Objective: To investigate the feasibility of alveolar-arterial oxygen partial pressure difference (AaDO2) for early warning and prediction of severity in patients with acute respiratory distress syndrome (ARDS).Methods: This retrospective cohort study is based on the MIMIC-IV database. In this study, both patients without and with ARDS were included. The AaDO2 value was calculated by blood gas analysis and ventilation parameters from the first day to the seventh day of the patient's admission to the ICU, and the most sensitive AaDO2 value was found by comparing the different days with the AUC curve. The Youden index was used to calculate the best threshold for predicting the incidence of ARDS. AUC curve was drawn to analyze the cutoff value of AaDO2 value for predicting mild, moderate, and severe ARDS, as well as the difference. Logistic regression analysis, lowess smoothing, and restricted cubic spline model were used to predict the association between AaDO2 and the incidence of ARDS.Results: The study included 2580 patients, of whom 1358 had no ARDS and 1222 had ARDS (690 mild, 474 moderate, and 58 severe). Our study found that the earliest time point at which AaDO2 predicted the incidence of ARDS was on the first day of ICU admission AUC was 0.85, 95% CI (0.83-0.86) with a cut-off value of 195.94 mmHg. AaDO2 on the first day predicted mild ARDS: AUC was 0.74, 95% CI (0.71-0.76) with a cut-off value of 136.39 mmHg; AaDO2 on the first day predicted moderate ARDS: AUC of 0.90, 95% CI (0.89-0.92), cut-off value 233.07 mmHg; AaDO2 on the first day predicted severe ARDS: AUC of 0.94, 95% CI (0.91-0.97), cut-off value 251.7 mmHg, linear Correlation analysis found a negative correlation between AaDO2 and PaO2/FiO2 (R= -0.84, R2= -0.64, P<001). In addition, logistic regression analysis, Lowess smoothing, and Restricted cubic spline model analysis showed a correlation between elevated AaDO2 and poor outcome.Conclusion: Increased AaDO2, in addition to the PaO2/FiO2 ratio, may serve as a valuable parameter for identifying patients at risk of developing ARDS. This study demonstrates the potential of AaDO2 in predicting the incidence of ARDS and stratifying its severity.
The effect of atmospheric turbulent environment on the performance of free-space quantum communication is analysed. Based on the existing mathematical model of atmospheric turbulent fluid motion, the three most influential physical factors, i.e. wind speed, temperature and humidity, are abstracted out and simulated to study the variation of quantum channel utilisation, quantum channel entanglement, channel capacity and fidelity in amplitude damped channels under the atmospheric turbulent environment parameters. After a comprehensive analysis of the simulation results, it can be preliminarily judged that an increase in all three physical factors will increase the intensity of atmospheric turbulence and reduce the performance of quantum communication, and the magnitude of the influence is: temperature > wind speed > humidity. Therefore, corresponding adjustment strategies should be adopted to improve the communication performance of quantum communication and ensure the reliability of communication.
Abstract Background Airway pressure release ventilation (APRV) has become increasingly popular for the management of acute respiratory distress syndrome (ARDS); however, its clinical impact remains a topic of debate. Furthermore, there is a gap between the guidelines and the actual clinical practices in mechanical ventilation management for ARDS. This survey aimed to explore the utilization of APRV and mechanical ventilation strategies for ARDS in Chinese intensive care unit (ICU) clinicians. Methods A comprehensive 34‐item survey was distributed online platforms amongst ICU clinicians across mainland China from June to August 2019. Results A total of 420 valid responses were collected, with 57.4% (241) originating from academic hospitals and 42.6% (179) from non‐academic hospitals. Of the respondents, 98.6% (414) recognized the significance of low tidal volume ventilation for ARDS prognosis, 85.2% adhered to a tidal volume below 8 mL/kg predicted body weight, and most (46.4%) selected the initial positive end‐expiratory pressure within the range of 5–10 cmH 2 O based on experience. Among the respondents, 62.1% (261) reported familiarity with APRV and 41.9% (176) had implemented APRV. Of those who had utilized APRV, 93.2% (164) believed in its effectiveness for ARDS patients and 69.3% (122) advocated for early application of APRV. Substantial variations were noted regarding APRV initiation settings and the preservation of spontaneous breathing during APRV. Academic hospitals exhibited higher usage rates of lung recruitment, neuromuscular blockade, prone ventilation, and acquaintance with and utilization of APRV compared to non‐academic hospitals (all p values ≤ 0.001). Conclusions Our findings highlight opportunities for improvement in mechanical ventilation management for ARDS, particularly in non‐academic hospitals. Additionally, a significant proportion of clinicians demonstrated limited knowledge of APRV, and there was a lack of consensus on its application. Further training and larger‐scale clinical trials are required to validate the efficacy and utilization of APRV in managing ARDS.
