[Construction and external validation of a risk prediction model for unplanned interruption during continuous renal replacement therapy].

OBJECTIVE: To identify the independent factors of unplanned interruption during continuous renal replacement therapy (CRRT) and construct a risk prediction model, and to verify the clinical application effectiveness of the model. METHODS: A retrospective study was conducted on critically ill adult patients who received CRRT treatment in the intensive care unit (ICU) of Zhejiang Hospital from January 2021 to August 2022 for model construction. According to whether unplanned weaning occurred, the patients were divided into two groups. The potential influencing factors of unplanned CRRT weaning in the two groups were compared. The independent influencing factors of unplanned CRRT weaning were screened by binary Logistic regression and a risk prediction model was constructed. The goodness of fit of the model was verified by a Hosmer-Lemeshow test and its predictive validity was evaluated by receiver operator characteristic curve (ROC curve). Then embed the risk prediction model into the hospital's ICU multifunctional electronic medical record system for severe illness, critically ill patients with CRRT admitted to the ICU of Zhejiang Hospital from November 2022 to October 2023 were prospectively analyzed to verify the model's clinical application effect. RESULTS: (1) Model construction and internal validation: a total of 331 critically ill patients with CRRT were included to be retrospectively analyzed. Among them, there were 238 patients in planned interruption group and 93 patients in unplanned interruption group. Compared with the planned interruption group, the unplanned interruption group was shown as a lower proportion of males (80.6% vs. 91.6%) and a higher proportion of chronic diseases (60.2% vs. 41.6%), poor blood purification catheter function (31.2% vs. 6.3%), as a higher platelet count (PLT) before CRRT initiation [×109/L: 137 (101, 187) vs. 109 (74, 160)], lower level of blood flow rate [mL/min: 120 (120, 150) vs. 150 (140, 180)], higher proportion of using pre-dilution (37.6% vs. 23.5%), higher filtration fraction [23.0% (17.5%, 32.9%) vs. 19.1% (15.7%, 22.6%)], and frequency of blood pump stops [times: 19 (14, 21) vs. 9 (6, 13)], the differences of the above 8 factors between the two groups were statistically significant (all P < 0.05). Binary Logistic regression analysis showed that chronic diseases [odds ratio (OR) = 3.063, 95% confidence interval (95%CI) was 1.200-7.819], blood purification catheter function (OR = 4.429, 95%CI was 1.270-15.451), blood flow rate (OR = 0.928, 95%CI was 0.900-0.957), and frequency of blood pump stops (OR = 1.339, 95%CI was 1.231-1.457) were the independent factors for the unplanned interruption of CRRT (all P < 0.05). These 4 factors were used to construct a risk prediction model, and ROC curve analysis showed that the area under the curve (AUC) predicted by the model was 0.952 (95%CI was 0.930-0.973, P = 0.003 0), with a sensitivity of 88.2%, a specificity of 89.9%, and a maximum value of 1.781 for the Youden index. (2) External validation: prospective inclusion of 110 patients, including 63 planned interruption group and 47 unplanned interruption group. ROC curve analysis showed that the AUC of the risk prediction model was 0.919 (95%CI was 0.870-0.969, P = 0.004 3), with a sensitivity of 91.5%, a specificity of 79.4%, and a maximum value of the Youden index of 1.709. CONCLUSIONS: The risk prediction model for unplanned interruption during CRRT has a high predictive efficiency, allowing for rapid and real-time identification of the high risk patients, thus providing references for preventative nursing.

Multi-organ spatiotemporal information aware model for sepsis mortality prediction.

