Systemic Immune-inflammation Index Research Articles

Abstract 2339: Development of a blood cell analysis-based AI model for detecting early ovarian cancer signals

Ovarian cancer (OC) remains one of the most lethal gynecologic malignancies, primarily due to delayed detection. Blood cell analysis presents a promising non-invasive diagnostic approach, capable of detecting systemic signals associated with early OC. This study focuses on developing and validating an artificial intelligence (AI)-based diagnostic model to detect OC risk signals using comprehensive blood cell data. To minimize confounding factors, samples from male participants, symptomatic controls, drug-treated individuals, and pre-anesthesia collections were excluded. Additionally, samples with missing data were removed. A total of 185 samples, including 85 for training and 100 for testing, were analyzed, comprising OC, benign tumor, and control groups. The model utilized 24 features: 18 directly measured blood cell parameters (via Sysmex) and 6 derived composite indices, including the systemic immune-inflammation index (SII: [NEUT×PLT]/LYM). A Random Forest algorithm, optimized through 5-fold GridSearch cross-validation and soft voting, was trained and validated with balanced accuracy (BA) as the performance metric. The optimal cutoff values were determined based on the training set’s best BA. In the training set, the model achieved a sensitivity of 100.0%, specificity of 92.6%, a positive predictive value (PPV) of 77.3%, and a negative predictive value (NPV) of 100.0%, with an area under the curve (AUC) of 0.987. For the test dataset, the model demonstrated a sensitivity of 75.0%, specificity of 92.9%, PPV of 66.7%, and NPV of 95.1%, with an AUC of 0.923. The platelet-to-lymphocyte ratio (PLT/LYM) and SII emerged as the most influential features, contributing significantly to model performance. The specificity for control samples, excluding benign tumors, was 1 in the training set, and validation confirmed a specificity of 0.984, demonstrating clear separation between the OC and control groups. Composite indices further improved the model’s reliability in detecting OC risk signals. This AI-driven blood cell analysis model exhibited strong performance in identifying early ovarian cancer signals, achieving high sensitivity and specificity. By leveraging non-invasive biomarkers, it holds significant promise for clinical implementation in OC screening. Future efforts will focus on expanding the diversity of analyzed samples to better distinguish cancer-specific signals from those associated with other diseases and incorporating molecular markers to enhance the accuracy and precision of the diagnostic algorithm. Citation Format: Eunyong Ahn, Sungmin Park, Sarah Kim, Se Ik Kim, Hyejin Lee, Heeyeon Kim, Seong Eun Kang, Ji Won Park, TaeJin Ahn, Yong-Sang Song. Development of a blood cell analysis-based AI model for detecting early ovarian cancer signals [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2339.

Effect of CBC-Derived Inflammatory Indicators in Predicting Chronic Kidney Disease Risk in Hypertrophic Cardiomyopathy Patients

Background: Hypertrophic cardiomyopathy (HCM) is a prevalent condition that often coexists with chronic kidney disease (CKD), significantly impacting patient prognosis. This study aimed to investigate the predictive value of complete blood cell counts derived inflammatory indicators in assessing CKD risk in HCM patients. Methods: This study enrolled HCM patients and categorized them into CKD and non-CKD group based on discharge diagnoses. Analyzed indicators included systemic inflammation response index (SIRI), systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). Least absolute shrinkage and selection operator (LASSO) logistic and multivariable logistic regression were employed to identified independent risk factors for CKD, which were subsequently utilized to develop a nomogram. Results: A total of 1795 HCM patients were included, including 112 (6.24%) individuals assigned to the CKD group. In univariate analyses, NLR (AUC: 0.755; 95%CI: 0.711–0.800) demonstrated superior accuracy compared to others. Eighteen baseline characteristics exhibiting statistical difference were incorporated into LASSO-logistic regression. Six factors were further selected by multivariable logistic regression. The results identified male gender (OR: 2.622; 95% CI: 1.565–4.393, p < 0.001), Hb (OR: 0.972; 95% CI: 0.962–0.981, p < 0.001), Pro-BNP (OR: 1.000; 95% CI: 1.000–1.000, p < 0.001), SIRI (OR: 1.037; 95% CI: 1.026–1.049, p < 0.001), and SII (OR: 1.000; 95% CI: 1.000–1.001, p = 0.003) as risk factors. These five factors were used to construct a nomogram, which exhibited good accuracy and consistency. Conclusions: Male gender, Hb, Pro-BNP, SIRI, and SII were identified as risk factors for CKD risk in HCM patients. A nomogram was developed using these factors, which may facilitate early identification and management of high-risk individuals.

