Platelet-to-neutrophil Ratio Research Articles

Neutrophil-platelet ratio as a predictor of acute kidney injury in severe COVID-19.

Acute kidney injury (AKI) is one of the most seen complications of coronavirus-2019 (COVID-19) infection. Patients with AKI caused by COVID-19 likely have higher neutrophil counts and lower platelet and lymphocyte levels. Therefore, the predictive value of many inflammation indexes calculated from the total blood count has been investigated to predict the AKI in COVID-19. According to our clinical experience, we thought that neutrophilia and thrombocytopenia may be more common in the development of AKI. For this reason, this study aimed to evaluate the predictive value of the neutrophil-to-platelet ratio (NPR) for AKI in severe COVID-19 patients. This retrospective study included 334 severe COVID-19 patients followed up in the intensive care unit (ICU). Predictive factors for AKI were analyzed. ROC curve analysis was performed to determine the inflammation indexes' cutoff values for the AKI prediction. Multivariate analyses were performed to determine correlations between the inflammation indexes and AKI. In this study, AKI was determined at the rate of 43% (n:145). Independent risk factors affecting AKI were determined to be age (HR = 1.047, 95% confidence interval [CI]: 1.021-1.072, P < .001), the need for invasive mechanical ventilation (HR = 3.003, 95% CI: 1.645-5.481, P = .001) and the need for vasopressor (HR = 8.111, 95% CI: 3.786-17.375, P < .001). The optimal cutoff values predicting AKI were determined to be 3.9 for the NPR (AUC = 0.679, 95% CI: 0.622-0.737, P < .001) with 71.7% sensitivity and 61.9% specificity, 16.1 for the neutrophil-to-lymphocyte ratio (NLR) (AUC = 0.634, 95% CI: 0.575-0.694, P < .001) with 65.5% sensitivity and 56.1% specificity, and 3872.5 × 109L for the systemic inflammatory index (SII) (AUC = 0.566, 95% CI: 0.504-0.629, P = .038) with 60% sensitivity and 55.6% specificity. In the regression model, only NPR values above the cutoff were related to AKI (HR = 3.817, 95% CI: 1.782-8.177, P = .001). The NPR has more predictive value than the NLPR, NLR, and SII in developing AKI in severe COVID-19 patients in the ICU. NPR is a new helpful index that can help clinicians predict early AKI in critical COVID-19.

Platelet-Related Indicators: Potential Role in Early Prediction of Bronchopulmonary Dysplasia

Background: Bronchopulmonary dysplasia (BPD) is the most common chronic respiratory disease in extreme preterm infants, and inflammation is the main contributor that initiates this lung injury. As platelet-related indicators such as platelet-to-lymphocyte ratio (PLR) and platelet-to-neutrophil ratio (PNR) are potential systemic inflammatory biomarkers and it has been shown to be good predictors of lung diseases. The objective of this study was to assess the potential role of platelet-related indicators in early prediction for BPD. Methods: Neonates with gestational ages &lt;32 weeks (w) from two tertiary neonatal intensive care units between January 2019 and April 2022 were included and the association between the platelet-related indicators and BPD were analyzed by logistic regression analysis and receiver operating characteristic curve. Results: 533 preterm infants were admitted, including 165 preterm infants with BPD and 368 preterm infants without BPD. The infants in the BPD group had higher PLR and PNR at birth, lower platelet (P) count at 2 w than those in the without BPD group. The high PLR at birth, high PNR at birth and low P at 2 w were independently associated with the risk of BPD. PLR at birth represented a predictive value for BPD with the area under the curve (AUC) being 0.589, sensitivity was 0.661, and specificity was 0.579 when the threshold was 135.33. PNR at birth represented a predictive value for BPD with the AUC being 0.576, sensitivity was 0.612, and specificity was 0.589 when the threshold was 129.12. P at 2 w represented a predictive value for BPD with the AUC being 0.668, sensitivity was 0.548, and specificity was 0.711 when the threshold was 285.5. The predictive value of the model was improved when including PLR at birth, PNR at birth, P at 2 w, and gestational age, with AUC being 0.798, sensitivity was 0.754, and specificity was 0.737. Conclusions: Combining PLR at birth, PNR at birth, P at 2 w, and gestational age improved the value in early prediction of BPD.

Changes in systemic immune-inflammation index (SII) predict the prognosis of patients with hepatitis B-related hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors.