BACKGROUND: Sepsis is a syndrome involving multi-organ dysfunction, and the mortality in sepsis patients correlates with the number of lesioned organs. Precise prognosis models play a pivotal role in enabling healthcare practitioners to administer timely and accurate interventions for sepsis, thereby augmenting patient outcomes. Nevertheless, the majority of available models consider the overall physiological attributes of patients, overlooking the asynchronous spatiotemporal interactions among multiple organ systems. These constraints hinder a full application of such models, particularly when dealing with limited clinical data. To surmount these challenges, a comprehensive model, denoted as recurrent Graph Attention Network-multi Gated Recurrent Unit (rGAT-mGRU), was proposed. Taking into account the intricate spatiotemporal interactions among multiple organ systems, the model predicted in-hospital mortality of sepsis using data collected within the 48-hour period post-diagnosis. MATERIAL AND METHODS: Multiple parallel GRU sub-models were formulated to investigate the temporal physiological variations of single organ systems. Meanwhile, a GAT structure featuring a memory unit was constructed to capture spatiotemporal connections among multi-organ systems. Additionally, an attention-injection mechanism was employed to govern the data flowing within the network pertaining to multi-organ systems. The proposed model underwent training and testing using a dataset of 10,181 sepsis cases extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. To evaluate the model's superiority, it was compared with the existing common baseline models. Furthermore, ablation experiments were designed to elucidate the rationale and robustness of the proposed model. RESULTS: Compared with the baseline models for predicting mortality of sepsis, the rGAT-mGRU model demonstrated the largest area under the receiver operating characteristic curve (AUROC) of 0.8777 ± 0.0039 and the maximum area under the precision-recall curve (AUPRC) of 0.5818 ± 0.0071, with sensitivity of 0.8358 ± 0.0302 and specificity of 0.7727 ± 0.0229, respectively. The proposed model was capable of delineating the varying contribution of the involved organ systems at distinct moments, as specifically illustrated by the attention weights. Furthermore, it exhibited consistent performance even in the face of limited clinical data. CONCLUSION: The rGAT-mGRU model has the potential to indicate sepsis prognosis by extracting the dynamic spatiotemporal interplay information inherent in multi-organ systems during critical diseases, thereby providing clinicians with auxiliary decision-making support.

Chinese critical care certified course in intensive care unit: a nationwide-based analysis.

BACKGROUND: A training program for intensive care unit (ICU) physicians entitled "Chinese Critical Care Certified Course" (5 C) started in China in 2009, intending to improve the quality of intensive care provision. This study aimed to explore the associations between the 5 C certification of physicians and the quality of intensive care provision in China. METHODS: This nationwide analysis collected data regarding 5 C-certified physicians between 2009 and 2019. Fifteen ICU quality control indicators (three structural, four procedural, and eight outcome-based) were collected from the Chinese National Report on the Services, Quality, and Safety in Medical Care System. Provinces were stratified into three groups based on the cumulative number of 5 C certified physicians per million population. RESULTS: A total of 20,985 (80.41%) physicians from 3,425 public hospitals in 30 Chinese provinces were 5 C certified. The deep vein thrombosis (DVT) prophylaxis rate in the high 5 C physician-number provinces was significantly higher than in the intermediate 5 C physician-number provinces (67.6% vs. 55.1%, p = 0.043), while ventilator-associated pneumonia (VAP) rate in the low 5 C physician-number provinces was significantly higher than in the high 5 C physician-number provinces (14.9% vs. 8.9%, p = 0.031). CONCLUSIONS: The higher number of 5 C-certified physicians per million population seemed to be associated with higher DVT prophylaxis rates and lower VAP rates in China, suggesting that the 5 C program might have a beneficial impact on the quality of intensive care provision.

Reinforcement Learning Model for Managing Noninvasive Ventilation Switching Policy.

