Value Of Prognostic Nutritional Index Research Articles

Effects of prognostic nutritional index and obesity on 1-year mortality in patients with acute heart failure

Abstract Background Increased body mass index (BMI) has paradoxically been linked to improved survival rates among patients with acute heart failure (AHF). However, the impact of different nutritional statuses on this obesity paradox on 1-year mortality remains unclear. The prognostic nutritional index (PNI) is a simple tool to assess nutrition status. Recently, landmark findings such as the STEP-HFpEF (Semaglutide Treatment Effect in People With Obesity and HFpEF) trial showed at 1-year, semaglutide not only reduced weight considerably, but also significantly improved health-related quality of life, functional status, and CRP levels. Also, the semaglutide effects on heart disease and stroke in patients with overweight or obesity (SELECT) trial demonstrated a 20% reduction in cardiovascular outcomes. Purpose To investigate the association between obesity and AHF on all-cause mortality according to nutritional status. Methods The cohort study comprised 7258 patients with a cardiopulmonary condition, all of whom were admitted and included on-site at the Emergency Department (ED) in a large University Hospital in Denmark, between March 2020 and March 2022. The follow-up time was at least 365 days. 408 patients with AHF were identified by chart review by trained cardiologists for analysis. PNI was calculated according to the formula: 10×serum albumin (g/ dL) + 5×total lymphocyte count×109/L. A value ≥ 38 is considered well-nourished, and a value < 38 is considered malnourished. Patients were divided into High-PNI (≥38) and Low-PNI (<38) according to cut-off value of PNI. The Kaplan-Meier (KM) curves were used to analyze cumulative survival in each subgroup, and the log-rank test was used to compare differences between groups. Cox regression models were used to analyze associa­tions between BMI, nutritional status and outcomes, expressed as hazard ratios (HR) and relative 95% confi­dence intervals (CI). Results Amongst 408 hospitalized AHF patients, 384 (mean age 75.6 ±11.7) with available PNI scores were enrolled into this study. 55.7% (n=214) were male, 56.4% (n=93) died in the Low-PNI group and 34.2% (n=75) died in the High-PNI group within 1 year. After adjusting for potential confounders in the High-PNI population, compared with the under/normal and overweight BMI group, obesity was negatively associated with 1-year mortality, with adjusted HR of 0.47, 95% CI (0.24–0.94), P = 0.033. However, this correlation disappeared in the Low-PNI group (obese BMI: HR 1.2, 95% CI 0.71–2.1, P = 0.457). In addition, those who were overweight in the Low-PNI group had an 80% increase in 1-year risk of death. Conclusion Obesity (BMI>30) was associated with reduced 1-year mortality only in AHF-patients with good nutritional status. However, this association disappeared in malnourished patients. Consequently, additional research is essential to formulate weight loss recommendations for malnourished obese patients with AHF.

The relationship between the Prognostic Nutritional Index and lymphovascular and perineural invasion of the tumor in patients diagnosed with gastric cancer, and its effect on overall survival.

A low Prognostic Nutritional Index (PNI) value, lymphovascular invasion (LVI), and perineural invasion (PeNI) have been identified as indicators of poor prognosis for many malignancies. We aimed to evaluate the relationship between PNI and LVI/PeNI, their prognostic significance, and their effect on overall survival in gastric cancer patients who underwent curative gastrectomy. A cutoff value of 39.8 was taken for the PNI, and PNI < 39.8 was defined as moderate to severe malnutrition. Patients were grouped as PNI-low (PNI < 39.8) and PNI-high (PNI ≥ 39.8). Paraffin-embedded tissue sections of surgical specimens were used to evaluate PeNI as defined by previously reported criteria. The study included 270 patients with ages ranging from 23 to 90 years. The mean PNI was calculated as 39.8 ± 6.35. PeNI was detected in 232 patients (85.93%), and LVI was identified in 248 patients (91.85%). It was observed that the PNI value of patients with an expired status in the PNI < 39.8 group was lower compared to those who survived, and in patients with PNI > 39.8, those without PeNI had better survival. The presence of PeNI in patients with PNI > 39.8 increased the mortality risk by 2.088 units, while in patients with PNI > 39.8, it was found that those without LVI had better survival, and the presence of LVI increased the mortality risk by 3.171 units. Mortality developed in 166 patients (61.48%) during the five-year follow-up period. The five-year overall survival was found to be 31.02 ± 21.73 months. In patients with gastric cancer, the PNI, LVI, and PeNI are independent prognostic factors for overall survival in postoperative patients. A low PNI score is an inherently poor prognostic factor. In patients with a high PNI score, the presence of positive LVI and PeNI negatively impacts survival. We found that in patients with a low PNI, the rates of PeNI and LVI are higher compared to those with a high PNI, and this significantly affects mortality.

