Background: Red blood cell (RBC) distribution width (RDW) to albumin ratio is a novel biomarker and its prognostic effect on critically ill patients with sepsis has not been extensively investigated. The objective of this study was to identify the prognostic value of the RDW to albumin ratio in these patients. Methods: Data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. A Cox proportional hazards model and restricted cubic spline model were used to determine the association of RDW to albumin ratio with mortality. Receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were applied, and the area under the curve (AUC) was used to compare the predictive value. Results: A total of 3,969 eligible patients were enrolled. The median RDW to albumin ratio was significantly higher in non-survivors than in survivors at 30 and 90 days. Patients were divided into groups according to the RDW to albumin ratio, and the risk of 30- and 90-day mortality markedly increased in the group with a higher ratio. The relationship between the RDW to albumin ratio as a continuous variable and 30-day mortality also showed an upward trend in the restricted cubic spline. The AUC of the RDW to albumin ratio was 0.633 in discriminating 30-day mortality which was similar to that of the lactate to albumin ratio (AUC =0.617; P=0.133) and higher than that of the neutrophil percentage to albumin ratio (AUC =0.559; P<0.001). Conclusions: The RDW to albumin ratio is a promising biomarker for assessing the prognosis of critically ill patients with sepsis. Its predictive value in determining mortality was found to be similar to that of the lactate to albumin ratio and superior to that of the neutrophil percentage to albumin ratio.

Shock is a common critical illness characterized by microcirculatory disorders and insufficient tissue perfusion. Patients with shock and hemodynamic instability generally require vasopressors to maintain the target mean arterial pressure. Enteral nutrition (EN) is an important therapeutic intervention in critically ill patients and has unique benefits for intestinal recovery. However, the initiation of early EN in patients with shock receiving vasopressors remains controversial. Current guidelines make conservative and vague recommendations regarding early EN support in patients with shock. Increasing studies demonstrates that early EN delivery is safe and feasible in patients with shock receiving vasopressors; however, this evidence is based on observational studies. Changes in gastrointestinal blood flow vary by vasopressor and inotrope and are complex. The risk of gastrointestinal complications, especially the life-threatening complications of non-occlusive mesenteric ischemia and non-occlusive bowel necrosis, cannot be ignored in patients with shock during early EN support. It remains a therapeutic challenge in critical care nutrition therapy to determine the initiation time of EN in patients with shock receiving vasopressors and the safe threshold region for initiating EN with vasopressors. Therefore, the current review aimed to summarize the evidence on the optimal and safe timing of early EN initiation in patients with shock receiving vasopressors to improve clinical practice.

(1) Background: Acute muscle inflammation leads to increased sonographic echodensity. We developed a technique to characterize the echodensity of the gastric antrum wall and assess its feasibility in evaluating the severity of acute gastrointestinal injury (AGI); (2) Methods: The B-mode images of the gastric antrum of each enrolled patient were obtained daily by point-of-care ultrasound (POCUS). The 50th percentile, 85th percentile, and mean value of the grayscale distribution according to histogram analysis (ED₅₀, ED₈₅, and ED_mean, respectively) were used to characterize the gastric antrum echodensity. Consistency and correlation analyses were performed to evaluate the feasibility and reproducibility of gastric antrum echodensity measurement. The association of gastric antrum echodensity with the severity of AGI and its ability to predict feeding intolerance (FI) were analyzed; (3) Results: In total, 206 POCUS images of 43 patients were analyzed. The gastric antrum echodensity measurements had sufficient intra- and inter-investigator reliabilities (intraclass correlation coefficient >0.9 for all parameters). The ED₅₀ showed a significant upward trend as AGI severity increased, as well as ED₈₅ and ED_mean (p for trend <0.001, respectively). Patients who experienced FI had a higher ED₅₀ (67.8 vs. 56.1, p = 0.02), ED₈₅ (85.6 vs. 71.2, p = 0.01), and ED_mean (70.3 vs. 57.6, p = 0.01) upon enteral feeding initiation; (4) Conclusions: Measurement of gastric antrum echodensity was technically feasible and reproducible in ventilated patients. Increased gastric antrum echodensity was associated with greater severity of AGI. Patients with higher gastric antrum echodensity upon enteral nutrition initiation via a nasogastric tube were more likely to develop FI.

