Easier and more explanatory indices by integrating leukocyte lymphocyte ratio (LLR) and prognostic nutritional index (PNI) to IPS systems in cases with classical Hodgkin lymphoma

The prognostic value of prognostic nutritional index in postoperative onset of PAH in children with isolated VSD: a prospective cohort study based on propensity score matching analysis

Background

The mechanism of pulmonary arterial hypertension (PAH) after surgery/intervention for isolated venticlular septal defect (VSD) in children is unknown. Reliable prognostic indicators for predicting postoperative PAH are urgently needed. Prognostic nutration index (PNI) is widely used to predict postoperative complications and survival in adults, but it is unclear whether it can be used as an indicator of prognosis in children.

Methods

A total of 251 children underwent VSD repair surgery or interventional closure in Hunan Children's Hospital from 2020 to 2023 were collected. A 1:1 propensity score matching (PSM) analysis was performed using the nearest neighbor method with a caliper size of 0.2 Logistics regression analysis is used to examine factors associated with the development of PAH.

Results

The cut-off value for PNI was determined as 58.0. After 1:1 PSM analysis, 49 patients in the low PNI group were matched with high PNI group. Children in the low PNI group had higher risk of postoperative PAH (P = 0.002) than those in the high PNI group. Multivariate logistics regression analysis showed that PNI (RR: 0.903, 95% CI: 0.816-0.999, P = 0.049) and tricuspid regurgitation velocity (RR: 4.743, 95% CI: 1.131-19.897, P = 0.033) were independent prognostic factors for the development of PAH.

Conclusion

PNI can be used as a prognostic indicator for PAH development after surgery/intervention in children with isolated VSD.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

The prognostic nutritional index, an independent predictor of overall survival for newly diagnosed follicular lymphoma in China

Objective

The prognostic nutritional index (PNI) is an important prognostic factor for survival outcomes in various hematological malignancies. The current study focused on exploring the predictive value of the PNI in newly diagnosed follicular lymphoma (FL) in China.

Materials and methods

The clinical indicators and follow-up data of 176 patients who received chemotherapy or immunotherapy combined with chemotherapy with FL in our hospital from January 2016 to March 2022 were retrospectively analyzed. Cox proportional hazard model was used for univariate and multivariate analyses. Kaplan–Meier curves were used to calculate survival rates and draw survival curves. The log-rank test was applied to compare differences between groups.

Results

The optimal cut-off value of PNI was 44.3. All patients were divided into a high PNI group (>44.3) and a low PNI group (≤44.3). The low PNI group had a low CR rate and a high risk of death, with a tendency toward POD24, and Both OS and PFS were worse than those in the high PNI group. PNI was able to predict OS and PFS in FL patients and was the only independent predictor of OS (P = 0.014 HR 5.024; 95%CI 1.388∼18.178) in multivariate analysis. PNI could re-stratify patients into groups of high FLIPI score, high FLIPI2 score, no POD24, and rituximab combined with chemotherapy. Moreover, integrating PNI into the FLIPI and FLIPI2 models improved the area under the curve (AUC) for more accurate survival prediction and prognosis.

Conclusion

PNI is a significant prognostic indicator for newly diagnosed FL in China that can early identify patients with poor prognosis and guide clinical treatment decisions.

Prognostic Factors for Clinical Outcomes in Patients with Newly Diagnosed Advanced-stage Hodgkin Lymphoma: A Nationwide Retrospective Study

Introduction

While Hodgkin lymphoma (HL) is mostly curable, outcomes for advanced-stage HL remain unsatisfactory. The International Prognostic Score and its modifications were developed to predict HL prognosis; however, more straightforward prognostic factors are needed. This study aimed to identify simpler prognostic factors for advanced-stage newly diagnosed HL (NDHL).

Methods

This retrospective study used the Taiwan National Health Insurance Research Database and the Taiwan Cancer Registry. Patients with advanced-stage NDHL receiving ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) or ABVD-like regimens between 2009 and 2016 were enrolled. Cox proportional hazards models were used to identify prognostic factors for the time to next treatment (TTNT) and overall survival (OS). We used the time-dependent area under the receiver operating characteristic curve (AUROC) to evaluate model performance.

Results

The study included 459 patients with advanced-stage NDHL. A bimodal age distribution (peaks 20-44 and >65 years) was observed. Over a median follow-up of 4.7 years, the complete remission and OS rates were 52% and 76%, respectively. Age ≥60 years (adjusted hazard ratio [aHR]: 1.73, 95% confidence interval [CI]: 1.23-2.43), extranodal involvement (1.40, 1.05-1.87), B symptoms (1.53, 1.13-2.06), and Charlson Comorbidity Index (CCI) ≥1 (1.49, 1.08-2.06) were significantly associated with a shorter TTNT. The time-dependent AUROC was .65. With a time-dependent AUROC of .81, age ≥60 years (4.55, 2.90-7.15) and CCI ≥1 (1.86, 1.18-2.91) were risk factors for worse OS.

Conclusion

Older age and more comorbidities were risk factors for an inferior OS in advanced-stage NDHL, while older age, extranodal involvement, B-symptoms, and higher CCI were significantly associated with disease relapse.

Long-term and short-term prognostic value of the prognostic nutritional index in cancer: a narrative review

Objective

To perform a narrative review of the prognostic value of prognostic nutritional index (PNI) in cancers.

Background

Prognostic estimation greatly determines the treatment approach in various cancers. The PNI, calculated using the serum albumin level and total lymphocyte count, is a useful indicator to assess nutritional and immunological conditions. The PNI represents a low-cost, easy-to-perform, noninvasive, rapid, and standardized tool for estimating the prognosis of cancer. Many studies have aimed to clarify the prognostic value of PNI for various types of cancer.

Methods

We summarize the studies, particularly the systematic reviews and meta-analyses, that have examined the prognostic value of PNI in common cancers.

Conclusions

The relevant studies indicate that low PNI is an independent prognostic factor for decreasing overall survival in many types of cancers. Disease-free survival and progression-free survival were also associated with PNI in some types of cancer including lung cancer and renal cell carcinoma. Therefore, we suggest that the measurement of PNI is a useful method to identify cancer patients that have a worse prognosis and that the treatment strategy for these patients be adjusted accordingly. We hypothesize that maintaining good nutritional status during treatment may improve outcomes of various cancers.

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

Introduction

Immunonutritional status is associated with the survival of DLBCL. This multicenter retrospective study aimed to explore the prognostic value of Prognostic Nutrition Index (PNI) in DLBCL patients by using propensity score matched analysis (PSM).

Methods

A total of 990 DLBCL cases were recruited from 5 centers of Huaihai Lymphoma Working Group (HHLWG). A 1:1 PSM analysis was performed using the nearest-neighbor method, with a caliper size of 0.02. Cox regression analysis was used to examine factors associated with survival.

Results

The median age at diagnosis was 62 years and 52.5% were males, with the 3-y overall survival of 65.1%. According to the MaxStat analysis, 44 was the optimal cut-off point of PNI. After PSM analysis, a total of 282 patients in PNI < 44 group could be propensity matched to PNI ≥ 44 patients, creating a group of 564 patients. Multivariable analysis revealed that PNI, age, central nervous system involvement and International Prognostic Index (IPI) were independent prognostic factors for DLBCL. Kaplan–Meier analysis indicated that patients with low PNI in Ann Arbor Stage (III/VI), ECOG (<2), IPI (LR+LIR), GCB, and BCL-2 negative groups had a poor prognosis.

Discussion

PNI could accurately stratify the prognosis of DLBCL after PSM analysis.