Noninvasive ventilation (NIV) has been recognized as a first-line treatment for respiratory failure in patients with chronic obstructive pulmonary disease (COPD) and hypercapnia respiratory failure, which can reduce mortality and burden of intubation. However, during the long-term NIV process, failure to respond to NIV may cause overtreatment or delayed intubation, which is associated with increased mortality or costs. Optimal strategies for switching regime in the course of NIV treatment remain to be explored.For the goal of reducing 28-day mortality of the patients undergoing NIV, Double Dueling Deep Q Network (D3QN) of offline-reinforcement learning algorithm was adopted to develop an optimal regime model for making treatment decisions of discontinuing ventilation, continuing NIV, or intubation. The model was trained and tested using the data from Multi-Parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) and evaluated by the practical strategies. Furthermore, the applicability of the model in majority disease subgroups (Catalogued by International Classification of Diseases, ICD) was investigated. Compared with physician's strategies, the proposed model achieved a higher expected return score (4.25 vs. 2.68) and its recommended treatments reduced the expected mortality from 27.82% to 25.44% in all NIV cases. In particular, for these patients finally received intubation in practice, if the model also supported the regime, it would warn of switching to intubation 13.36 hours earlier than clinicians (8.64 vs. 22 hours after the NIV treatment), granting a 21.7% reduction in estimated mortality. In addition, the model was applicable across various disease groups with distinguished achievement in dealing with respiratory disorders. The proposed model is promising to dynamically provide personalized optimal NIV switching regime for patients undergoing NIV with the potential of improving treatment outcomes.

The risk factors for deep venous thrombosis in critically ill older adult patients: a subgroup analysis of a prospective, multicenter, observational study.

BACKGROUND: Older adult patients mainly suffer from multiple comorbidities and are at a higher risk of deep venous thrombosis (DVT) during their stay in the intensive care unit (ICU) than younger adult patients. This study aimed to analyze the risk factors for DVT in critically ill older adult patients. METHODS: This was a subgroup analysis of a prospective, multicenter, observational study of patients who were admitted to the ICU of 54 hospitals in Zhejiang Province from September 2019 to January 2020 (ChiCTR1900024956). Patients aged > 60 years old on ICU admission were included. The primary outcome was DVT during the ICU stay. The secondary outcomes were the 28- and 60-day survival rates, duration of stay in ICU, length of hospitalization, pulmonary embolism, incidence of bleeding events, and 60-day coagulopathy. RESULTS: A total of 650 patients were finally included. DVT occurred in 44 (2.3%) patients. The multivariable logistic regression analysis showed that age (≥75 vs 60-74 years old, odds ratio (OR) = 2.091, 95% confidence interval (CI): 1.308-2.846, P = 0.001), the use of analgesic/sedative/muscarinic drugs (OR = 2.451, 95%CI: 1.814-7.385, P = 0.011), D-dimer level (OR = 1.937, 95%CI: 1.511-3.063, P = 0.006), high Caprini risk score (OR = 2.862, 95%CI: 1.321-2.318, P = 0.039), basic prophylaxis (OR = 0.111, 95%CI: 0.029-0.430, P = 0.001), and physical prophylaxis (OR = 0.322, 95%CI: 0.109-0.954, P = 0.041) were independently associated with DVT. There were no significant differences in 28- and 60-day survival rates, duration of stay in ICU, total length of hospitalization, 60-day pulmonary embolism, and coagulation dysfunction between the two groups, while the DVT group had a higher incidence of bleeding events (2.6% vs. 8.9%, P < 0.001). CONCLUSION: In critically ill older adult patients, basic prophylaxis and physical prophylaxis were found as independent protective factors for DVT. Age (≥75 years old), the use of analgesic/sedative/muscarinic drugs, D-dimer level, and high Caprini risk score were noted as independent risk factors for DVT. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR1900024956).URL: http://www.chictr.org.cn/listbycreater.aspx .

[Value of high-flow oxygen therapy after weaning for successful extubation of critical ill patients with mechanical ventilation].