Abstract Background Associated with increased morbidity and mortality, postoperative pulmonary complications (PPCs) often occur after major abdominal surgery. Diaphragmatic dysfunction is suggested to play an important role in the development of PPCs and diaphragm echodensity can be used as an indicator of diaphragm function. This study aimed to determine whether diaphragm echodensity could predict the occurrence of PPCs in patients after major abdominal surgery. Methods Diaphragm ultrasound images of patients after major abdominal surgery were collected during spontaneous breathing trials. Echodensity was quantified based on the right-skewed distribution of grayscale values (50th percentile, ED50; 85th percentile, ED85; mean, EDmean). Intra- and inter-analyzer measurement reproducibility was determined. Outcomes including occurrence of PPCs, reintubation rate, duration of ventilation, and length of ICU stay were recorded. Results Diaphragm echodensity was measured serially in 117 patients. Patients who developed PPCs exhibited a higher ED50 (35.00 vs. 26.00, p < 0.001), higher ED85 (64.00 vs. 55.00, p < 0.001) and higher EDmean (39.32 vs. 33.98, p < 0.001). In ROC curve analysis, the area under the curve of ED50 for predicting PPCs was 0.611. The optimal ED50 cutoff value for predicting the occurrence of PPCs was 36. According to this optimal ED50 cutoff value, patients were further divided into a high-risk group (ED50 > 36, n = 35) and low-risk group (ED50 ≤ 36, n = 82). Compared with the low-risk group, the high-risk group had a higher incidence of PPCs (unadjusted p = 0.003; multivariate-adjusted p < 0.001). Conclusion Diaphragm echodensity can be feasibly and reproducibly measured in mechanically ventilated patients. The increase in diaphragm echodensity during spontaneous breathing trials was related to an increased risk of PPCs in patients after major abdominal surgery.
ABSTRACT Background Data regarding critical care for patients with severe COVID-19 are limited. We aimed to describe the clinical course, multi-strategy management, and respiratory support usage for the severe COVID-19 at the provincial level. Methods Using data from Sichuan Provincial Department of Health and the multicentre cohort study, all microbiologically confirmed COVID-19 patients in Sichuan who met the national severe criteria were included and followed-up from the day of inclusion (D1), until discharge, death, or the end of the study. Findings Out of 539 COVID-19 patients, 81 severe cases (15.0%) were identified. The median (IQR) age was 50 (39-65) years, 37% were female, and 53.1% had chronic comorbidities. All severe cases were identified before requiring mechanical ventilation and treated in the intensive care units (ICUs), among whom 51 (63.0%) were treated in provisional ICUs and 77 patients (95.1%) were admitted by D1. On D1, 76 (93.8%) were administered by respiratory support, including 55 (67.9%) by conventional oxygen therapy (COT), 8 (9.9%) by high-flow nasal cannula (HFNC) and 13 (16.0%) by non-invasive ventilation (NIV). By D28, 53 (65.4%) were discharged, three (3.7%) were deceased, and 25 (30.9%) were still hospitalized. COT, administered to 95.1% of the patients, was the most commonly used respiratory support and met 62.7% of the respiratory support needed, followed by HFNC (19.3%), NIV ventilation (9.4%) and IV 8.5%. Interpretation The multi-strategy management for severe COVID-19 patients including early identification and timely critical care may contribute to the low case-fatailty. Preparation of sufficient conventional oxygen equipment should be prioritized. Trial registration number ChiCTR2000029758.