This study aimed to evaluate the prognostic significance of changes in inflammatory markers in patients with Hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) treated with first-line lenvatinib plus a programmed cell death protein 1 (PD-1) inhibitor. This study retrospectively included 117 HBV-HCC patients treated with first-line lenvatinib in combination with a PD-1 inhibitor. Independent factors affecting progression-free survival (PFS) and overall survival (OS) were explored based on baseline indicators and inflammatory markers changes after one treatment cycle. Multivariate analysis revealed that an alpha-fetoprotein (AFP) level 400 ng/mL [hazard ratio (HR), 1.69; 95% confidence interval (CI), 1.11-2.58; P = 0.01] was identified as an independent risk factor, platelet-to-neutrophil ratio (PNR) 65.43 (HR 0.50; 95% CI 0.30-0.84; P and SII 539.47 (HR 0.54; 95% CI 0.30-0.96; P = 0.03) were identified as independent protective factors for PFS. Additionally, multivariate analysis demonstrated that AFP 400 ng/mL, HBV-HCC patients with diabetes mellitus (DM), and SII were independent risk factors of OS. The patients whose SII had increased after one cycle of treatment showed a poorer PFS (HR 1.61; 95 %CI 1.10-2.37; P = 0.015) and OS (HR 1.76; 95 % CI 1.15-2.70; P = 0.009) than patients whose SII had decreased. The objective response rate (ORR) was higher in the SII-decreased patients (47.5% vs 32.5%, P = 0.11). Mann-Whitney test found a significant difference in therapeutic response between the SII-increased patients and the SII-decreased patients (P = 0.04). SII can be associated with outcomes in patients with HBV-HCC treated with first-line lenvatinib plus PD-1 inhibitors.

Clostridioides difficile Infection: Use of Inflammatory Biomarkers and Hemogram-Derived Ratios to Predict Mortality Risk in Hospitalized Patients.

Clostridioides difficile infection (CDI) is a significant cause of mortality, especially in healthcare environments. Reliable biomarkers that can accurately predict mortality in CDI patients are yet to be evaluated. Our study aims to evaluate the accuracy of several inflammatory biomarkers and hemogram-derived ratios in predicting mortality in CDI patients, such as the neutrophil-to-lymphocyte ratio (NLR), the systemic immune-inflammation index (SII), the platelet-to-neutrophil ratio (PNR), the derived neutrophil-to-lymphocyte ratio (dNLR), C-reactive protein (CRP), the platelet-to-lymphocyte ratio (PLR), and procalcitonin (PCT). NLR showed a sensitivity of 72.5% and a specificity of 58.42% with an area under curve (AUC) = 0.652. SII had a sensitivity of 77.5%, a specificity of 54.74%, and an AUC = 0.64. PNR, neutrophils, dNLR, and lymphocytes had lower AUCs which ranged from 0.595 to 0.616, with varied sensitivity and specificity. CRP, leukocytes, and platelets showed modest predictive values with AUCs below 0.6. PCT had a sensitivity of 100%, a low specificity of 7.41%, and an AUC = 0.528. We conducted a retrospective analysis of CDI patients from two different hospital settings in Italy and Romania during the COVID-19 pandemic, from 1 January 2020 to 5 May 2023. Statistical analyses included t-tests, Wilcoxon rank-sum tests, χ2 tests, and multivariate logistic regression to identify predictors of mortality. ROC analysis assessed the accuracy of biomarkers and hemogram-derived ratios. A p value < 0.05 was considered significant. Neutrophils, dNLR, NLR, SII, and PNR are valuable biomarkers for predicting mortality in CDI patients. Understanding these predictors can improve risk stratification and clinical outcomes for CDI patients.

Predictive Value of Lymphocyte-to-Neutrophil Ratio and Platelet-to-Neutrophil Ratio on PD-L1 Expression in Lung Cancer.

This study aimed to examine the predictive effect of the lymphocyte-to-neutrophil ratio (LNR) and the platelet-to-neutrophil ratio (PNR) on the expression of programmed death receptor ligand 1 (PD-L1) in patients diagnosed with lung cancer. The clinical records of 86 patients diagnosed with lung cancer between January 2020 and February 2022 at Fu Yang People's Hospital were retrospectively analyzed. The records included information on age, gender, smoking history, hematological indices at the time of admission, staging of the lung malignancy, histopathological subtype, comorbidities, and the expression levels of PD-L1. Patients were stratified into two distinct cohorts based on their PD-L1 expression levels: Those with an expression level greater than or equal to 1% were classified into the PD-L1 positive expression group, while the remainder were categorized as the PD-L1 negative expression group. Univariate analysis and multivariate logistic regression analysis were used to identify the influencing factors of PD-L1, and the diagnostic efficacy was calculated using the receiver operating characteristic (ROC) curve. Upon analysis, the PD-L1 positive expression group manifested notably lower values as compared to their counterparts in the PD-L1 negative expression group (LNR: 0.262 ± 0.105 vs. 0.390 ± 0.201; PNR: 41.03 [29.64, 50.11] vs. 49.50 [37.38, 73.83]), and these differences were statistically significant. There was a notable disparity in PD-L1 expression based on gender, with males exhibiting a statistically significant higher positivity rate compared to females. Furthermore, patients in Stages I-III of the disease demonstrated a markedly elevated PD-L1 positivity rate compared to those in Stage IV (p < 0.05). Incorporating univariates with statistical differences into multivariate logistic regression analysis suggests that stage and LNR are independent risk factors for PD-L1 negative expression. ROC curve analyses revealed that the area under the ROC curve (AUC) for LNR as an indicator for PD-L1 positive expression stood at 0.706, while the AUC for PNR was calculated at 0.687. PD-L1 expression is correlated with gender and lung cancer staging, and LNR and PNR have a predictive value for PD-L1 expression.

