A prognostic model using the neutrophil-albumin ratio and PG-SGA to predict overall survival in advanced palliative lung cancer

Construction and validation of a nomogram to predict 1-year mortality risk in patients with HIV/AIDS undergoing maintenance hemodialysis

This single-center retrospective study aimed to explore the 1-year mortality risk factors in 166 patients with human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) undergoing maintenance hemodialysis (MH) between 6 June 2017 and 6 June 2023, and construct a 1-year mortality prediction model. The patients were classified into survival and mortality groups based on the 1-year follow-up results, and into training and validation sets at a ratio of 1:1 (53 mortalities and 53 survivors in the training set and 48 mortalities and 58 survivors in the validation set). Stepwise logistic regression was used to construct a 1-year mortality prediction model and to visualize it as a nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, and decision curves were used for nomogram evaluation in the training set and validation in the validation set. Age (≥52 years) (OR (95% CI): 2.05 (3.191-18.892), p < .001), neutrophil to albumin ratio (NAR) (≥0.135) (OR (95% CI): 4.753 (2.011-11.234), p < .001), and HIV-RNA (≥24,650) (OR (95% CI): 13.786 (5.493-34.598), p < .001), represents three of five independent risk factors of 1-year mortality in HIV/AIDS undergoing MH. The AUC of the nomogram for the training and validation sets were 0.908 (95% CI: 0.853-0.963) and 0.939 (95% CI: 0.896-0.983), respectively. The 1-year mortality prediction showed good separation capacity, calibration capacity, and clinical net benefit, which may benefit the management of patients with HIV/AIDS undergoing MH.

Relationship Between Coronary Collateral Circulation and the Neutrophil-Percentage-to-Albumin Ratio in Patients with Chronic Coronary Syndrome

Background and Objectives: The neutrophil-percentage-to-albumin ratio (NPAR) has been recognized as an independent risk factor for cardiovascular diseases. In our study, we investigated whether the NPAR is associated with the formation of coronary collateral circulation (CCC) in patients with chronic coronary syndrome (CCS). Materials and Methods: A total of 681 patients with CCS were included in this study. Of these patients, 571 had chronic total occlusion in at least one major vessel and developed collateral vessels. In total, 110 patients were in the control group, who had CCS but did not have complete occlusion in a major vessel and did not develop collateral vessels. Patients with collateral vessels on coronary angiography were divided into two groups according to the Rentrop score: poor CCC (Rentrop 0-1) and good CCC (Rentrop 2-3). Blood samples were taken for the NPAR and other biochemical parameters in all patients during hospitalization. The NPAR was calculated as the neutrophil-percentage-to-albumin ratio. Results: The group of patients with poor CCC had a higher white blood count (WBC), neutrophil, C-reactive protein (CRP), neutrophil-lymphocyte ratio (NLR), CRP/albumin ratio (CAR), and NPAR values than patients with good CCC (p < 0.001, for all). Multivariate logistic regression analysis showed that high NPAR levels were an independent predictor of poor CCC (OR: 2.79, 95% CI:1.7-4.6, p < 0.001), accompanied by neutrophil, CRP, CAR, and NLR levels. In the receiver operator characteristic curve (ROC analysis), the cut-off value for the NPAR to indicate poor CCC was 1.78 with a sensitivity of 76.6% and specificity of 81.4% (area under ROC curve = 0.804 95% CI (0.753-0.854), p < 0.001). Conclusions: We demonstrated that the NPAR may be an independent predictor of poor CCC development in clinical practice.

Development and validation of a nomogram model for predicting overall survival in patients with gastric carcinoma

BACKGROUND

The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China, with the disease's intricate and varied characteristics further amplifying its health impact. Precise forecasting of overall survival (OS) is of paramount importance for the clinical management of individuals afflicted with this malignancy.

AIM

To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric carcinoma.

METHODS

Data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and 2020. Least absolute shrinkage and selection operator, univariate, and multivariate Cox regression analyses were employed to identify independent prognostic factors. A nomogram model was developed to predict gastric cancer patient outcomes. The model's predictability and discriminative ability were evaluated via receiver operating characteristic curves. To evaluate the clinical utility of the model, Kaplan-Meier and decision curve analyses were performed.

