Prognostic nomogram for bladder cancer with brain metastases: a National Cancer Database analysis

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.

A Systematic Review of Prognostic Factors in Patients with Cancer Receiving Palliative Radiotherapy: Evidence-Based Recommendations

(1) Background: Prognostication in patients with cancer receiving palliative radiotherapy remains a challenge. To improve the process, we aim to identify prognostic factors in this population from the literature and offer evidence-based recommendations on prognostication in patients undergoing palliative radiotherapy for non-curable or advanced cancers. (2) Methods: A systematic review was performed on the medical literature from 2005 to 2023 to extract papers on the prognosis of palliative radiotherapy patients with advanced cancer. The initial selection was performed by at least two authors to determine study relevance to the target area. Studies were then classified based on type and evidence quality to determine final recommendations. (3) Results: The literature search returned 57 papers to be evaluated. Clinical and biological prognostic factors were identified from these papers to improve clinical decision making or construct prognostic models. Twenty prognostic models were identified for clinical use. There is moderate evidence supporting (i) evidence-based factors (patient, clinical, disease, and lab) in guiding decision making around palliative radiation; (ii) that certain biological factors are of importance; (iii) prognostication models in patients with advanced cancer; and that (iv) SBRT or re-irradiation use can be guided by predictions of survival by prognostic scores or clinicians. Patients with more favorable prognoses are generally better suited to SBRT or re-irradiation, and the use of prognostic models can aid in this decision making. (4) Conclusions: This evaluation has identified several factors or tools to aid in prognosis and clinical decision making. Future studies should aim to further validate these tools and factors in a clinical setting, including the leveraging of electronic medical records for data availability. To increase our understanding of how causal factors interact with palliative radiotherapy, future studies should also examine and include prediction of response to radiation as an outcome.

A Competing Risk Nomogram for Prediction of Prognosis in Patients With Primary Squamous Cell Thyroid Carcinoma

Objective: Primary squamous cell thyroid carcinoma (PSCTC) is an extremely rare carcinoma, accounting for less than 1% of all thyroid carcinomas. However, the factors contributing to PSCTC outcomes remain unclear. This study aimed to identify the prognostic factors and develop a prognostic predictive model for patients with PSCTC. Methods: The analysis included patients diagnosed with thyroid carcinoma between 1975 and 2016 from the Surveillance, Epidemiology, and End Results database. Prognostic differences among the 5 pathological types of thyroid carcinomas were analyzed. To determine prognostic factors in PSCTC patients, the Cox regression model and Fine-Gray competing risk model were utilized. Based on the Fine-Gray competing risk model, a nomogram was established for predicting the prognosis of patients with PSCTC. Results: A total of 198,757 thyroid carcinoma patients, including 218 PSCTC patients, were identified. We found that PSCTC and anaplastic thyroid cancer had the worst prognosis among the 5 pathological types of thyroid carcinoma (P < .001). According to univariate and multivariate Cox regression analyses, age (71-95 years) was an independent risk factor for poorer overall survival and disease-specific survival in PSCTC patients. Using Fine-Gray regression analysis, the total number of in situ/malignant tumors for patient (Number 1) (≥2) was identified as an independent protective factor for prognosis of PSCTC. The area under the curve, the concordance index (C-index), calibration curves and decision curve analysis revealed that the nomogram was capable of predicting the prognosis of PSCTC patients accurately. Conclusion: The competing risk nomogram is highly accurate in predicting prognosis for patients with PSCTC, which may help clinicians to optimize individualized treatment decisions.

Predictive nomograms for risk and prognostic factors in metastatic bladder cancer: a population-based study

Background

Given the poor prognosis of patients with metastatic bladder cancer (MBC), the development of an effective diagnostic and prognostic model is significant in cancer management and for guidance in clinical practice.

