The Value of Prognostic Nutritional Index (PNI) on Newly Diagnosed Diffuse Large B-Cell Lymphoma Patients: A Multicenter Retrospective Study of HHLWG Based on Propensity Score Matched Analysis

Clinical Significance of the Prognostic Nutritional Index in Predicting Delirium among Critically Ill Patients: A Retrospective Cohort Study

Delirium is a serious and complex problem facing critically ill patients. This retrospective study aimed to explore the association between prognostic nutrition index (PNI) and delirium in critically ill patients in the intensive care unit (ICU). This study was based on the Medical Information Mart Intensive Care IV (MIMIC IV) database. Patients over 18 years of age were enrolled. Univariate and multivariate logistic regression analyses were performed to explore the association between PNI and delirium. Tendency analysis, subgroups analysis, and restricted cubic spline (RCS) were selected to further certify the association. The receiver operating characteristic curve (ROC) was adopted to assess the predictability of PNI to delirium. Propensity score matching (PSM) was used to reduce the interference of confounders. A total of 3,105 patients participated in this study. As the grade of malnutrition increases, the incidence of delirium increases in all models. The odds ratios (OR) of delirium in the fully adjusted model were 1.00 (reference), 1.04 (0.80, 1.36), 1.53 (1.17, 1.99), and 1.93 (1.44, 2.59). Strong U-shaped curves were found with RCS analysis between delirium and PNI in the subgroups of patients over 60 years of age and patients with chronic obstructive pulmonary disease (COPD). After PSM, the ORs of delirium were 1.44 (1.16, 1.79) and 1.53 (1.22, 1.93), respectively, in the univariate and multivariate logistic regression models. PNI is negatively associated with the prevalence of delirium in critically ill adults in the ICU. PNI is an independent risk factor for the incidence of delirium in adults in the ICU.

Clinical significance of prognostic nutritional index in myelodysplastic syndrome

BACKGROUND

Prognostic nutritional index has been found to be related to the clinical outcomes of patients with cancer. However, its role in myelodysplastic syndromes (MDS) patients is unclear. We aimed to assess the value of nutritional status in predicting the prognosis of MDS patients.

METHODS

Totally 121 MDS patients were analyzed retrospectively. The prognostic nutritional index (PNI) was used to assess nutritional status of the patients. The bio-informatics tool X-tile was used to define the threshold, and accordingly patients were divided into PNI^low and PNI^high groups, the characteristics were compared between two groups.

RESULTS

The PNI^high was associated with better OS (Overall Survival) than PNI^low in MDS patients (Median OS, 28.03 months versus 19.63 months, P = 0.0205). But there were no statistical differences in PFS (Progression-Free-Survival) between the two groups (P = 0.9373). The univariable and multivariable COX proportional hazard analysis adjusted for age, gender, platelet count, HB level and IPSS-R scores, and the results showed that PNI is a useful index in the evaluation of the OS of MDS (HR 0.588, 95%CI 0.374-0.926, P = 0.024).

CONCLUSION

PNI would be a simple and immediately available tool for predicting the prognosis of MDS.

A nomogram prognostic model for diffuse large B-cell lymphoma based on SUVmax and GNRI in elderly patients

To establish a nomogram for elderly patients with diffuse large B-cell lymphoma (DLBCL) based on nutritional and imaging features. The data of 221 elderly pretreatment DLBCL patients were retrospectively analyzed. All cases were randomly separated into the training group and validation group. A nomogram was built based on the results of multivariate analysis. A nomogram was established based on maximum standardized uptake value (SUVmax), geriatric nutritional risk index (GNRI), and lactate dehydrogenase. The concordance index (C-index) of the nomogram was 0.772 for the training group and 0.729 for the validation group, and similar results were found in the area under the curve (AUC). The calibration curve showed favorable consistency between prediction and real survival. The decision curve analysis (DCA) also showed that the nomogram had favorable clinical effectiveness. The new risk-stratification model divided patients into three groups with obvious survival. The C-index and AUCs for the new model were greater than those of IPI and NCCN-IPI. The DCA curve suggested that the new model had better clinical effectiveness than the IPI and NCCN-IPI. The nomogram prognostic model based on SUVmax and GNRI performed superior to NCCN-IPI and equal to IPI for risk stratification of elderly DLBCL patients.