The effect of immunonutrition support on the prognostic nutritional index in the postoperative period in brain tumors

Objective: The prognostic nutritional index (PNI) reflects the immunological response and nutritional status. We evaluated the effect of immunonutrition on PNI in patients with brain tumors receiving chemoradiotherapy and immunonutritional support. Methods: Demographic, laboratory and clinical data were collected retrospectively from 30 consecutive brain tumor patients who received brain chemoradiotherapy between 2019 and 2022 in our clinic and who were given immunonutrition support during their treatment. The cut-off PNI value before adjuvant therapy was calculated in patients who received immunonutrition support in the postoperative period and compared with the PNI values after adjuvant therapy. Results: While glioblastoma patients constitute the majority (60%) of all patients diagnosed as pathological, different histopathological brain tumors were also included in the study (meningioma, oligodendroglioma). The mean albümin value before adjuvant treatment was 4.04 g/dL, while the mean albümin value after adjuvant treatment increased to 4.16 g/dL (p=0,057). The optimal cut-off value for PNI was found to be 45.5 by ROC analysis. PNI was calculated as 49.38 ± 6.03 SD before adjuvant treatment and 49.40 ± 6.12 SD after adjuvant treatment (p&gt;0.05). Retrospective analysis was conducted on over 30 HGG patients who did not receive immunonutritional supplementation containing Arg/gln/HMB (Arginine/glutamine/Beta-Hydroxy Beta-Methylbutyrate). Interestingly, the analysis revealed that the average PNI was 45.15 before adjuvant therapy and decreased to 42.26 after adjuvant therapy, indicating a statistically significant decline in PNI among those without immunonutritional supplementation. This finding suggests a potential beneficial impact of immunonutritional supplementation on PNI. Conclusions: Immunonutrition support has positive effects on PNI and albumin levels in brain tumor patients who will undergo postoperative radiotherapy/chemoradiotherapy. It can be thought that low PNI, which may be an indicator of hematological and nutritional toxicity predicted by brain chemoradiotherapy, can be prevented by immunonutrition support.

Malnutrition is associated with severe outcome in elderly patients hospitalised with COVID-19

Some studies have identified influencing factors of COVID-19 illness in elderly, such as underlying diseases, but research on the effect of nutritional status is still lacking. This study retrospectively examined the influence of nutritional status on the outcome of elderly COVID-19 inpatients. A retrospective analysis of the clinical data of 4241 COVID-19 patients who were admitted to a third-class hospital of Nanchang between November 1, 2022 and January 31, 2023 was conducted. Nutritional status was assessed using the prognostic nutritional index (PNI) and controlling nutritional status score (CONUT). The influence of nutritional status on the outcome of COVID-19 patients was determined through multivariate adjustment analysis, restrictive cubic spline, and receiver operating characteristic curve (ROC). Compared with mild/no malnutrition, severe malnutrition substantially increased the critical outcome of COVID-19. A linear relationship was observed between the odds ratio (OR) and PNI and CONUT (P > 0.05). The area under the ROC curve indicated that PNI was the better predictor. The optimal cutoff value of PNI was 38.04 (95%CI: 0.797 ~ 0.836, AUC = 0.817), with a sensitivity of 70.7% and a specificity of 79.6%. The critical illness of elderly COVID-19 patients shows a linear relationship with malnutrition at admission. The use of PNI to assess the prognosis of COVID-19 eldeely patients is reliable, highlighting the importance for doctors to closely pay attention to the nutritional status of COVID-19 patients. Focusing on nutritional status in clinical practice can effectively reduce the critical illness of elderly COVID-19 patients.

