Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data.

BACKGROUND: Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO proliferation. Timely identification of patients at high risk for MDRO can aid in curbing transmission, enhancing patient outcomes, and maintaining the cleanliness of the ICU environment. This study focused on developing a machine learning (ML) model to identify patients at risk of MDRO during the initial phase of their ICU stay. METHODS: Utilizing patient data from the First Medical Center of the People's Liberation Army General Hospital (PLAGH-ICU) and the Medical Information Mart for Intensive Care (MIMIC-IV), the study analyzed variables within 24 h of ICU admission. Machine learning algorithms were applied to these datasets, emphasizing the early detection of MDRO colonization or infection. Model efficacy was evaluated by the area under the receiver operating characteristics curve (AUROC), alongside internal and external validation sets. RESULTS: The study evaluated 3,536 patients in PLAGH-ICU and 34,923 in MIMIC-IV, revealing MDRO prevalence of 11.96% and 8.81%, respectively. Significant differences in ICU and hospital stays, along with mortality rates, were observed between MDRO positive and negative patients. In the temporal validation, the PLAGH-ICU model achieved an AUROC of 0.786 [0.748, 0.825], while the MIMIC-IV model reached 0.744 [0.723, 0.766]. External validation demonstrated reduced model performance across different datasets. Key predictors included biochemical markers and the duration of pre-ICU hospital stay. CONCLUSIONS: The ML models developed in this study demonstrated their capability in early identification of MDRO risks in ICU patients. Continuous refinement and validation in varied clinical contexts remain essential for future applications.

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.

Interpretable machine learning models for predicting the incidence of antibiotic- associated diarrhea in elderly ICU patients.

BACKGROUND: Antibiotic-associated diarrhea (AAD) can prolong hospitalization, increase medical costs, and even lead to higher mortality rates. Therefore, it is essential to predict the incidence of AAD in elderly intensive care unit(ICU) patients. The objective of this study was to create a prediction model that is both interpretable and generalizable for predicting the incidence of AAD in elderly ICU patients. METHODS: We retrospectively analyzed data from the First Medical Center of the People's Liberation Army General Hospital (PLAGH) in China. We utilized the machine learning model Extreme Gradient Boosting (XGBoost) and Shapley's additive interpretation method to predict the incidence of AAD in elderly ICU patients in an interpretable manner. RESULTS: A total of 848 adult ICU patients were eligible for this study. The XGBoost model predicted the incidence of AAD with an area under the receiver operating characteristic curve (ROC) of 0.917, sensitivity of 0.889, specificity of 0.806, accuracy of 0.870, and an F1 score of 0.780. The XGBoost model outperformed the other models, including logistic regression, support vector machine (AUC = 0.809), K-nearest neighbor algorithm (AUC = 0.872), and plain Bayes (AUC = 0.774). CONCLUSIONS: While the XGBoost model may not excel in absolute performance, it demonstrates superior predictive capabilities compared to other models in forecasting the incidence of AAD in elderly ICU patients categorized based on their characteristics.

Advances in the Application of AI Robots in Critical Care: Scoping Review.