Prognostic value of prognostic nutritional index on extranodal natural killer/T-cell lymphoma patients: A multicenter propensity score matched analysis of 1022 cases in Huaihai Lymphoma Working Group

Prognostic nutritional index (PNI), comprised of serum albumin level and lymphocyte count, is associated with the prognosis of several malignant diseases, while the prognostic value of PNI in extranodal natural killer/T cell lymphoma, nasal type (ENKTL) remains unclear. This retrospective multicenter study aimed to investigate the value of PNI in predicting the prognosis of newly diagnosed ENKTL patients by using propensity score matched analysis (PSM). A total of 1022 newly diagnosed ENKTL patients were retrieved from Huaihai Lymphoma Working Group and clinicopathological variables were collected. MaxStat analysis was used to calculate the optimal cut-off points of PNI and other continuous variables. The median age at diagnosis was 47 years and 69.4% were males, with the 5-year OS of 71.7%. According to the MaxStat analysis, 41 was the optimal cut-off point for PNI. The Pseudo R2 before matching was 0.250, and it decreased to less than 0.019 after matching. Confounding factors of the two groups were well balanced after PSM. Multivariable analysis revealed that PNI, Korean Prognostic Index (KPI), eastern cooperative oncology group performance status (ECOG PS), the prognostic index of natural killer lymphoma (PINK) and hemoglobin were independent prognostic factors for ENKTL. The results of subgroup analysis demonstrated that patients with low PNI could predict worse prognosis and re-stratify patients in ECOG PS ≥ 2, EBER-positive, the International Prognostic Index (IPI) (HIR + HR), and PINK (HR) groups. PNI combined with IPI, PINK and KPI could improve the prediction efficiency. In conclusion, PNI could accurately stratify the prognosis of ENKTL by PSM analysis and patients with low PNI had poorer prognosis.

A Novel and Validated Inflammation-Based Prognosis Score (IBPS) Predicts Outcomes in Patients with Diffuse Large B-Cell Lymphoma

Purpose

We aimed to create a novel prognostic score, the inflammation-based prognosis score (IBPS). In addition, we attempted to establish and validate a nomogram to predict the overall survival (OS) of patients with DLBCL.

Patients and Methods

We retrospectively investigated the data of 213 patients with DLBCL diagnosed and treated in the Affiliated Hospital of Jiangnan University and used these data to develop nomograms. At the same time, 89 patients diagnosed and treated in Wuxi People's Hospital Affiliated with Nanjing Medical University from January 2015 to June 2021 were collected as an external validation cohort. We developed IBPS through the least absolute shrinkage and selection operator (LASSO) Cox regression. The univariate and multivariate Cox regression method was used to develop the nomogram. We used the concordance index (C-index), calibration chart, time-dependent receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and the Kaplan-Meier curve were used to assess the nomogram.

Results

The systemic immune inflammation index (SII), prognostic nutrition index (PNI), and modified Glasgow prognostic score (mGPS) were used to construct IBPS. The Eastern Cooperative Oncology Group performance status (ECOG PS), IBPS, response to treatment, and whether accept surgery were used to construct the nomogram to predict the OS of DLBCL patients. The C-index in the training and validation cohorts were 0.844 and 0.828, respectively. According to the time-dependent ROC curve and DCA, the nomogram has good predictive accuracy and clinical net benefit. The Kaplan-Meier curve showed that according to the nomogram score, patients in the training and validation cohorts could be classified into three risk groups.

Conclusion

In patients with DLBCL, baseline IBPS was a reliable predictor of OS. The survival probability of DLBCL patients can be precisely predicted using the prognosis nomogram based on IBPS.