OBJECTIVE: To investigate the value of high-flow oxygen therapy after weaning in successful extubation of critically ill patients with mechanical ventilation. METHODS: A retrospective study was conducted. The weaned patients who were older than 18 years old and underwent mechanical ventilation for the first time due to cerebrovascular accidents, surgical operations, cardiovascular diseases, and pneumonia admitted to the department of critical care medicine of Zhejiang Hospital from January 2018 to June 2020 were enrolled. Among the patients, 40 cases received high-flow oxygen therapy after weaning, and 37 cases received Venturi combined with the humidifier. The patient's gender, age, primary disease, severity score, duration of mechanical ventilation before weaning, heart rate (HR), blood pressure, pulse oxygen saturation (SpO2) at 0, 6, 12, 18, and 24 hours after weaning, and pH value, arterial partial pressure of oxygen (PaO2), arterial partial pressure of carbon dioxide (PaCO2) at 6, 12, 18, and 24 hours after weaning, the rate of performing mechanical ventilation after weaning, extubation time after weaning, and the rate of reintubation after extubation for 72 hours were collected. RESULTS: There was no significant difference in baseline data such as gender, age, primary disease, severity score, and duration of mechanical ventilation before weaning between the two groups. After weaning, the vital signs of the two groups were stable, and there was no significant difference in HR, systolic blood pressure (SBP), diastolic blood pressure (DBP) or SpO2 at each time point between the two groups. After weaning, the pH of arterial blood gas analysis in the two groups and the fluctuations of PaO2 and PaCO2 in the high-flow group were not obvious. In the Venturi group, PaO2 gradually decreased after weaning, PaCO2 increased significantly at 12 hours, and slowly decreased after 12 hours. The PaO2 from 6 hours and PaCO2 from 12 hours in the high-flow group were significantly lower than those in the Venturi group, and continued to 24 hours [PaO2 (mmHg, 1 mmHg ≈ 0.133 kPa): 112.34±38.25 vs. 156.76±68.44 at 6 hours, 110.92±38.66 vs. 150.64±59.07 at 12 hours, 111.12±36.77 vs. 141.30±39.05 at 18 hours, 110.82±39.37 vs. 139.65±41.50 at 24 hours; PaCO2 (mmHg): 41.30±7.51 vs. 47.42±7.54 at 12 hours, 40.97±6.98 vs. 45.83±8.63 at 18 hours, 40.10±7.06 vs. 46.14±9.15 at 24 hours, all P < 0.01]. The rate of performed mechanical ventilation after weaning and the rate of reintubation after extubation for 72 hours in the high-flow group were significantly lower than those in the Venturi group [17.5% (7/40) vs. 40.5% (15/37), 6.2% (2/32) vs. 31.8% (7/22), both P < 0.05], and the extubation time after weaning was significantly shorter than that in the Venturi group (hours: 22.43±11.72 vs. 28.07±10.42, P < 0.05). CONCLUSIONS: Using high-flow oxygen therapy to the extubation process of critically ill mechanical ventilation patients can reduce the incidence of carbon dioxide retention and the rate of performed mechanical ventilation after weaning, shorten the extubation time after weaning, and reduce the rate of reintubation after extubation for 72 hours.

A model of the pulmonary acinar circulatory system with gas exchange function to explore the influence of alveolar diameter.

Lung diseases such as acute respiratory distress syndrome affect the patient's lung compliance, which in turn affects the ability of gas exchange. Changes in alveolar diameter relate to local lung compliance. How alveolar diameter affects gas exchange, particularly oxygen concentrations in alveolar capillaries, is a topic of concern for researchers, and can be studied using mathematical models. The level of small-scale mathematical models of the pulmonary circulatory system was the alveolar capillaries, but existing models do not consider the gas-exchange function and fail to reflect the influence of alveolar diameter. Therefore, we proposed a pulmonary acinar capillary model with gas exchange function, and most importantly, introduced alveolar diameter into the model, to analyze the effect of alveolar diameter on the gas exchange function of the pulmonary acini. The model was tested by three respiratory function simulation experiments. According to the simulation results of changing diameter, we found that the alveolar diameter mainly affects the alveolar gas exchange function of lung acinar inlets and the middle section compared with the peripheral section.

Synthesis and biological evaluation of novel hybrids of phenylsulfonyl furoxan and phenstatin derivatives as potent anti-tumor agents.

