Lower body mass index is an independent predictor of mortality in older patients with acute respiratory distress syndrome.

Background: Acute respiratory distress syndrome (ARDS) is associated with high mortality. The impacts of body mass index (BMI) on the morality of older patients with ARDS remain unclear. Methods: This is a single-center cohort study which was conducted at Taichung Veterans General Hospital, Taiwan. Adult patients admitted to the ICU needing mechanical ventilation with ARDS were included for analysis. We compared the data of older patients (age ≥65 years) with those of younger patients (Age <65 years). The factors associated with in-hospital mortality of older patients were investigated. Results: This study included a total of 728 (mean age: 66 years; men: 63%) patients, and 425 (58.4%) of them aged ≥65 years. Older patients exhibited lower body mass index (BMI) (23.8 vs 25.2), higher Acute Physiology and Chronic Health Evaluation (APACHE) II scores (28.9 vs 26.3), higher Charlson Comorbidity Index (CCI) (4.0 vs 3.4), and lower Sequential Organ Failure Assessment (SOFA) scores (10.0 vs 11.1) than younger patients. Furthermore, older patients had mortality rates similar to younger patients (40.5% vs 42.9%, P = 0.542), but had longer length of stay in the ICU (17.6 vs 15.6 days, P = 0.047). For older patients, BMI <18.5 (odds ratio [OR], 2.78; 95% confidence interval [CI], 1.45-5.34), high SOFA score (OR, 1.20; 95% CI, 1.12-1.28), and moderate (OR, 1.95; 95% CI 1.20-3.14) or severe ARDS (OR, 2.30; 95% CI 1.26-4.22) were independent risk factors for mortality. Conclusions: In this cohort, critical ill older patients with ARDS had lower BMI, more comorbidities, and higher APACHE II scores than younger patients. Mortality rate was similar between older and younger patients. Low BMI, high SOFA score, and moderate or severe ARDS were independently associated with mortality in older patients with ARDS.

A combination of oxygenation and driving pressure can provide valuable information in predicting the risk of mortality in ARDS patients.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common life-threatening condition in critically ill patients. Itis also an important public health issue because it can cause substantial mortality and health care burden worldwide. The objective of this study was to investigate therisk factors that impact ARDS mortality in a medical center in Taiwan. METHODS: This was a single center, observational study thatretrospectively analyzed data from adults in 6 intensive care units (ICUs) at Taichung Veterans General Hospital in Taiwan from 1st October, 2018to30th September, 2019. Patients needing invasive mechanical ventilation and meeting the Berlin definition criteria were included for analysis. RESULTS: A total of 1,778 subjects were screened in 6 adult ICUs and 370 patients fulfilled the criteria of ARDS in the first 24 hours of the ICU admission. Among these patients, the prevalenceof ARDS was 20.8% and the overall hospital mortality rate was 42.2%. The mortality rates of mild, moderate and severe ARDS were 35.9%, 43.9% and 46.5%, respectively. In a multivariate logistic regression model, combination of driving pressure (DP) > 14cmH2O and oxygenation (P/F ratio)≤150 was an independent predictor of mortality (OR2.497, 95% CI 1.201-5.191, p = 0.014). Patients with worse oxygenation and a higher driving pressure had the highest hospital mortality rate(p<0.0001). CONCLUSIONS: ARDS is common in ICUs and the mortality rate remains high. Combining oxygenation and respiratory mechanics may better predict the outcomes of these ARDS patients.

Classifying ball trajectories in invasion sports using dynamic time warping: A basketball case study.

Comparison and classification of ball trajectories can provide insight to support coaches and players in analysing their plays or opposition plays. This is challenging due to the innate variability and uncertainty of ball trajectories in space and time. We propose a framework based on Dynamic Time Warping (DTW) to cluster, compare and characterise trajectories in relation to play outcomes. Seventy-two international women's basketball games were analysed, where features such as ball trajectory, possession time and possession outcome were recorded. DTW was used to quantify the alignment-adjusted distance between three dimensional (two spatial, one temporal) trajectories. This distance, along with final location for the play (usually the shot), was then used to cluster trajectories. These clusters supported the conventional wisdom of higher scoring rates for fast breaks, but also identified other contextual factors affecting scoring rate, including bias towards one side of the court. In addition, some high scoring rate clusters were associated with greater mean change in the direction of ball movement, supporting the notion of entropy affecting effectiveness. Coaches and other end users could use such a framework to help make better use of their time by honing in on groups of effective or problematic plays for manual video analysis, for both their team and when scouting opponent teams and suggests new predictors for machine learning to analyse and predict trajectory-based sports.

Artificial intelligence-aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs.

Objective: The aim of this study was to develop an artificial intelligence-based model to detect the presence of acute respiratory distress syndrome (ARDS) using clinical data and chest X-ray (CXR) data. Method: The transfer learning method was used to train a convolutional neural network (CNN) model with an external image dataset to extract the image features. Then, the last layer of the model was fine-tuned to determine the probability of ARDS. The clinical data were trained using three machine learning algorithms-eXtreme Gradient Boosting (XGB), random forest (RF), and logistic regression (LR)-to estimate the probability of ARDS. Finally, ensemble-weighted methods were proposed that combined the image model and the clinical data model to estimate the probability of ARDS. An analysis of the importance of clinical features was performed to explore the most important features in detecting ARDS. A gradient-weighted class activation mapping (Grad-CAM) model was used to explain what our CNN sees and understands when making a decision. Results: The proposed ensemble-weighted methods improved the performances of the ARDS classifiers (XGB + CNN, area under the curve [AUC] = 0.916; RF + CNN, AUC = 0.920; LR + CNN, AUC = 0.920; XGB + RF + LR + CNN, AUC = 0.925). In addition, the ML model using clinical data to present the top 15 important features to identify the risk factors of ARDS. Conclusion: This study developed combined machine learning models with clinical data and CXR images to detect ARDS. According to the results of the Shapley Additive exPlanations values and the Grad-CAM techniques, an explicable ARDS diagnosis model is suitable for a real-life scenario.