Combined with prognostic nutritional index and IgM for predicting the clinical outcomes of gastric cancer patients who received surgery

Objective

Although the survival rate of patients who undergo surgery for gastric cancer has greatly improved, still many patients have a poor prognosis. This retrospective study aimed to investigate the predictive ability of the PNI-IgM score, a combined prognostic nutritional index (PNI), and immunoglobulin M (IgM), on the prognosis of patients undergoing surgery for gastric cancer.

Methods

340 patients with gastric cancer who underwent surgery from January 2016 to December 2017 were selected. The PNI-IgM score ranged from 1 to 3: score of 1, low PNI (< 48.45) and low IgM (< 0.87); score of 2, low PNI and high IgM, or high PNI and low IgM; score of 3, high PNI and high IgM. We compared the differences in disease-free survival (DFS) and overall survival (OS) among the three groups, while univariate and multivariate analyses calculated prognostic factors for DFS and OS. In addition, the nomograms were constructed based on the results of multivariate analysis to estimate the 1-, 3- and 5-year survival probability.

Results

There were 67 cases in the PNI-IgM score 1 group, 160 cases in the PNI-IgM score 2 group, and 113 cases in the PNI-IgM score 3 group. The median survival times of DFS in the PNI-IgM score group 1, the PNI-IgM score group 2, and the PNI-IgM score group 3 were 62.20 months, not reached, and not reached, and 67.57 months vs. not reached vs. not reached in three groups for OS. Patients in the PNI-IgM score group 1 had a lower DFS than the PNI-IgM score group 2 (HR = 0.648, 95% CI: 0.418-1.006, P = 0.053) and the PNI-IgM score group 3 (HR = 0.337, 95% CI: 0.194-0.585, P < 0.001). In stratified analysis, PNI-IgM score 1 had a worse prognosis in the age < 60 years group and CA724 < 2.11 U/m group.

Conclusion

PNI-IgM score is a novel combination of nutritional and immunological markers that can be used as a sensitive biological marker for patients with gastric cancer who undergo surgery. The lower the PNI-IgM score, the worse the prognosis.

The prognostic value of controlling nutritional status (CONUT) score-based nomogram on extranodal natural killer/T cell lymphoma patients

Controlling nutritional status (CONUT) score as an original nutritional assessment tool can be used to assess the prognosis of patients with a variety of malignancies. However, the predictive power of CONUT in extranodal natural killer/T cell lymphoma (ENKTL) patients has never been demonstrated. Our retrospective multicenter study aimed to explore the prognostic value of CONUT in newly diagnosed ENKTL. A total of 1085 newly diagnosed ENKTL patients between 2003 and 2021 were retrospectively retrieved. Cox proportional hazard model was used to explore the prognostic factors of overall survival (OS). The survival rate of ENKTL was evaluated using Kaplan-Meier analysis, and log-rank test was applied to the difference between groups. We investigated the prognostic performance of CONUT, the International Prognostic Index (IPI), the Korean Prognostic Index (KPI), and the Prognostic Index of Natural Killer Cell Lymphoma (PINK) using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). The median age at diagnosis for the whole cohort was 47 years, and the male to female ratio was 2.2:1. The 5-year OS for all patients was 72.2%. Multivariable analysis showed that CONUT, age, bone marrow involvement, ECOG PS score, and Chinese Southwest Oncology Group and Asia Lymphoma Study Group ENKTL stage were identified as independent predictive factors for OS. Based on multivariable results, a prognostic nomogram was developed. Subgroup analysis demonstrated that patients with severe malnutrition had poorest clinical outcome. In addition, ROC curves and DCA analysis proved that compared with IPI, KPI, and PINK models, the CONUT score-based nomogram showed a better prognostic predictive efficiency of ENKTL. CONUT could effectively stratify the prognosis of ENKTL and the proposed nomogram based on CONUT was an effective prognostic model for prediction.