A risk assessment model was constructed using differentially expressed long non‑coding (lnc)RNAs for the prognosis of glioma. Transcriptome sequencing of the lncRNAs and mRNAs from glioma samples were obtained from the TCGA database. The samples were divided into bad and good prognosis groups based on survival time, then differently expressed lncRNAs between these two groups were screened using DEseq and edgeR packages. Multivariate Cox regression analysis was performed to establish a risk assessment system according to the weighted regression coefficient of lncRNA expression. Survival analysis and receiver operating characteristic curve were conducted for the risk assessment model. Furthermore, the co‑expression network of the screened lncRNAs was constructed, followed by the functional enrichment analysis for associated genes. A total of 117 lncRNAs were screened using edgeR and DEseq packages. Among all differently expressed lncRNAs, five lncRNAs (RP3‑503A6, LINC00940, RP11‑453M23, AC009411 and CDRT7) were identified to establish the risk assessment model. The risk assessment model demonstrated a good prognostic function with high area under the curve values in the training, validation and entire sets. The risk score was certified as an independent prognostic factor for gliomas. Multiple genes were screened to be co‑expressed with these five lncRNAs. Functional enrichment analysis demonstrated that they were involved in cytoskeleton, adhesion and Janus kinase/signal transducer and activator of transcription signaling pathway‑associated processes. The present study established a risk assessment model integrating five significantly different expressed lncRNAs, which may help to assess the prognosis of patients with glioma with increased accuracy.
The mechanisms of lung injury in acute respiratory distress syndrome (ARDS) are not well understood.Piezo1 was recently identified as a mechanotransduction protein. The present study found the expression of Piezo1 in type II pneumocytes and investigated its role in mediating ARDS-related lung injury.
Objective: To identify subclasses of acute pancreatitis (AP) patients in the intensive care unit (ICU) by analyzing blood urea nitrogen (BUN) trajectories. Methods: AP patients in West China Hospital System (development cohort) and three public databases in the United States (validation cohort) were included. Latent class trajectory modelling was used to identify subclasses based on BUN trajectories within the first 21 days after ICU admission. Clinical characteristics and outcomes were compared, and results were externally validated. Results: The study comprised 2971 and 930 patients in the development and validation cohorts, respectively, with five subclasses: Class 1 ("Moderate-azotemia, slow decreasing"), Class 2 ("Non-azotemia"), Class 3 ("Severe-azotemia, slow decreasing"), Class 4 ("Moderate-azotemia, rapid increasing"), and Class 5 ('Moderate-azotemia, slow increasing) identified. Azotemia patients showed significantly higher 30-day mortality risk in development and validation cohorts. Specifically, Class 4 patients exhibited notably highest mortality risk in both the development cohort (HR 5.32, 95% CI 2.62– 10.82) and validation cohort (HR 6.23, 95% CI 2.93– 13.22). Regarding clinical characteristics, AP patients in Class 4 showed lower mean arterial pressure and a higher proportion of renal disease. We also created an online early classification model to further identify Class 4 patients among all patients with moderate azotemia at baseline. Conclusion: This multinational study uncovers heterogeneity in BUN trajectories among AP patients. Patients with "Moderate-azotemia, rapid increasing" trajectory, had a higher mortality risk than patients with severe azotemia at baseline. This finding complements studies that solely rely on baseline BUN for risk stratification and enhanced our understanding of longitudinal progression of AP. Keywords: acute pancreatitis, BUN, trajectories analysis, ICU
To explore the effects of blood glucose levels on liver functions in patients with sepsis treated in intensive care unit (ICU).We performed a retrospective study on 93 patients with sepsis between March 2014 to January 2015. Patients were divided into two groups according to the initial blood glucose levels when admitted to ICU: high glucose group (blood glucose>10 mmol/L) and normal glucose group(blood glucose≤10 mmol/L). Routine blood test results, liver functions and coagulations function were compared between the two groups.On the first day of ICU admission, patients in normal glucose group had lower blood platelet count, higher total bilirubin (TB) and direct bilirubin (DB) levels, lower high density lipoprotein cholesterol (HDL-C) level, lower low density lipoprotein cholesterol (LDL-C) level, longer prothrombin time (PT), longer international normalized ratio (INR), longer activated partial thromboplastin time (APTT) and lower antithrombin Ⅲ (AT Ⅲ) level ( P<0.05). However, seven days after ICU admission, there were no significant differences between the two groups ( P>0.05).In patients with sepsis, normal blood glucose level might represent severe liver damage.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)