Methods

We acquired data of 23,180 bladder cancer patients from Surveillance Epidemiology and End Results (SEER) database registered from 2010 to 2019. The optimal cut-off value for patient age and tumor size was determined by x-tile software. Independent risk factors for MBC were identified by univariate and multivariate logistic regression analyses and prognosis factors were identified by univariate and multivariate cox regression analyses, and risk and prognostic nomograms were constructed. The accuracy of the nomograms was verified by receiver operating characteristic (ROC) curves, calibration curves, and its clinical utility was determined by decision curve analysis (DCA) curves and clinical impact curves (CIC). Kaplan-Meier (K-M) survival curves further confirmed the clinical validity of the prognostic model.

Results

Through logistic regression analyses, we derived that age, histological type, tumor size, T stage, and N stage were independent risk factors for metastasis in bladder cancer patients. By cox regression analyses, age, chemotherapy, histological type, bone, lung and liver metastases were identified as risk factors influencing prognosis of MBC patients. Area under the curve (AUC) of the risk nomogram was 0.80, the AUC values of 1/2/3 years were 0.74/0.71/0.71 in the training group and 0.81/0.77/0.77 in the validation group. Based on calibration curves, DCA curves, CIC and K-M curves, the nomograms were validated with excellent predictive performance and clinical utility for MBC.

Conclusions

The nomograms we constructed have perfect predictive accuracy and clinical practicality for MBC patients, enabling clinicians to provide treatment advice and clinical guidance to patients.

Mortality prediction models after radical cystectomy for bladder tumour: A systematic review and critical appraisal

INTRODUCTION

To identify risk-predictive models for bladder-specific cancer mortality in patients undergoing radical cystectomy and assess their clinical utility and risk of bias.

METHODS

Systematic review (CRD42021224626:PROSPERO) in Medline and EMBASE (from their creation until 31/10/2021) was screened to include articles focused on the development and internal validation of a predictive model of specific cancer mortality in patients undergoing radical cystectomy. CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and Prediction model Risk Of Bias ASsessment Tool (PROBAST) were applied.

RESULTS

Nineteen observational studies were included. The main predictors were sociodemographic variables, such as age (18 studies, 94.7%) and sex (17, 89.5% studies), tumour characteristics (TNM stage (18 studies, 94.7%), histological subtype/grade (15 studies, 78.9%), lymphovascular invasion (10 studies, 52.6%) and treatment with chemotherapy (13 studies, 68.4%). C-index values were presented in 14 studies. The overall risk of bias assessed using PROBAST led to 100% of studies being classified as high risk (the analysis domain was rated to be at high risk of bias in all the studies), and 52.6% showed low applicability. Only 5 studies (26.3%) included an external validation and 2 (10.5%) included a prospective study design.

CONCLUSIONS

Using clinical predictors to assess the risk of bladder-specific cancer mortality is a feasibility alternative. However, the studies showed a high risk of bias and their applicability is uncertain. Studies should improve the conducting and reporting, and subsequent external validation studies should be developed.

Characterization of Different Subtypes of Immune Cell Infiltration in Glioblastoma to Aid Immunotherapy

Glioblastoma multiforme (GBM) has been identified as a frequently occurring adult primary brain cancer that is highly aggressive. Currently, the prognostic outcome for GBM patients is dismal, even with intensive treatment, and the median overall survival (OS) is 14.6 months. Immunotherapy, which is specific at the cellular level and can generate persistent immunosurveillance, is now becoming a promising tool to treat diverse cancers. However, the complicated nature of the tumor microenvironment (TME) makes it challenging to develop anti-GBM immunotherapy because several cell types, cytokines, and signaling pathways are involved in generating the immunosuppressive environment. Novel immunotherapies can illustrate novel tumor-induced immunosuppressive mechanisms. Here, we used unsupervised clustering analysis to identify different subtypes of immune cell infiltration that actuated different prognoses, biological actions, and immunotherapy responses. Gene cluster A, with a hot immune cell infiltration phenotype, had high levels of immune-related genes (IRGs), which were associated with immune pathways including the interferon-gamma response and interferon-alpha response, and had low IDH1 and ATRX mutation frequencies. Gene cluster B, a cold immune cell infiltration subtype, exhibited a high expression of the KCNIP2, SCRT1, CPLX2, JPH3, UNC13A, GABRB3, ARPP21, DLGAP1, NRXN1, DLL3, CA10, MAP2, SEZ6L, GRIA2, and GRIA4 genes and a low expression of immune-related genes, i.e., low levels of immune reactivity. Our study highlighted the complex interplay between immune cell infiltration and genetic mutation in the establishment of the tumor immune phenotype. Gene cluster A was identified as an important subtype with a better prognosis and improved immunotherapy response.

Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method

Background: Heart failure (HF) is the main cause of mortality in hemodialysis (HD) patients. However, it is still a challenge for the prediction of HF in HD patients. Therefore, we aimed to establish and validate a prediction model to predict HF events in HD patients.

Methods: A total of 355 maintenance HD patients from two hospitals were included in this retrospective study. A total of 21 variables, including traditional demographic characteristics, medical history, and blood biochemical indicators, were used. Two classification models were established based on the extreme gradient boosting (XGBoost) algorithm and traditional linear logistic regression. The performance of the two models was evaluated based on calibration curves and area under the receiver operating characteristic curves (AUCs). Feature importance and SHapley Additive exPlanation (SHAP) were used to recognize risk factors from the variables. The Kaplan–Meier curve of each risk factor was constructed and compared with the log-rank test.

Results: Compared with the traditional linear logistic regression, the XGBoost model had better performance in accuracy (78.5 vs. 74.8%), sensitivity (79.6 vs. 75.6%), specificity (78.1 vs. 74.4%), and AUC (0.814 vs. 0.722). The feature importance and SHAP value of XGBoost indicated that age, hypertension, platelet count (PLT), C-reactive protein (CRP), and white blood cell count (WBC) were risk factors of HF. These results were further confirmed by Kaplan–Meier curves.

Conclusions: The HF prediction model based on XGBoost had a satisfactory performance in predicting HF events, which could prove to be a useful tool for the early prediction of HF in HD.

Integrated Analysis of a Competing Endogenous RNA Network Reveals a Prognostic lncRNA Signature in Bladder Cancer

Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs) to regulate mRNA expression through sponging microRNA in tumorigenesis and progression. However, following the discovery of new RNA interaction, the differentially expressed RNAs and ceRNA regulatory network are required to update. Our study comprehensively analyzed the differentially expressed RNA and corresponding ceRNA network and thus constructed a potentially predictive tool for prognosis. “DESeq2” was used to perform differential expression analysis. Two hundred and six differentially expressed (DE) lncRNAs, 222 DE miRNAs, and 2,463 DE mRNAs were found in this study. The lncRNA-mRNA interactions in the miRcode database and the miRNA-mRNA interactions in the starBase, miRcode, and mirTarBase databases were searched, and a competing endogenous RNA (ceRNA) network with 186 nodes and 836 interactions was subsequently constructed. Aberrant expression patterns of lncRNA NR2F1-AS1 and lncRNA AC010168.2 were evaluated in two datasets (GSE89006, GSE31684), and real-time polymerase chain reaction was also performed to validate the expression pattern. Furthermore, we found that these two lncRNAs were independent prognostic biomarkers to generate a prognostic lncRNA signature by univariate and multivariate Cox analyses. According to the lncRNA signature, patients in the high-risk group were associated with a poor prognosis and validated by an external dataset. A novel genomic-clinicopathologic nomogram to improve prognosis prediction of bladder cancer was further plotted and calibrated. Our study deepens the understanding of the regulatory ceRNA network and provides an easy-to-do genomic-clinicopathological nomogram to predict the prognosis in patients with bladder cancer.