Predictive value of prognostic nutritional index in patients undergoing gastrectomy for gastric cancer: A systematic review and meta-analysis.

The value of prognostic nutritional index (PNI) in gastrectomy remains controversial. This meta-analysis aimed to evaluate the predictive value of PNI in patients undergoing gastrectomy for malignancy. We retrieved studies from medical literature databases to analyze the endpoints of overall survival, cancer-specific survival, recurrence-free survival, and clinicopathologic features. The hazard ratio (HR) and 95% confidence interval (CI) were used to access the survival prognostic value of PNI in patients after gastrectomy. Odds ratio and mean difference were used to evaluate the relationship between the low PNI and clinicopathologic features. In total, we included 38 articles (39 trial comparisons) which contained 23,756 gastrectomy patients. The results showed that low PNI was associated with shorter overall survival (HR: 1.82, 95% CI 1.62-2.03), shorter cancer-specific survival (HR: 1.44, 95% CI 1.24-1.67), and shorter recurrence-free survival (HR: 2.52, 95% CI 1.41-4.47). Besides, patients with low PNI had a higher risk of postoperative complications compared with high PNI (HR: 1.65, 95% CI 1.30-2.09). And low PNI group was found to be related to older, lower BMI, larger tumor size, deeper tumor invasion, poorer differentiation, more advanced tumor stage, total gastrectomy, and the presence of lymph node metastasis, lymphatic invasion, and vessel invasion. PNI was significantly associated with survival and postoperative complications of gastric cancer patients undergoing gastrectomy. Therefore PNI has the potential to be a prognostic predictor for gastrectomy.

Can maternal inflammatory and nutritional status, evaluated by the hemoglobin, albumin, lymphocyte, and platelet (HALP) score and the prognostic nutritional index (PNI) in the first trimester, predict late-onset fetal growth restriction?

ObjectiveThe aim of this study was to evaluate the potential of immunonutritional markers, specifically the hemoglobin, albumin, lymphocyte, and platelet (HALP) score and the prognostic nutritional index (PNI), in predicting late-onset fetal growth restriction (LO-FGR) during the first trimester.Materials and methodsThis retrospective study was conducted at a tertiary care center between October 2022 and August 2023. The study included a total of 213 singleton pregnancies, with 99 women in the LO-FGR group and 114 in the healthy control group, matched by maternal age and gestational age at delivery. All blood samples were collected between 11 and 14 weeks of gestation (during the first-trimester screening test). We analyzed first-trimester laboratory parameters, specifically focusing on hemoglobin levels, white blood cells (WBCs), lymphocytes, platelets, and albumin levels. Afterwards, we calculated the HALP score and PNI, and then compared the values of both groups.ResultsBoth HALP score (3.58 ± 1.31 vs. 4.19 ± 1.8, p = 0.012) and PNI (36.75 ± 2.9 vs. 39.37 ± 3.96, p < 0.001) were significantly lower in the FGR group than in the control group. The HALP score cut-off value of < 3.43 in predicting FGR had a sensitivity of 62.3% and specificity of 54.5% (AUC = 0.600, 95% CI: 0.528–0.672, p = 0.012). The PNI cut-off value of < 37.9 in predicting FGR had a sensitivity of 65.8% and specificity of 62.9% (AUC = 0.707, 95% CI: 0.632–0.778, p < 0.001). While the HALP score was not a significant predictor of composite adverse neonatal outcomes in the FGR group, PNI showed a cut-off value of < 37.7 with a sensitivity of 60.9% and specificity of 59.7% (AUC = 0.657, 95% CI: 0.581–0.733, p < 0.001).ConclusionThe HALP score and PNI are valuable prognostic tools for predicting the risk of FGR in the first trimester. Low PNI values are also associated with composite adverse neonatal outcomes in pregnancies complicated by FGR.

Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer.

This study aimed to compare three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer (GC). Objective nutritional screening tools including geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), and controlling nutritional status (CONUT) score, were evaluated in patients with GC at our institution. Malnutrition was diagnosed according to the GLIM criteria. The diagnostic value of GNRI, PNI, and COUNT scores in identifying GLIM-defined malnutrition was assessed by conducting Receiver Operating Characteristic (ROC) curves and calculating the area under the curve (AUC). Additionally, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were determined. The Kappa coefficient (k) was used to assess agreement between three objective nutritional screening tools and GLIM criteria. A total of 316 patients were enrolled in this study, and malnutrition was diagnosed in 151 (47.8%) patients based on the GLIM criteria. The GNRI demonstrated good diagnostic accuracy (AUC = 0.805, 95% CI: 0.758-0.852) for detecting GLIM-defined malnutrition, while the PNI and COUNT score showed poor diagnostic accuracy with AUCs of 0.699 (95% CI: 0.641-0.757) and 0.665 (95% CI: 0.605-0.725) respectively. Among these objective nutritional screening tools, the GNRI-based malnutrition risk assessment demonstrated the highest specificity (80.0%), accuracy (72.8%), PPV (74.8%), NPV (71.4%), and consistency (k = 0.452) with GLIM-defined malnutrition. Compared to PNI and COUNT scores, GNRI demonstrated superior performance as an objective nutritional screening tool for identifying GLIM-defined malnutrition in GC patients.

Prognostic Nutritional Index and the Survival of Patients with Endometrial cancer: A Meta-analysis.

The prognostic nutritional index (PNI) has emerged as a potential predictor of clinical outcomes in various cancers. However, a quantativetily analysis of its role in endometrial cancer (EC) remains lacking. This meta-analysis aims to evaluate the prognostic value of PNI on the survival outcomes of patients with EC. A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science, Wanfang, and CNKI to identify relevant cohort studies. Studies were included if they provided sufficient data to calculate hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS) based on PNI levels. Data extraction and quality assessment were performed independently by two reviewers. Pooled HRs with 95% confidence intervals (CIs) were calculated using a random-effects model to account for heterogeneity. A total of 10 studies, encompassing 3656 patients, met the inclusion criteria. The meta-analysis revealed that a low PNI was significantly associated with poorer OS (HR = 2.01, 95% CI = 1.62-2.49, p < 0.05; I2 = 54%) and PFS (HR = 2.75, 95% CI = 1.74-4.33, p < 0.05; I2 = 78%) in patients with EC. Subgroup analyses indicated that the prognostic impact of PNI was consistent in studies from Asian and non-Asian countries, and across studies with different ages of the patients, cutoff values of PNI, and follow-up duration (p for subgroup difference all > 0.05). In conclusion, the PNI is a prognostic marker for survival in patients with EC.

Predictive value of prognostic nutritional index as prognostic biomarkers in patients with lymphoma: a systematic review and meta-analysis.

Several research have indicated the significant potential of the Prognostic Nutritional Index (PNI) as a prognostic biomarker in lymphoma patients. However, there is some inconsistency in the findings of a few studies. Hence, to offer a thorough evaluation of the predictive significance of PNI in lymphoma patients, we performed a meta-analysis to examine the prognostic value of PNI for survival outcomes in lymphoma patients. We conducted a comprehensive search for pertinent works published up until December 2023 in databases such as PubMed, EMBASE, Cochrane Library, and Web of Science. We obtained hazard ratio (HR) data related to survival outcomes and computed aggregated HRs with their corresponding 95% confidence intervals (CIs) to evaluate the correlation between PNI and both overall survival (OS) and progression-free survival (PFS) in lymphoma patients. By analyzing data from 1260 patients in 28 studies, we found that PNI levels were associated with prognosis in lymphoma patients. High PNI levels predicted that patients had longer OS (HR: 0.46, 95% CI 0.37-0.58, P < 0.05) and better PFS (HR: 0.56, 95% CI 0.45-0.70, P < 0.05). Subgroup analyses showed that the predictive ability of PNI for patient prognosis may differ depending on the type of lymphoma. In addition, we found that the critical PNI value had greater predictive potential at 40-45 and above 45. Our study suggests a strong association between PNI and prognostic outcomes in lymphoma patients, indicating that PNI holds substantial prognostic value in this population.

