Background. Sepsis-related acute kidney injury (S-AKI) is a frequent complication of hospitalized patients and is linked to increased morbidity and mortality. Early prediction and detection remain conducive to optimizing treatment strategies and limiting further insults. This study was aimed at evaluating the potential predictive value of the combined prognostic nutrition index (PNI) and neutrophil-to-lymphocyte ratio (NLR) to predict the risk of AKI in septic patients. Methods. In this retrospective study, 1238 adult patients with sepsis who were admitted to the First Affiliated Hospital of Xi’an Jiaotong University from January 2015 to June 2021 were enrolled. Patients were divided into two groups: the non-AKI group ( ) and the S-AKI group ( ). Univariate and multivariate logistic regression analyses were performed to screen the independent predictive factors of S-AKI. A receiver operating characteristic (ROC) curve was used to evaluate the predictive value of PNI and NLR. Results. Multivariate logistic regression analysis indicated that age, chronic liver disease, cardiovascular disease, respiratory rate (RR), white blood cells (WBC), blood urea nitrogen (BUN), creatinine (CRE), international normalized ratio (INR), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutrition index (PNI) were independent prognostic factors of S-AKI. In the three models, the adjusted OR of PNI for S-AKI was 0.802 (0.776-0.829), 0.801 (0.775-0.829), and 0.717 (0.666-0.772), while that of NLR was 1.094 (1.078-1.111), 1.097 (1.080-1.114), and 1.044 (1.016-1.072), respectively. In addition, the area under the ROC curve of the PNI plus NLR group was significantly greater than that of the CRE plus BUN group (0.801, 95% CI: 0.775-0.827 vs. 0.750, 95% CI: 0.722-0.778, respectively; ). Conclusions. PNI and NLR have been identified as readily available and independent predictors in septic patients with S-AKI. PNI, in combination with NLR, is of vital significance for early warning and efficient intervention of S-AKI and is superior to combined BUN and CRE.

Background: In sepsis patients, kidney damage is among the most dangerous complications, with a high mortality rate. In addition, major adverse kidney events within 30 days (MAKE30) served as a comprehensive and unbiased clinical outcome measure for sepsis patients due to the recent shift toward targeting patient-centered renal outcomes in clinical research. However, the underlying predictive model for the prediction of MAKE30 in sepsis patients has not been reported in any study.Methods: A cohort of 2,849 sepsis patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database was selected and subsequently allocated into a training set (n = 2,137, 75%) and a validation set (n = 712, 25%) through randomization. In addition, 142 sepsis patients from the Xi’An No. 3 Hospital as an external validation group. Univariate and multivariate logistic regression analyses were conducted to ascertain the independent predictors of MAKE30. Subsequently, a nomogram was developed utilizing these predictors, with an area under curve (AUC) above 0.6. The performance of nomogram was assessed through calibration curve, receiver operating characteristics (ROC) curve, and decision curve analysis (DCA). The secondary outcome was 30-day mortality, persistent renal dysfunction (PRD), and new renal replacement therapy (RRT). MAKE30 were a composite of death, PRD, new RRT.Results: The construction of the nomogram was based on several independent predictors (AUC above 0.6), including age, respiratory rate (RR), PaO2, lactate, and blood urea nitrogen (BUN). The predictive model demonstrated satisfactory discrimination for MAKE30, with an AUC of 0.740, 0.753, and 0.821 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the simple prediction model exhibited superior predictive value compared to the SOFA model in both the training (AUC = 0.710) and validation (AUC = 0.692) cohorts. The nomogram demonstrated satisfactory calibration and clinical utility as evidenced by the calibration curve and DCA. Additionally, the predictive model exhibited excellent accuracy in forecasting 30-day mortality (AUC = 0.737), PRD (AUC = 0.639), and new RRT (AUC = 0.846) within the training dataset. Additionally, the model displayed predictive power for 30-day mortality (AUC = 0.765), PRD (AUC = 0.667), and new RRT (AUC = 0.783) in the validation set.Conclusion: The proposed nomogram holds the potential to estimate the risk of MAKE30 promptly and efficiently in sepsis patients within the initial 24 h of admission, thereby equipping healthcare professionals with valuable insights to facilitate personalized interventions.