Pneumatosis cystoides intestinalis (PCI) is a rare condition characterized by the presence of air collection within the subserosa and/or submucosa of the gastrointestinal wall. Due to the lack of specific symptoms, PCI is likely to be misdiagnosed or missed without the use of imaging techniques or gastrointestinal endoscopy. Here, we report a patient who complained of abdominal distention and constipation after chemotherapy for hematological malignancies, and was diagnosed with secondary PCI via computed tomography (CT) and exploratory laparotomy. Pneumoperitoneum was no longer observed after two weeks of conservative treatments. Notably, the possibility of intra-abdominal pressure (IAP) as a predictor for surgical intervention was proposed. Furthermore, we conducted a literature review on PCI after chemotherapy in hematological malignancies to raise awareness of etoposide-related PCI, while whether PCI could be identified as an adverse event of etoposide requires more evidence.

Hospital acquired thrombocytopenia (HAT) is a common hematological complication after surgery. This research aimed to develop and compare the performance of seven machine learning (ML) algorithms for predicting patients that are at risk of HAT after surgery. We conducted a retrospective cohort study which enrolled adult patients transferred to the intensive care unit (ICU) after surgery in West China Hospital of Sichuan University from January 2016 to December 2018. All subjects were randomly divided into a derivation set (70%) and test set (30%). ten-fold cross-validation was used to estimate the hyperparameters of ML algorithms during the training process in the derivation set. After ML models were developed, the sensitivity, specificity, area under the curve (AUC), and net benefit (decision analysis curve, DCA) were calculated to evaluate the performances of ML models in the test set. A total of 10,369 patients were included and in 1354 (13.1%) HAT occurred. The AUC of all seven ML models exceeded 0.7, the two highest were Gradient Boosting (GB) (0.834, 0.814–0.853, p < 0.001) and Random Forest (RF) (0.828, 0.807–0.848, p < 0.001). There was no difference between GB and RF (0.834 vs. 0.828, p = 0.293); however, these two were better than the remaining five models (p < 0.001). The DCA revealed that all ML models had high net benefits with a threshold probability approximately less than 0.6. In conclusion, we found that ML models constructed by multiple preoperative variables can predict HAT in patients transferred to ICU after surgery, which can improve risk stratification and guide management in clinical practice.

Objective This study aimed to predict the molecular features of endometrial carcinoma (EC) and the overall survival (OS) of EC patients using histopathological imaging. Methods The patients from The Cancer Genome Atlas (TCGA) were separated into the training set (n = 215) and test set (n = 214) in proportion of 1:1. By analyzing quantitative histological image features and setting up random forest model verified by cross-validation, we constructed prognostic models for OS. The model performance is evaluated with the time-dependent receiver operating characteristics (AUC) over the test set. Results Prognostic models based on histopathological imaging features (HIF) predicted OS in the test set (5-year AUC = 0.803). The performance of combining histopathology and omics transcends that of genomics, transcriptomics, or proteomics alone. Additionally, multi-dimensional omics data, including HIF, genomics, transcriptomics, and proteomics, attained the largest AUCs of 0.866, 0.869, and 0.856 at years 1, 3, and 5, respectively, showcasing the highest discrepancy in survival (HR = 18.347, 95% CI [11.09–25.65], p < 0.001). Conclusions The results of this experiment indicated that the complementary features of HIF could improve the prognostic performance of EC patients. Moreover, the integration of HIF and multi-dimensional omics data might ameliorate survival prediction and risk stratification in clinical practice.