Objective To investigated the early risk factors of AIDS severe pneumonia complicated with acute respiratory distress syndrome in order to carry out early recognition and intervention of ARDS and improve the prognosis of patients. Methods The clinical data of 232 patients with severe AIDS pneumonia admitted to Chengdu Public Health Clinical Medical Center from January 2017 to December 2020 were retrospectively analyzed, including general data, vital signs, laboratory examination indexes, basic diseases, etc. Firstly influential indexes for complicated with ARDS were screened by single factor logistic regression analysis, then the multicollinearity assessment indicators were filtered out in multi-factor logistic stepwise regression analysis, finally the receiver operating characteristic (ROC) curves were drawn and the predictive value of the indicators were assessed. Results Thirty-three of 232 AIDS patients with severe pneumonia were complicated with ARDS. The mortality rate in ARDS group was 81.8%. The intra-group mortality of non-ARDS group was 33.7%. Single factor logistic regression analysis showed that pH, acute physiology and chronic health evaluation Ⅱ grade, sequential organ failure assessment grade, white blood cell count, lactate dehydrogenase, α-hydroxybutyric acid dehydrogenase (α-HBDH), alanine aminotransferase (ALT), aspartic acid aminotransferase (AST), calcium, fibrinogen degradation produc (FDP) and D-dimer, total 11 indicators were associated with the incidence of ARDS. The multicollinearity analysis of the 11 indicators showed that there was no multicollinearity problem among the other 9 indicators except the variance inflation factor of ALT and AST which was greater than 10. Multivariate logistic stepwise regression analysis showed α-HBDH (OR=1.001, 95% confidence interval 1.000 - 1.002, P=0.045) and D-dimer (OR=1.044, 95% confidence interval 1.006 - 1.083, P=0.024) were independent factors. ROC curve indicated the following: alpha hydroxy butyric acid dehydrogenase (the area under ROC curve=0.667, P=0.002, the optimal threshold was 391 U/L, the corresponding sensitivity and specificity was 78.8% and 61.8%, respectively), D-dimer (the area under ROC curve=0.602, P=0.062, the optimal threshold was 4.855 μg/mL, the corresponding sensitivity and specificity was 42.4% and 82.9%, respectively). Conclusion AIDS severe pneumonia complicated with ARDS is associated with many factors, among whichα-HBDH (≥391 U/L) and D-dimer (≥ 4.855 μg/mL) on admission are independent risk factors, which have great early predictive value and can provide reference for early clinical identification of ARDS high-risk patients.
A novel coronavirus (SARS-CoV-2) that broke out at the end of 2019 is a newly discovered highly pathogenic human coronavirus and has some similarities with severe acute respiratory syndrome coronavirus (SARS-CoV). Angiotensin-converting enzyme 2 (ACE2) is the receptor for infected cells by SARS-CoV. SARS-CoV can invade cells by binding to ACE2 through the spike protein and SARS-CoV-2 may also infect cells through ACE2. Meanwhile, ACE2 also plays an important role in the course of pneumonia. Therefore the possible role of ACE2 in SARS and coronavirus disease 2019 (COVID-19) is worth discussing. This paper briefly summarized the role of ACE2 in SARS, and discussed the possible function of ACE2 in COVID-19 and potential risk of infection with other organs. At last, the function of ACE2 was explored for possible treatment strategies for SARS. It is hoped to provide ideas and theoretical support for clinical treatment of COVID-19.
As an extracorporeal life support technology, veno-venous extracorporeal membrane oxygenation (VV-ECMO) has been demonstrated its role in the treatment of patients with severe respiratory failure. Its main advantages include the ability to maintain adequate oxygenation and remove excess CO2, increase oxygen delivery, improve tissue perfusion and metabolism, and implement lung protection strategies. Clinicians should accurately assess and identify the patient's condition, timely and accurately carry out VV-ECMO operation and management. This article will review the patient selection, cannulation strategy, anticoagulation, clinical management and weaning involved in the application of VV-ECMO.
Mechanical ventilation is an importmant life-sustaining treatment for patients with acute respiratory distress syndrome. Its clinical outcomes depend on patients’ characteristics of lung recruitment. Estimation of lung recruitment characteristics is valuable for the determination of ventilatory maneurvers and ventilator parameters. There is no easily-used, bedside method to assess lung recruitment characteristics. The present paper proposed a method to estimate lung recruitment characteristics from the static pressure-volume curve of lungs. The method was evaluated by comparing with published experimental data. Results of lung recruitment derived from the presented method were in high agreement with the published data, suggesting that the proposed method is capable to estimate lung recruitment characteristics. Since some advanced ventilators are capable to measure the static pressure-volume curve automatedly, the presented method is potential to be used at bedside, and it is helpful for clinicians to individualize ventilatory manuevers and the correpsonding ventilator parameters.
Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic at the end of December 2019, more than 85% of the population in China has been infected. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mainly affects the respiratory system, especially the lungs. The mortality rate of patients with severe infection is high. A percentage of 6% to 10% of patients will eventually develop into COVID-related acute respiratory distress syndrome (CARDS), which requires mechanical ventilation and extracorporeal membrane oxygenation (ECMO) support. Some patients who survive acute lung injury will subsequently develop post COVID-19 pulmonary fibrosis (PCPF). Both fully treated CARDS and severe PCPF are suitable candidates for lung transplantation. Due to the special course, evaluation strategies are different from those used in patients with common end-stage lung disease. After lung transplantation in COVID-19 patients, special treatment is required, including standardized nucleic acid testing for the novel coronavirus, adjustment strategy of immunosuppressive drugs, and rational use of antiviral drugs, which is a big challenge for the postoperative management of lung transplantation. This consensus was evidence-based written and was reached by experts after multiple rounds of discussions, providing reference for assessment and postoperative management of patients with interstitial pneumonia after COVID-19 infection.
Objective To explore the efficacy of prone positioning ventilation in patients with acute respiratory distress syndrome (ARDS) after acute Stanford type A aortic dissection (STAAD) surgery. Methods From November 2019 to September 2021, patients with ARDS who was placed prone position after STAAD surgery in the Xiamen Cardiovascular Hospital of Xiamen University were collected. Data such as the changes of blood gas, respiratory mechanics and hemodynamic indexes before and after prone positioning, complications and prognosis were collected for statistical analysis. ResultsA total of 264 STAAD patients had surgical treatment, of whom 40 patients with postoperative ARDS were placed prone position. There were 37 males and 3 females with an average age of 49.88±11.46 years. The oxygen partial pressure, oxygenation index and peripheral blood oxygen saturation 4 hours and 12 hours after the prone positioning, and 2 hours and 6 hours after the end of the prone positioning were significantly improved compared with those before prone positioning ventilation (P<0.05). The oxygenation index 2 hours after the end of prone positioning which was less than 131.42 mm Hg, indicated that the patient might need ventilation two or more times of prone position. Conclusion Prone position ventilation for patients with moderate to severe ARDS after STAAD surgery is a safe and effective way to improve the oxygenation.
Objective To investigate the titration of best positive end-expiratory pressure (Best PEEP) based on mechanical power (MP) and transpulmonary pressure monitoring during lung reexpansion in patients with acute respiratory distress syndrome (ARDS), and to analyze the value of both in evaluating the prognosis of ARDS patients.Methods ARDS patients treated in the intensive care Unit of the First Affiliated Hospital of Jinzhou Medical University from September 2021 to March 2023 were selected and divided into survival group and death group according to the 28-day mortality rate. After full sedation, esophageal pressure tube was inserted through the nasal passage, and lung recruitment maneuvers (RM) was performed by incremental PEEP method. The Best PEEP method was titrated based on MP and transpulmonary pressure. Pearson correlation analysis was used to analyze the correlation between MP at RM 30 min and 2 h and transpulmonary pressure. The changes of clinical indicators at 30 minutes and 2 hours after RM were compared between the two groups with different outcomes. Receiver operating characteristic (ROC) curve was used to analyze the predictive value of 2 h MP and transpulmonary pressure for 28-day mortality in ARDS patients. Results MP and transpulmonary pressure in the survival group decreased significantly at 30 min and 2 h, while MP and transpulmonary pressure in the death group showed a significant upward trend (P < 0.05). The Best PEEP and RR at 30 min and 2 h of the RM in the survival group were lower than those in the death group (P < 0.05). Pearson correlation analysis showed that MP at RM 30 min and 2 h was significantly correlated with transpulmonary pressure (r = 0.710 and 0.804, P < 0.05). The area under the ROC curve of MP and transpulmonary pressure were 0.812 and 0.795, respectively. 95% confidencial interval: 0.704 - 0.920 and 0.687 - 0.903 (P < 0.05); The sensitivity was 86.95% and 82.50%, respectively. The specificity were 76.67% and 59.40%; The positive predictive values were 0.851 and 0.688; The negative predictive values were 0.793 and 0.759; The optimal cut-off values were 15.5 and 17.5, respectively. RM 2 h MP and transpulmonary pressure have good predictive value for 28-day mortality in ARDS patients. Conclusion Monitoring MP and transpulmonary pressure during lung recruitment maneuver can effectively guide the titration of Best PEEP in ARDS patients, and both have good evaluation value for the prognosis of ARDS patients.