The prognostic nutritional index represents a novel inflammation-nutrition-based prognostic factor for nasopharyngeal carcinoma

Purpose

This study explored the relationship between the prognostic nutritional index (PNI) and overall survival rate (OS) in patients with nasopharyngeal carcinoma (NPC), and established and validated an effective nomogram to predict clinical outcomes.

Methods

This study included 618 patients newly diagnosed with locoregionally advanced NPC. They were divided into training and validation cohorts at a ratio of 2:1 based on random numbers. The primary endpoint of this study was OS, progression-free survival (PFS) was the second endpoint. A nomogram was drawn from the results of multivariate analyses. Harrell's concordance index (C-index), area under the receiver operator characteristic curve (AUC), and decision curve analysis (DCA) were used to evaluate the clinical usefulness and predictive ability of the nomogram and were compared to the current 8th edition of the International Union Against Cancer/American Joint Committee (UICC/AJCC) staging system.

Results

The PNI cutoff value was 48.1. Univariate analysis revealed that age (p < 0.001), T stage (p < 0.001), N stage (p = 0.036), tumor stage (p < 0.001), PNI (p = 0.001), lymphocyte-neutrophil ratio (NLR, p = 0.002), and lactate dehydrogenase (LDH, p = 0.009) were significantly associated with OS, age (p = 0.001), T-stage (p < 0.001), tumor stage (p < 0.001), N-stage (p = 0.011), PNI (p = 0.003), NLR (p = 0.051), and LDH (p = 0.03) were significantly associated with PFS. Multivariate analysis showed that age (p < 0.001), T-stage (p < 0.001), N-stage(p = 0.02), LDH (p = 0.032), and PNI (p = 0.006) were significantly associated with OS, age (p = 0.004), T-stage (<0.001), N-stage (<0.001), PNI (p = 0.022) were significantly associated with PFS. The C-index of the nomogram was 0.702 (95% confidence interval [CI]: 0.653-0.751). The Akaike information criterion (AIC) value of the nomogram for OS was 1142.538. The C-index of the TNM staging system was 0.647 (95% CI, 0.594-0.70) and the AIC was 1163.698. The C-index, DCA, and AUC of the nomogram demonstrated its clinical value and higher overall net benefit compared to the 8th edition of the TNM staging system.

Conclusion

The PNI represents a new inflammation-nutrition-based prognostic factor for patients with NPC. In the proposed nomogram, PNI and LDH were present, which led to a more accurate prognostic prediction than the current staging system for patients with NPC.

An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers

Background: Systemic inflammatory indicators are clinically significant in guiding diffuse large B-cell lymphoma (DLBCL) prognosis. However, which inflammatory markers are the best predictors of DLBCL prognosis is still unclear. In this study, we aimed to create a nomogram based on the best inflammatory markers and clinical indicators to predict the overall survival of patients with DLBCL. Patients and methods: We analyzed data from 423 DLBCL patients from two institutions and divided them into a training set, an internal validation set, and an external validation set (n = 228, 97, and 98, respectively). The least absolute shrinkage and selection operator and Cox regression analysis were used to develop nomograms. We assessed model fit using the Akaike information criterion and Bayesian information criterion. The concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the nomogram's predictive performance and clinical net benefit and compared with the International Prognostic Index (IPI) and National Comprehensive Cancer Network (NCCN)-IPI. Results: The inclusion variables for the nomogram model were age, Eastern Cooperative Oncology Group performance status, lactate dehydrogenase level, the systemic immune-inflammation index (SII), the prognostic nutritional index (PNI), and β-2 microglobulin (β-2 MG) level. In the training cohort, the nomogram showed better goodness of fit than the IPI and NCCN-IPI. The C-index of the nomogram (0.804, 95% CI: 0.751-0.857) outperformed the IPI (0.690, 95% CI: 0.629-0.751) and NCCN-IPI (0.691, 95% CI: 0.632-0.750). The calibration curve, ROC curve, and DCA curve analysis showed that the nomogram has satisfactory predictive power and clinical utility. Similar results were found in the validation cohort. Conclusion: The nomogram integrated with the clinical characteristics and inflammatory markers is beneficial to predict the prognosis of patients with DLBCL.