Hybridization of nitric oxide (NO) donors with known anti-cancer agents have been emerged as a strategy to achieve improved therapeutic effect and to overcome chemo-resistance in cancer therapy. In this study, furoxan moiety as an efficient NO donor was introduced to phenstatin, a microtubule-interfering agent (MIA), leading to the design and synthesis of a series of furoxan-based NO-releasing arylphenones derivatives. In biological evaluation, the synthesized compounds showed moderate to potent anti-tumor activities against several human cancer cell lines. Among them, compound 15h showed the most potent activities against both chemo-sensitive and resistant cancer cell lines with IC50 values ranging from 0.008 to 0.021 µM. Further mechanistic studies revealed that 15h worked as a bifunctional agent exhibiting both tubulin polymerized inhibition and NO-releasing activities, resulting in potent anti-angiogenesis, colony formation inhibition, cell cycle arrest and apoptosis induction effects. In the nude mice xenograft model, 15h significantly inhibited the paclitaxel-resistant tumor growth with low toxicity, demonstrating the promising potential for further preclinical evaluation as a therapeutic agent, particularly for the treatment of chemo-resistant cancers.

Feature extraction based on microstate sequences for EEG-based emotion recognition.

Recognizing emotion from Electroencephalography (EEG) is a promising and valuable research issue in the field of affective brain-computer interfaces (aBCI). To improve the accuracy of emotion recognition, an emotional feature extraction method is proposed based on the temporal information in the EEG signal. This study adopts microstate analysis as a spatio-temporal analysis for EEG signals. Microstates are defined as a series of momentary quasi-stable scalp electric potential topographies. Brain electrical activity could be modeled as being composed of a time sequence of microstates. Microstate sequences provide an ideal macroscopic window for observing the temporal dynamics of spontaneous brain activity. To further analyze the fine structure of the microstate sequence, we propose a feature extraction method based on k-mer. K-mer is a k-length substring of a given sequence. It has been widely used in computational genomics and sequence analysis. We extract features that are based on the D 2 ∗ statistic of k-mer. In addition, we also extract four parameters (duration, occurrence, time coverage, GEV) of each microstate class as features at the coarse level. We conducted experiments on the DEAP dataset to evaluate the performance of the proposed features. The experimental results demonstrate that the fusion of features in fine and coarse levels can effectively improve classification accuracy.

Prevention, treatment, and risk factors of deep vein thrombosis in critically ill patients in Zhejiang province, China: a multicenter, prospective, observational study.

PURPOSE: The aim of this study is to investigate the prevention and treatment patterns of deep vein thrombosis (DVT) in critically ill patients and to explore the risk factors for DVT in people from Zhejiang Province, China. MATERIALS AND METHODS: This study prospectively enrolled patients admitted in intensive care units (ICUs) of 54 hospitals from 09/16/2019 to 01/16/2020. The risk of developing DVT and subsequent prophylaxis was evaluated. The primary outcome was DVT occurrence during ICU hospitalisation. Univariate and multivariate logistic regression were performed to determine the risk factors for DVT. RESULTS: A total of 940 patients were included in the study. Among 847 patients who received prophylaxis, 635 (75.0%) patients received physical prophylaxis and 199 (23.5%) patients received drug prophylaxis. Fifty-eight (6.2%) patients were diagnosed with DVT after admission to the ICU, and 36 patients were treated with anticoagulants (all patients received low molecular weight heparin [LMWH]). D-dimer levels (OR = 1.256, 95% CI: 1.132-1.990), basic prophylaxis (OR = 0.092, 95% CI: 0.016-0.536), and physical prophylaxis (OR = 0.159, 95% CI: 0.038-0.674) were independently associated with DVT in ICU patients. The short-term survival was similar between DVT and non-DVT patients. CONCLUSIONS: DVT prophylaxis is widely performed in ICU patients. Prophylaxis is an independent protective factor for DVT occurrence. The most common treatment of DVT patients is LMWH, although it might increase the rate of bleeding.Key messagesThis is the only multicenter and prospective study of DVT in ICUs in China.d-dimer levels were independently associated with DVT in ICU patients.Prophylaxis was an independent protective factor for DVT occurrence in ICU.