BACKGROUND: In recent epochs, the field of critical medicine has experienced significant advancements due to the integration of artificial intelligence (AI). Specifically, AI robots have evolved from theoretical concepts to being actively implemented in clinical trials and applications. The intensive care unit (ICU), known for its reliance on a vast amount of medical information, presents a promising avenue for the deployment of robotic AI, anticipated to bring substantial improvements to patient care. OBJECTIVE: This review aims to comprehensively summarize the current state of AI robots in the field of critical care by searching for previous studies, developments, and applications of AI robots related to ICU wards. In addition, it seeks to address the ethical challenges arising from their use, including concerns related to safety, patient privacy, responsibility delineation, and cost-benefit analysis. METHODS: Following the scoping review framework proposed by Arksey and O'Malley and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a scoping review to delineate the breadth of research in this field of AI robots in ICU and reported the findings. The literature search was carried out on May 1, 2023, across 3 databases: PubMed, Embase, and the IEEE Xplore Digital Library. Eligible publications were initially screened based on their titles and abstracts. Publications that passed the preliminary screening underwent a comprehensive review. Various research characteristics were extracted, summarized, and analyzed from the final publications. RESULTS: Of the 5908 publications screened, 77 (1.3%) underwent a full review. These studies collectively spanned 21 ICU robotics projects, encompassing their system development and testing, clinical trials, and approval processes. Upon an expert-reviewed classification framework, these were categorized into 5 main types: therapeutic assistance robots, nursing assistance robots, rehabilitation assistance robots, telepresence robots, and logistics and disinfection robots. Most of these are already widely deployed and commercialized in ICUs, although a select few remain under testing. All robotic systems and tools are engineered to deliver more personalized, convenient, and intelligent medical services to patients in the ICU, concurrently aiming to reduce the substantial workload on ICU medical staff and promote therapeutic and care procedures. This review further explored the prevailing challenges, particularly focusing on ethical and safety concerns, proposing viable solutions or methodologies, and illustrating the prospective capabilities and potential of AI-driven robotic technologies in the ICU environment. Ultimately, we foresee a pivotal role for robots in a future scenario of a fully automated continuum from admission to discharge within the ICU. CONCLUSIONS: This review highlights the potential of AI robots to transform ICU care by improving patient treatment, support, and rehabilitation processes. However, it also recognizes the ethical complexities and operational challenges that come with their implementation, offering possible solutions for future development and optimization.

Uplift modeling to predict individual treatment effects of renal replacement therapy in sepsis-associated acute kidney injury patients.

Renal replacement therapy (RRT) is a crucial treatment for sepsis-associated acute kidney injury (S-AKI), but it is uncertain which S-AKI patients should receive immediate RRT. Identifying the characteristics of patients who may benefit the most from RRT is an important task. This retrospective study utilized a public database and enrolled S-AKI patients, who were divided into RRT and non-RRT groups. Uplift modeling was used to estimate the individual treatment effect (ITE) of RRT. The validity of different models was compared using a qini curve. After labeling the patients in the validation cohort, we characterized the patients who would benefit the most from RRT and created a nomogram. A total of 8289 patients were assessed, among whom 591 received RRT, and 7698 did not receive RRT. The RRT group had a higher severity of illness than the non-RRT group, with a Sequential Organ Failure Assessment (SOFA) score of 9 (IQR 6,11) vs. 5 (IQR 3,7). The 28-day mortality rate was higher in the RRT group than the non-RRT group (34.83% vs. 14.61%, p < 0.0001). Propensity score matching (PSM) was used to balance baseline characteristics, 458 RRT patients and an equal number of non-RRT patients were enrolled for further research. After PSM, 28-day mortality of RRT and non-RRT groups were 32.3% vs. 39.3%, P = 0.033. Using uplift modeling, we found that urine output, fluid input, mean blood pressure, body temperature, and lactate were the top 5 factors that had the most influence on RRT effect. The area under the uplift curve (AUUC) of the class transformation model was 0.068, the AUUC of SOFA was 0.018, and the AUUC of Kdigo-stage was 0.050. The class transformation model was more efficient in predicting individual treatment effect. A logistic regression model was developed, and a nomogram was drawn to predict whether an S-AKI patient can benefit from RRT. Six factors were taken into account (urine output, creatinine, lactate, white blood cell count, glucose, respiratory rate). Uplift modeling can better predict the ITE of RRT on S-AKI patients than conventional score systems such as Kdigo and SOFA. We also found that white blood cell count is related to the benefits of RRT, suggesting that changes in inflammation levels may be associated with the effects of RRT on S-AKI patients.

Predicting hypovitaminosis C with LASSO algorithm in adult critically ill patients in surgical intensive care units: a bi-center prospective cohort study.