LASSO Model Better Predicted the Prognosis of DLBCL than Random Forest Model: A Retrospective Multicenter Analysis of HHLWG

Background. Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous non-Hodgkin’s lymphoma with great clinical challenge. Machine learning (ML) has attracted substantial attention in diagnosis, prognosis, and treatment of diseases. This study is aimed at exploring the prognostic factors of DLBCL by ML. Methods. In total, 1211 DLBCL patients were retrieved from Huaihai Lymphoma Working Group (HHLWG). The least absolute shrinkage and selection operator (LASSO) and random forest algorithm were used to identify prognostic factors for the overall survival (OS) rate of DLBCL among twenty-five variables. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were utilized to compare the predictive performance and clinical effectiveness of the two models, respectively. Results. The median follow-up time was 43.4 months, and the 5-year OS was 58.5%. The LASSO model achieved an Area under the curve (AUC) of 75.8% for the prognosis of DLBCL, which was higher than that of the random forest model (AUC: 71.6%). DCA analysis also revealed that the LASSO model could augment net benefits and exhibited a wider range of threshold probabilities by risk stratification than the random forest model. In addition, multivariable analysis demonstrated that age, white blood cell count, hemoglobin, central nervous system involvement, gender, and Ann Arbor stage were independent prognostic factors for DLBCL. The LASSO model showed better discrimination of outcomes compared with the IPI and NCCN-IPI models and identified three groups of patients: low risk, high-intermediate risk, and high risk. Conclusions. The prognostic model of DLBCL based on the LASSO regression was more accurate than the random forest, IPI, and NCCN-IPI models.

Prognostic value of the controlling nutritional status score in patients with myelodysplastic syndromes

Background

Myelodysplastic syndromes (MDS) are a heterogeneous spectrum of clonal hematopoietic disorders with varying degrees of cytopenia and morphologic dysplasia. The controlling nutritional status (CONUT) score, an easy-to-use tool for assessing the nutritional status, was reported as an independent prognostic factor in cancer patients. However, its role in patients with MDS is unclear.

Objective

We aimed to explore the impact of CONUT score on the prognosis of patients with MDS, which is of great significance for clinical treatment.

Methods

A total of 121 patients with MDS were analyzed. The CONUT score was calculated prior to therapy. The bio-informatics tool X-tile was used to define the CONUT score and the threshold of 4 points was determined to predict the prognosis. Patients were divided into CONUT^low and CONUT^high groups, and the characteristics were compared between two groups.

Results

Results show that CONUT^low was associated with better overall survival (OS) than CONUT^high patients (Median OS, 30.20 vs. 19.63 months, p = 0.0003). However, there were no statistical differences in progression-free survival (PFS) between the two groups (p = 0.2683). Results of univariate and multivariate COX proportional hazard analysis adjusted for bone marrow blasts level, platelet count, International Prognostic Scoring System (IPSS) scores, gender, and hemoglobin (Hb) level showed that the CONUT score was useful in the evaluation standard of OS of MDS (hazard ratio (HR) 2.297, 95% CI 1.441–3.663, p < 0.001).

Conclusions

The CONUT, as a novel immuno-nutritional biomarker, may be useful in predicting the OS of MDS.

Effect of the normal liver mean dose on intrahepatic recurrence in patients with hepatocellular carcinoma after receiving liver stereotactic body radiation therapy

•

The normal liver mean dosage is rarely studied in its association with metastasis related survival.

•

Higher normal liver mean dosage was related to a better intra-hepatic progression free survival.

•

No associations were found between normal liver mean dose with Local control rate, Overall Survival, Distant Metastasis Free Survival, Progression Free Survival.

Background and purpose

This study aims to evaluate whether dosimetric parameters affect the intrahepatic out-field recurrence or distant metastasis-free survival following the stereotactic body radiation therapy (SBRT) in patients with hepatocellular carcinoma (HCC).