The Relationship Between Neutrophil-to-Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, and Systemic Immune-Inflammation Index Markers and Response to Biological Therapy in Patients with Psoriasis

Plaque psoriasis is a chronic, immune-mediated inflammatory skin disease characterized by the formation of thick, scaly plaques. The disease is driven by dysregulation of the immune response, primarily involving T-helper cells, which create a persistent inflammatory environment. In recent years, several biomarkers reflecting systemic inflammation have been identified, including indices derived from a complete blood count, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and Systemic Immune-Inflammation Index (SII). The aim of our study was to explore the role of these markers in patients with psoriasis undergoing biological treatment. Medical records of 159 patients with plaque psoriasis receiving biologics were retrospectively reviewed. The NLR, PLR, and SII values were calculated from the hemograms of the patients. Additionally, demographic and psoriasis severity data were analyzed. During the 18-month follow-up, the mean NLR, PLR, SII, and CRP values were significantly decreased in comparison to the baseline (p < 0.05). No significant differences between anti-TNF, anti-IL-12/23, anti-IL-17, and anti-IL-23 drugs were identified (p > 0.05). The initial values of NLR, PLR, and SII were positively correlated with psoriasis severity. No relationship between the analyzed biomarkers and age, sex, psoriasis duration, and prior exposure to biological drugs was identified. CBC-derived biomarkers may be useful for monitoring inflammation reduction in psoriasis patients treated with biological drugs.

Low-grade systemic inflammation is associated with risk of psoriasis in a general population study of more than 100,000 individuals.

Biomarkers of low-grade systemic inflammation have been reported to be higher in patients with psoriasis than in healthy controls. However, it is unknown whether this low-grade systemic inflammation contributes to the development of psoriasis or is merely a consequence. To investigate if low-grade systemic inflammation, measured as systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), or C-reactive protein (CRP), is an independent risk factor for psoriasis. We used data from the Copenhagen General Population Study, a prospective cohort study of the Danish general population where individuals aged 20-100 were enrolled between 2003 and 2015. Upon enrolment in the study, all individuals underwent a physical examination, completed an extensive self-reported questionnaire regarding lifestyle, and provided blood samples, from which SII, NLR, and CRP were measured. Psoriasis was identified using ICD codes by individual-linkage to the Danish National Patient Registry. Associations between SII, NLR, and CRP and psoriasis were estimated using hazard ratios from Cox proportional hazard regression models. Analyses were adjusted for potential confounders including sex, age, smoking, alcohol consumption, physical activity, educational level, hypertension, dyslipidaemia, and obesity. We included 105,418 individuals with a median age of 58 years of whom 55% were women. The risk of receiving a diagnose of psoriasis increased with increasing levels of SII, NLR, and CRP. In individuals with high levels (>90 percentile) of SII, NLR, and CRP, the multivariable adjusted hazard ratios were 1.78 (95% confidence interval 1.41-2.24), 1.56 (1.22-1.99), and 2.83 (2.27-3.51), respectively, compared with individuals with low levels. Results were similar but slightly attenuated when we used topical calcipotriol (alone or in combination with corticosteroids) for mild psoriasis. We found that low-grade systemic inflammation, as measured by SII, NLR, and CRP, was an independent risk factor for psoriasis, especially moderate-to-severe disease. These findings support the hypothesis that low-grade systemic inflammation may contribute to the pathogenesis of psoriasis rather than simply being a consequence of the disease.

