphocytes collected shortly before commencement of Pd treatment using the equation: SII = N × P/L, where N, P, and L are the respective counts per litre of peripheral blood for neutrophils, platelets, and lymphocytes.

RESULTS

The study group consisted of 54 patients. Most patients received Pd as the third (38.9%) or fourth (37.0%) line of treatment. The median number of completed treatment cycles was 5 (IQR: 1-12). The median progression-free survival (PFS) was 6.8 months and overall survival (OS) 14.8 months. High SII (> 374) was an independent prognostic factor for PFS (HR = 3.0, 95% CI: 1.4-6.3, p < 0.01) and OS (HR = 2.2, 95% CI: 1.0-4.6, p = 0.04). In the low SII group, the respective median PFS and OS values were 9.6 and 21.7 months, compared to 2.6 (p = 0.018) and 5.5 months (p = 0.035) in the high SII group.

CONCLUSIONS

The systemic inflammation index has prognostic significance in MM patients treated with Pd. A high SII predicts a poorer outcome in pretreated MM patients undergoing Pd treatment evaluation. As such, it may well be a key factor for guiding subsequent treatment decisions.

Machine learning for identifying benign and malignant of thyroid tumors: A retrospective study of 2,423 patients

Thyroid tumors, one of the common tumors in the endocrine system, while the discrimination between benign and malignant thyroid tumors remains insufficient. The aim of this study is to construct a diagnostic model of benign and malignant thyroid tumors, in order to provide an emerging auxiliary diagnostic method for patients with thyroid tumors. The patients were selected from the Chongqing General Hospital (Chongqing, China) from July 2020 to September 2021. And peripheral blood, BRAFV600E gene, and demographic indicators were selected, including sex, age, BRAFV600E gene, lymphocyte count (Lymph#), neutrophil count (Neu#), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), red blood cell distribution width (RDW), platelets count (PLT), red blood cell distribution width-coefficient of variation (RDW-CV), alkaline phosphatase (ALP), and parathyroid hormone (PTH). First, feature selection was executed by univariate analysis combined with least absolute shrinkage and selection operator (LASSO) analysis. Afterward, we used machine learning algorithms to establish three types of models. The first model contains all predictors, the second model contains indicators after feature selection, and the third model contains patient peripheral blood indicators. The four machine learning algorithms include extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), and adaptive boosting (AdaBoost) which were used to build predictive models. A grid search algorithm was used to find the optimal parameters of the machine learning algorithms. A series of indicators, such as the area under the curve (AUC), were intended to determine the model performance. A total of 2,042 patients met the criteria and were enrolled in this study, and 12 variables were included. Sex, age, Lymph#, PLR, RDW, and BRAFV600E were identified as statistically significant indicators by univariate and LASSO analysis. Among the model we constructed, RF, XGBoost, LightGBM and AdaBoost with the AUC of 0.874 (95% CI, 0.841-0.906), 0.868 (95% CI, 0.834-0.901), 0.861 (95% CI, 0.826-0.895), and 0.837 (95% CI, 0.802-0.873) in the first model. With the AUC of 0.853 (95% CI, 0.818-0.888), 0.853 (95% CI, 0.818-0.889), 0.837 (95% CI, 0.800-0.873), and 0.832 (95% CI, 0.797-0.867) in the second model. With the AUC of 0.698 (95% CI, 0.651-0.745), 0.688 (95% CI, 0.639-0.736), 0.693 (95% CI, 0.645-0.741), and 0.666 (95% CI, 0.618-0.714) in the third model. Compared with the existing models, our study proposes a model incorporating novel biomarkers which could be a powerful and promising tool for predicting benign and malignant thyroid tumors.