RESULTS

A total of ten independent prognostic factors were identified, including body mass index, tumor-node-metastasis (TNM) stage, radiation, chemotherapy, surgery, albumin, globulin, neutrophil count, lactate dehydrogenase, and platelet-to-lymphocyte ratio. The area under the curve (AUC) values for the 1-, 3-, and 5-year survival prediction in the training set were 0.843, 0.850, and 0.821, respectively. The AUC values were 0.864, 0.820, and 0.786 for the 1-, 3-, and 5-year survival prediction in the validation set, respectively. The model exhibited strong discriminative ability, with both the time AUC and time C-index exceeding 0.75. Compared with TNM staging, the model demonstrated superior clinical utility. Ultimately, a nomogram was developed via a web-based interface.

CONCLUSION

This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients, which demonstrated strong predictive ability. Based on these findings, this model can aid clinicians in implementing personalized interventions for patients with gastric cancer.

Combined impact of prognostic nutritional index, fibrinogen-to-albumin ratio, and neutrophil-to-lymphocyte ratio on surgical outcomes and prognosis in hepatocellular carcinoma

This study evaluated the predictive value of the prognostic nutritional index (PNI), fibrinogen-to-albumin ratio (FAR), and neutrophil-to-lymphocyte ratio (NLR) for overall survival in hepatocellular carcinoma (HCC) patients. A total of 283 HCC cases from Hunan Provincial People's Hospital were included in the analysis, with 45 additional patients as external validation. The relationship between these indices and patient prognosis was further evaluated using the Kaplan-Meier method and Cox regression analysis. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive performance of these indices for overall survival (OS) and to determine the optimal cutoff values. ROC curve analysis revealed that the area under the curve (AUC) for PNI, FAR, and NLR was 0.723, 0.857, and 0.872, respectively. Multivariate analysis identified hepatitis history, intraoperative blood transfusion, FAR, NLR, and PNI as independent prognostic factors (all P<0.05). The resulting prediction model demonstrated strong performance in both the training (C-index =0.917) and external validation (C-index =0.853) cohorts, with AUCs of 0.889 and 0.931 for 6-month and 1-year prediction in the validation set, respectively. These findings suggest that preoperative levels of peripheral blood PNI, FAR, and NLR are closely associated with the surgical prognosis of HCC patients. The prognostic prediction model developed based on these indices exhibits good predictive efficacy.

Development and validation of a prognostic nomogram incorporating neutrophil-to-albumin ratio for predicting overall survival in patients with nasopharyngeal carcinoma undergoing concurrent chemoradiotherapy

BACKGROUND

Recent research suggests that the emerging neutrophil-albumin ratio (NAR) has a significant correlation with the survival outcomes across a range of tumors, yet its predictive significance for nasopharyngeal carcinoma (NPC) remains insufficiently investigated. This study aimed to evaluate the relationship between the neutrophil-to-albumin ratio (NAR) and overall survival (OS) in patients with NPC, as well as to develop a corresponding prognostic model.

METHODS

This retrospective analysis included 861 NPC patients treated with concurrent chemoradiotherapy (CCRT), who were randomly divided into a training group (n = 605) and a validation group (n = 256). To identify factors associated with OS and construct a prognostic nomogram, both univariate and multivariate Cox regression analyses were performed. The nomogram's prognostic accuracy was evaluated and independently validated.

RESULTS

The NAR score successfully segregated NPC patients into two categories with significantly different OS (HR = 0.536; 95 % CI: 0.296-0.972, P = 0.040). Through multivariate analysis, factors such as age, T stage, N stage, and NAR score were identified as independent predictors of OS, leading to the creation of a prognostic nomogram. This nomogram demonstrated superior predictive capability for OS [C-index = 0.702 (95 % CI: 0.636-0.768)], surpassing that of the conventional staging system [C-index = 0.651 (95 % CI: 0.549-0.752)]. The findings underwent internal validation within an independent cohort.

CONCLUSIONS

The NAR, an emergent biomarker combining nutritional and inflammatory status, offers a practical, low-cost, and non-invasive prognostic measure for NPC patients treated with CCRT. Additionally, the prognostic nomogram derived from NAR surpasses traditional staging systems in predictive accuracy.

Associations between Patient-Generated Subjective Global Assessment criteria and all-cause mortality among cancer patients: Evidence from baseline and longitudinal analyses

OBJECTIVES

The prognostic effects of the Patient-Generated Subjective Global Assessment (PG-SGA) criteria in cancer survivors have been observed but require validation in clinical practice. This study was designed to evaluate the prognostic effects of baseline and longitudinal changes in PG-SGA scores on all-cause mortality among Chinese cancer patients in a real-world setting.

METHODS

Study patients were selected from one representative tertiary hospital in West China. Kaplan-Meier curves and Cox regression analyses were used to estimate the prognostic effect of baseline and dynamic changes in PG-SGA scores on the all-cause mortality of cancer patients. Receiver operating characteristic curves and a concordance index were used to evaluate the predictive accuracy of PG-SGA criteria.

