The cuff-leak test was widely used for the prediction of post-extubation stridor, but controversial results limit its clinical application. The current study used real-time ultrasonography to evaluate the air-leak and hypothesised that the air-column width, measured by ultrasonography, may be correlated to the development of post-extubation stridor.From June 1, 2001 to March 1, 2002, a total of 51 planned extubations in 51 consecutively intubated patients were included. All of the patients received ultrasonographical examinations of their vocal cords and larynx in addition to an air-column width measurement within 24 h prior to extubation.The overall post-extubation stridor rate was 7.8%. The air-leak volume presented as median (interquartile range) were 300 (350) mL and 25 (20) mL, respectively, for the nonstridor and stridor groups. The air-column width during cuff deflation was 6.4 (2) mm and 4.5 (0.8) mm, respectively. They were found to be statistically significant.In conclusion, the authors demonstrated that laryngeal ultrasonography could be a reliable, noninvasive method, in the evaluation of vocal cords, laryngeal morphology and the ease of airflow, which passed through vocal cords or subglottic area due to laryngeal oedema. The air-column width during cuff deflation was a potential predictor of post-extubation stridor.

There has been increasing evidence that angiotensin II may play an important role in the pathogenesis and in the evolution of acute lung injury. It was therefore hypothesized that polymorphisms of the angiotensin-converting enzyme gene affects the risk and outcome of acute respiratory distress syndrome (ARDS). Prospective, observational study. The ARDS group consisted of 101 patients treated at the medical intensive care unit; the control groups consisted of 138 “at-risk” patients treated at the medical intensive care unit due to acute respiratory failure but did not meet the ARDS criteria throughout the hospital course, and 210 non–at-risk subjects. None. The ARDS patients and control subjects were genotyped for the insertion/deletion (I/D) polymorphism of the angiotensin-converting enzyme gene. Association of the polymorphism and the risk and the outcome of ARDS was analyzed. There was no significant difference in the frequencies of the genotypes between the ARDS, at-risk, and non–at-risk groups. The 28-day mortality rates were significantly different between the three angiotensin-converting enzyme genotypes (42%, 65%, and 75% for II, ID, and DD, respectively; p = .036). Survival analysis showed that the II genotype favorably affected 28-day survival (hazard ratio, 0.46; 95% confidence interval, 0.26–0.81; p = .007), whereas ARDS caused by hospital-acquired pneumonia had a negative effect (hazard ratio, 2.34; 95% confidence interval, 1.25–4.40; p = .008). The II genotype (hazard ratio, 0.53; 95% confidence interval, 0.32–0.87; p = .012) and ARDS caused by hospital-acquired pneumonia (hazard ratio, 2.13; 95% confidence interval, 1.24–3.68; p = .006) were also significant prognostic factors for the in-hospital mortality. The angiotensin-converting enzyme I/D polymorphism is a significant prognostic factor for the outcome of ARDS. Patients with the II genotype have a significantly better chance of survival. This study did not show an increased risk for ARDS in Chinese patients with the D allele.

The aim of this study was to identify sonographic predictors of patient outcomes or need for surgical intervention of acute thoracic empyema. All patients with a clinical diagnosis of thoracic empyem...

Objectives Current mortality prediction models used in the intensive care unit (ICU) have a limited role for specific diseases such as influenza, and we aimed to establish an explainable machine learning (ML) model for predicting mortality in critically ill influenza patients using a real-world severe influenza data set. Study design A cross-sectional retrospective multicentre study in Taiwan Setting Eight medical centres in Taiwan. Participants A total of 336 patients requiring ICU-admission for virology-proven influenza at eight hospitals during an influenza epidemic between October 2015 and March 2016. Primary and secondary outcome measures We employed extreme gradient boosting (XGBoost) to establish the prediction model, compared the performance with logistic regression (LR) and random forest (RF), demonstrated the feature importance categorised by clinical domains, and used SHapley Additive exPlanations (SHAP) for visualised interpretation. Results The data set contained 76 features of the 336 patients with severe influenza. The severity was apparently high, as shown by the high Acute Physiology and Chronic Health Evaluation II score (22, 17 to 29) and pneumonia severity index score (118, 88 to 151). XGBoost model (area under the curve (AUC): 0.842; 95% CI 0.749 to 0.928) outperformed RF (AUC: 0.809; 95% CI 0.629 to 0.891) and LR (AUC: 0.701; 95% CI 0.573 to 0.825) for predicting 30-day mortality. To give clinicians an intuitive understanding of feature exploitation, we stratified features by the clinical domain. The cumulative feature importance in the fluid balance domain, ventilation domain, laboratory data domain, demographic and symptom domain, management domain and severity score domain was 0.253, 0.113, 0.177, 0.140, 0.152 and 0.165, respectively. We further used SHAP plots to illustrate associations between features and 30-day mortality in critically ill influenza patients. Conclusions We used a real-world data set and applied an ML approach, mainly XGBoost, to establish a practical and explainable mortality prediction model in critically ill influenza patients.

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide, with approximately 70% to 80% of adults with COPD being undiagnosed. Patients with undiagnosed COPD are at increased risk of poor outcomes and a worsened quality of life, making early detection a crucial strategy to mitigate the impact of COPD and reduce the burden on healthcare systems. In the past decade, increased interest has been focused on the development of effective strategies and instrument for COPD early detection. However, identifying undiagnosed cases of COPD is still challenging. Both screening and case-finding approaches have been adopted to identify undiagnosed COPD, with case-finding being recommended by the 2023 Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline and the updated United States Preventive Services Task Force (USPTF) recommendation. Nonetheless, the approaches, criteria, and instruments used for early detection of COPD are varied. However, advances in the taxonomy and risk factors of COPD are continuously being investigated. It is important to continuously assess the current state of knowledge on COPD early detection, given the challenges associated with identifying undiagnosed COPD. This review aims to highlight recent advances in early detection of COPD. To discuss the current challenge and opportunity in COPD early detection, providing an overview of existing literature on COPD case-finding strategies, including the approaches, criteria for subjects, and instruments. The review also summarizes the current progress in COPD case-findings and proposes a COPD case-finding flowchart as an efficient method for identifying at risk COPD patients.