The prognostic value of the combined neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-platelet ratio (NPR) in sepsis

Sepsis is a severe disease characterized by high mortality rates. Our aim was to develop an early prognostic indicator of adverse outcomes in sepsis, utilizing easily accessible routine blood tests. A retrospective analysis of sepsis patients from the MIMIC-IV database was conducted. We performed univariate and multivariate regression analyses to identify independent risk factors associated with in-hospital mortality within 28 days. Logistic regression was utilized to combine the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-platelet ratio (NPR) into a composite score, denoted as NLR_NPR. We used ROC curves to compare the prognostic performance of the models and Kaplan–Meier survival curves to assess the 28 day survival rate. Subgroup analysis was performed to evaluate the applicability of NLR_NPR in different subpopulations based on specific characteristics. This study included a total of 1263 sepsis patients, of whom 179 died within 28 days of hospitalization, while 1084 survived beyond 28 days. Multivariate regression analysis identified age, respiratory rate, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelet ratio (NPR), hypertension, and sequential organ failure assessment (SOFA) score as independent risk factors for 28 day mortality in septic patients (P < 0.05). Additionally, in the prediction model based on blood cell-related parameters, the combined NLR_NPR score exhibited the highest predictive value for 28 day mortality (AUC = 0.6666), followed by NLR (AUC = 0.6456) and NPR (AUC = 0.6284). Importantly, the performance of the NLR_NPR score was superior to that of the commonly used SOFA score (AUC = 0.5613). Subgroup analysis showed that NLR_NPR remained an independent risk factor for 28 day in-hospital mortality in the subgroups of age, respiratory rate, and SOFA, although not in the hypertension subgroup. The combined use of NLR and NPR from routine blood tests represents a readily available and reliable predictive marker for 28 day mortality in sepsis patients. These results imply that clinicians should prioritize patients with higher NLR_NPR scores for closer monitoring to reduce mortality rates.

Correlation between novel inflammatory markers and carotid atherosclerosis: A retrospective case-control study.

Carotid atherosclerosis is a chronic inflammatory disease, which is a major cause of ischemic stroke. The purpose of this study was to analyze the relationship between carotid atherosclerosis and novel inflammatory markers, including platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to neutrophil ratio (PNR), neutrophil to lymphocyte platelet ratio (NLPR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI), in order to find the best inflammatory predictor of carotid atherosclerosis. We included 10015 patients who underwent routine physical examinations at the physical examination center of our hospital from January 2016 to December 2019, among whom 1910 were diagnosed with carotid atherosclerosis. The relationship between novel inflammatory markers and carotid atherosclerosis was analyzed by logistic regression, and the effectiveness of each factor in predicting carotid atherosclerosis was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC). The level of PLR, LMR and PNR in the carotid atherosclerosis group were lower than those in the non-carotid atherosclerosis group, while NLR, NLPR, SII, SIRI and AISI in the carotid atherosclerosis group were significantly higher than those in the non-carotid atherosclerosis group. Logistic regression analysis showed that PLR, NLR, LMR, PNR, NLPR, SII, SIRI, AISI were all correlated with carotid atherosclerosis. The AUC value of NLPR was the highest, which was 0.67, the cut-off value was 0.78, the sensitivity was 65.8%, and the specificity was 57.3%. The prevalence rate of carotid atherosclerosis was 12.4% below the cut-off, 26.6% higher than the cut-off, and the prevalence rate increased by 114.5%. New inflammatory markers were significantly correlated with carotid atherosclerosis, among which NLPR was the optimum inflammatory marker to predict the risk of carotid atherosclerosis.

Is it possible to identify COVID-19 infection-related biomarkers during pregnancy?: A prospective study in Turkish population.