Potential of Nutritional Markers as Predictors After Immunotherapy in Advanced Head and Neck Squamous Cell Carcinoma.

Immune checkpoint inhibitors (ICIs) are the standard treatment for advanced head and neck squamous cell carcinoma (HNSCC). Programmed death-ligand 1 (PD-L1) is clinically assessed before initiating ICIs; however, there are no established biomarkers for predicting the response to immunotherapy. In this study, inflammatory and nutritional parameters were examined to determine the therapeutic outcomes of ICIs for HNSCC. Sixty-five patients with metastatic or recurrent HNSCC who received programmed death-1 (PD-1) blockade were enrolled. Inflammatory and nutritional indices were correlated with patient outcomes, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI). Patients aged <70 years were significantly associated with a high NLR, whereas those with a performance status of 2 or 3 were closely related to a high NLR, high SII, and low PNI. Although all patients achieved an objective response rate of 24.6% and a disease control rate of 36.9%, the NLR, PLR, SII, and PNI values were not significantly different between responders and non-responders. Univariate analysis showed that the NLR, PLR, SII, and PNI were significant predictors of progression-free survival (PFS) and overall survival (OS). Multivariate analysis identified PNI as an independent predictor of PFS and OS. PNI, as a nutritional marker, was identified as a significant predictor of outcomes following PD-1 blockade administration in patients with advanced HNSCC, compared to inflammatory markers, such as NLR, PLR, and SII.

Performance of nutritional and inflammatory markers in patients with pancreatic cancer.

Systemic inflammation and nutrition play pivotal roles in cancer progression and can increase the risk of delayed recovery after surgical procedures. To assess the significance of inflammatory and nutritional indicators for the prognosis and postoperative recovery of patients with pancreatic cancer (PC). Patients who were diagnosed with PC and underwent surgical resection at our hospital between January 1, 2019, and July 31, 2023, were enrolled in this retrospective observational cohort study. All the data were collected from the electronic medical record system. Seven biomarkers - the albumin-to-globulin ratio, prognostic nutritional index (PNI), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), nutritional risk index (NRI), and geriatric NRI were assessed. A total of 446 patients with PC met the inclusion criteria and were subsequently enrolled. Patients with early postoperative discharge tended to have higher PNI values and lower SII, NLR, and PLR values (all P < 0.05). Through multivariable logistic regression analysis, the SII value emerged as an independent risk factor influencing early recovery after surgery. Additionally, both univariable and multivariable Cox regression analyses revealed that the PNI value was the strongest prognostic marker for overall survival (OS; P = 0.028) and recurrence-free survival (RFS; P < 0.001). The optimal cutoff PNI value was established at 47.30 using X-tile software. Patients in the PNI-high group had longer OS (P < 0.001) and RFS (P = 0.0028) times than those in the PNI-low group. Preoperative systemic inflammatory-nutritional biomarkers may be capable of predicting short-term recovery after surgery as well as long-term patient outcomes.

Neutrophil-to-Lymphocyte Ratio and Prognostic Nutritional Index Are Predictors for Overall Survival after Primary Pancreatic Resection of Pancreatic Ductal Adenocarcinoma: A Single Centre Evaluation.