Evaluation of blood-count-derived inflammatory markers in patients with idiopathic epiretinal membrane

BackgroundTo assess the role of inflammation in the pathogenesis of idiopathic epiretinal membrane (iERM) by evaluating blood-count-derived inflammatory marker levels.MethodsThe medical records of patients diagnosed with iERM and cataract patients with normal fundus examinations were analyzed retrospectively. Levels of neutrophils, monocytes, lymphocytes, and thrombocytes were obtained from blood samples. Systemic inflammatory markers, including neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammatory response index (SIRI) were calculated and compared between the two groups. The receiver operating characteristic curve (ROC) analysis was performed to determine the best cutoff value of NLR, PLR, SII, and SIRI in iERM.ResultsIn total, 91 iERM cases and 95 controls were included in the study. iERM patients had significantly higher NLR (2.25 vs. 1.91, p = 0.003), PLR (117.22 vs. 113.33, p = 0.042), SII (529.45 vs. 472.57, p = 0.003), and SIRI (1.25 vs. 0.90, p < 0.001). The area under the curve of NLR, PLR, SII, and SIRI in differentiating patients with iERM and controls was 0.637, 0.608, 0.645 and 0.660, respectively, according to ROC analysis. The best cutoff values (with sensitivity and specificity) were 1.95 (60.4% and 52.6%) for NLR, 116.7 (54.9% and 55.7%) for PLR, 498.03 (58.2% and 58.9%) for SII, and 1.07 (62.6% and 64.6%) for SIRI.No significant differences in inflammatory markers were found across iERM stages.ConclusionPatients with iERM exhibit higher levels of blood-count-derived inflammatory markers, suggesting a link between systemic subclinical inflammation and iERM development. However, these markers do not correlate with iERM severity. Further research with larger cohorts and broader inflammatory marker analysis is needed to elucidate the role of systemic inflammation in iERM pathogenesis.

Survival Outcomes of Preoperative Serum Biomarkers in Surgically Treated Intrahepatic Cholangiocarcinoma Patients.

Intrahepatic cholangiocarcinoma (ICC) is a highly malignant tumor, often diagnosed at advanced stages, with recurrence and metastasis significantly affecting survival. The combined prognostic value of biomarkers such as the Systemic Immune-Inflammation Index (SII), Fibrosis-4 (FIB-4), and Prognostic Nutritional Index (PNI) remains underexplored. A retrospective analysis of 280 ICC patients who underwent curative resection was performed. The prognostic significance of FIB-4, SII, and PNI for overall survival (OS) and disease-free survival (DFS) was assessed using clinical, pathological, and follow-up data. Statistical analysis included Cox regression and Kaplan-Meier survival curves. High PNI was significantly associated with better OS (P = 0.014) and DFS (P = 0.025). High FIB-4 levels were correlated with worse OS (P = 0.0076) and DFS (P = 0.023). High SII was strongly associated with poor OS (P < 0.0001) and DFS (P = 0.00041). The combination of high SII, low PNI, and high FIB-4 was linked to significantly worse OS (HR = 2.633, P = 0.002) and DFS (HR = 2.475, P = 0.004). Preoperative serum biomarkers, including PNI, FIB-4, and SII, are significant independent prognostic factors for ICC patients. Their combined use may help refine prognostic assessment and guide personalized treatment strategies.

Pronounced effects of the sepsis–obesity paradox in elderly and male individuals without septic shock and the role of immune–inflammatory status: an analysis of MIMIC-IV data