The coronavirus disease 2019 (COVID-19) has raised concerns about the potential complications it may cause in pregnant women. Therefore, biomarkers that can predict the course of COVID-19 in pregnant women may be of great benefit as they would provide valuable insights into the prognosis and, thus, the management of the disease. In this context, the objective of this study is to identify the biomarkers that can predict COVID-19 progression in pregnant women, focusing on composite hemogram parameters and systemic inflammatory and spike markers. The population of this single-center prospective case-control study consisted of all consecutive pregnant women with single healthy fetuses who tested positive for COVID-19 and who were admitted to Bakirköy Dr Sadi Konuk Training and Research Hospital in Istanbul, Turkey, a COVID-19 referral hospital, between April 2020 and March 2021, with an obstetric indication, during their second or third trimester. The control group consisted of consecutive pregnant women with a single healthy fetus who were admitted to the same hospital within the same date range, had demographic and obstetric characteristics matching the patient group, but tested negative for COVID-19. The patient and control groups were compared in terms of platelet-to-lymphocyte ratio (PLR), platelet-to-neutrophil ratio (PNR), and neutrophil-to-lymphocyte ratio (NLR), and systemic inflammatory and spike markers, including C-reactive protein (CRP), interleukin-6 (IL-6), interleukin-10 (IL-10), cluster of differentiation 26 (CD26), and B7 homolog 4 (B7H4). There were 45 (51.1%) and 43 (48.8%) pregnant women in the patient and control groups, respectively. There was no significant difference between the groups in demographic and obstetric characteristics (P > .05). The PNR, PLR, and CRP values were significantly higher in the patient group than in the control group (P < .05). On the other hand, there was no significant difference between the groups in IL-6, IL-10, CD26, and B7H4 levels (P > .05). The findings of our study showed that specific inflammatory markers, such as CRP, PLR, and PNR, can potentially predict the course of COVID-19 in pregnant women. However, more comprehensive, well-controlled studies are needed to corroborate our study's findings and investigate other potential inflammatory markers.

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms.

This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model's performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients' average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses.

An inflammation-based model for identifying severe acute pancreatitis: a single-center retrospective study

Background and aimsSevere acute pancreatitis (SAP) is potentially lethal. Considering the role of inflammation in the progression of acute pancreatitis (AP), this study aims to develop a model based on inflammatory indexes for identifying the presence of SAP.MethodsOverall, 253 patients with AP who were consecutively admitted between July 2018 and November 2020 were screened, of whom 60 had SAP. Systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-platelet ratio (NPR), systemic inflammation response index (SIRI), platelet-to-albumin ratio (PAR), C-reactive protein-to-albumin ratio (CAR), C-reactive protein-to-lymphocyte ratio (CLR), and triglyceride glucose (TyG) index were calculated. Multivariate logistic regression analyses were performed to identify independent risk factors of SAP. Then, inflammation-based models were established. Receiver operating characteristics (ROC) curve analyses were performed. Area under ROC curve (AUROC) was calculated.ResultsDiabetes mellitus, fatty liver, high white blood cell count (WBC), C-reactive protein (CRP), red blood cell distribution width (RDW), procalcitonin (PCT), SII, NLR, NPR, CAR, CLR, and TyG index, and a low LMR were significantly associated with SAP. Considering the collinearity among these variables, 10 multivariate logistic regression analyses were separately performed. Finally, four independent inflammation-based models were established. Of them, the best one, which was calculated as follows: 1.204*fatty liver (yes = 1; no = 0) + 0.419*PCT + 0.005*CLR - 2.629, had an AUROC of 0.795 with a specificity of 73.4% and a sensitivity of 71.7%.ConclusionThe inflammation-based model consisting of fatty liver, PCT, and CLR has a good diagnostic performance for SAP.

Predictive role of neutrophil-to-platelet ratio in futile recanalization of patients after endovascular therapy.

Aim: To explore the association between the neutrophil-to-platelet ratio (NPR) and futile recanalization (FR) in patients with acute ischemic strokedue to large vascular occlusions after endovascular therapy (EVT). Methods: FR after EVT was defined as a poor 90-day prognosis (modified Rankin scale [mRS] score ≥3) despite successful reperfusion (modified thrombolysis in cerebral infarction grade 2b-3). Patients were divided into high NPR (>35; n=115) and low NPR (≤35; n=81) groups. Results: The FR rate was significantly higher in the high NPR group than low NPR group (81.74vs 55.56%;p=0.000). NPR was independently associated with FR (odds ratio: 2.107; 95% CI: 1.017-4.364; p=0.045). Conclusion: High NPR was associated with the risk of FR in patients with acute ischemic stroke due to large vascular occlusions.

Complete blood count inflammation derived indexes as predictors of metabolic syndrome in type 2 diabetes mellitus.