Vitamin C played pleiotropic roles in critical illness and vitamin C insufficiency was predictive of the development of multiple organ failure. Currently, the prevalence of vitamin C insufficiency in Chinese critically ill patients is rarely determined and there are no established bedside tools to predict hypovitaminosis C. To develop a nomogram to identify patients with high risk of hypovitaminosis C, we performed a bi-center prospective cohort study at two ICUs of the first and sixth medical center in PLA General Hospital, Beijing, China from May 6th to July 31st, 2021 We identified 322 eligible patients. 62.4% patients were hypovitaminosis C. 7 features, including source of infection, the level of serum albumin, age, male gender, sepsis, vascular disease, and wasting of vitamin C by the kidney, were selected using LASSO algorithm and therefore included in the nomogram. In the testing set, our model showed moderate discrimination ability with areas under the curve of 0.75 [0.64-0.84]. Variable importance evaluated by SHAP value highlighted two novel important predictors, i.e., abdominal infection and the level of serum albumin. In conclusion, we first reported a high burden of vitamin C insufficiency in Chinese adult patient in the ICU. We also constructed a prediction model to timely identify patients with high risk of hypovitaminosis C, which allows the clinicians to choose appropriate candidates for Vitamin C repletion in clinical practice or clinical trials.

First/second-order predefined-time convergent ZNN models for time-varying quadratic programming and robotic manipulator application.

Zeroing neural network (ZNN) model, an important class of recurrent neural network, has been widely applied in the field of computation and optimization. In this paper, two ZNN models with predefined-time convergence are proposed for the time-varying quadratic programming (TVQP) problem. First, in the framework of the traditional ZNN model, the first-order predefined-time convergent ZNN (FPTZNN) model is proposed in combination with a predefined-time controller. Compared with the existing ZNN models, the proposed ZNN model is error vector combined with sliding mode control technique. Then, the FPTZNN model is further extended and the second-order predefined-time convergent ZNN (SPTZNN) model is developed. Combined with the Lyapunov method and the concept of predefined-time stability, it is shown that the proposed FPTZNN and SPTZNN models have the properties of predefined-time convergence, and their convergence time can be flexibly adjusted by predefined-time control parameters. Finally, the proposed FPTZNN and SPTZNN models are compared with the existing ZNN models for the TVQP problem in simulation experiment, and the simulation experiment results verify the effectiveness and superior performance of the proposed FPTZNN and SPTZNN models. In addition, the proposed FPTZNN model for robot motion planning problem is applied and successfully implemented to verify the practicality of the model.

Acute lung injury caused by sepsis: how does it happen?

Sepsis is a systemic inflammatory disease caused by severe infections that involves multiple systemic organs, among which the lung is the most susceptible, leaving patients highly vulnerable to acute lung injury (ALI). Refractory hypoxemia and respiratory distress are classic clinical symptoms of ALI caused by sepsis, which has a mortality rate of 40%. Despite the extensive research on the mechanisms of ALI caused by sepsis, the exact pathological process is not fully understood. This article reviews the research advances in the pathogenesis of ALI caused by sepsis by focusing on the treatment regimens adopted in clinical practice for the corresponding molecular mechanisms. This review can not only contribute to theories on the pathogenesis of ALI caused by sepsis, but also recommend new treatment strategies for related injuries.

Protective and immunomodulatory effects of mesenchymal stem cells on multiorgan injury in male rats with heatstroke.

Heatstroke (HS) causes multiple organ dysfunction syndrome (MODS) with a mortality rate of 60% after hospitalization. Currently, there is no effective and targeted approach for the treatment of HS. Despite growing evidence that mesenchymal stem cells (MSCs) may reduce multiorgan damage and improve survival through immunomodulatory effects in several diseases, no one has tested whether MSCs have immunomodulatory effects in heatstroke. The present study focused on pathological changes and levels of the cytokines and immunoglobulins to investigate the mechanisms underlying the protective effect and the anti-inflammatory effects of MSCs. We found that MSCs treatment significantly reduced the 28-day mortality rate (P < 0.05), the levels of hepatic and renal function markers on day 1 (P < 0.01) and the pathological lesion scores of multiple organs in HS rats. The levels of IgG1, IgM, and IgA of the HS + MSC group was significantly higher than that in HS group on days 3 and 28(P < 0.05). In conclusion, MSCs contribute to protecting against multiorgan injury, reducing pro-inflammatory cytokines, stabilizing immunoglobulins, and reducing the mortality rate of HS rats.