Background: In the intensive care unit (ICU), approximately 45.6% of patients diagnosed with chronic obstructive pulmonary disease (COPD) also presented with sepsis, and this cohort exhibited a significantly higher 28-day mortality rate compared to sepsis patients without COPD (23.6% versus 16.4%). A novel nomogram is necessary to predict the risk of mortality within 28 days for sepsis patients with COPD. Methods: Clinical data from 501 sepsis patients with COPD were sourced from the MIMIC-IV database. These data were randomly allocated into a training cohort and a validation cohort in a 3:1 ratio. Independent predictors of 28-day mortality were identified through both univariate and multivariate logistic regression analyses. Subsequently, a nomogram model was developed, and its performance was assessed using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis. Results: The 28-day mortality rates in the training and validation cohorts were 32.7% and 27.2%, respectively. Multivariate regression analysis identified age, heart rate (HR), respiratory rate (RR), blood urea nitrogen (BUN), creatinine (Cr), lactate levels, pH, and urine output as independent risk factors for 28-day mortality in sepsis patients with COPD. Furthermore, the nomogram demonstrated superior predictive performance, with an area under the curve (AUC) of 0.784 for the training group and 0.689 for the validation group. Conclusion: This nomogram integrates laboratory indicators pertinent to the patient’s metabolic status, hypoxia status, and organ function, thereby enhancing the accuracy of early prediction of 28-day mortality in sepsis patients with COPD. Additionally, the model’s comparative advantage over existing scoring systems (eg, SOFA) would enhance its impact. Our findings hold substantial implications for early prognostic assessment and clinical decision-making in this patient population. Therefore, earlier diagnosis within 24 hours of admission and proper identification of high-risk patients may reduce disease-related mortality by promoting timely treatment.

Background: Sepsis is a life-threatening condition often complicated by organ dysfunction and is associated with a high mortality rate. The dysregulation of immune response, inflammation, and nutritional status are critical factors contributing to its pathogenesis. This study aimed to develop a nomogram that integrates prognostic immune-inflammatory-nutritional indicators with other clinical information to predict 28-day mortality in sepsis patients with acute respiratory distress syndrome (ARDS). Methods: Clinical data from 635 adult sepsis patients with ARDS were obtained from Shaanxi Provincial People's Hospital and randomly divided into a training set ( n = 477) and a validation set ( n = 158). To identify predictors of 28-day mortality in sepsis patients with ARDS, univariate analysis, and least absolute shrinkage and selection operator (LASSO) regression were utilized. Subsequently, a multivariate logistic regression model was constructed to identify independent predictors. A nomogram was then developed by integrating the selected indicators. The model's performance was assessed with respect to discrimination, calibration, and clinical utility through the use of the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). Results: The independent predictors utilized for the construction of the nomogram included the albumin-alkaline phosphatase ratio (AAPR), albumin-bilirubin grade (ALBI), neutrophil–lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), prognostic nutritional index (PNI), systemic immune-inflammation index (SII), and lactate-albumin ratio (LAR). Notably, the nomogram exhibited superior predictive performance, with an AUC of 0.873 in the training set and 0.837 in the validation set, as compared to the SOFA score, which showed an AUC of 0.689 in the training set and 0.684 in the validation set, for predicting 28-day mortality in sepsis patients with ARDS. The calibration plots demonstrated excellent consistency. DCA confirmed the model's clinical utility, showing a positive net benefit across a wide range of clinically relevant threshold probabilities (approximately 10% to 70%), which supports its potential to guide clinical decision-making. Conclusion: We have successfully developed and validated a robust nomogram that integrates seven readily accessible immune-inflammatory-nutritional indicators. This model serves as an individualized and precise tool for predicting the 28-day mortality risk in sepsis patients with acute respiratory distress syndrome (ARDS), thereby potentially enhancing early risk stratification and informing clinical decision-making.