Metabolic syndrome (MetS) is a group of comorbidities related to regulating hyperglycemia and acute cardiovascular incidents and complications. With the increasing prevalence in individuals with type 2 diabetes mellitus (T2DM), MetS represents an increasing public health problem and clinical challenge, and early diagnosis is necessary to avoid the accelerated development of diabetic complications. To investigate the role of Complete Blood Count-derived Inflammation Indexes (CBCIIs) in predicting MetS in T2DM individuals. The study was designed as a two-year prospective study and included 80 T2DM individuals divided into MetS and non-MetS groups based on MetS development over two years. The sera samples were analyzed for complete blood count parameters and C-reactive protein (CRP). Based on the laboratory test results, 13 CBCIIs were calculated and analyzed. The receiver operating characteristic (ROC) curve and their corresponding areas under the curve (AUC) were used to determine prognostic accuracy. There were significant differences between T2DM participants with Mets and those without MetS concerning Neutrophil to Platelet Ratio (NPR) values (p< 0.001), Neutrophil to Lymphocyte and Platelet Ratio (NLPR) (p< 0.001), Platelet to Lymphocyte Ratio (PLR) (p< 0.001), Lymphocyte to C-reactive protein Ratio (LCR) (p< 0.001), C-reactive protein to Lymphocyte Ratio (CRP/Ly) (p< 0.001), Systemic immune inflammation index (SII) (< 0.001), and Aggregate Index of Systemic Inflammation (AISI) (p= 0.005). The results of ROC curve analysis have shown that the LCR (AUC of 0.907), CRP/Ly (AUC of 0.907) can serve as excellent predictors, but NPR (AUC of 0.734), NLRP (AUC of 0.755), PLR (AUC of 0.823), SII (AUC of 0.745), and AISI (AUC of 0.688) as good predictors of MetS in T2 DM individuals. This study confirms the reliability of the CBCIIs as novel, simple, low cost and valuable predictors of MetS developing in T2DM.

Limited value of platelet-related markers in diagnosing periprosthetic joint infection

ObjectiveTo evaluate the diagnostic values of serum platelet count (PC), mean platelet volume ratio (MPV), platelet count to mean platelet volume ratio (PVR), platelet to lymphocyte ratio (PLR), platelet to neutrophil ratio (PNR), PC/Albumin-globulin ratio (PC/AGR), and PC/C-reactive protein (PC/ CRP) in the diagnosis of periprosthetic joint infection (PJI).MethodsThe medical records were retrospectively analyzed of the 158 patients who had undergone hip or knee revisions from January 2018 to May 2022. Of them, 79 cases were diagnosed with PJI and 79 with aseptic loosening (AL). PJI was defined using the Musculoskeletal Infection Society criteria. The plasma levels of CRP, the erythrocyte sedimentation rate (ESR), PC, MPV, PVR, PLR, PNR, PC/AGR, and PC/CRP in the 2 groups were recorded and analyzed. In addition, tests were performed according to different joint types. The receiver operating characteristic curve was used to calculate the sensitivity and specificity of each indicator. The diagnostic value for each indicator was calculated according to the area under the curve (AUC).ResultsThe PC, PVR, PLR and PC/AGR levels in the PJI group were significantly higher than those in the AL group, while PC/CRP levels were significantly lower (P < 0.001). The AUC for PC/CRP, and PC/AGR was 0.804 and 0.802, respectively, which were slightly lower than that of CRP (0.826) and ESR (0.846). ROC analysis for PC/CRP, and PC/AGR revealed a cut-off value of 37.80 and 160.63, respectively, which provided a sensitivity of 73.42% and 84.81% and a specificity of 75.95% and 65.82% for PJI. The area under the curve of PLR and PC was 0.738 and 0.702. The area under the curve values for PVR, PNR, and MPV were 0.672, 0.553, and 0.544, respectively.ConclusionsThe results of this study suggest that PC, PLR, PC/CRP, and PC/AGR values do not offer significant advantages over ESR or CRP values when employed for the diagnosis of PJI. PVR, PNR, and MPV were not reliable in the diagnosis of PJI.

The predictive value of the neutrophil/platelet ratio on in-hospital adverse events and long-term prognosis in patients with coronary artery disease after percutaneous coronary intervention and its possible internal mechanism.

The neutrophil-to-platelet ratio (NPR) is considered to be an indicator of inflammatory status. The value of the NPR in predicting in-hospital adverse events (AEs) and long-term prognosis after percutaneous coronary intervention (PCI) in coronary artery disease (CAD) patients has not yet been reported. Meanwhile, the mechanisms behind its predictive value for long-term prognosis remain unreported as well. The study retrospectively enrolled 7284 consecutive patients with CAD undergoing PCI from January 2012 to December 2018. Multivariable logistic regression analysis, multivariable Cox regression analysis, Kaplan‒Meier (KM) curve analysis, restricted cubic spline (RCS) curve analysis, and sensitivity analysis were used in the study. All-cause death was the endpoint of the study. According to the median value of the NPR, the patients were divided into two groups: the high group (NPR ≥ 0.02, n = 3736) and the low group (NPR < 0.02, n = 3548). Multivariate logistic regression analysis demonstrated that a high NPR was a risk factor for in-hospital AEs [odds ratio (OR) = 1.602, 95% CI 1.347-1.909, p = 0.001]. During a mean follow-up period of 3.01 ± 1.49years, the multivariate Cox regression analysis showed that a high NPR affected the long-term prognosis of patients (HR 1.22, 95% CI 1.03-1.45, p = 0.025) and cardiac death (HR 1.49, 95% CI 1.14-1.95, p = 0.003). The subgroup analysis showed that the NPR was affected by age and sex. The mediation analysis identified that the effect of the NPR on long-term outcomes is partially mediated by serum creatinine (Scr) and triglycerides. The NPR may be a convenient indicator of in-hospital AEs and poor long-term and cardiac outcomes in CAD patients. It might have impacted prognosis through effects on kidney function and lipid metabolism.