BackgroundObesity has been shown to reduce short-term mortality in sepsis patients, but the main subgroups and its role in immune-related inflammatory status require further research. The aim of this study was to identify the primary beneficiaries of the sepsis–obesity paradox and to investigate the involvement of immune–inflammatory status.MethodsIn this study, we analyzed data from 6602 sepsis patients from the MIMIC-IV database. Body mass index (BMI) was divided into quartiles, and mortality rates were assessed for each interval. Logistic trend tests and subgroup and restricted cubic spline (RCS) analyses were performed. Blood biochemical indicators were compared across different BMI ranges and between survivors and non-survivors. The receiver operating characteristic (ROC) curve for 28-day mortality was also evaluated.ResultsThe 28-day mortality of sepsis patients followed a U-shaped pattern with increasing BMI. Trend analysis confirmed that BMI was a significant risk factor for 28-day mortality (p < 0.05). Subgroup analysis revealed an interactive effect of BMI on 28-day mortality in elderly (≥ 65 years old), male, and non-septic shock individuals (p < 0.05). A higher BMI was associated with an increased lymphocyte proportion and decreased neutrophil proportion, neutrophil-to-lymphocyte ratio (NLR), and systemic immune–inflammation index (SII) (p < 0.05). Compared with survivors, non-survivors had lower lymphocyte proportions and higher neutrophil proportions, NLRs, and SIIs. ROC analysis revealed that the lymphocyte and neutrophil proportions, NLR, and SII had predictive value for 28-day mortality. Subgroup and RCS analyses revealed that increased BMI was associated with reduced 28-day mortality in sepsis patients, mainly in elderly, male, and septic shock individuals, with protective BMIs ranging from 27.8 ~ 41.7 kg/cm2, 28.4 ~ 37.7 kg/cm2, and > 28.6 kg/cm2, respectively.ConclusionsThe sepsis–obesity paradox significantly affects elderly (≥ 65 years old), male, and non-septic shock individuals, displaying a U-shaped pattern for 28-day mortality. BMI may mediate this phenomenon by influencing the body’s immune–inflammatory status.

The Prognostic Value of the Systemic Immune-Inflammation Index in Glioblastoma Patients and the Establishment of a Nomogram.

The systemic immune-inflammation index (SII) has recently attracted significant interest as a new biomarker for predicting the prognosis of patients with glioblastoma (GBM). However, the predictive significance of it is still a subject of debate. This study intended to assess the clinical effectiveness of the SII in GBM and establish a nomogram. Receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off values of the SII. Kaplan-Meier (KM) survival curves were used to analyze the median overall survival (OS). Cox regression analysis was carried out to evaluate the associations between OS and different clinical factors. Based on the SII and clinical characteristics, a nomogram was constructed, and its value in clinical application was evaluated by means of decision curve analysis. The optimal SII cut-off value was 610.13. KM analysis revealed that GBM patients with higher SII values had shorter OS (15.0 vs. 34.0 months, P = 0.044). Multivariate analysis demonstrated that a high SII was an independent predictor of poor outcome in GBM (HR = 1.79, P = 0.029). The nomogram incorporating the preoperative SII showed good predictive accuracy for GBM patient prognosis (C-index = 0.691). The SII is an independent predictive indicator for GBM. Patients with elevated SII levels tend to have a poorer prognosis. A nomogram combining the SII with clinical and molecular pathological features can assist clinicians in assessing the risk of death in GBM patients, providing a basis for individualized treatment decisions.

Systemic Immune-Inflammation Index for the Diagnosis of Acute Appendicitis: A Systematic Review.

Systemic Immune-Inflammation Index for the Diagnosis of Acute Appendicitis: A Systematic Review.

Clinical features of immune checkpoint inhibitor-related pneumonitis in older patients with lung cancer receiving immune checkpoint inhibitors-based therapy: a retrospective study

BackgroundOlder patients with lung cancer are underrepresented in pivotal trials of immune checkpoint inhibitors (ICIs). This study primarily retrospectively evaluated the older patients with lung cancer treated with ICIs to determine which factors are related to the occurrence and prognosis of ICI-related pneumonitis (CIP).MethodsWe conducted a single-center, retrospective study of patients age ≥ 65 years diagnosed with lung cancer who received ICIs between January 2018 and June 2023 at the First Hospital of China Medical University. Clinical characteristics and blood parameters at baseline (before ICIs), at onset of pneumonitis (in the CIP group), and before the last dose of ICIs (in the non-CIP group) were collected and compared.ResultsA total of 205 older patients with lung cancer were included, of which 51 (24%) patients developed CIP. Radiotherapy history, first line treatment, and the increased baseline systemic immune-inflammation index (SII), and CD4/CD8 were significantly and independently associated with the risk of CIP. Significant increase in CRP and decrease in albumin (ALB), prognostic nutritional index (PNI), and PaO2 were observed from baseline to CIP during treatment with ICIs. The PD-L1 expression status < 50% (P = 0.022) was the risk factor affecting their progression free survival (PFS). ECOG PS ≥ 2 (P = 0.031) and high-CRP (P = 0.007) of older patients were significantly correlated with their overall survival (OS), and patients who experienced CIP had a better OS than non-CIP (P = 0.001). The older patients with interstitial lung abnormalities (ILA) showed a shorter PFS than those without ILA (P = 0.036), and the PD-L1 expression status < 50% (P = 0.005), and low ALB (P = 0.023) was correlated with the OS in CIP.ConclusionsRadiotherapy history, first line treatment (mostly in combination therapy), and increased baseline SII and CD4/CD8 were associated with the occurrence of CIP in older patients with lung cancer. PD-L1 expression status < 50%, ECOG PS ≥ 2 and high-CRP were associated with worse prognosis in all older patients. ILA, PD-L1 expression status < 50% and low-ALB at onset of CIP were related to poor prognosis in CIP.