The duration of acute kidney injury is an additional parameter to predict 1-year survival in very elderly patients.

Background: Acute kidney injury (AKI) is primarily defined and classified according to the magnitude of the elevation of serum creatinine (Scr). We aimed to determine whether the duration of AKI adds prognostic value in addition to that obtained from the magnitude of injury alone. Methods: This retrospective study enrolled very elderly inpatients (≥75 years) in the Chinese PLA General Hospital from January 2007 to December 2018. AKI was stratified by magnitude according to KDIGO stage (1, 2, and 3) and duration (1-2 days, 3-4 days, 5-7 days, and >7 days). The primary outcome was the 1-year mortality after AKI. Multivariable Cox regression analysis was performed to identify covariates associated with the 1-year mortality. The probability of survival was estimated using the Kaplan-Meier method, and curves were compared using the log-rank test. Results: In total, 688 patients were enrolled, with the median age was 88 (84-91) years, and the majority (652, 94.8%) were male. According to the KDIGO criteria, 317 patients (46.1%) had Stage 1 AKI, 169 (24.6%) had Stage 2 AKI, and 202 (29.3%) had Stage 3 AKI. Of the 688 study subjects, 61 (8.9%) with a duration of AKI lasted 1-2 days, 104 (15.1%) with a duration of AKI lasted 3-4 days, 140 (20.3%) with a duration of AKI lasted 5-7 days, and 383 (55.7%) with a duration of AKI lasted >7 days. Within each stage, a longer duration of AKI was slightly associated with a higher rate of 1-year mortality. However, within each of the duration categories, the stage of AKI was significantly associated with 1-year mortality. When considered separately in multivariate analyses, both the duration of AKI (3-4 days: HR=3.184; 95% CI: 1.733-5.853; P <0.001, 5-7 days: HR=1.915; 95% CI: 1.073-3.416; P=0.028; >7 days: HR=1.766; 95% CI: 1.017-3.065; P=0.043) and more advanced AKI stage (Stage 2: HR=3.063; 95% CI: 2.207-4.252; P <0.001; Stage 3: HR=7.333; 95% CI: 5.274-10.197; P <0.001) were independently associated with an increased risk of 1-year mortality. Conclusions: In very elderly AKI patients, both a higher stage and duration were independently associated with an increased risk of 1-year mortality. Hence, the duration of AKI adds additional information to predict long-term mortality.

[Mechanism of regulatory T cells in heat stroke-induced acute kidney injury].