Novel Hematological Parameters in the Assessment of the Extent of Cardiac Implantable Electronic Device-Related Infections

Background: Patients with infectious complications related to the presence of cardiac implantable electronic devices (CIED) constitute a heterogeneous group, ranging from local pocket infection (PI) to lead-related infectious endocarditis (LRIE) infection spreading along the leads to the endocardium. The detection of isolated LRIE and the assessment of the spread of infection in a patient with PI is often difficult and requires complex imaging and microbiological tests. The aim of the current study is to evaluate the usefulness of new simple hematological parameters in detecting infectious complications in patients with CIED, differentiating vegetation and vegetation-like masses, and assessing the extent of infections in patients with PI. Methods: A retrospective analysis of clinical data of 2909 patients (36.37% with CIED-related infections), undergoing transvenous lead extraction (TLE) procedures in three high-volume centres in the years 2006–2020, was conducted. Receiver operating characteristic (ROC) curve analysis was used to assess the sensitivity and specificity of neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelet ratio (NPR), and lymphocyte-to-platelet ratio (LPR) in the diagnosis of CIED infections, evaluate the spread of the infectious process in patients with PI and differentiate additional structures related to the presence of lead. Results: The values of NLR and NPR were significantly higher in infectious patients than non-infectious controls (3.07 vs. 2.59; p &lt; 0.001, and 0.02 vs. 0.01; p = 0.008) and the area under the ROC curve (AUC) was 0.59; p &lt; 0.001 and 0.56; p &lt; 0.001, respectively. The high specificity of the new markers in detecting the infectious process was demonstrated: 72.82% for NLR (optimal cut-off value: 3.06) and 79.47% for NPR (optimal cut off value: 0.02). The values of NLR and NPR were significantly higher in patients with vegetations than in non-infectious patients with the presence of additional lead-related masses (3.37 vs. 2.61; p &lt; 0.001 and 0.03 vs. 0.02; p = 0.008). The AUC of NLR and NPR for the prediction of vegetations was 0.65; p &lt; 0.001 and 0.60; p &lt; 0.001 with the highest specificity of NPR (82.78%) and an optimal cut-off value of 0.03. NLR and NPR were higher in patients with LRIE compared to isolated PI (4.11 vs. 2.56; p &lt; 0.001 and 0.03 vs. 0.02; p &lt; 0.001) and the ROC curve analysis for coexistence LRIE with PI showed the AUC for NLR: 0.57; p &lt; 0.001 and AUC for NPR: 0.55; p = 0.001. High specificity in the detection of coexistence between PI and LRIE was demonstrated for NLR (87.33%), with an optimal cut-off value of 3.13. Conclusions: Novel hematological markers (NLR and NPR) are characterized by high specificity in the initial diagnosis of CIED infections, with optimal cut-off values of 3.06 and 0.02. NLR is also useful in the assessment of the spread of infection in patients with PI, with a calculated optimal cut-off value of 3.13. NPR may be helpful in the differentiation of vegetation and vegetation-like masses with an optimal cut-off value of 0.03.

Diagnostic and prognostic values of immunocyte ratios in patients with sepsis in the intensive care unit.

The aim of this study was to evaluate the diagnostic and prognostic values of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), platelet-to-neutrophil ratio (PNR), monocyte-to-lymphocyte ratio (MLR), neutrophils-to-lymphocytes and platelets ratio (N/LPR), mean platelet volume-to-platelet count ratio (MPV/PC), red blood cell distribution width-to-platelet count ratio (RDW/PC), and platelet volume distribution width-to-platelet count ratio (PDW/PC) in patients with sepsis. A total of 203 patients with sepsis admitted to an emergent intensive care unit (EICU) were enrolled in this retrospective study. Basic data, inflammatory factors, NLR, PLR, PNR, MLR, N/LPR, MPV/PC, RDW/PC, PDW/PC were compared between survival and non-survival groups. Receiver operating characteristic (ROC) curve was used to assess the diagnostic values. The univariate and multivariate regression analyses were used for constructing a prognostic model for sepsis. There were significant differences in acute physiology and chronic health evaluation (APACHEII) score, mechanical ventilation, use of vasopressors, acute kidney injury (AKI), continuous renal replacement therapy (CRRT), long-term antiplatelet drug use, lymphocyte, monocyte, hemoglobin, procalcitonin (PCT), interleukin-6 (IL-6), C-reactive protein (CRP)*PCT, and N/LPR. APACHE II had the highest diagnostic value [Area Under Curve (AUC) = 0.999], followed by CRP*PCT (AUC = 0.718). The prognoses were different between patients stratified according to CRP, IL-6, lactic acid (Lac), PNR, PLR, PDW/PC, and APACHEII. Lac, CRP*PCT, PDW/PC, MPV/PC and APACHE II were independent prognostic factors of sepsis. Both N/LPR and CRP*PCT had high values to predict mortality in sepsis patients. CRP*PCT, PDW/PC and MPV/PC were independent factors to predict the prognosis of sepsis.