The Role of Systemic Immune-Inflammation Index and Systemic Inflammation Response Index in the Diagnosis of Bell’s Palsy

The Role of Systemic Immune-Inflammation Index and Systemic Inflammation Response Index in the Diagnosis of Bell’s Palsy

Systemic immune-inflammation index is associated with adverse outcomes in patients hospitalized for AECOPD: A multicenter cohort study.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with increased morbidity and mortality. The novel inflammatory biomarker, systemic immune-inflammation index (SII), may have prognostic value. This study aimed to assess the association between SII and short-term and long-term adverse outcomes among AECOPD inpatients. This was a multicenter, retrospective analysis of a prospectively collected cohort of AECOPD inpatients. We initially compared SII and other clinical characteristics between survivors and non-survivors during hospitalization, adjusting for primary comorbidities using propensity score matching (PSM). We assessed the short-term and long-term adverse outcomes, particularly focusing on in-hospital mortality and 2-year all-cause mortality, across different levels of SII. Multivariate Cox analysis was employed to evaluate the associations of SII, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) with in-hospital mortality of AECOPD patients. Restricted cubic spline (RCS) models investigated the nonlinear relationships between these biomarkers and in-hospital mortality. To compare the predictive values of SII, NLR, and PLR for in-hospital mortality, receiver operating characteristic (ROC) curve analysis was performed. Subgroup analysis was carried out to further determine the predictive capacity of SII among diverse subgroups. The study included 12,551 AECOPD inpatients, among whom 180 (1.4%) died in hospital. Whether before or after PSM adjusting for comorbidities, the levels of SII, NLR, and PLR in non-survivors were significantly higher than those in survivors (all P < 0.001). Elevated SII levels (divided into quartiles) were associated with increased in-hospital mortality (Q1 vs. Q2 vs. Q3 vs. Q4: 0.6% vs. 0.8% vs. 1.5% vs. 2.8%) and 2-year all-cause mortality (15.4% vs. 22.6% vs. 22.2% vs. 27.8%), as well as other adverse outcomes (all P < 0.05). After adjusting for covariates, higher levels of SII and NLR consistently remained associated with increased in-hospital mortality. RCS analysis revealed a consistent linear relationship between SII and in-hospital mortality, while NLR and PLR exhibited nonlinear relationships. Furthermore, ROC curve analysis indicated that SII showed inferiority to NLR but superiority to PLR in predicting in-hospital mortality among AECOPD patients (area under the curve for SII vs. NLR vs. PLR: 0.670 vs. 0.731 vs. 0.609). Subgroup analysis revealed that the association between SII and in-hospital mortality varied across different subgroups. Elevated SII is associated with increased risks of short-term and long-term adverse outcomes in AECOPD inpatients, making it potential prognostic factor used to identify high-risk patients and guide the management of AECOPD.