Materials and methods

A total of 76 patients with HCC who were treated with SBRT from January 2015 to May 2020 were included in this retrospective study. The main clinical endpoints considered were intrahepatic out-field free survival (OutFFS) and distant metastasis-free survival (DMFS). The target parameters and the liver were documented including tumor diameters, gross tumor volume (GTV), Liver minus GTV volume (LGV), and Liver minus GTV mean dose (LGD). Multivariable Cox regression with forward stepwise selection was performed to identify independent risk factors for OutFFS and DMFS. Maximally selected rank statistics were used to determine the most informative cut-off value for age and LGD.

Results

The median follow-up was 28.2 months (range, 7.7–74.5 months). LGD higher than 12.54 Gy [HR, 0.861(0.747–0.993); p = 0.040] and age greater than 67-year-old [HR, 0.966(0.937–0.997); p = 0.030] are two independent predictors of OutFFS, previous TACE treatment [HR, 0.117(0.015–0.891); p = 0.038] was an independent predictor of DMFS.

Conclusions

The results of this study suggested that the higher the dose received by the normal liver (greater than 12.54 Gy) the better the intrahepatic out-field recurrence-free survival (RFS) rate. Further study is warranted to confirm and to better understand this phenomenon.

Value of the prognostic nutritional index (PNI) in patients with newly diagnosed, CD5-positive diffuse large B-cell lymphoma: A multicenter retrospective study of the Huaihai Lymphoma Working Group

BACKGROUND

CD5-positive diffuse large B-cell lymphoma (DLBCL) is a clinically rare subtype of DLBCL with aggressive clinical manifestations and a poor prognosis. It has been demonstrated that the prognostic nutritional index (PNI), an indicator of nutritional status and systemic inflammation, is a significant prognostic factor for several types of lymphoma. The objective of this multicenter retrospective study was to explore the prognostic value of the PNI in patients with CD5-positive DLBCL.

METHODS

In total, 207 patients with CD5-positive DLBCL were recruited from 11 centers of the Huaihai Lymphoma Working Group. Maximally selected rank statistics analysis was used to identify optimal cutoff points for the PNI. A Cox proportional hazards model was used for univariable and multivariable analyses. Kaplan-Meier curves were used to calculate survival rates and draw survival curves, and the log-rank test was used to compare differences between groups.

RESULTS

The median age at diagnosis was 61 years, and the 5-year overall survival rate was 47.5%. According to the maximally selected rank statistics analysis, a score of 49.7 was the optimal cutoff point for the PNI. Subgroup analysis showed that the PNI could re-stratify patients in BCL-2-negative, MYC-negative, high-intermediate-risk and high-risk International Prognostic Index, BCL-6-positive and BCL-6-negative, high Ki-67 score (≥0.9), Ann Arbor stage III/IV, Eastern Cooperative Oncology Group performance status ≥2, and germinal center B subgroups. Multivariable analysis revealed that PNI, age, Eastern Cooperative Oncology Group performance status, albumin level, and red blood cell count were independent prognostic factors for CD5-positive DLBCL.

CONCLUSIONS

The PNI was a significant prognostic indicator for CD5-positive DLBCL and was able to re-stratify the prognosis for clinicopathologic subgroups of patients with CD5-positive DLBCL.

Cachexia is a risk factor for negative clinical and functional outcomes in patients receiving chimeric antigen receptor T-cell therapy for B-cell non-Hodgkin lymphoma

Cachexia is a muscle-wasting syndrome that is known to impact the clinical course of several cancer populations but has not been specifically investigated in patients receiving chimeric antigen receptor T (CAR-T) cell therapy. In this study, we investigated the relationship between cachexia markers and several cancer and functional outcomes in a pilot population of aggressive B-cell non-Hodgkin lymphoma patients receiving CAR-T. We found that the prognostic nutritional index was linked to progression-free survival, overall survival, and disability-free survival, while several additional weight and serum-based markers of cachexia were also associated with negative outcomes. These data prompt further investigation of cachexia markers in populations receiving CAR-T cell therapy.