Sepsis and acute-on-chronic liver failure (ACLF) constitute a severe clinical interaction, resulting in alarmingly high short-term mortality rates. Current prognostic models, such as the Sequential Organ Failure Assessment (SOFA) score, lack specificity for this distinct patient group. The pathophysiology of both conditions is critically influenced by immune dysfunction, systemic inflammation, and nutritional deficits. This study sought to develop and validate a novel prognostic model that integrates immune-inflammatory-nutritional indicators to enhance the prediction of 28-day mortality in sepsis patients with ACLF. A retrospective cohort study was conducted involving 246 sepsis patients with ACLF admitted to Shaanxi Provincial People’s Hospital. Participants were randomly divided into a training cohort (n=185) and a validation cohort (n=61). Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analyses were utilized to identify independent predictors and construct a prognostic nomogram. The model’s efficacy was thoroughly evaluated based on its discriminative capacity (area under the receiver operating characteristic curve, AUC), calibration (calibration curves), and clinical utility (decision curve analysis, DCA). The 28-day mortality rate was observed to be 37.0%. Six independent predictors were identified and integrated into the nomogram: hepatic encephalopathy, D-dimer levels, platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), ALBI grade, and the systemic inflammation response index (SIRI). The nomogram exhibited exceptional discriminative ability, with AUC values of 0.907 and 0.862 in the training and validation cohorts, respectively, significantly surpassing the performance of the SOFA score (AUCs: 0.651 and 0.624). Calibration curves indicated excellent concordance between predicted and observed outcomes, while DCA confirmed a substantial clinical net benefit across a broad range of threshold probabilities. We have successfully developed and validated a robust nomogram that utilizes routinely available immune-inflammatory-nutritional indicators to provide individualized risk estimation of 28-day mortality in sepsis patients with ACLF. This model represents an advanced prognostic instrument when compared to traditional scoring systems, with the potential to enable early detection of high-risk patients and inform individualized therapeutic interventions.

Background: In the clinical management of patients with invasive breast cancer (IBC), the precise identification of axillary lymph node metastasis (ALNM) is of paramount importance for guiding axillary surgery and formulating corresponding treatment strategies. Currently, the clinical need to accurately predict the risk of ALNM in patients with IBC, so as to determine whether axillary surgery (including sentinel lymph node biopsy) can be safely omitted, has not been fully met. Methods: The clinical data of 454 patients with IBC were retrospectively analyzed, and the patients were randomly divided into a training group and a validation group at a ratio of 3:1. Independent predictors related to ALNM were identified through univariate and multivariate Logistic regression analysis. Accordingly, a nomogram model integrating clinicopathological, ultrasound and serological indicators along with additional clinical parameters was constructed and validated by receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA). The primary outcome measure was the incidence of ALNM. Results: The ALNM rates were 31.4% and 27.4% in the training and verification groups, respectively. Multivariate regression analysis indicated that tumor size, circumscribe margin, ultrasonic lymph node status, WBC, ER, and BI-RADS were independent risk factors for ALNM in patients with IBC. In both the training group and the validation group, the nomogram exhibited modest predictive performance (training group AUC = 0.741; validation group AUC = 0.705). The Ultrasonic lymph node status (normal vs. abnormal) alone yielded an AUC of 0.620 in the training cohort and 0.627 in the validation cohort for predicting ALNM. In comparison, our nomogram achieved significantly higher AUCs (training: 0.741, P = 0.0017; validation: 0.705, P = 0.1772; DeLong test). Additionally, the nomogram demonstrated satisfactory calibration and clinical utility as evidenced by the calibration curve and DCA. Conclusion: By integrating clinicopathological, ultrasonic and serological indicators, this nomogram can enhance the accuracy of predicting ALNM in patients with IBC. However, given its moderate discriminative ability, the model can support preoperative risk stratification but cannot replace standard axillary staging procedures (including sentinel lymph node biopsy). Our findings provide a basis for decision-making regarding individualized axillary lymph node surgery.