Association between systemic inflammation biomarkers and incident cardiovascular disease in 423,701 individuals: evidence from the UK biobank cohort

BackgroundThe associations between systemic inflammation biomarkers and cardiovascular disease (CVD) remain not well explored. This study aimed to investigate associations between different systemic inflammation biomarkers and incident CVD and main CVD subtypes - ischaemic heart disease (IHD), stroke, and heart failure - explore dose–response relationships, and compare their predictive performance.MethodsThis prospective cohort study included 423,701 UK Biobank participants free of CVD at baseline. Baseline neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and system inflammation response index (SIRI) were derived. Cox-proportional regression models were used to investigate the associations.ResultsNLR, PLR, SII, and SIRI was positively and LMR was negatively associated with all four of the outcomes investigated. The relationships were non-linear for all biomarkers with CVD and were linear for NLR, SII, and SIRI and non-linear for LMR and PLR with IHD, stroke and heart failure. Compared with the more established biomarkers, all four of the novel biomarkers had statistically superior predictive performance for three of the outcomes investigated (CVD, IHD and heart failure) and three of them were superior at predicting stroke. Compared to a model of CVD prediction with classical risk factors (C-index = 0.702), discrimination was improved on the addition of inflammation markers for CVD (C-index change 0.0069, 95% CI 0.0033 to 0.0107), IHD (C-index change 0.0054, 95% CI 0.0013 to 0.0095), and heart failure (C-index change 0.0153, 95% CI 0.0089 to 0.0218).ConclusionsThere were independent and dose–response relationships between the novel systemic inflammation biomarkers and CVD outcomes. Addition of the inflammation biomarkers including novel inflammation biomarkers showed improved discrimination of the traditional risk prediction model. With accumulated evidence, these biomarkers should be considered for inclusion in risk tools and prevention.Graphical abstract

Pretreatment platelet level is associated with tumor proliferation and prognosis in malignant pleural mesothelioma.

The present study aimed to investigate the relationship between serum-based inflammatory biomarkers and MPM tumor biology and prognosis. A total of 83 patients with MPM who were diagnosed and started treatment between January 1998 and December 2010 were studied. Clinicopathological variables were evaluated, including age, sex, clinical stage, histology, surgical resection, and chemotherapy. The cut-off values for pretreatment levels of white blood cell count, neutrophil count, lymphocyte count, platelet count, C-reactive protein, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index were determined using receiver operating characteristic curve analysis for predicting 5-year survival. Univariate and multivariate Cox regression analyses were performed to estimate the prognostic impact on 5-year overall survival. The mean Ki-67 proliferation index in MPM cells was 35.1 ± 29.5% and the median overall survival of patients was 15.0months. The Ki-67 proliferation index in MPM cells was significantly higher in the platelet-high group compared with that in the platelet-low group (42.1 ± 31.9 vs. 27.7 ± 25.1%; P = 0.027). Multivariate Cox regression analyses identified platelet count (hazard ratio = 1.929; P = 0.022) and PLR (hazard ratio = 1.776; P = 0.040) as significant prognostic factors. Pretreatment platelet level may be a useful prognostic marker for 5-year overall survival related to tumor proliferation in patients with MPM.

Exploring the Predictive Potential of Systemic Immune Inflammation Index and Hematologic Markers in Preterm Labor Risk Assessment

Exploring the Predictive Potential of Systemic Immune Inflammation Index and Hematologic Markers in Preterm Labor Risk Assessment

Nomogram model for predicting pressure injury in COPD patients using SII: a Chinese clinical study

ObjectivesThis study aims to investigate the association between the Systemic Immune-Inflammation Index (SII) and the development of pressure injuries (PI) in patients with chronic obstructive pulmonary disease (COPD). Additionally, a nomogram model based on the SII will be constructed to predict the probability of pressure injury (PI) occurrence in patients with COPD.MethodsA retrospective analysis was performed on the clinical data of 844 patients with COPD who were admitted to the Affiliated Hospital of Guangdong Medical University between June 2018 and December 2019. Logistic regression analysis was employed to identify risk factors associated with the development of PI, and the Wald chi-square test was used to select variables for constructing a predictive nomogram. The performance of the nomogram was assessed, followed by internal validation. Additionally, clinical data from 452 patients with COPD admitted to the Second Affiliated Hospital of Guangdong Medical University between January 2024 and December 2024 were prospectively collected for external validation.ResultsA total of 844 patients with COPD were included in this study, with 590 cases in the training group and 254 cases in the internal validation group. The predictors included in the nomogram model were age, respiratory rate [Breathe (R)], duration of COPD history, Serum albumin (ALB), SII, paralysis, edema, and activities of daily living (ADL). The nomogram demonstrated strong predictive performance and calibration. The area under the curve and 95% confidence intervals were 0.77 (0.72–0.82) for the training group, 0.77 (0.70–0.85) for the internal validation group, and 0.73 (0.66–0.81) for the external validation group.ConclusionThis study identified the SII, age, respiratory rate, duration of COPD history, ALB, paralysis, and ADL as independent risk factors for the development of PI in patients with COPD. A nomogram model was successfully developed based on SII and validated through both internal and external testing. The findings suggest that SII is a reliable predictor of PI development in patients with COPD, and the model demonstrates strong predictive performance.