ObjectiveTo improve clinicians' understanding of severe cytokine release syndrome (CRS) through reporting the clinical manifestation, diagnosis, treatment, and prognosis of CRS after chimeric antigen receptor T (CAR-T) cell therapy in a patient with solid tumor. Methods A patient with ovarian cancer who suffered severe CRS after CAR-T cell therapy in the Department of Critical Care Medicine, the First Affiliated Hospital of Nanjing Medical University was reviewed. Relevant studies were searched for literature review. Results The patient, a 55-year-old woman, was diagnosed with ovarian cancer in early 2016 and continued to progress despite multiple lines of treatment, so she received CAR-T cell therapy on September 16, 2022. The patient developed a fever 2 days after infusion, and developed dyspnea and shortness of breath with oxygen desaturation 2 days later. Her condition kept deteriorating with respiratory distress and severe hypoxia 6 days after infusion, and the level of interleukin-6 and interferon-gamma continued to be elevated. Chest CT showed pleural effusion and massive exudation of both lungs. Considered to have acute respiratory distress syndrome (ARDS) due to severe CRS, she was transferred to the intensive care unit (ICU). The patient was treated with tocilizumab, high-dose intravenous glucocorticoid pulses, mechanical ventilation, and sivelestat sodium for ARDS. Her symptoms were gradually relieved, and the results of laboratory tests were gradually stabilized. The patient was extubated 6 days after ICU admission and discharged from ICU a week later. Six patients were screened out with ARDS or acute respiratory failure caused by CRS after CAR-T cell therapy, whose treatments were mainly anticytokine agents combined with high-flow oxygen therapy or invasive mechanical ventilation. One of them died. ConclusionsClinicians should be alert to severe CRS during the administration of CAR-T cell. Rapid interruption of the inflammation development is the key to all treatments. If respiratory and/or circulatory dysfunction occurs, patients should be transferred to ICU in time for organ support therapy.
Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index. However, blood gas analysis is an invasive operation, and can’t continuously monitor the development of the disease. In response to the above problems, in this study, we proposed a new algorithm for identifying the severity of ARDS disease. Based on a variety of non-invasive physiological parameters of patients, combined with feature selection techniques, this paper sorts the importance of various physiological parameters. The cross-validation technique was used to evaluate the identification performance. The classification results of four supervised learning algorithms using neural network, logistic regression, AdaBoost and Bagging were compared under different feature subsets. The optimal feature subset and classification algorithm are comprehensively selected by the sensitivity, specificity, accuracy and area under curve (AUC) of different algorithms under different feature subsets. We use four supervised learning algorithms to distinguish the severity of ARDS (P/F ≤ 300). The performance of the algorithm is evaluated according to AUC. When AdaBoost uses 20 features, AUC = 0.832 1, the accuracy is 74.82%, and the optimal AUC is obtained. The performance of the algorithm is evaluated according to the number of features. When using 2 features, Bagging has AUC = 0.819 4 and the accuracy is 73.01%. Compared with traditional methods, this method has the advantage of continuously monitoring the development of patients with ARDS and providing medical staff with auxiliary diagnosis suggestions.
ObjectiveTo develop a predictive model for acute respiratory distress syndrome (ARDS) following cardiac mechanical valve replacement under cardiopulmonary bypass (CPB) using artificial intelligence algorithms, providing a novel method for early identification of high-risk ARDS patients. MethodsPatients undergoing CPB-assisted cardiac mechanical valve replacement surgery in the Department of Cardiovascular Surgery at the First Hospital of Lanzhou University from January 2023 to March 2025 were retrospectively and consecutively enrolled. Data processing and model construction were performed using Python software. Variables with missing data proportions ≥30% were excluded, while multiple imputation combined with sensitivity analysis and standardization was applied to the remaining dataset. The dataset was randomly partitioned into training (70%) and testing (30%) sets. Feature selection was conducted using the Boruta algorithm and least absolute shrinkage and selection operator regression. The synthetic minority over-sampling technique edited nearest neighbors (SMOTEEN) algorithm was applied to balance samples in the training set. Six machine learning models, including random forest, light gradient boosting machine, extreme gradient boosting, categorical boosting (CatBoost), gradient boosting decision tree, and logistic regression, were developed through 5-fold nested cross-validation for parameter optimization. Model performance was evaluated via area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, average precision, recall rate, and F1 score. The optimal model was determined based on AUC values and validated through Hosmer-Lemeshow (HL) goodness-of-fit test. Decision curve analysis was performed for all models, while SHAP algorithm was employed for feature interpretation and visualization. External validation was conducted using clinical data from patients who underwent CPB-assisted mechanical valve replacement between April 1 and October 1, 2025. ResultsA total of 352 patients were included [training set: n=246, 135 males, 111 females, aged (51.71±11.03) years; testing set: n=106, 62 males, 44 females, aged (53.27±9.67) years], with 34 (9.7%) patients developing early ARDS in ICU. Key predictors included cardioplegia duration, right atrial transverse diameter, right ventricular transverse diameter, indirect bilirubin, and rewarming time. The CatBoost model demonstrated superior performance (AUC=0.828) with HL test P=0.64. In the single-center temporal validation cohort [n=41, 25 males, 16 females, aged (52.18±10.56) years], the CatBoost model achieved AUC=0.771. ConclusionCardiac arrest duration, right atrial transverse diameter, right ventricular transverse diameter, indirect bilirubin, and rewarming time are identified as critical factors influencing postoperative ARDS development after CPB-assisted mechanical valve replacement. The CatBoost model exhibits excellent accuracy, consistency, and clinical applicability.