RESULTS

A total of 1415 cancer patients were included in this study, with a mean age of 46 years old. Cox regression analysis showed that baseline malnourished status was significantly associated with the survival of cancer patients (PG-SGA 4-8: hazard ratio [HR] = 1.46, 95% confidence interval [CI]: 1.09-1.96, P = 0.012; PG-SGA ≥9: HR = 1.78, 95% CI: 1.34-2.37, P < 0.001). Cancer patients with longitudinal increased PG-SGA scores (>2 points) were observed to have high risks for mortality (HR = 1.69, 95% CI: 1.04-2.74, P = 0.033). Compared with longitudinal changes in PG-SGA scores, baseline malnourished status showed higher predictive power in identifying the risk subgroup (concordance index: 0.646 vs. 0.586). Sensitivity analyses supported the main findings.

CONCLUSIONS

This study highlights the prognostic value of baseline and dynamic changes in PG-SGA scores for cancer patients, which can help improve their outcomes.

Development and validation of a novel nomogram model predicting the unfavorable outcome based on NAR and collaterals status for patients with AIS

INTRODUCTION

Stroke is a leading cause of disability and mortality globally. This study aimed to develop a prognostic nomogram based on neutrophil-to-albumin ratio (NAR) and collateral status in acute ischemic stroke (AIS) patients with anterior large vessel occlusion (LVO).

MATERIAL & METHOD

590 AIS patients with LVO assessed for regional leptomeningeal collateral (rLMC) were retrospectively enrolled, and randomly divided into a training set (n = 414) and a testing set (n = 176). Unfavorable functional outcome was defined as a modified Rankin scale (mRS) score of 3 to 6 at 3 months. We assessed the accuracy and clinical utility of the nomogram using calibration plots, area under the curve (AUC), decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI).

RESULTS

Both NAR and rLMC were independently associated with unfavorable outcome at 3 months (OR=8.96, p=0.0341; OR=0.89, p=0.0002, respectively). The developed nomogram (akaike information criterion (AIC)=398.77), which included NAR, rLMC and other factors, showed good performance (the AUC for the development and validation cohorts was 0.848 and 0.840 respectively) and improved the predictive value compared to a model without NAR and rLMC, according to an overall NRI of 3.27% (p=0.2401), overall IDI of 3.27% (p=0.2414), and a higher AUC (0.848 vs 0.831).

CONCLUSIONS

NAR can serve as an independent predictor in AIS patients with anterior LVO, and the nomogram incorporating NAR and rLMC is reliable in predicting unfavorable outcome. Further studies with larger sample sizes are needed to validate and extend these findings.

Predictive Value of Pretreatment Neutrophil to Albumin Ratio in Response to Neoadjuvant Chemotherapy of Breast Cancer

Background

The immune system appears to play a crucial role in how breast cancer responds to chemotherapy. In this study, we investigated a peripheral marker of immune and inflammation named the neutrophil to albumin ratio (NAR) to explore its potential relationship with pathological complete response (pCR) in locally advanced breast cancer patients who underwent neoadjuvant chemotherapy (NAC).

Methods

We conducted a retrospective analysis of 212 consecutive breast cancer patients who received NAC. The NAR was calculated by examining the complete blood cell count and albumin level in peripheral blood before starting NAC. Through ROC curve analysis, we determined the optimal cutoff value for NAR as 0.0877. We used Pearson's chi-square test or Fisher's exact test to evaluate the relationship between NAR and pCR, as well as other clinical and pathological characteristics. Logistic regression models were employed for univariate and multivariate analyses.

Results

The results of both univariate and multivariate logistic regression analyses showed that NAR was associated with tumor pathological regression. The NAR high group had a higher pCR rate compared to the NAR low group (OR 3.127 [95% CI 1.545-6.328]; p = 0.002).

Conclusion

According to this study, it was observed that patients with breast cancer who had high levels of NAR were more likely to achieve pCR when undergoing NAC.

Inflammatory indexes predict mortality in non-malignant patients in palliative care

BACKGROUND

The literature on the use of inflammatory indexes for palliative care patients without malignancy is scarce.

AIMS

To determine which inflammatory indexes are associated with the mortality risks of non-malignant patients hospitalised and receiving palliative care.

METHODS

Discharged or deceased patients in a palliative care unit of a secondary care hospital were included. The laboratory values were obtained during the first 48 hours of hospitalisation.