OBJECTIVE: To investigate the mechanism of regulatory T cells (Treg) in heat stroke (HS)-induced acute kidney injury (AKI). METHODS: Male SPF Balb/c mice were randomly divided into control group, HS group (HS+Rat IgG), HS+PC61 group, and HS+Treg group (n = 6). The HS mice model was established by making the body temperature of the mice reach 42.7 centigrade at room temperature 39.5 centigrade with relative humidity 60% for 1 hour. In HS+PC61 group, 100 µg PC61 antibody (anti-CD25) was injected through the tail vein in consecutive 2 days before the model was established to eliminate Tregs. Mice in HS+Treg group was injected with 1×106 Treg via tail vein immediately after successful modeling. The proportion of Treg infiltrated in the kidney, serum creatinine (SCr) and histopathology, levels of interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α) both in the serum and kidney tissue, as well as proportion of neutrophils and macrophages located in the kidney were observed at 24 hours after HS. RESULTS: HS dampened renal function and exaggerated kidney injury, up-regulated levels of inflammatory cytokines both in local kidney and circulation, and increased infiltration of neutrophils and macrophages to the injured kidneys. The proportion of Treg (Treg/CD4+) infiltrated in kidney was significantly decreased in HS group, compared with control group [(3.40±0.46)% vs. (7.67±0.82)%, P < 0.01]. Compared with HS group, local Tregs in kidney were almost completely depleted via PC61 antibody [(0.77±0.12)% vs. (3.40±0.46)%, P < 0.01]. Depletion of Tregs could exacerbate HS-AKI, indicating by increased serum creatinine [SCr (mmol/L): 348.22±35.36 vs. 254.42±27.40, P < 0.01] and pathological injury (Paller score: 4.70±0.20 vs. 3.60±0.20, P < 0.01), incremental levels of IFN-γand TNF-α both in injured kidney and serum [serum IFN-γ (ng/L): 747.70±64.52 vs. 508.46±44.79, serum TNF-α (ng/L): 647.41±26.62 vs. 464.53±41.80, both P < 0.01], and more infiltrated neutrophils and macrophages in the injured kidney [neutrophil proportion: (6.63±0.67)% vs. (4.37±0.43)%, macrophage proportion: (38.70±1.66)% vs. (33.19±1.55)%, both P < 0.01]. On the contrast, adoptive transfer of Tregs could reverse the aforementioned effects of Treg depletion, indicating by incremental proportion of Tregs in the injured kidney [(10.58±1.19)% vs. (3.40±0.46)%, P < 0.01], decreased serum creatinine [SCr (mmol/L): 168.24±40.56 vs. 254.42±27.40, P < 0.01] and pathological injury (Paller score: 2.73±0.11 vs. 3.60±0.20, P < 0.01), reduced levels of IFN-γ and TNF-α both in injured kidney and serum [serum IFN-γ (ng/L): 262.62±22.68 vs. 508.46±44.79, serum TNF-α (ng/L): 206.41±22.58 vs. 464.53±41.80, both P < 0.01], and less infiltrated neutrophils and macrophages in the injured kidney [neutrophil proportion: (3.04±0.33)% vs. (4.37±0.43)%, macrophage proportion: (25.68±1.93)% vs. (33.19±1.55)%, both P < 0.01]. CONCLUSIONS: Treg might be involved in HS-AKI, possibly via down-regulation of pro-inflammatory cytokines and infiltration of inflammatory cells.

Intelligent Intensive Care Unit: Current and Future Trends.

There is a growing demand for intensive care units, but there is a relative shortage of medical staff. Intensive care work is heavy and stressful. Optimizing the working conditions and processes of the intensive care unit is of great significance for improving the work efficiency and the level of diagnosis and treatment in the intensive care unit. The intelligent intensive care unit is a new ward management model gradually developed on the basis of modern science and technology such as communication technology, internet of things, artificial intelligence, robots, and big data. Under this model, the potential risks caused by human factors are greatly reduced, and the monitoring and treatment of patients has been significantly improved. This paper reviews the progress in related fields.

Effect of an Herbal-Based Injection on 28-Day Mortality in Patients With Sepsis: The EXIT-SEP Randomized Clinical Trial.

Importance: Previous research has suggested that Xuebijing injection (XBJ), an herbal-based intravenous preparation, may reduce mortality among patients with sepsis. Objective: To determine the effect of XBJ vs placebo on 28-day mortality among patients with sepsis. Design, Setting, and Participants: The Efficacy of Xuebijing Injection in Patients With Sepsis (EXIT-SEP) trial was a multicenter, randomized double-blind, placebo-controlled trial conducted in intensive care units at 45 sites and included 1817 randomized patients with sepsis (sepsis 3.0) present for less than 48 hours. Patients aged 18 to 75 years with a Sequential Organ Failure Assessment score of 2 to 13 were enrolled. The study was conducted from October 2017 to June 2019. The final date of follow-up was July 26, 2019. Data analysis was performed from January 2020 to August 2022. Interventions: The patients were randomized to receive either intravenous infusion of XBJ (100 mL, n = 911) or volume-matched saline placebo (n = 906) every 12 hours for 5 days. Main Outcomes and Measures: The primary outcome was 28-day mortality. Results: Among the 1817 patients who were randomized (mean [SD] age, 56.5 [13.5] years; 1199 [66.0%] men), 1760 (96.9%) completed the trial. In these patients, the 28-day mortality rate was significantly different between the placebo group and the XBJ group (230 of 882 patients [26.1%] vs 165 of 878 patients [18.8%], respectively; P < .001). The absolute risk difference was 7.3 (95% CI, 3.4-11.2) percentage points. The incidence of adverse events was 222 of 878 patients (25.3%) in the placebo group and 200 of 872 patients (22.9%) in the XBJ group. Conclusions and Relevance: In this randomized clinical trial among patients with sepsis, the administration of XBJ reduced 28-day mortality compared with placebo. Trial Registration: ClinicalTrials.gov Identifier: NCT03238742.