Prognostic inflammation-based parameters have been reported as useful tools in various oncologic diseases. Pancreatic ductal adenocarcinoma (PDAC) is characterized by a high mortality rate, making reliable prognostic markers highly desirable. However, there is still inconsistency in the literature regarding the efficacy of the different available scores. A total of 207 patients, who underwent primary resection of PDAC from January 2000 to December 2018 at the University Hospital of Erlangen, were included in this retrospective single-center study. Different biomarkers, including the preoperative neutrophil-lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR), the c-reactive protein (CRP)-albumin ratio (CAR), the lymphocyte-CRP ratio (LCR), the prognostic nutritional index (PNI) and the modified Glasgow prognostic score (mGPS) were analyzed for their ability to predict overall survival (OS). In our cohort, the median overall survival was 20.7 months. Among the investigated biomarkers, NLR and PNI were identified as independent prognostic markers (Hazard Ratio (HR) 1.6 (1.0-2.5), p = 0.048 and HR 0.6 (0.4-0.9), p = 0.018), whereas PLR, CAR, LCR and mGPS did not reach significance in the multivariate analysis. Subgroup analysis revealed that the prognostic value of NLR and PNI is particularly evident in locally advanced tumor stages (pT3/4 and pN+). The NLR and PNI could serve as valuable tools for estimating prognosis in patients with PDAC undergoing pancreatic resection in curative intention, especially in locally advanced tumor stages. However, conflicting results in the current literature highlight the need for further prospective studies to validate these findings.

Prognostic nutritional index predicts lateral lymph node metastasis and recurrence free survival in papillary thyroid carcinoma

BackgroundPreoperative hematological parameters are predictors of pathological features and recurrence-free survival (RFS) in various malignancies. However, comprehensive studies of preoperative indicators associated with papillary thyroid carcinoma (PTC) are scarce. The present study investigated the association between preoperative indicators and RFS in patients with PTC. Accordingly, we explored the clinical impact of the prognostic nutritional index (PNI) on lymph node metastasis and RFS in patients with PTC.MethodsA total of 619 PTC patients were retrospectively reviewed between Jan 2013 and Dec 2017. Laboratory values were measured and calculated. Receiver operating characteristic curves were generated to calculate the cutoff value. Univariate and multivariate analyses using the COX proportional hazard model were performed for RFS. The effects of PNI and age on RFS were investigated by the Kaplan-Meier method. Clinical characteristics and PNI were tested with the chi-square test. Univariate and multivariate logistic analyses were conducted to evaluate the predictive value of PNI for lymph node metastasis.ResultsIn the multivariate Cox analysis, age, PNI and lymph node metastasis were independent prognostic indicators for RFS. The Kaplan-Meier method showed that the lower PNI group and age older than 55 years group displayed poor RFS. A low preoperative PNI was remarkably correlated with age, sex, extrathyroidal invasion, T stage, N stage and TNM stage. PNI was the only preoperative hematological indicator for lateral lymph node metastasis.ConclusionsAmong the preoperative hematological indicators, PNI may serve as a promising and effective predictor for RFS and lateral lymph node metastasis in PTC patients.

Comparison of the Clinical Features and Prognostic Value of Inflammation-Based Markers in Uterine Leiomyosarcoma

Aim: Inflammation-related markers are the factors affecting prognosis in many types of cancer. In this study, we aimed to investigate the relationship between inflammation-related markers, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI) with prognosis of patients with uterine leiomyosarcoma (uLMS). Methods: Patients diagnosed with uLMS were reviewed retrospectively. NLR, PLR, SII, and PNI values were calculated at the diagnosis and before treatment. Totally 35 patients were included in the study. Results: Median overall survival (OS) in the low-NLR (

The Effect of Prognostic Nutritional Index and Systemic Inflammatory Index in Endometrioid Type Endometrial Cancer