FINDINGS

As a result of univariate Cox regression analysis, 14-day mortality rate was affected by lymphocyte ratio, neutrophil-to-albumin ratio (NAR), C-reactive protein/albumin ratio (CAR), multi-inflammatory indexes (MII-1) and MII-2 (p<0.001, p=0.001, p=0.002, p=0.009 and p=0.003, respectively); NLR, CLR, NAR, CAR, MII-1 and MII-2 (respectively p=0.005, p<0.001, p<0.001, p<0.001, p=0.001 and p<0.001) affected 28-day mortality rate. Indexes that statistically significantly increased both 14-day and 28-day mortality rates independently of other variables were CLR, NAR, CAR, MII-1 and MII-2.

CONCLUSION

High values in inflammatory indexes, including C-reactive protein and albumin increase the risk of 14-day and 28-day mortality rates in palliative care non-malignant patients.

Prognostic Biomarkers of Systemic Inflammation in Non-Small Cell Lung Cancer: A Narrative Review of Challenges and Opportunities

Non-small cell lung cancer (NSCLC) is a common malignancy and is associated with poor survival outcomes. Biomarkers of systemic inflammation derived from blood tests collected as part of routine clinical care offer prognostic information for patients with NSCLC that may assist clinical decision making. They are an attractive tool, as they are inexpensive, easily measured, and reproducible in a variety of healthcare settings. Despite the wealth of evidence available to support them, these inflammatory biomarkers are not yet routinely used in clinical practice. In this narrative review, the key inflammatory indices reported in the literature and their prognostic significance in NSCLC are described. Key challenges limiting their clinical application are highlighted, including the need to define the optimal biomarker of systemic inflammation, a lack of understanding of the systemic inflammatory landscape of NSCLC as a heterogenous disease, and the lack of clinical relevance in reported outcomes. These challenges may be overcome with standardised recording and reporting of inflammatory biomarkers, clinicopathological factors, and survival outcomes. This will require a collaborative approach, to which this field of research lends itself. This work may be aided by the rise of data-driven research, including the potential to utilise modern electronic patient records and advanced data-analysis techniques.

Neutrophil-to-albumin Ratio as a Prognostic Factor in Patients with Acute Ischemic Stroke

BACKGROUND

Neutrophil-To-Albumin Ratio (NAR) is a novel inflammatory biomarker. However, the potential prognostic value of NAR in acute ischemic stroke (AIS) remains unclear. This study aimed to evaluate whether NAR levels correlated with the 3-month modified Rankin scale (mRS) in patients with AIS.

METHODS

AIS patients were included in this retrospective study. NAR was calculated as the ratio of absolute neutrophil count to serum albumin level. Logistic regression analyses were used to investigate the effect of NAR on 3-month mRS of AIS. The predictive values of NAR, albumin level, and neutrophil count were compared utilizing receiver operating characteristic (ROC) curves. Moreover, subgroup analyses and interaction tests were conducted to evaluate the consistency of NAR's effect on AIS prognosis.

RESULTS

Of the 780 patients included, 403 (51.67%) had a poor clinical outcome (mRS 3-6) at 3 months. NAR was independently correlated to 3-month poor functional outcome after adjusting for confounders (Odds ratios (OR), 9.34; 95% confidence intervals (CI), 1.09 to 80.13; p =0.0417). Subgroup analysis showed a relative effect consistent with the overall population results, and no statistical interactions were found in the subgroups (all p for interaction > 0.05). The ROC curve showed that the prognosis-related cutoff value for NAR was 0.123, with corresponding specificity and sensitivity of 53.55% and 63.94%, respectively. When comparing the predictive power, NAR (0.590; 95%CI 0.549-0.630) exhibited the highest area under the curve (AUC) of ROC compared to neutrophils (0.584; 95%CI 0.543-0.624) and albumin (0.540; 95%CI 0.500-0.581).

CONCLUSION

There is a positive relationship between NAR levels and 3-month poor functional outcomes in AIS patients, supporting the potential of NAR as a readily available and economic serum biomarker for the early identification of AIS prognosis. Further studies are required to validate the prognostic value and clinical utility of the NAR.

Routine laboratory parameters to support decision on parenteral nutrition in palliative care

Introduction

Parenteral nutrition (PN) is widely used in palliative care (PC), but there is limited evidence to support its use at the end of life (EOL). This aim of this was to investigate the relationship between routine laboratory parameters and survival in patients receiving PN, and to develop a decision tree model to support clinicians decide whether to start or forgo PN.