A Simple Nomogram for Predicting Hospital Mortality of Patients Over 80 Years in ICU: An International Multicenter Retrospective Study.

OBJECTIVES: This study aimed to develop and validate an easy-to-use intensive care unit (ICU) illness scoring system to evaluate the in-hospital mortality for very old patients (VOPs, over 80 years old). METHODS: We performed a multicenter retrospective study based on the electronic ICU (eICU) Collaborative Research Database (eICU-CRD), Medical Information Mart for Intensive Care Database (MIMIC-III CareVue and MIMIC-IV), and the Amsterdam University Medical Centers Database (AmsterdamUMCdb). Least Absolute Shrinkage and Selection Operator regression was applied to variables selection. The logistic regression algorithm was used to develop the risk score and a nomogram was further generated to explain the score. RESULTS: We analyzed 23 704 VOPs, including 3 726 deaths (10 183 [13.5% mortality] from eICU-CRD [development set], 12 703 [17.2%] from the MIMIC, and 818 [20.8%] from the AmsterdamUMC [external validation sets]). Thirty-four variables were extracted on the first day of ICU admission, and 10 variables were finally chosen including Glasgow Coma Scale, shock index, respiratory rate, partial pressure of carbon dioxide, lactate, mechanical ventilation (yes vs no), oxygen saturation, Charlson Comorbidity Index, blood urea nitrogen, and urine output. The nomogram was developed based on the 10 variables (area under the receiver operating characteristic curve: training of 0.792, testing of 0.788, MIMIC of 0.764, and AmsterdamUMC of 0.808 [external validating]), which consistently outperformed the Sequential Organ Failure Assessment, acute physiology score III, and simplified acute physiology score II. CONCLUSIONS: We developed and externally validated a nomogram for predicting mortality in VOPs based on 10 commonly measured variables on the first day of ICU admission. It could be a useful tool for clinicians to identify potentially high risks of VOPs.

[Chinese consensus of cardiopulmonary resuscitation guides prevention, treatment and rescue of cardiac arrest in pregnancy].

Pregnant women are a group of people in a special period, once sudden cardiac arrest (CA) occurs, it will threaten the life of both mother and child. It has become a great challenge for hospital, doctors and nurses to minimize maternal mortality during pregnancy. All the efforts should ensure the safety of both mother and child throughout the perinatal period. Because difference of the cardiopulmonary resuscitation strategies for common CA patients of the same age, the resuscitation strategies for CA patients during pregnancy need consider the patient's gestational age and fetal condition. Different resuscitation techniques, such as manual left uterine displacement (MLUD), will involve perimortem cesarean delivery (PMCD). At the same time, drugs should be reasonably used for different causes of CA during pregnancy, such as hypoxemia, hypovolemia, hyperkalemia or hypokalemia and other electrolyte disorders and hypothermia in 4Hs, as well as thrombosis, pericardial tamponade, tension pneumothorax and toxicosis in 4Ts. In view of the fact that many causes of CA in pregnancy are preventable, it is more necessary to introduce guidelines for CA in pregnancy in line with our national conditions for clinical guidance. This paper systematically reviewed the pathophysiological characteristics of CA during pregnancy, the high-risk factors of CA during pregnancy, and identified the correct resuscitation methods and prevention and treatment strategies of CA during pregnancy.

