ObjectiveTo determine the predictive value of the preoperative prognostic nutritional index (PNI) regarding the development of acute kidney injury (AKI) after non-coronary artery bypass grafting (CABG) cardiac surgery.MethodsThe clinical data of 584 patients who underwent elective non-CABG cardiac surgery with cardiopulmonary bypass (CPB) in our hospital from May to September 2019 were reviewed. There were 268 (45.9%) males and 316 (54.1%) females, with a mean age of 52.1±11.6 years. The mean cardiopulmonary time and aortic-clamp time was 124.8±50.1 min and 86.4±38.9 min, respectively. Totally 449 (76.9%) patients received isolate valve surgery. We developed the risk prediction model of AKI using multivariable logistic regression. The predictive values of preoperative PNI, Cleveland Clinic Score (CCS) and risk prediction model were estimated by the area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness-of-fit test. The improvement of preoperative PNI to predictive values of CCS or AKI risk prediction models were defined by the net reclassification index (NRI) and variation of AUC.ResultsThe preoperative PNI could neither effectively predict the occurrence of AKI following non-CABG cardiac surgery (AUC=0.553, 95%CI 0.489-0.617, P=0.095) nor improve the predictive effect of other AKI predictive models. The risk prediction model of AKI structured by our study had high predictive value on AKI or severe AKI (stage 2-3) (AUC=0.741, 95%CI 0.686-0.796, P<0.001) and superior to CCS (AUC=0.512, 95%CI 0.449-0.576, P=0.703).ConclusionThe preoperative PNI can neither predict the occurrence of AKI following elective non-CABG cardiac surgery nor improve the prediction values of other AKI prediction models.
Objective To explore the association of pretreatment prognostic nutritional index (PNI) with the prognosis of small cell lung cancer (SCLC) patients. Methods The PubMed, Web of Science, Embase, China National Knowledge Infrastructure, WanFang and VIP databases were searched for relevant literature which identified the prognostic role of PNI in SCLC up to March 9th, 2022. The primary endpoint was overall survival (OS) and the secondary endpoint was progression-free survival (PFS). The hazard ratio (HR) and 95% confidence interval (CI) were combined by Stata 12.0 software. Results A total of 19 retrospective cohort studies were included, involving 5999 participates. The pooled results indicated that low pretreatment PNI predicted poorer OS [HR=1.58, 95%CI (1.37, 1.83), P<0.001] and PFS [HR=1.51, 95%CI (1.03, 2.22), P=0.037]. Conclusion Low pretreatment PNI may be a risk factor for poor prognosis of SCLC patients and could be applied for the evaluation of prognosis and formulation of therapy strategy.
Objective To investigate the relationship between the level of prognostic nutritional index (PNI) and 28-day mortality in patients after cardiopulmonary resuscitation. Methods A total of 955 patients admitted to intensive care unit after cardiopulmonary resuscitation between 2008 and 2019 were selected from the MIMIC-IV database and grouped according to the optimal cut-off value of PNI for retrospective cohort analysis. Primary outcome was defined as 28-day all-cause mortality. After adjusting for confounding factors by propensity score matching, the outcomes between high PNI and low PNI groups were compared. PNI and Sequential Organ Failure Assessment (SOFA) score were incorporated into a Cox proportional risk model to construct a predictive model, and the predictive effect was assessed using the concordance index, the net reclassification index, and the integrated discriminant improvement. Results After propensity score matching, compared with the high PNI group, the low PNI group had lower 28-day survival (P<0.001), higher doses of vasoactive drugs used during intensive care unit stay (P<0.001), higher SOFA score (P<0.001) and higher Logistic Organ Dysfunction System score (P=0.002). The admission PNI and SOFA score had similar predictive effects on 28-day mortality, with the area under the receiver operating characteristic curve of 0.639 and 0.638, respectively. In addition, compared with SOFA score alone, PNI combined with SOFA score improved the predictive performance, with an area under the curve of 0.673, the concordance index increasing from 0.598 to 0.622, and the net reclassification index and the integrated discriminant improvement estimates of 0.144 (P<0.001) and 0.027 (P<0.001), respectively. Conclusions PNI can be used as a new predictor of all-cause death risk within 28 days after cardiopulmonary resuscitation. SOFA score combined with PNI can improve the prediction effect.