Methods

The laboratory parameters of 113 patients with advanced diseases who were admitted to a specialized palliative care unit (PCU) were analyzed at two points in time: T0 = before PN, T1 = two weeks after initiation of PN. Univariate Mann-Whitney U-tests and multivariate linear regression models, as well as a decision tree analysis were computed; all in relation to survival time.

Results

The final regression model was significant with p = 0.001 (adjusted R2 = 0.15) and included two predictors for survival time after PN initiation: the CRP/albumin ratio and urea at T1 (ps = 0.019). Decision tree analysis revealed three important predictors for classification of survival time after PN initiation: CRP, urea, and LDH (all at T0).

Discussion

The decision tree model may help to identify patients likely to benefit from PN, thus supporting the clinical decision whether or not to start PN.

Inflammatory and nutritional markers predict the risk of post-operative delirium in elderly patients following total hip arthroplasty

Objectives

This study intended to explore whether albumin-associated inflammatory and nutritional markers could predict post-operative delirium (POD) in older patients after total hip arthroplasty (THA). In addition, we established a nomogram model for POD prediction.

Methods

Totally, 254 elderly cases who received THA were included. Clinical and laboratory data of these patients were retrospectively collected. Albumin-associated inflammatory and nutritional markers included neutrophil-to-albumin ratio (NAR), CRP-to-albumin ratio (CAR), prognostic nutritional index (PNI), and systemic inflammation score (SIS). The LASSO, univariate and multivariate logistic regression analyses were utilized to screen risk factors. A nomogram model was developed according to the results of multivariate regression analyses.

Results

Among 254 patients, 49 cases had POD with an incidence of 19.3%. LASSO regression and multivariate logistic analyses suggested that preoperative NAR, preoperative PNI, preoperative SIS, and age >75 years were risk factors for POD. A nomogram model was developed according to the results of multivariate logistic analyses. The calibration curve suggested that the predicted probability of this nomogram model was in good line with the actual probability. The DCA showed that this nomogram model had net benefits for the prediction of POD for elderly patients following THA.

Conclusion

Albumin-associated inflammatory and nutritional markers including NAR, PNI, and SIS could predict POD in elderly patients following THA.

Prognostic utility of neutrophil-to-albumin ratio in surgically treated oral squamous cell carcinoma

BACKGROUND

We aimed to evaluate the prognostic significance of preoperative neutrophil-to-albumin ratio (NAR) in oral squamous cell carcinoma (OSCC).

METHODS

A total of 622 patients with surgically treated OSCC were enrolled. NAR was defined as the absolute neutrophil count divided by the serum albumin level in peripheral blood before the radical surgery. Cox proportional hazards model were used to discover survival outcome-associated factors.

RESULTS

The optimal cut-off of NAR to predict overall survival (OS) was determined to be 0.1. In Cox model, high NAR was identified as an independent negative prognosticator of OS, cancer-specific survival, and recurrence-free survival (adjusted hazard ratio: 1.503, 1.958, and 1.727, respectively; all p < 0.05). The NAR-based nomogram accurately predicted OS (concordance index: 0.750).

CONCLUSION

Our study suggests that preoperative NAR is a convenient and effective prognostic marker for OSCC and NAR-based nomogram can be a promising prognostic tool in clinical setting.

Prognostic Value of Neutrophil Percentage-to-Albumin Ratio in Patients with Oral Cavity Cancer

This study investigated preoperative neutrophil percentage-to-albumin ratio (NPAR) for predicting oral cavity squamous cell carcinoma (OSCC) survival. We retrospectively analyzed 368 patients who received curative OSCC surgery between 2008 and 2017. Receiver operating characteristic curve analyses were employed to identify the optimal NPAR cutoff (16.93), and the patients were then separated into low-NPAR and high-NPAR groups. Intergroup differences in survival were determined through Kaplan−Meier analysis and log-rank tests. Disease-free survival (DFS) and overall survival (OS) predictors were identified using Cox proportional-hazards models. A nomogram integrating independent prognostic factors was proposed to increase the accuracy of OS prediction. A high NPAR (≥16.93) was associated with worse median OS and DFS than was a low NPAR (both p < 0.001); this finding was confirmed through multivariate analyses (hazard ratio (HR) for OS = 2.697, p < 0.001; and HR for DFS = 1.671, p = 0.008). The nomogram’s favorable predictive ability was confirmed by the calibration plots and concordance index (0.784). The preoperative NPAR is thus a promising prognostic biomarker in patients with OSCC after external validation in a larger cohort. Our nomogram can facilitate clinical use of the NPAR and provides accurate individualized OS predictions.