Diagnostic value of systemic immune-inflammation index and prognostic nutritional index combined with CEA in gastric cancer with lymph node metastasis

BackgroundCarcinoembryonic antigen (CEA), systemic immune-inflammation index(SII), and prognostic nutritional index (PNI) are diagnostic markers for cancer, but their combined significance in gastric cancer (GC) with lymph node metastasis remains unclear. The aim of this study was to evaluate the association between these serum biomarkers and lymph node metastasis in patients with GC.MethodsRecords of patients with GC were reviewed retrospectively. Univariate and multivariate logistic regression were performed to examine the association between tumor markers, serum biomarkers and lymph node metastasis in GC. Based on the results of multivariate regression, a nomogram was developed and verified.ResultsOf the 395 patients aged 68.5 ± 9.1 years, 192 (48.6%) were diagnosed with lymphatic node metastasis. After adjusting for confounding factors, CEA (Odd ratio (OR):2.21; 95%CI: 1.17-3.81) and SII (OR:1.02; 95%CI: 1.01-1.04) was identified as significant risk factors, while PNI (OR:0.90; 95%CI: 0.85~0.96) was a protective factor for lymph node metastasis. The established nomogram by incorporating CEA, SII, PNI, differentiation, and tumor diameter can effectively predict lymph node metastasis in GC.ConclusionCEA, SII, PNI, differentiation, and tumor diameter were significantly associated with lymph node metastasis in patients with GC, and the combination of CEA, SII, PNI, differentiation, and tumor diameter has a better diagnostic value than either index alone.

Neutrophil-to-lymphocyte ratio; platelet-to-lymphocyte ratio; systemic immune-inflammatory Index: inflammatory indicators of cognitive impairment in schizophrenia patients

ObjectiveThe present study sought to evaluate the correlation between cognitive impairment (CI) and inflammatory indicators such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammatory index (SII) in schizophrenia patients.MethodsThis study included 331 schizophrenia inpatients. General data and laboratory findings (neutrophil, lymphocyte, platelet, etc.) were gathered, and then calculating NLR, PLR, and SII. A Chinese version of the Mini Mental State Examination (MMSE) was used for the assessment of cognitive function, and then the patients in the CI group were categorized into mild CI, moderate CI, and severe CI groups. Comparing the differences in NLR, PLR, and SII between the CN group and the CI group, as well as different CI groups, and analyzing the relationship between the NLR, PLR, and SII and the mechanism of CI in schizophrenia.ResultsThere were 145 (43.8%) patients with cognitive impairment. Compared to the CN group, the CI group had higher NLR, PLR, and SII than the CN group, although their lymphocyte was lower. The NLR and SII were higher in the moderate CI group than in the mild CI group. NLR, PLR, and SII were significantly inversely correlated with the total score of cognitive function and scores across all aspects, whereas lymphocytes were considerably positively correlated. Higher NLR, PLR, and SII were substantially related to an increased risk of CI, but higher lymphocytes were associated with a decreased risk of CI.ConclusionNLR, PLR, and SII may be serum inflammatory markers of CI in schizophrenia, and lymphocytes may be protective variables for cognitive function in schizophrenia.

