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 ±â€…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 ±â€…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.

Assessing the effectiveness of ACS surgical risk calculator versus P-POSSUM in predicting mortality and morbidity for major hepatobiliary surgery: An observational study.

Risk assessment is difficult yet would provide valuable data for both the surgeons and the patients in major hepatobiliary surgeries. An ideal risk calculator should improve workflow through efficient, timely, and accurate risk stratification. The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC) and Portsmouth Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (P-POSSUM) are surgical risk stratification tools used to assess postoperative morbidity. In this study, preoperative data from 300 patients undergoing major hepatobiliary surgeries performed at a single tertiary university hospital were retrospectively collected from electronic patient records and entered into the ACS-SRC and P-POSSUM systems, and the resulting risk scores were calculated and recorded accordingly. The ACS-NSQIP-M1 (C-statistics = 0.725) and M2 (C-statistics = 0.791) models showed better morbidity discrimination ability than P-POSSUM-M1 (C-statistics = 0.672) model. The P-POSSUM-M2 (C-statistics = 0.806) model showed better differentiation success in morbidity than other models. The ACS-NSQIP-M1 (C-statistics = 0.888) and M2 (C-statistics = 0.956) models showed better mortality discrimination than P-POSSUM-M1 (C-statistics = 0.776) model. The P-POSSUM-M2 (C-statistics = 0.948) model showed better mortality differentiation success than the ACS-NSQIP-M1 and P-POSSUM-M1 models. The use of ACS-SRC and P-POSSUM calculators for major hepatobiliary surgeries offers quantitative data to assess risks for both the surgeon and the patient. Integrating these calculators into preoperative evaluation practices can enhance decision-making processes for patients. The results of the statistical analyses indicated that the P-POSSUM-M2 model for morbidity and the ACS-NSQIP-M2 model for mortality exhibited superior overall performance.