Can the Neutrophil-to-Platelet Ratio Be Used to Predict Postoperative Mortality in Geriatric Patients with Hip Fractures?

Objective: Hip fracture is a condition frequently encountered in geriatric patients and is correlated with high postoperative mortality and morbidity. Numerous factors may impact mortality, but recent studies have focused on its correlation with inflammation. The neutrophil-to-platelet ratio has been evaluated as a systemic inflammation marker. The aim of our study was to examine the correlation between preoperative neutrophil-to-platelet ratio (NPR) values and postoperative one-year mortality in geriatric patients with hip fractures. Material and Method: In our retrospective, multicenter study, 50 patients over 65 years of age who had undergone hip fracture surgery under spinal anesthesia were examined. Patients’ preoperative neutrophil-to-platelet ratio values at the time of hospitalization, age, gender, American Society of Anesthesiologists scores, fracture types, comorbidities, durations of surgery, lengths of hospital stay, and survival in the postoperative one-year period were checked. Results: The mean age of all the patients was 85.44 ± 6.93 years. Of the patients, 60% were female (n = 30), and 40% were male (n = 20). The optimal cut-off value for preoperative neutrophil-to-platelet ratio was specified as 38.286. The length of hospital stay was 8.94 ± 3.05 in the deceased patients and 6.94 ± 2.63 days (p = 0.02) in the surviving group. The mortality rate was higher in men than in women (p = 0.01). Conclusion: A preoperative neutrophil-to-platelet ratio value over 38.286 is an indicator of postoperative one-year mortality in elderly hip fracture patients. Additionally, length of hospital stay and the male gender were revealed to be correlated with mortality.

Development of machine learning models for predicting outcome in patients with distal medium vessel occlusions: a retrospective study.

While numerous prognostic factors have been reported for large vessel occlusion (LVO)-acute ischemic stroke (AIS) patients, the same cannot be said for distal medium vessel occlusions (DMVOs). We used machine learning (ML) algorithms to develop a model predicting the short-term outcome of AIS patients with DMVOs using demographic, clinical, and laboratory variables and baseline computed tomography (CT) perfusion (CTP) postprocessing quantitative parameters. In this retrospective cohort study, consecutive patients with AIS admitted to two comprehensive stroke centers between January 1, 2017, and September 1, 2022, were screened. Demographic, clinical, and radiological data were extracted from electronic medical records. The clinical outcome was divided into two categories, with a cut-off defined by the median National Institutes of Health Stroke Scale (NIHSS) shift score. Data preprocessing involved addressing missing values through imputation, scaling with a robust scaler, normalization using min-max normalization, and encoding of categorical variables. The data were split into training and test sets (70:30), and recursive feature elimination (RFE) was employed for feature selection. For ML analyses, XGBoost, LightGBM, CatBoost, multi-layer perceptron, random forest, and logistic regression algorithms were utilized. Performance evaluation involved the receiver operating characteristic (ROC) curve, precision-recall curve (PRC), the area under these curves, accuracy, precision, recall, and Matthews correlation coefficient (MCC). The relative weights of predictor variables were examined using Shapley additive explanations (SHAP). Sixty-nine patients were included and divided into two groups: 35 patients with favorable outcomes and 34 patients with unfavorable outcomes. Utilizing ten selected features, the XGBoost algorithm achieved the best performance in predicting unfavorable outcomes, with an area under the ROC curve (AUROC) of 0.894 and an area under the PRC curve (AUPRC) of 0.756. The SHAP analysis ranked the following features in order of importance for the XGBoost model: mismatch volume, time-to-maximum of the tissue residue function (Tmax) >6 s, diffusion-weighted imaging (DWI) volume, neutrophil-to-platelet ratio (NPR), mean corpuscular volume (MCV), Tmax >10 s, hemoglobin, potassium, hypoperfusion index (HI), and Tmax >8 s. Our ML models, trained on baseline quantitative laboratory and CT parameters, accurately predicted the short-term outcome in patients with DMVOs. These findings may aid clinicians in predicting prognosis and may be helpful for future research.

Characterization of peripheral blood inflammatory indicators and OCT imaging biological markers in diabetic retinopathy with or without nephropathy.