Dimethyl itaconate alleviates the pyroptosis of macrophages through oxidative stress.

Macrophages are involved in the pathophysiology of many diseases as critical cells of the innate immune system. Pyroptosis is a form of macrophage death that induces cytokinesis of phagocytic substances in the macrophages, thereby defending against infection. Dimethyl itaconate (DI) is an analog of itaconic acid with anti-inflammatory effects. However, the effect of dimethyl itaconate on macrophage pyroptosis has not been elucidated clearly. Thus, the present study aimed to analyze the effect of DI treatment on a macrophage pyroptosis model (Lipopolysaccharide, LPS + Adenosine Triphosphate, ATP). The results showed that 0.25 mM DI ameliorated macrophage pyroptosis and downregulated interleukin (IL)-1ß expression. Then, real-time quantitative polymerase chain reaction (RT-qPCR) was used to confirm the result of RNA-sequencing of the upregulated oxidative stress-related genes (Gclc and Gss) and downregulated inflammation-related genes (IL-12ß and IL-1ß). In addition, Gene Ontology (GO) enrichment analysis showed that differential genes were associated with transcript levels and DNA replication. Kyoto encyclopedia of genes and genomes (KEGG) enrichment showed that signaling pathways, such as tumor necrosis factor (TNF), Jak, Toll-like receptor and IL-17, were altered after DI treatment. N-acetyl-L-cysteine (NAC) reversed the DI effect on the LPS + ATP-induced macrophage pyroptosis and upregulated the IL-1ß expression. Oxidative stress-related protein Nrf2 is involved in the DI regulation of macrophage pyroptosis. Taken together, these findings suggested that DI alleviates the pyroptosis of macrophages through oxidative stress.

Association Between Oxygen Partial Pressure Trajectories and Short-Term Outcomes in Patients With Hemorrhagic Brain Injury.

Objectives: Arterial hyperoxia is reportedly a risk factor for poor outcomes in patients with hemorrhagic brain injury (HBI). However, most previous studies have only evaluated the effects of hyperoxia using static oxygen partial pressure (PaO2) values. This study aimed to investigate the association between overall dynamic oxygenation status and HBI outcomes, using longitudinal PaO2 data. Methods: Data were extracted from the Medical Information Mart for Intensive Care III database. Longitudinal PaO2 data obtained within 72 h of admission to an intensive care unit were analyzed, using a group-based trajectory approach. In-hospital mortality was used as the primary outcomes. Multivariable logistic models were used to explore the association between PaO2 trajectory and outcomes. Results: Data of 2,028 patients with HBI were analyzed. Three PaO2 trajectory types were identified: Traj-1 (mild hyperoxia), Traj-2 (transient severe hyperoxia), and Traj-3 (persistent severe hyperoxia). The initial and maximum PaO2 of patients with Traj-2 and Traj-3 were similar and significantly higher than those of patients with Traj-1. However, PaO2 in patients with Traj-2 decreased more rapidly than in patients with Traj-3. The crude in-hospital mortality was the lowest for patients with Traj-1 and highest for patients with Traj-3 (365/1,303, 209/640, and 43/85 for Traj-1, Traj-2, and Traj-3, respectively; p < 0.001), and the mean Glasgow Coma Scale score at discharge (GCSdis) was highest for patients with Traj-1 and lowest in patients with Traj-3 (13 [7-15], 11 [6-15], and 7 [3-14] for Traj-1, Traj-2, and Traj-3, respectively; p < 0.001). The multivariable model revealed that the risk of death was higher in patients with Traj-3 than in patients with Traj-1 (odds ratio [OR]: 3.3, 95% confidence interval [CI]: 1.9-5.8) but similar for patients with Traj-1 and Traj-2. Similarly, the logistic analysis indicated the worst neurological outcomes in patients with Traj-3 (OR: 3.6, 95% CI: 2.0-6.4, relative to Traj-1), but similar neurological outcomes for patients in Traj-1 and Traj-2. Conclusion: Persistent, but not transient severe arterial hyperoxia, was associated with poor outcome in patients with HBI.

