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.
Lung injury could be classified as acute and chronic injuries, such as acute respiratory distress syndrome and chronic obstructive pulmonary disease. Lung function recovery mainly depends on inflammation adjusting, lung and airway remodeling, endogenous stem cell proliferation and differentiation, and tissue repair. The principles of clinical therapy include inhibition of inflammation, balancing coagulation and fibrinolysis, and protective lung ventilation for acute lung injury; while reduction of hyper-secretion, bronchodilation, adjusting airway mucosal inflammation and immunity, as well as improving airway remodeling for chronic obstructive pulmonary disease. The functional recovery of lung and airway depends on endogenous stem cell proliferation and repair. The purpose of clinical treatment is to provide assistance for lung and airway repair besides pathophysiological improvement.
Objective Exploring the correlation between intravesical pressure (IP) and diaphragm excursion (DE) in patients with severe acute pancreatitis (SAP) and acute respiratory distress syndrome (ARDS), and evaluating its predictive value for weaning outcomes. Methods A retrospective analysis was conducted on the clinical data of 144 SAP patients with ARDS admitted between 2020 and 2023. By collecting the outcome of weaning, collect data on gender, age, acute physiology and chronic health score II (APACHE II), oxygenation index, and IP and DE before weaning and extubation for all patients. Based on weaning outcomes, divide patients into successful and failed groups, and compare the differences in various indicators between the two groups; Use binary logistic regression to analyze whether IP and DE are risk factors affecting weaning in SAP patients with ARDS, and use Pearson correlation analysis to examine the correlation between IP and DE; Use receiver operating characteristic curve (ROC curve) to analyze the predictive value of IP and DE on weaning outcomes in SAP patients with ARDS. ResultsA total of 144 SAP patients with ARDS were included, of which 108 were successfully weaned and 36 were unsuccessful. There were no statistically significant differences in gender, age, and APACHE II scores between the successful and failed groups (males: 62.96% (68/108) compared to 69.44% (25/36), age (years): 41.91 ± 8.14 compared to 42.42 ± 6.22, APACHE II score (points): 18.28 ± 2.22 compared to 18.97 ± 1.83, P>0.05). The IP of the successful group was significantly lower than that of the failed group, and the DE was significantly higher than that of the failed group [IP (mmHg): 18.45 ± 3.76 compared to 23.92 ± 5.65, DE (mm): 16.18 ± 4.23 compared to 12.28 ± 4.44, all P<0.05]. All patients showed a significant negative correlation between IP and DE (r=–0.457, P<0.001). ROC curve analysis showed that the area under the curve (AUC) of IP predicting the withdrawal outcome of SAP patients with ARDS was 0.805, with a 95% confidence interval (95%CI) of 0.724-0.885 and P<0.001. When the cutoff value was 19.5 mmHg, the sensitivity was 91.57% and the specificity was 47.54%; The AUC for predicting the withdrawal outcome of SAP patients with ARDS by DE was 0.738, with a 95%CI of 0.641-0.834 and P<0.001. When the cutoff value was 11.5 points, the sensitivity was 84.82% and the specificity was 59.38%. Conclusions There is a significant negative correlation between IP and DE in SAP combined with ARDS patients, and both have certain predictive value for weaning outcomes.
ObjectiveTo investigate the independent risk factors associated with postoperative acute respiratory distress syndrome in patients undergoing type A aortic dissection surgery.MethodsThe clinical data of 147 patients who underwent acute type A aortic dissection surgery in the First Affiliated Hospital of Anhui Medical University from 2015 to 2019 were retrospectively analyzed. There were 110 males at age of 51.9±10.1 years and 37 females at age of 54.3±11.1 years. According to whether the patients developed ARDS after surgery, all of the patients were divided into a ARDS group or a non-ARDS group. Logistic regress analysis was utilized to establish the predictive mode to identify the independent risk factors related to ARDS.ResultsOf the patients, 25 developed postoperative ARDS. Among them, 5 patients were mild ARDS, 13 patients were moderate, and 7 patients were severe ARDS. Multivariate logistic regression analysis showed that deep hypothermic circulatory arrest time [odds ratio (OR)=1.067, 95% confidence interval (CI) 1.014-1.124, P=0.013], cardiopulmonary bypass time (OR=1.012, 95%CI 1.001-1.022, P=0.027) and perioperative plasma input (OR=1.001, 95%CI 1.000-1.002, P=0.011) were independently associated with ARDS in patients undergoing acute A aortic dissection surgery. Receiver operating characteristic (ROC) curve analysis demonstrated a good discrimination ability of the logistic regression model, with an area under the curve of 0.835 (95%CI 0.740-0.929, P=0.000).ConclusionDuration of deep hypothermic circulatory arrest, cardiopulmonary bypass time and perioperative plasma are independent risk factors for postoperative ARDS in patients undergoing type A aortic dissection surgery.
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.
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.
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.
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.
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.