Activation of glucagon-like peptide-1 receptor in microglia exerts protective effects against sepsis-induced encephalopathy via attenuating endoplasmic reticulum stress-associated inflammation and apoptosis in a mouse model of sepsis.

Sepsis-induced encephalopathy (SAE) is a detrimental complication in patients with severe sepsis, while there is still no effective treatment. Previous studies have elucidated the neuroprotective effects of glucagon-like peptide-1 receptor (GLP-1R) agonists. However, the role of GLP-1R agonists in the pathological process of SAE is unclear. Here, we found that GLP-1R was up-regulated in the microglia of septic mice. The activation of GLP-1R with Liraglutide could inhibit endoplasmic reticulum stress (ER stress) and associated inflammatory response as well as apoptosis triggered by LPS or tunicamycin (TM) in BV2 cells. In vivo experiments confirmed the benefits of Liraglutide in the regulation of microglial activation, ER stress, inflammation, and apoptosis in the hippocampus of septic mice. Additionally, the survival rate and cognitive dysfunction of septic mice were also improved after Liraglutide administration. Mechanically, cAMP/PKA/CREB signaling is involved in the protection of ER stress-induced inflammation and apoptosis in cultured microglial cells under LPS or TM stimulations. In conclusion, we speculated that GLP-1/GLP-1R activation in microglia might be a potential therapeutic target for the treatment of SAE.

Mesenchymal stem cell-derived exosomes regulate microglia phenotypes: a promising treatment for acute central nervous system injury.

There is growing evidence that long-term central nervous system (CNS) inflammation exacerbates secondary deterioration of brain structures and functions and is one of the major determinants of disease outcome and progression. In acute CNS injury, brain microglia are among the first cells to respond and play a critical role in neural repair and regeneration. However, microglial activation can also impede CNS repair and amplify tissue damage, and phenotypic transformation may be responsible for this dual role. Mesenchymal stem cell (MSC)-derived exosomes (Exos) are promising therapeutic agents for the treatment of acute CNS injuries due to their immunomodulatory and regenerative properties. MSC-Exos are nanoscale membrane vesicles that are actively released by cells and are used clinically as circulating biomarkers for disease diagnosis and prognosis. MSC-Exos can be neuroprotective in several acute CNS models, including for stroke and traumatic brain injury, showing great clinical potential. This review summarized the classification of acute CNS injury disorders and discussed the prominent role of microglial activation in acute CNS inflammation and the specific role of MSC-Exos in regulating pro-inflammatory microglia in neuroinflammatory repair following acute CNS injury. Finally, this review explored the potential mechanisms and factors associated with MSC-Exos in modulating the phenotypic balance of microglia, focusing on the interplay between CNS inflammation, the brain, and injury aspects, with an emphasis on potential strategies and therapeutic interventions for improving functional recovery from early CNS inflammation caused by acute CNS injury.

Bibliometric analysis of ferroptosis in acute kidney injury from 2014 to 2022.

INTRODUCTION: Acute kidney injury (AKI) is a global disease with high morbidity and mortality. At present, the treatment of AKI still lacks targeted measures. Ferroptosis, a form of regulated cell death, plays an essential role in the initiation and progression of AKI. Current evidence proves that targeting ferroptosis is supposed to be a novel potential strategy to cure AKI. In this study, we aim to use bibliometric analysis to identify research trends and hotspots in the field of "ferroptosis in AKI". METHODS: We chose the Science Citation Index Expanded (SCI-EXPANDED) index of the Web of Science Core Collection (WoSCC) as the source database for data retrieval. Data were retrieved from the WoSCC on May 24, 2022. Full records and cited references of all the documents in WoSCC were collected. The R software and the Online Analysis Platform of Literature Metrology were used for data analysis and visual analysis. RESULTS: There were 120 documents on "ferroptosis in AKI" in the WOSCC from 2014 to 2022 (May 24, 2022). There was a clear upward trend each year in the number of documents published. According to WoS report, China, the United States, and Germany were the top three countries involved in this research area, the majority of publications were included in the subject area "Cell Biology". Technical University of Dresden contributed the most publications, followed by Central South University and University of Pittsburgh. The Journal of Cell Death and Disease had the highest H-index and contributed the most publications. Linkermann A authored 16 articles and had the highest H-index. Multifactorial analysis of the keywords show that the research field is divided into two clusters. The most contributing publications and the most cited publications were also determined by factorial analysis. CONCLUSION: This bibliometric analysis provides a comprehensive analysis of research trends and hot spots on the topic of "ferroptosis in AKI". The study of ferroptosis-related AKI research remains in its early stages. There will be a dramatically increasing number of publications on this field. Further research should focus on exploring the mechanisms of crosstalk between ferroptosis and other programmed cell deaths, and improves clinical applications and therapeutic effects against AKI.