To observe the distribution characteristics of peripheral blood inflammatory indexes and retinal macular area optical coherence tomography (OCT) imaging biomarkers in patients with diabetic retinopathy (DR) with or without diabetic nephropathy (DN), in order to seek clinical biomarkers that can predict the development of DR and DN. A total of 169 inpatients with DR who visited the ophthalmology department of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine from October 2020 to June 2022 and had complete clinical data were collected, and the patients with DR were divided into two major groups, DR and DR/DN, according to whether they had DN, and then further divided into four subgroups, Non-proliferative DR(NPDR), proliferative DR(PDR), NPDR/DN and PDR/DN, according to the stage of DR. The distribution characteristics of peripheral blood inflammatory indexes [Neutrophil to lymphocyte ratio(NLR) and Platelet to neutrophil ratio(PLR)], renal function indexes [Cystatin-C(CYS-C), Creatinine(Crea), Uric acid(UA)and Urinary albumin to creatinine ratio(UACR)] and OCT imaging indexes [Hyperreflective foci(HRF), Disorgnization of retinal inner layers(DRIL), Outer retinal tubulations(ORTs), Central retinal thickness(CRT), Retinal nerve fiber layer(RNFL) and Ganglion cell layer(GCL)] were analyzed between the above subgroups. There was no difference between DR and DR/DN groups in terms of gender, family history of diabetes, duration of diabetes and Body mass index(BMI) (P>0.05), the mean age of the DR/DN group was significantly lower than that of the DR group (P<0.05), and the proportion of the DR/DN group with a history of hypertension was significantly higher than that of the DR group (P<0.05); there was no significant difference in hemoglobin A1C(HbA1c) between DR and DR/DN groups (P>0.05). (P>0.05), Hemoglobin(HGB) was significantly higher in the DR group than in the DR/DN group (P <0.05), NLR, PLR, Crea, UA and CYS-C were significantly higher in the DR/DN group than in the DR group (P<0.05); there was no significant difference in the comparison of HRF, DRIL, ORTs positive rate and CRT between the DR and DR/DN groups (P>0.05). RNFL and GCL thickness were significantly lower in the DR/DN group than in the DR group (P<0.05); history of hypertension (OR=2.759), NLR (OR=1.316), PLR (OR=1.009), Crea (OR=1.018), UA (OR=1.004), CYS-C (OR=3.742) were the independent (OR=0.951), age (OR=0.951), HGB (OR=0.976), RNFL (OR=0.909) and GCL (OR=0.945) were independent protective factors for DR/DN; RNFL (OR=0.899) and GCL (OR=0.935) were independent protective factors for NPDR/DN, RNFL (OR=0.852) and GCL (OR=0.928) were independent protective factors for PDR/DN. ROC curve analysis showed that the area under the curve (AUC) for CYS-C, PLR, Crea, UA and the combination of the four indicators to predict DR/DN were 0.717, 0.625, 0.647, 0.616 and 0.717, respectively. (1) Low age combined with hypertension HGB, NLR, PLR, CYS-C, Crea and UA may be serum biological markers for predicting DN in DR; meanwhile, PLR, CYS-C, Crea, UA and the combination of the four indicators can be used for risk assessment and adjunctive diagnosis of DN in DR combined with hypertension. (2) The RNFL and GCL thickness in the temporal aspect of the central macular sulcus may be imaging biological markers for predicting DN in DR; meanwhile, GCL thickness may have important value for risk prediction and diagnosis of DN in combination with DR.

The preoperative platelet to neutrophil ratio and lymphocyte to monocyte ratio are superior prognostic indicators compared with other inflammatory biomarkers in ovarian cancer.

Previous studies have suggested that the ratios of immune-inflammatory cells could serve as prognostic indicators in ovarian cancer. However, which of these is the superior prognostic indicator in ovarian cancer remains unknown. In addition, studies on the prognostic value of the platelet to neutrophil ratio (PNR) in ovarian cancer are still limited. A cohort of 991 ovarian cancer patients was analyzed in the present study. Receiver operator characteristic (ROC) curves were utilized to choose the optimal cut-off values of inflammatory biomarkers such as neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and PNR. The correlation of inflammatory biomarkers with overall survival (OS) and relapse-free survival (RFS) was investigated by Kaplan-Meier methods and log-rank test, followed by Cox regression analyses. Kaplan-Meier curves suggested that LMR<3.39, PLR≥181.46, and PNR≥49.20 had obvious associations with worse RFS (P<0.001, P=0.018, P<0.001). Multivariate analysis suggested that LMR (≥3.39 vs. <3.39) (P=0.042, HR=0.810, 95% CI=0.661-0.992) and PNR (≥49.20 vs. <49.20) (P=0.004, HR=1.351, 95% CI=1.103-1.656) were independent prognostic indicators of poor RFS. In addition, Kaplan-Meier curves indicated that PLR≥182.23 was significantly correlated with worse OS (P=0.039). Taken together, PNR and LMR are superior prognostic indicators compared with NLR, PLR, and SII in patients with ovarian cancer.