Prognostic significance of prognostic nutritional index and systemic immune-inflammation index in patients after curative breast cancer resection: a retrospective cohort study

Value of Contrast-Enhanced Ultrasound Combined with Immune-Inflammatory Markers in Predicting Axillary Lymph Node Metastasis of Breast Cancer

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with immune-inflammatory markers in predicting axillary lymph node metastasis (ALNM) in breast cancer patients.

METHODS

From January 2020 to June 2023, the clinicopathological data and ultrasound features of 401 breast cancer patients who underwent biopsy or surgery were recorded. Patients were randomly divided into a training set (321 patients) and a validation set (80 patients). The risk factors for ALNM were determined using univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, and prediction models were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess their diagnostic performance.

RESULTS

Logistic regression analysis demonstrated that systemic immunoinflammatory index (SII), CA125, Ki67, pathological type, lesion size, enhancement pattern and Breast Imaging Reporting and Data System (BI-RADS) category were significant risk factors for ALNM. Three different models were constructed, and the combined model yielded an AUC of 0.903, which was superior to the clinical model (AUC=0.790) and ultrasound model (AUC=0.781). A nomogram was constructed based on the combined model, calibration curves and DCA demonstrated its satisfactory performance in predicting ALNM.

CONCLUSION

The nomogram combining ultrasound features and immune-inflammatory markers could serve as a valuable instrument for predicting ALNM in breast cancer patients.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Prognostic nutritional index during hospitalization correlates with adverse outcomes in elderly patients with acute myocardial infarction: a single-center retrospective cohort study

BACKGROUND AND AIMS

Acute myocardial infarction (AMI) is one of the most prevalent illnesses endangering the elderly's health. The predictive nutritional index (PNI) has been shown in several studies to be a good predictor of nutritional prognosis. In this study, we explored the correlation between PNI during hospitalization and the outcome of elderly AMI patients.

METHODS

Elderly AMI patients in the Cardiac Intensive Care Unit of Huadong Hospital from September 2017 to April 2020 were recruited for analysis. The clinical and laboratory examination data of subjects were retrieved. All enrolled patients were monitored following discharge. The primary clinical endpoints encompass major adverse cardiovascular events (MACEs) and Composite endpoint (MACEs and all-cause mortality). Survival analyses were conducted via the Kaplan-Meier and the log-rank analyses, and the Cox, proportional hazards model, was employed for hazard rate (HR) calculation.

RESULTS

307 subjects were recruited for analysis. The optimal PNI threshold is 40.923. Based on the Kaplan-Meier analysis, the elevated PNI group experienced better prognosis (P < 0.001). Cox analysis demonstrated that the PNI group was a stand-alone predictor for elderly AMI patient prognosis (HR = 1.674, 95% CI 1.076-2.604, P = 0.022). Subgroup analysis showed that the HR of the PNI group was the highest in the ST-segment elevation myocardial infarction (STEMI) subgroup (HR = 3.345, 95% CI 1.889-5.923, P = 0.05), but no discernible difference was observed in the non-ST-segment elevation myocardial infarction (NSTEMI) subgroup.

CONCLUSION

Based on our analyses, the PNI during hospitalization can accurately predict the prognosis of elderly STEMI patients but not that of elderly NSTEMI patients.

Prognostic Nutritional Index as a Predictive Biomarker of Post-Operative Infectious Morbidity in Gynecological Cancer Patients: A Prospective Cohort Study

Malnutrition significantly impacts the post-operative process of gynecological cancer patients. A prominent variable for determining perioperative morbidity is the Prognostic Nutritional Index (PNI). To investigate PNI's predictive value on the risk of post-operative infections, we conducted a prospective cohort study involving women who underwent surgery for gynecological malignancies. Out of the 208 patients enrolled, 28 (13.5%) were malnourished and post-operative infections occurred in 43 patients. Notably, there was a significant difference in PNI between patients who developed infections and those who did not (p = 0.027), as well as between malnourished patients and those with normal nutritional status (p = 0.043). Univariate analysis showed that preoperative PNI predicts the risk of post-operative infections better than post-operative white blood cell count (AUC of 0.562 vs 0.375). However, the most accurate diagnostic results in the multivariate analysis were obtained from random forest and classification tree models (AUC of 0.987 and 0.977, respectively). Essentially, PNI and post-operative white blood cell count provided the best information gain according to rank probabilities. In conclusion, PNI appears to be a critical parameter that merits further investigation during the preoperative evaluation of gynecological malignancies.

The role of nutrition-based index in predicting survival of breast cancer patients: A systematic review and meta-analysis

Background

Prognostic nutritional index (PNI) and Controlling Nutritional Status (CONUT) are two model that incorporates the role of inflammation and nutrition factors to predict the progression of tumor. The primary objective of this investigation is to examine the ability of PNI and CONUT score for predicting the survival in breast cancer patients.