Evaluation of hemogram parameters in patients with atopic dermatitis

Aims: This study aimed to evaluate the hemogram parameters of patients with atopic dermatitis (AD) and contribute to the existing literature. Methods: This cross-sectional study retrospectively analyzed data from pediatric patients diagnosed with AD, who presented to the Pediatric Allergy and Immunology outpatient clinic at Ümraniye Training and Research Hospital between January 1, 2024, and August 15, 2024. The sociodemographic characteristics, hemogram parameters, total IgE values, allergy history, and allergy test results of the patients were evaluated. Neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), neutrophil/lymphocyte/platelet ratio (NLPR), and pan-immune-inflammation value (PIV) were calculated. Allergen-specific IgE measurements were performed using the ImmunoCAP method, and skin prick tests were conducted for food and inhalant allergens. The severity of AD was classified as mild or moderate-to-severe based on the potency of the topical corticosteroids used by the patients. Statistical analyses were performed using SPSS version 29.0. Results: A total of 346 patients diagnosed with AD were included in the study. Among the patients, 53.8% were male, and the median age was 32.5 months. Mild AD was identified in 46.5% of the patients, while 53.5% had moderate-to-severe AD. Although sensitization to house dust mites, cats, eggs, and nuts was more frequent in patients with moderate-to-severe AD, no statistically significant difference was found between allergen sensitization and AD severity. Eosinophil count and eosinophillymphocyte ratio (ELR) were significantly higher in moderate-to-severe AD patients compared to those with mild AD (p=0.008 and p=0.004, respectively). No significant difference was found for other hemogram parameters and inflammatory markers. Patients with allergen sensitization had significantly higher WBC, basophil, eosinophil counts, and total IgE levels (p&lt;0.05). Conclusion: This study evaluated the relationship between hematological parameters and allergen sensitization in AD patients. Eosinophil count and ELR were significantly elevated with increasing AD severity. However, NLR, SII, SIRI, NLPR, and PIV were not associated with AD severity. Patients with allergen sensitization had significantly higher WBC, eosinophil, basophil counts, and total IgE levels. Our findings suggest that eosinophil count and total IgE levels may serve as predictive markers for AD severity and allergen sensitization. Routine measurement of these parameters could offer a practical approach in determining AD severity and allergen sensitization. Further research is needed to clarify the clinical significance of other hematological parameters.

Hematological and biochemical markers and cytokine levels in hospitalized psychiatric patients with COVID-19

BackgroundMultiple lines of evidence indicate a connection between the pathogenesis of coronavirus disease 2019 (COVID-19) and psychiatric diseases (PDs). To improve the treatment and management of individuals with psychosis and COVID-19, we evaluated biomarkers of PD patients, including those with schizophrenia (SCZ), bipolar disorder (BD), and major depression (MDD), along with the biomarkers of COVID-19.MethodsIn this study, 104 inpatients with concurrent PD and COVID-19 (PD+), the same 104 PD patients after they had recovered from COVID-19 (PD-), and 97 healthy controls (HCs) were evaluated. We analyzed the peripheral blood hematological parameters, serum biochemical parameters, and cytokine levels of the participants and compared the results among the three groups.ResultsThe monocyte count; neutrophil-to-lymphocyte ratio (NLR); monocyte-to-lymphocyte ratio (MLR); systemic immune-inflammation index (SII); and C-reactive protein (CRP), serum CK isoenzyme MB (CK-MB), glucose (GLU), and interleukin (IL)-6 levels were significantly greater (P &amp;lt; 0.05), whereas the magnesium (Mg) level was lower (P &amp;lt; 0.05) in both the PD+ and PD- groups than in the HC group. Moreover, the above indicators were significantly different between the PD+ and PD- groups (P &amp;lt; 0.05).ConclusionNeutrophil count, monocyte count, NLR, MLR, SII, CRP, CK-MB, GLU and IL-6 levels were positively correlated with COVID-19 and PD. The Mg level was negatively correlated with COVID-19 and PD. Our findings suggest that Mg supplementation might be considered a potential treatment approach for PD patients with COVID-19. Despite these insights, the underlying pathophysiological mechanisms remain unclear, highlighting the vital need for further research to validate and build upon these findings.