ObjectiveTo explore the application value of prognostic nutritional index (PNI) in the postoperative complications of McKeown surgery for da Vinci robotic esophageal cancer. MethodsThe clinical data of the patients who underwent da Vinci robotic McKeown surgery for esophageal cancer in the Department of Thoracic Surgery of the First Hospital of Lanzhou University from January 2019 to June 2022 were retrospectively collected. According to the receiver operating characteristic (ROC) curve, the optimal cut-off value of PNI for predicting postoperative complications was explored. The patients were divided into a high PNI group and a low PNI group according to the cut-off value, and the differences in basic characteristics, surgery-related indexes and postoperative complications between the two groups were analyzed. According to the occurrence of postoperative complications, the patients were divided into a non-complication group and a complication group. Univariate and multivariate analyses were used to explore the influence of relevant indicators on the occurrence of postoperative complications in da Vinci robotic McKeown surgery for esophageal cancer. ResultsFinally 120 patients were collected, including 95 males and 25 females, with an average age of 62.82 years. The preoperative hemoglobin content, preoperative blood lymphocyte count, preoperative serum albumin and preoperative blood total cholesterol in the high PNI group were higher than those in the low PNI group (P<0.05). There were statistical differences between the two groups in the incidences of postoperative overall complications, pulmonary infection, pleural effusion and poor incision healing (P<0.05). The relevant indicators that may cause postoperative complications were included in univariate analysis, and the results showed that age, operation time, intraoperative blood loss, preoperative blood lymphocyte count, preoperative hemoglobin content, preoperative blood mononuclear cell count, preoperative blood monocyte count, serum albumin level and PNI were possible influencing factors of postoperative complications after da Vinci robotic McKeown surgery for esophageal cancer. Incorporating these influencing factors into multivariate analysis, the results showed that age, PNI, operation time and intraoperative blood loss were independent influencing factors of postoperative complications. ConclusionPNI has certain predictive value in the postoperative complications of da Vinci robotic McKeown surgery for esophageal cancer. PNI is an independent factor affecting postoperative complications. Improving the level of PNI in esophageal cancer patient before surgery may help reduce the occurrence of postoperative complications.
Objective To investigate the predictive value of the prognostic nutritional index (PNI) for 28-day all-cause mortality in patients with chronic obstructive pulmonary disease (COPD) in intensive care unit (ICU). Methods The relationship between PNI and short-term mortality in COPD patients was analysed using COX proportional hazards and restricted cubic spline (RCS) models. Receiver operating characteristic (ROC) curves were plotted and area under the ROC curve (AUC) was calculated to assess the predictive performance of PNI. The optimal cut-off value for PNI was determined using the Youden index, and the data were divided into a low PNI group and a high PNI group. Kaplan-Meier curves were then constructed and the log-rank test was used to assess differences in survival between the two groups. Results A total of 980 COPD patients were included in the study. Multivariable COX regression analysis showed that PNI was an independent factor influencing short-term mortality in the severe COPD patients (HR=0.972, 95%CI 0.948 - 0.995, P=0.019). RCS curve results showed a non-linear relationship between PNI and short-term mortality in the severe COPD patients (P for non-linear=0.032), with the risk of death gradually decreasing as PNI increased. The ROC curve indicated that PNI had some predictive power, comparable to that of SOFA score [(AUCPNI=0.693) vs. (AUCSOFA=0.672)]. Kaplan-Meier curve analysis showed a significant difference in survival time between the low (≤38.3) PNI group and the high (>38.3) PNI group (P<0.05). Conclusions PNI has a certain predictive role for short-term all-cause mortality in patients with severe COPD. Patients with low PNI at ICU admission have a higher risk of short-term mortality.