Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study.

BACKGROUND: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving outcomes and reducing mortality rates. Nonetheless, clinical tools for predicting sepsis among patients with major trauma are limited. This study aimed to develop and validate an artificial intelligence (AI) platform for predicting the risk of sepsis among patients with major trauma. METHODS: This study involved 961 patients, with prospective analysis of data from 244 patients with major trauma at our hospital and retrospective analysis of data from 717 patients extracted from a database in the United States. The patients from our hospital constituted the model development cohort, and the patients from the database constituted the external validation cohort. The patients in the model development cohort were randomly divided into a training cohort and an internal validation cohort at a ratio of 8:2. The machine learning algorithms used to train models included logistic regression (LR), decision tree (DT), extreme gradient boosting machine (eXGBM), neural network (NN), random forest (RF), and light gradient boosting machine (LightGBM). RESULTS: The incidence of sepsis for the model development cohort was 43.44%. Twelve predictors, including gender, abdominal trauma, open trauma, red blood cell count, heart rate, respiratory rate, injury severity score, sequential organ failure assessment score, Glasgow coma scale, smoking, total protein concentrations, and hematocrit, were used as features in the final model. Internal validation showed that the NN model had the highest area under the curve (AUC) of 0.932 (95% CI: 0.917-0.948), followed by the LightGBM and eXGBM models with AUCs of 0.913 (95% CI: 0.883-0.930) and 0.912 (95% CI: 0.880-0.935), respectively. In the external validation cohort, the eXGBM model (AUC: 0.891, 95% CI: 0.866-0.914) had the highest AUC value, followed by the LightGBM model (AUC: 0.886, 95% CI: 0.860-0.906), and the AUC value of the NN model was only 0.787 (95% CI: 0.751-0.829). Considering the predictive performance for both the internal and external validation cohorts, the LightGBM model had the highest score of 82, followed by the eXGBM (81) and NN (76) models. Thus, the LightGBM was emerged as the optimal model, and it was deployed online as an AI application. CONCLUSIONS: This study develops and validates an AI application to effectively assess the susceptibility of patients with major trauma to sepsis. The AI application equips healthcare professionals with a valuable tool to promptly identify individuals at high risk of developing sepsis. This will facilitate clinical decision-making and enable early intervention.

CHRONIC CRITICAL ILLNESS-INDUCED MUSCLE ATROPHY: INSIGHTS FROM A TRAUMA MOUSE MODEL AND POTENTIAL MECHANISM MEDIATED VIA SERUM AMYLOID A.

ABSTRACT: Background: Chronic critical illness (CCI), which was characterized by persistent inflammation, immunosuppression, and catabolism syndrome (PICS), often leads to muscle atrophy. Serum amyloid A (SAA), a protein upregulated in critical illness myopathy, may play a crucial role in these processes. However, the effects of SAA on muscle atrophy in PICS require further investigation. This study aims to develop a mouse model of PICS combined with bone trauma to investigate the mechanisms underlying muscle weakness, with a focus on SAA. Methods: Mice were used to examine the effects of PICS after bone trauma on immune response, muscle atrophy, and bone healing. The mice were divided into two groups: a bone trauma group and a bone trauma with cecal ligation and puncture group. Tibia fracture surgery was performed on all mice, and PICS was induced through cecal ligation and puncture surgery in the PICS group. Various assessments were conducted, including weight change analysis, cytokine analysis, hematological analysis, grip strength analysis, histochemical staining, and immunofluorescence staining for SAA. In vitro experiments using C2C12 cells (myoblasts) were also conducted to investigate the role of SAA in muscle atrophy. The effects of inhibiting receptor for advanced glycation endproducts (RAGE) or JAK2 on SAA-induced muscle atrophy were examined. Bioinformatic analysis was conducted using a dataset from the GEO database to identify differentially expressed genes and construct a coexpression network. Results: Bioinformatic analysis confirmed that SAA was significantly upregulated in muscle tissue of patients with intensive care unit-induced muscle atrophy. The PICS animal models exhibited significant weight loss, spleen enlargement, elevated levels of proinflammatory cytokines, and altered hematological profiles. Evaluation of muscle atrophy in the animal models demonstrated decreased muscle mass, grip strength loss, decreased diameter of muscle fibers, and significantly increased expression of SAA. In vitro experiment demonstrated that SAA decreased myotube formation, reduced myotube diameter, and increased the expression of muscle atrophy-related genes. Furthermore, SAA expression was associated with activation of the FOXO signaling pathway, and inhibition of RAGE or JAK2/STAT3-FOXO signaling partially reversed SAA-induced muscle atrophy. Conclusions: This study successfully develops a mouse model that mimics PICS in CCI patients with bone trauma. Serum amyloid A plays a crucial role in muscle atrophy through the JAK2/STAT3-FOXO signaling pathway, and targeting RAGE or JAK2 may hold therapeutic potential in mitigating SAA-induced muscle atrophy.