OBJECTIVES: The primary goal of the cohort was to show the relationship between systemic inflammatory index (SII) and prognostic nutritional index (PNI) values and the prognostic factors of endometrial cancer (EC), especially lymph node (LN) involvement, and lymphovascular space invasion (LVSI), which can not generally be evaluated preoperatively. STUDY DESIGN: This is a retrospective cohort based on reviews of patient records who were diagnosed with EC and underwent treatment between January 2014 and 2021. Complete blood counts and albumin level measurements were performed within 2 weeks before treatment. RESULTS: Two hundred twenty-seven patients with endometrioid type EC were included in the cohort. The mean follow-up was 35.4 ± 19.6 months. LVSI positivity was 8.2% in the presence of low SII and 44.1% in the presence of high SII (p&lt;0.001). LVSI positivity was 23.3% in the presence of low PNI and 11.1% in the presence of high PNI (p=0.025). Low and high PNI values do not affect disease-free (DFS) and overall survival (OS) significantly. 5-year mean DFS and OS differ significantly with low and high SII values. CONCLUSİON: SII and PNI values can be used to predict the presence of LVSI in the preoperative period, and to determine the extent of LN dissection and the surgical procedure Patients with high SII values had worse survival outcomes.

The effect of prognostic nutritional index on diabetic patients with myocardial infarction

BackgroundThe prognostic nutritional index (PNI), a simple and comprehensive predictor of nutritional and immunological health, is connected to cancer and cardiovascular disease. The effects of PNI on myocardial infarction (MI) in individuals with diabetes remain unclear. Thus, we aim to investigate the correlation of PNI with predictive outcomes in this specific population group to inform therapeutic decision-making.MethodsThis prospective observational study included 417 diabetic patients with MI who underwent coronary angiography intervention at Fujian Medical University Union Hospital from May 2017 to May 2020. We collected follow-up and prognostic data from these patients at 6, 12, 18, and 24 months post-procedure via outpatient visits or phone interviews. The main focus of the study was on major adverse cardiovascular events (MACE) in the two years after surgery. Based on the median PNI, patients were categorized into two groups: high PNI (H-PNI) and low PNI (L-PNI). Data were analyzed using IBM SPSS 25.0. Kalpan-Meier survival curves and Cox proportional hazards regression analysis were utilized to examine the associations between preoperative PNI and the prognosis of diabetic patients with MI.ResultsIn the study, 417 participants were observed for two years. Of these patients, 159 (38.1%) had MACE. According to the Kaplan–Meier curves, patients in the L-PNI group had more MACE than those in the H-PNI group (log-rank p < 0.001) and had a heightened susceptibility to all categories of MACE. After adjusting for confounding variables, the corrected hazard ratio for developing unstable angina in the L-PNI group was 2.55 (95% CI 1.57–4.14, p < 0.001).ConclusionLow PNI levels are associated with MACE after coronary angiography intervention in diabetic patients with myocardial infarction. This highlights the prognostic value of PNI and broadens its potential use in larger populations.Trial registration: Not applicable.

Prognostic Nutritional Index (PNI) and Systemic Immune-Inflammatory Index (SII) as markers of severity among patients having COVID-19 infection.

Prognostic nutritional index (PNI) and systemic immune-inflammatory index (SII) are two novel markers that have emerged as potential candidates as an early indication of the severity of the disease in coronavirus disease 2019 (COVID-19) patients. The objective of the study is to assess the utility of the prognostic nutritional index (PNI) and systemic immune-inflammatory index (SII) as markers of severity among patients with COVID-19 infection. This is a retrospective study conducted in a tertiary care centre in South India. A total of 80 patients diagnosed with COVID-19 were included in the study. The patients were divided into mild, moderate, and severe groups based on the clinical parameters as per Indian Council of Medical Research guidelines. Lab values taken at admission were obtained from patient records, using which the PNI and SII were calculated using standard formulae. These markers were correlated with the severity of the COVID-19 illness. PNI and SII were significantly elevated in the patients with severe COVID-19 illness as compared with mild COVID-19 illness. The mean PNI among subjects with mild COVID-19 and severe COVID-19 being 46.62 ± 6.51 and 34.09 ± 5.81, respectively. The mean SII among subjects with mild COVID-19 was 9,52,287.2 ± 1,42,113, and among subjects with severe COVID-19 was 15,39,461 ± 8,04,285. The cut-off value for PNI and SII for predicting severity of COVID-19 illness was 35.93 and 5,82,400, respectively. The sensitivity for PNI was 87.5, and the SII was 95. The present study showed a significant correlation between the SII and PNI as markers used to determine the severity of COVID-19. Based on these findings, it can be effectively used independently of other markers to predict critical illness among COVID-19 patients.