Emerging principles of brain immunology and immune checkpoint blockade in brain metastases

Brain metastases are the most common type of brain tumours, harbouring an immune microenvironment that can in principle be targeted via immunotherapy. Elucidating some of the immunological intricacies of brain metastases has opened a therapeutic window to explore the potential of immune checkpoint inhibitors in this globally lethal disease. Multiple lines of evidence suggest that tumour cells hijack the immune regulatory mechanisms in the brain for the benefit of their own survival and progression. Nonetheless, the role of the immune checkpoint in the complex interplays between cancers cells and T cells and in conferring resistance to therapy remains under investigation. Meanwhile, early phase trials with immune checkpoint inhibitors have reported clinical benefit in patients with brain metastases from melanoma and non-small cell lung cancer. In this review, we explore the workings of the immune system in the brain, the immunology of brain metastases, and the current status of immune checkpoint inhibitors in the treatment of brain metastases.

Construction and validation of a machine learning-based nomogram: A tool to predict the risk of getting severe coronavirus disease 2019 (COVID-19)

Background

Identifying patients who may develop severe coronavirus disease 2019 (COVID‐19) will facilitate personalized treatment and optimize the distribution of medical resources.

Methods

In this study, 590 COVID‐19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by two machine learning methods in the training set, 5 out of 31 clinical features were selected into the model building to predict the risk of developing severe COVID‐19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in two different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance.

Results

From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C‐reactive protein (CRP), lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS), and erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID‐19. In the training cohort, the area under curves (AUCs) were 0.822 (95% CI, 0.765–0.875) and the internal validation cohort was 0.762 (95% CI, 0.768–0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI, 0.627–0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and the actual situation. And DCA analysis also conferred high clinical net benefit.

Conclusion

In this study, our predicting model based on five clinical characteristics of COVID‐19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

Type and timing of systemic therapy use predict overall survival for patients with brain metastases treated with radiation therapy

INTRODUCTION

This study aimed to investigate whether systemic therapy (ST) use surrounding radiation therapy (RT) predicts overall survival (OS) after RT for patients with brain metastases (BMs).

METHODS

Provincial RT and pharmacy databases were used to review all adult patients in British Columbia, Canada, who received a first course of RT for BMs between 2012 and 2016 (n = 3095). Multivariate analysis on a randomly selected subset was used to develop an OS nomogram.

RESULTS

In comparison to the 2096 non-recipients of ST after RT, the median OS of the 999 recipients of ST after RT was 5.0 (95% Confidence interval (CI) 4.1-6.0) months longer (p < 0.0001). Some types of ST after RT were independently predictive of OS: targeted therapy (hazard ratio (HR) 0.42, CI 0.37-0.48), hormone therapy (HR 0.45, CI 0.36-0.55), cytotoxic chemotherapy (HR 0.71, CI 0.64-0.79), and immunotherapy (HR 0.64, CI 0.37-1.06). Patients who discontinued ST after RT had 0.9 (CI 0.3-1.4) months shorter median OS than patients who received no ST before or after RT (p < 0.0001). In the multivariate analysis of the 220-patient subset, established prognostic variables (extracranial disease, performance status, age, cancer diagnosis, and number of BMs), and the novel variables "ST before RT" and "Type of ST after RT" independently predicted OS. The nomogram predicted 6- and 12-month OS probability and median OS (bootstrap-corrected Harrell's Concordance Index = 0.70).

CONCLUSIONS

The type and timing of ST use surrounding RT predict OS for patients with BMs.

Epidemiology of and prognostic factors for patients with sarcomatoid carcinoma: a large population-based study