Development and validation of an artificial intelligence mobile application for predicting 30-day mortality in critically ill patients with orthopaedic trauma.

BACKGROUND: Given the intricate and grave nature of trauma-related injuries in ICU settings, it is imperative to develop and deploy reliable predictive tools that can aid in the early identification of high-risk patients who are at risk of early death. The objective of this study is to create and validate an artificial intelligence (AI) model that can accurately predict early mortality among critical fracture patients. METHODS: A total of 2662 critically ill patients with orthopaedic trauma were included from the MIMIC III database. Early mortality was defined as death within 30 days in this study. The patients were randomly divided into a model training cohort and a model validation cohort. Various algorithms, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), support vector machine (SVM), random forest (RF), and neural network (NN), were employed. Evaluation metrics, including discrimination and calibration, were used to develop a comprehensive scoring system ranging from 0 to 60, with higher scores indicating better prediction performance. Furthermore, external validation was carried out using 131 patients. The optimal model was deployed as an internet-based AI tool. RESULTS: Among all models, the eXGBM demonstrated the highest area under the curve (AUC) value (0.974, 95%CI: 0.959-0.983), followed by the RF model (0.951, 95%CI: 0.935-0.967) and the NN model (0.922, 95%CI: 0.905-0.941). Additionally, the eXGBM model outperformed other models in terms of accuracy (0.915), precision (0.906), recall (0.926), F1 score (0.916), Brier score (0.062), log loss (0.210), and discrimination slope (0.767). Based on the scoring system, the eXGBM model achieved the highest score (53), followed by RF (42) and NN (39). The LR, DT, and SVM models obtained scores of 28, 18, and 32, respectively. Decision curve analysis further confirmed the superior clinical net benefits of the eXGBM model. External validation of the model achieved an AUC value of 0.913 (95%CI: 0.878-0.948). Consequently, the model was deployed on the Internet at https://30-daymortalityincriticallyillpatients-fnfsynbpbp6rgineaspuim.streamlit.app/, allowing users to input patient features and obtain predicted risks of early mortality among critical fracture patients. Furthermore, the AI model successfully stratified patients into low or high risk of early mortality based on a predefined threshold and provided recommendations for appropriate therapeutic interventions. CONCLUSION: This study successfully develops and validates an AI model, with the eXGBM algorithm demonstrating the highest predictive performance for early mortality in critical fracture patients. By deploying the model as a web-based AI application, healthcare professionals can easily access the tool, enabling them to predict 30-day mortality and aiding in the identification and management of high-risk patients among those critically ill with orthopedic trauma.

Knockdown of angiopoietin-like 4 suppresses sepsis-induced acute lung injury by blocking the NF-κB pathway activation and hindering macrophage M1 polarization and pyroptosis.

OBJECTIVE: Sepsis-induced acute lung injury (ALI) is a life-threatening disease. Macrophage pyroptosis has been reported to exert function in ALI. We aimed to investigate the mechanisms of ANGPTL4-mediated cell pyroptosis in sepsis-induced ALI, thus providing new insights into the pathogenesis and prevention and treatment measures of sepsis-induced ALI. METHODS: In vivo animal models and in vitro cell models were established by cecal ligation and puncture (CLP) method and lipopolysaccharide-induced macrophages RAW264.7. ANGPTL4 was silenced in CLP mice or macrophages, followed by the determination of ANGPTL4 expression in bronchoalveolar lavage fluid (BALF) or macrophages. Lung histopathology was observed by H&E staining, with pathological injury scores evaluated and lung wet and dry weight ratio recorded. M1/M2 macrophage marker levels (iNOS/CD86/Arg1), inflammatory factor (TNF-α/IL-6/IL-1ß/iNOS) expression in BALF, cell death and pyroptosis, NLRP3 inflammasome, cell pyroptosis-related protein (NLRP3/Cleaved-caspase-1/caspase-1/GSDMD-N) levels, NF-κB pathway activation were assessed by RT-qPCR/ELISA/flow cytometry/Western blot, respectively. RESULTS: ANGPTL4 was highly expressed in mice with sepsis-induced ALI, and ANGPTL4 silencing ameliorated sepsis-induced ALI in mice. In vivo, ANGPTL4 silencing repressed M1 macrophage polarization and macrophage pyroptosis in mice with sepsis-induced ALI. In vitro, ANGPTL4 knockout impeded LPS-induced activation and pyroptosis of M1 macrophages and hindered LPS-induced activation of the NF-κB pathway in macrophages. CONCLUSION: Knockdown of ANGPTL4 blocks the NF-κB pathway activation, hinders macrophage M1 polarization and pyroptosis, thereby suppressing sepsis-induced ALI.