Comparison of the diagnostic efficacy of systemic inflammatory indicators in the early diagnosis of ovarian cancer.

This study aimed to determine the diagnostic accuracy of CA125, HE4, systemic immune-inflammation index (SII), prognostic nutritional index (PNI), fibrinogen-to-albumin ratio (FAR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and the combination of the six inflammatory-nutritional markers for ovarian cancer (OC) to identify the best diagnostic indicator for OC early diagnosis. An extensive study was performed to establish the connection between these indicators and the pathological aspects of OC. A total of 170 individuals were included in this study, with 87 diagnosed with OC and 83 with benign ovarian tumors (BOTs). The diagnostic abilities of the variables were evaluated by calculating sensitivity, specificity, and area under the ROC curves. Through the use of DCA, we evaluated the variables' clinical value in the discrimination of ovarian masses. All markers showed significant diagnostic power for OC. CA125, HE4, SII, FAR, and MLR levels significantly increased from the BOTs group to the early-stage OC group. The advanced-stage OC group had significantly lower PNI values compared to the early-stage OC group but significantly higher levels of CA125, HE4, SII, NLR, and FAR. Moreover, the OC group with lymph node metastasis exhibited significantly higher levels of CA125, HE4, SII, NLR, PLR, and FAR, in contrast to the non-metastatic group, while PNI levels were significantly lower. Categorical factors, such as histological grade and pathological classification, showed noticeable discrepancies in CA125 and HE4 levels. NLR was significantly different among the pathological type groups. Among the six inflammatory-nutritional markers, the FAR displayed the greatest diagnostic value. In the analysis of logistic regression, it was observed that a combination marker containing all six inflammatory-nutritional markers exhibited a notably higher AUC value (0.881; 95% CI, 0.823 - 0.926) than any of the individual marker. PNI, NLR, PLR, MLR, SII, and FAR showed excellent diagnostic performance for OC. The combination of these markers demonstrated a superior diagnostic capability compared to each individual one. The systemic inflammatory indicators may be helpful to diagnose OC.

Prognostic Nutritional Index (PNI) as Independent Predictor of Poor Survival in Prostate Cancer: A Systematic Review and Meta-Analysis

BackgroundThe concentration of albumin and lymphocyte in the body can serve as indicators of both nutritional status and inflammation. The predictive significance of the prognostic nutritional index (PNI) has been documented in multiple cancer types. Consequently, a meta-analysis was conducted in order to investigate the prognostic impact of PNI on survival outcomes among individuals diagnosed with prostate cancer. MethodsA systematic search was conducted across 4 electronic databases to identify pertinent studies that evaluated the predictive significance of pretreatment PNI in patients with prostate cancer. The outcomes of interest in this study were overall survival (OS) and progression-free survival (PFS). The researchers utilized random-effect models to summarize the time-to-event outcomes, presenting the results as adjusted hazard ratios (aHR) along with their corresponding 95% confidence intervals (CI). ResultsA total of 2229 prostate cancer patients in 13 studies were included. Pooled analysis from these studies showed that low PNI value was associated with shorter OS [aHR 1.99 (95% CI, 1.45-2.72), P < .0001], and PFS [aHR 1.97 (95% CI, 1.55-2.51), P < .00001]. Sub-group analysis revealed that the ability of PNI to predict poor outcomes was better observed in patients with metastatic castration-resistant prostate cancer (mCRPC) and those who received androgen receptor pathway inhibitors (ARPIs). ConclusionsThis study suggests the role of PNI in predicting the survival and progression of prostate cancer. PNI values can be used in the risk stratification of patients with prostate cancer.

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data. To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients. Data of patients treated for colorectal cancer (n = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group (n = 60) and a control group (n = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model. More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (P < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility. This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.