Sarcomatoid carcinoma (SC) is regarded as a rare malignant neoplasm associated with poor outcomes. This study aimed to explore the epidemiological characteristics and prognostic factors of SC, and establish a clinical predictive model. The Surveillance, Epidemiology, and End Results database was used for data inquiry of patients with SC. Relevant population materials were used for age-adjusted incidence, limited-duration prevalence and prognostic analyses, and also for nomogram construction and validation. A total of 17,917 cases of SC were identified. Among them, 12,276 (68.52%) were women and 14,265 (79.62%) were white. Most cases occurred in the female genital system, accounting for 41.10% of all SCs. The median age at diagnosis was 68 years. The incidence and prevalence of SC increased substantially over time. The age-adjusted incidence increased from 0.31/100,000 in 1973 to 1.26/100,000 by 2014, a 4.06-fold change. Among site groups, the incidence of SC in the female genital and the respiratory system increased most significantly (P < 0.001). As for stage and grade, the incidence increased the most in distant and high-grade SC, respectively (P < 0.001). Moreover, the survival duration varied significantly by site, histology, stage and grade (P < 0.001). The multivariable analyses showed that the year of diagnosis, age, sex, race, grade, stage, and site were all significant prognostic factors (P < 0.001). Among these, stage and primary tumor site were the most valuable indicators of outcomes. Furthermore, a nomogram comprising age, histology, grade, stage and site were established to predict the 3-/5-year survival probability. The concordance indexes of the nomogram were 0.745 (95% confidence interval [CI]: 0.737-0.753) and 0.743 (95% CI: 0.728-0.756) for the internal and external validations, respectively. The calibration plot demonstrated satisfactory consistency between the actual and predicted outcomes in both the internal and external validations. In conclusion, increasing incidence and prevalence of SC was observed in our study, suggesting that SC is more prevalent than previously reported. Clinicians should be familiar with the characteristics of these tumors. Furthermore, the established nomogram could accurately predict the 3-/5-year survival rate of patients with SC, which may be of value for patient counselling and risk stratification.

Marital Status and Prognostic Nomogram for Bladder Cancer With Distant Metastasis: A SEER-Based Study

Background

To investigate the impact of marital status on overall survival (OS) and create a prognostic nomogram predicting OS in distant-metastatic bladder cancer (DMBC) patients.

Methods

The Surveillance, Epidemiology, and End Results (SEER) database was explored to recruit DMBC patients from 2010 to 2015. Kaplan–Meier survival analysis was used to compare survival differences among different marital status. Univariate and multivariate analyses were used to screen for prognostic factors and then constructed the nomogram based on Cox proportional hazard regression models. Calibration plot diagrams and concordance index (C-index) were used to verify the prognostic nomogram.

Results

Kaplan–Meier curves suggested the significant differences of OS among different marital status existed in total (P < 0.001), female (P = 0.011) and male (P = 0.001) DMBC patients, respectively. Multivariate analysis indicated marital status was an independent prognostic factor for OS of DMBC patients. Nomogram showed the contribution of marital status to predicting OS was small. Other independent prognostic factors included age, grade, histology type, surgery of primary site, chemotherapy, and metastasis pattern. By combining seven factors, we constructed a prognostic nomogram for DMBC patients. The C-index of this nomogram for OS prediction was 0.722 (95% CI 0.712–0.732). The calibration curves showed perfect consistency between observed and predictive survival.

Conclusions

Marital status was an independent prognostic factor for OS of DMBC patients, but its contribution to predicting OS was small. The prognostic nomogram will provide an individualized evaluation of OS and guidance for suitable treatments in DMBC patients.

Prognostic Score-based Clinical Factors and Metabolism-related Biomarkers for Predicting the Progression of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor representing more than 90% of primary liver cancer. This study aimed to identify metabolism-related biomarkers with prognostic value by developing the novel prognostic score (PS) model. Transcriptomic profiles derived from TCGA and EBIArray databases were analyzed to identify differentially expressed genes (DEGs) in HCC tumor samples compared with normal samples. The overlapped genes between DEGs and metabolism-related genes (crucial genes) were screened and functionally analyzed. A novel PS model was constructed to identify optimal signature genes. Cox regression analysis was performed to identify independent clinical factors related to prognosis. Nomogram model was constructed to estimate the predictability of clinical factors. Finally, protein expression of crucial genes was explored in different cancer tissues and cell types from the Human Protein Atlas (HPA). We screened a total of 305 overlapped genes (differentially expressed metabolism-related genes). These genes were mainly involved in "oxidation reduction," "steroid hormone biosynthesis," "fatty acid metabolic process," and "linoleic acid metabolism." Furthermore, we screened ten optimal DEGs (CYP2C9, CYP3A4, and TKT, among others) by using the PS model. Two clinical factors of pathologic stage (P < .001, HR: 1.512 [1.219-1.875]) and PS status (P <.001, HR: 2.259 [1.522-3.354]) were independent prognostic predictors by cox regression analysis. Nomogram model showed a high predicted probability of overall survival time, and the AUC value was 0.837. The expression status of 7 proteins was frequently altered in normal or differential tumor tissues, such as liver cancer and stomach cancer samples.We have identified several metabolism-related biomarkers for prognosis prediction of HCC based on the PS model. Two clinical factors were independent prognostic predictors of pathologic stage and PS status (high/low risk). The prognosis prediction model described in this study is a useful and stable method for novel biomarker identification.