Novel phenotypes of coronavirus disease: a temperature-based trajectory model.

BACKGROUND: Coronavirus disease has heterogeneous clinical features; however, the reasons for the heterogeneity are poorly understood. This study aimed to identify clinical phenotypes according to patients' temperature trajectory. METHOD: A retrospective review was conducted in five tertiary hospitals in Hubei Province from November 2019 to March 2020. We explored potential temperature-based trajectory phenotypes and assessed patients' clinical outcomes, inflammatory response, and response to immunotherapy according to phenotypes. RESULTS: A total of 1580 patients were included. Four temperature-based trajectory phenotypes were identified: normothermic (Phenotype 1); fever, rapid defervescence (Phenotype 2); gradual fever onset (Phenotype 3); and fever, slow defervescence (Phenotype 4). Compared with Phenotypes 1 and 2, Phenotypes 3 and 4 had a significantly higher C-reactive protein level and neutrophil count and a significantly lower lymphocyte count. After adjusting for confounders, Phenotypes 3 and 4 had higher in-hospital mortality (adjusted odds ratio and 95% confidence interval 2.1, 1.1-4.0; and 3.3, 1.4-8.2, respectively), while Phenotype 2 had similar mortality, compared with Phenotype 1. Corticosteroid use was associated with significantly higher in-hospital mortality in Phenotypes 1 and 2, but not in Phenotypes 3 or 4 (p for interaction < 0.01). A similar trend was observed for gamma-globulin. CONCLUSIONS: Patients with different temperature-trajectory phenotypes had different inflammatory responses, clinical outcomes, and responses to corticosteroid therapy.

Dynamic prediction of late noninvasive ventilation failure in intensive care unit using a time adaptive machine model.

BACKGROUND: Noninvasive ventilation (NIV) failure is strongly associated with poor prognosis. Nowadays, plenty of mature studies have been proposed to predict early NIV failure (within 48 hours of NIV), however, the prediction for late NIV failure (after 48 hours of NIV) lacks sufficient research. Late NIV failure delays intubation resulting in the increasing mortality of the patients. Therefore, it is of great significance to expeditiously predict the late NIV failure. In order to dynamically predict late NIV failure, we proposed a Time Updated Light Gradient Boosting Machine (TULightGBM) model. MATERIAL AND METHODS: In this work, 5653 patients undergoing NIV over 48 hours were extracted from the database of Medical Information Mart for Intensive Care Ⅲ (MIMIC-Ⅲ) for model construction. The TULightGBM model consists of a series of sub-models which learn clinical information from updating data within 48 hours of NIV and integrates the outputs of the sub-models by the dynamic attention mechanism to predict late NIV failure. The performance of the proposed TULightGBM model was assessed by comparison with common models of logistic regression (LR), random forest (RF), LightGBM, eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and long short-term memory (LSTM). RESULTS: The TULightGBM model yielded prediction results at 8, 16, 24, 36, and 48 hours after the start of the NIV with dynamic AUC values of 0.8323, 0.8435, 0.8576, 0.8886, and 0.9123, respectively. Furthermore, the sensitivity, specificity, and accuracy of the TULightGBM model were 0.8207, 0.8164, and 0.8184, respectively. The proposed model achieved superior performance over other tested models. CONCLUSIONS: The TULightGBM model is able to dynamically predict the late NIV failure with high accuracy and offer potential decision support for clinical practice.

Effect of IL-33 on pyroptosis of macrophages in mice with sepsis via NF-κB/p38 MAPK signaling pathway.