Protective Effects of Mesenchymal Stem Cells Against Central Nervous System Injury in Heat Stroke.

BACKGROUND: Heatstroke (HS) is a serious disease caused by central nervous system (CNS) injuries, such as delirium, convulsion, and coma. Currently, mesenchymal stem cells (MSCs) have demonstrated novel neuroprotective effects; therefore, this research explores the neuroprotective effects and mechanisms of MSCs against HS injury. METHODS: HS rat models were induced in a 40°C and 65% humidity environment until the rectal temperature reached 42°C. The verified HS injury model rats were divided into the HS and MSCs-treated groups. Each rat in the treated group was infused with 1x106 MSCs suspended in 0.3 ml physiological saline via the tail vein. The HS- or MSCs-treated rats were further divided into early-stage (3d) and late-stage (28d). HS rat models were induced by a high-temperature and high-humidity environment at a specific time, the mortality was analyzed, and an automatic biochemical analyzer measured levels of liver and kidney function indicators in the blood. The neurons' morphologic changes were observed through Nissl staining, and neurological deficit scores were performed. Moreover, the levels of inflammatory factors in brain tissue were measured using a multi-cytokine detection platform, and the expression of BDNF, phosphorylated TrkB and P38 were detected by the Western Bolt. RESULTS: MSCs injection significantly reduced mortality and alleviated liver and kidney function. Moreover, the neurological deficit and neuronic edema of the hippocampus caused by HS at 3d and 28d were significantly ameliorated by MSCs administration. Specifically, the injection of MSCs inhibited high levels of interleukin (IL)-1ß, IL-6, tumor necrosis factor-α (TNF-α), and IL-17A caused by HS but elevated the levels of IL-10 and IL-13 in the early period (3d); while in the later period (28d), MSCs significantly increased the levels of IL-10 and IL-13 continuously and inhibited the high level of IL-17A. Furthermore, MSCs injection increased the expressions of BDNF and phosphorylated TrkB (BDNF receptor), meanwhile inhibiting the expression of phosphorylated P38 (inflammatory factor) in the brains of HS rats in the early period (3d) but had no significant influence on the later period (28d). CONCLUSION: These results suggested that MSCs injection may provide therapeutic effects for HS in rats by improving liver and kidney function and reducing CNS damage. Moreover, MSCs injection inhibited the brain inflammatory response of HS rats, and the BDNF-TrkB and P38/MAPK signal pathways may be involved, providing a potential mechanism for HS therapy by MSCs administration.

Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study.

BACKGROUND: Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU. METHODS: The study analyzed older patients from 197 hospitals in the United States and 1 hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (≥80 years), which were separately used to develop and evaluate models including internal, external, and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHapley Additive exPlanations (SHAP) method to interpret predictions. RESULTS: Thirty-four thousand four hundred and ninety-seven young-old (11.3% mortality) and 21 330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9 046 U.S. patients was as follows: 0.87 and 0.82, respectively; discrimination of external validation models in 1 905 EUR patients was as follows: 0.86 and 0.85, respectively; and discrimination of temporal validation models in 8 690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like Sequential Organ Failure Assessment and Acute Physiology Score III. The Glasgow Coma Scale, Charlson Comorbidity Index, and Code Status emerged as top predictors of mortality. CONCLUSIONS: Our models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making.