Ribosome Binding Protein 1 Correlates with Prognosis and Cell Proliferation in Bladder Cancer

Introduction

Ribosome binding protein 1 (RRBP1) is reported to be correlated with tumor formation and progression. However, the role of RRBP1 in bladder cancer is unclear. In this study, we aimed to investigate the expression of RRBP1 and its influence on cell proliferation in bladder cancer.

Methods

Quantification real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) were used to detect the expression levels of RRBP1 in 138 bladder cancer and matched adjacent normal bladder tissues. Then, the clinical significance of RRBP1 in bladder cancer was evaluated. The effect of RRBP1 on cell proliferation and its potential mechanism were further explored.

Results

Results show that the mRNA levels of RRBP1 in bladder cancer were significantly higher compared with those in normal tissues (P< 0.001). IHC results show the high-expression rate of RRBP1 in bladder cancer was 68.8%, which was significantly greater than those in normal tissues (40.6%, P< 0.001). RRBP1 high-expression was significantly associated with differentiation, T stage and lymph node metastasis in bladder cancer (P< 0.05). The overall survival time of patients with RRBP1 high-expression was significantly reduced compared to those with RRBP1 low-expression. Moreover, RRBP1 overexpression significantly promoted cell proliferation, which was correlated with Smad1/Smad3/TGF-β1 signal pathway.

Conclusion

RRBP1 high-expression correlates with prognosis and promotes cell proliferation in bladder cancer, which could be a potential biomarker.

The Prognostic Value of Preoperative Geriatric Nutritional Risk Index in Patients with Pancreatic Ductal Adenocarcinoma

Purpose

Patients with malignancy are more likely to develop nutritional problems. The Geriatric Nutritional Risk Index (GNRI) is a new prognostic index for evaluating nutritional status. The objective of this study was to assess if preoperative GNRI could be a prognostic factor for patients with pancreatic ductal adenocarcinoma (PDAC) who underwent radical surgery.

Patients and Methods

This study included 282 consecutive patients with incident pancreatic ductal adenocarcinoma who were treated with radical surgery. The Cox regression analysis was performed to calculate the overall survival (OS) and assess the prognostic factors. A nomogram was developed based on the results of the multivariate analysis, and the predictive accuracy of the nomogram was assessed.

Results

Among the 282 patients, there are 117 males and 165 females. The patients had a mean age of 58.7 ±13.5 years, with the median follow-up time of 72.9 months (interquartile range, 0.7 to 115.2 months). They were classified into abnormal (GNRI ≤ 98) and normal (GNRI > 98) GNRI groups, respectively. Multivariate Cox analysis showed that age (HR = 1.023), drinking history (HR = 1.453), tumor grade (HR = 1.633), TNM stage (HR = 1.921), and GNRI (HR = 1.757) were significantly associated with OS. Based on the above variables, the nomogram was established. The concordance index (C-index) and time-dependent receiver operating characteristics curve (tdROC) showed the nomogram was superior to TNM grade and tumor grade in predicting the OS of patients with PDAC.

Conclusion

GNRI could be a useful prognostic indicator in patients with PDAC who received surgery. Based on the GNRI and the other clinical indicators, we developed a nomogram model that can provide an accurate estimation of OS in patients with PDAC after radical surgery.