PURPOSE: To demonstrate the effect of IL-33 on the macrophage pyroptosis in mice with sepsis through the NF-kB/p38 MAPK signal pathway. METHODS: In total, 24 C57BL/6 mice were divided into the sham operation group (sham) and the cecal ligation and puncture group (CLP). After CLP, 24 IL-33-/- mice were divided into the IL-33-/- group and the IL-33-/- intervention group. The latter group was intraperitoneally injected with IL-33. Mouse mortality was observed after CLP. Macrophage apoptosis in peritoneal lavage fluid was detected by flow cytometry. Serum inflammatory factor level was detected by ELISA. Apoptotic protein expression and NF-κB/p38 MAKP signaling pathway protein expression were detected by qRT-PCR and Western blot. RESULTS: Knocking out IL-33 significantly reduced the mortality of CLP mice, as well as the mRNA expression of IL-33 and the levels of serum inflammatory factors, including IL-33, IL-1ß, and IL-18. It also reduced the rate of macrophage apoptosis and the expression of the apoptotic protein caspase-1 p10; increased the expression of IκBα; and reduced the protein expression of NF-κB and p38 MAPK. These effects were reversed after exogenous injection of IL-33. CONCLUSIONS: IL-33 can increase the level of macrophage pyroptosis in mice with sepsis (by activating the NF-kB/p38MAPK signal pathway) and the mortality of these mice.

Impact of chronic respiratory diseases on re-intubation rate in critically ill patients: a cohort study.

Chronic respiratory diseases' (CRDs) impact on re-intubation rate remains unclear. We investigated the association between these factors in mechanically ventilated patients. Data were extracted from the freely available online Medical Information Mart for Intensive Care III database. CRDs were defined according to ICD-9 codes. Generalised linear regression and propensity score matching were performed. Of 13,132 patients, 7.9% required re-intubation. Patients with chronic obstructive pulmonary disease (COPD) had higher re-intubation (OR 2.48, 95% CI 1.83-3.33) and mortality rates (OR 1.64, 95% CI 1.15-2.34) than those without. Patients with asthma had a lower mortality rate (OR 0.63, 95% CI 0.43-0.92) but a similar re-intubation rate to those of patients without. These findings remained stable after propensity score matching and bootstrapping analysis. The association of COPD with re-intubation was significantly stronger in patients with high oxygen-partial pressure (PaO2) or mild disease severity but was independent of carbon dioxide partial pressure. Corticosteroid use was associated with increased re-intubation rates in subgroups without CRDs (OR 1.77-1.99, p < 0.001) but not in subgroups with CRDs. COPD patients with high post-extubation PaO2 or mild disease severity should be carefully monitored as they have higher re-intubation and mortality rates.

Macrophages enhance lipopolysaccharide induced apoptosis via Ang1 and NF-κB pathways in human umbilical vein endothelial cells.

Lipopolysaccharide (LPS) could induce apoptosis and dysfunction of endothelial cells. We aimed to reveal the effects of macrophages on cell proliferation and apoptosis in LPS induced human umbilical vein endothelial cells (HUVECs). THP-1 derived macrophages and HUVECs were co-cultured in the presence of LPS. Cell viability was measured by Cell Counting Kit-8 and apoptosis was analyzed by flow cytometry. Expression of Ang1, the NF-κB component p65 was evaluated by western blot and quantitative PCR. Small interfering RNAs (siRNAs) were used to knockdown the expression of proinflammatory cytokines and p65 in HUVECs. Plasmid transfection-mediated overexpression of Ang1 was employed to see its effects on cell proliferation and apoptosis in HUVECs. Macrophages enhanced LPS-induced proliferation impairments and apoptosis in HUVECs, which could be attenuated by siRNA-mediated knockdown of cytokines TNF-α, IL-1ß, IL-6 and IL-12p70 in macrophages. The dysfunction of HUVECs was tightly associated with reduced Ang1 expression and increased phosphorylated p65 (p-65). Overexpression of Ang1 in HUVECs significantly decreased p-p65, suggesting negatively regulation of p-p65 by Ang1. Overexpression of Ang1, adding recombinant Ang1 or silencing of p65 substantially attenuated the dysfunction of HUVECs in terms of cell proliferation and apoptosis. In conclusions, THP-1-derived macrophages enhance LPS induced dysfunction of HUVECs via Ang1 and NF-κB pathways, suggesting new therapeutic targets for sepsis.