Methods

A comprehensive search was conducted on the Cochrane Library, Scopus, Europe PMC, and Medline databases up until August 14th, 2023, utilizing a combination of relevant keywords. This review incorporates literature that examines the relationship between PNI, CONUT, and survival in breast cancer. We employed random-effect models to analyze the hazard ratio (HR) and present the outcomes together with their corresponding 95 % confidence intervals (CI).

Results

A total of sixteen studies were incorporated. The results of our meta-analysis indicated that high PNI was associated with better overall survival (OS) (HR 0.38; 95%CI: 0.28-0.51, p < 0.00001, I2 = 32 %), but not disease-free survival (DFS) (HR 0.60; 95%CI: 0.33-1.10, p = 0.10, I2 = 78 %) than low PNI in breast cancer patients. Meta-analysis also indicated that high CONUT was associated with worse OS (HR 1.66; 95%CI: 1.21-2.28, p = 0.002, I2 = 78 %) and worse DFS (HR 2.09; 95%CI: 1.60-2.73, p < 0.00001, I2 = 41 %) in breast cancer patients.

Conclusions

This study suggests the prognostic role of both PNI and CONUT score for predicting survival in breast cancer patients.

Predictive Significance of Systemic Immune-Inflammation Index in Patients with Breast Cancer: A Retrospective Cohort Study

Background

Peripheral blood inflammation indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), have become research hotspots in the diagnosis, treatment, and prognosis prediction of breast cancer, whereas existing research findings remain controversial.

Methods

Data pertaining to 1808 breast cancer patients were collected retrospectively to analyze the predictive value of NLR/PLR/SII for breast cancer clinicopathological characteristics, chemotherapy response, and relapse. 1489, 258, and 53 eligible breast cancer patients entered into the three analyses, respectively. Logistic regression analyses were used to assess the correlation between these indices and poor response to chemotherapy. A predictive scoring model was established to predict chemotherapeutic responses based upon the odds ratio values of significant variables identified in logistic regression analyses.

Results

Higher pretherapeutic NLR/PLR/SII values were significantly correlated with higher tumor stage, triple-negative breast cancer, premenopausal status, and younger age. Logistic regression analyses indicated that pretherapeutic high SII (as a continuous variable or with a cut-off value of 586.40) and HER2-negative status were independent predictors of poor response to neoadjuvant chemotherapy. A first-in-class SII-based predictive scoring model well distinguished patients who might not benefit from neoadjuvant chemotherapy, with an area under the curve of 0.751. In HR-positive cancers, SII was more strongly associated with clinicopathological features and chemotherapy response. In addition, a receiver operating characteristic curve analysis indicated that the specificity of follow-up SII in identifying cancer relapse was greater than 98.0% at a cut-off value of 900.

Conclusion

As a predictor of breast cancer, especially in the HR-positive subtype, SII may eclipse NLR/PLR. SII-high patients are more likely to have a worse chemotherapy response and a higher risk of recurrence.

Association between Posttreatment Serum Platelet-to-Lymphocyte Ratio and Distant Metastases in Patients with Hepatocellular Carcinoma Receiving Curative Radiation Therapy

Background: We sought to investigate whether serum immune and inflammatory parameters can help to predict distant metastasis (DM) in patients with unresectable hepatocellular carcinoma (HCC) undergoing curative radiation therapy (RT). Methods: A total of 76 RT courses were analyzed. The following variables were included in the analysis: systemic inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio (PLR), prognostic nutritional index (PNI), absolute lymphocyte count, lymphocyte-to-monocyte ratio, albumin, albumin-to-alkaline phosphatase ratio, RT-related parameters, and levels of total protein, hemoglobin, α-fetoprotein, and PIVKA-II. Distant control (DC) and overall survival (OS) rates were calculated and compared. Results: The mean age was 61.4 years, and most patients were men (n = 62, 81.6%). The median RT fraction number and fractional doses were 12 (range, 4–30) and 5 (range, 2–12) Gy, respectively. With a median follow-up of 12 (range, 3.1–56.7) months, the 1-year DC and OS rates were 64.4% and 55.2%, respectively. The development of DM significantly deteriorated OS (p = 0.013). In the multivariate analysis, significant independent prognostic indicators for DC and OS rates were the highest posttreatment PLR (≤235.7 vs. >235.7, p = 0.006) and the lowest posttreatment PNI (≤25.4 vs. >25.4, p < 0.001), respectively. Conclusions: Posttreatment serum PLR might be helpfully used as a predictive biomarker of DM in unresectable HCC patients undergoing RT. Future research is necessary to confirm our findings.