Background: The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with an accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality. Objective: The aim of this study was to develop a prediction system using lifestyle data, environmental factors, and patient symptoms for the early detection of AECOPD in the upcoming 7 days. Methods: This prospective study was performed at National Taiwan University Hospital. Patients with COPD that did not have a pacemaker and were not pregnant were invited for enrollment. Data on lifestyle, temperature, humidity, and fine particulate matter were collected using wearable devices (Fitbit Versa), a home air quality–sensing device (EDIMAX Airbox), and a smartphone app. AECOPD episodes were evaluated via standardized questionnaires. With these input features, we evaluated the prediction performance of machine learning models, including random forest, decision trees, k-nearest neighbor, linear discriminant analysis, and adaptive boosting, and a deep neural network model. Results: The continuous real-time monitoring of lifestyle and indoor environment factors was implemented by integrating home air quality–sensing devices, a smartphone app, and wearable devices. All data from 67 COPD patients were collected prospectively during a mean 4-month follow-up period, resulting in the detection of 25 AECOPD episodes. For 7-day AECOPD prediction, the proposed AECOPD predictive model achieved an accuracy of 92.1%, sensitivity of 94%, and specificity of 90.4%. Receiver operating characteristic curve analysis showed that the area under the curve of the model in predicting AECOPD was greater than 0.9. The most important variables in the model were daily steps walked, stairs climbed, and daily distance moved. Conclusions: Using wearable devices, home air quality–sensing devices, a smartphone app, and supervised prediction algorithms, we achieved excellent power to predict whether a patient would experience AECOPD within the upcoming 7 days. The AECOPD prediction system provided an effective way to collect lifestyle and environmental data, and yielded reliable predictions of future AECOPD events. Compared with previous studies, we have comprehensively improved the performance of the AECOPD prediction model by adding objective lifestyle and environmental data. This model could yield more accurate prediction results for COPD patients than using only questionnaire data.

Background Tuberculous pleurisy is traditionally indicated by extreme lymphocytosis in pleural fluid and low yield of effusion culture. However, there is considerable inconsistency among previous study results. In addition, these data should be updated due to early effusion studies and advances in culture methods. Methods From January 2004 to June 2009, patients with tuberculous pleurisy were retrospectively identified from the mycobacteriology laboratories and the pathology and tuberculosis registration databases of two hospitals in Taiwan where tuberculosis is endemic. Pleural fluid characteristics and yields of mycobacterial cultures using liquid media were evaluated. Results A total of 382 patients with tuberculous pleurisy were identified. The median lymphocyte percentage of total cells in pleural fluids was 84% (IQR 64–95%) and 17% of cases had a lymphocyte percentage of <50%. The lymphocyte percentage was negatively associated with the probability of a positive effusion culture (OR 0.97; 95% CI 0.96 to 0.99). The diagnostic yields were 63% for effusion culture, 48% for sputum culture, 79% for the combination of effusion and sputum cultures, and 74% for histological examination of pleural biopsy specimens. Conclusion The degree of lymphocyte predominance in tuberculous pleurisy was lower than was previously thought. The lymphocyte percentage in pleural fluid was negatively associated with the probability of a positive effusion culture. With the implementation of a liquid culture method, the sensitivity of effusion culture was much higher than has been previously reported, and the combination of effusion and sputum cultures provided a good diagnostic yield.

Introduction: The effectiveness of corticosteroid therapy on the mortality of acute respiratory distress syndrome (ARDS) remains under debate. We aimed to explore the grounds for the inconsistent results in previous studies and update the evidence. Methods: We searched MEDLINE, Cochrane Central Register of Controlled Trials and Web of Science up to December 2013. Eligible studies included randomized clinical trials (RCTs) and cohort studies that reported mortality and that had corticosteroid nonusers for comparison. The effect of corticosteroids on ARDS mortality was assessed by relative risk (RR) and risk difference (RD) for ICU, hospital, and 60-day mortality using a random-effects model. Results: Eight RCTs and 10 cohort studies were included for analysis. In RCTs, corticosteroids had a possible but statistically insignificant effect on ICU mortality (RD, −0.28; 95% confidence interval (CI), −0.53 to −0.03 and RR, 0.55; 95% CI, 0.24 to 1.25) but no effect on 60-day mortality (RD, −0.01; 95% CI, −0.12 to 0.10 and RR, 0.97; 95% CI, 0.75 to 1.26). In cohort studies, corticosteroids had no effect on ICU mortality (RR, 1.05; 95% CI, 0.74 to 1.49) but non-significantly increased 60-day mortality (RR, 1.30; 95% CI, 0.96 to 1.78). In the subgroup analysis by ARDS etiology, corticosteroids significantly increased mortality in influenza-related ARDS (three cohort studies, RR, 2.45, 95% CI, 1.40 to 4.27). Conclusions: The effects of corticosteroids on the mortality of ARDS differed by duration of outcome measures and etiologies. Corticosteroids did not improve longer-term outcomes and may cause harm in certain subgroups. Current data do not support routine use of corticosteroids in ARDS. More clinical trials are needed to specify the favorable and unfavorable subgroups for corticosteroid therapy.

Natural history of chronic obstructive pulmonary disease (COPD) is punctuated by exacerbations; however, little is known about prognosis of the first-ever COPD exacerbation and variables predicting its outcomes. A population-based cohort study among COPD patients with their first-ever exacerbations requiring hospitalizations was conducted. Main outcomes were in-hospital mortality and one-year mortality after discharge. Demographics, comorbidities, medications and in-hospital events were obtained to explore outcome predictors. The cohort comprised 4204 hospitalized COPD patients, of whom 175 (4%) died during the hospitalization. In-hospital mortality was related to higher age (odds ratio [OR]: 1.05 per year; 95% confidence interval [CI]: 1.03–1.06) and Charlson comorbidity index score (OR: 1.08 per point; 95% CI: 1.01–1.15); angiotensin II receptor blockers (OR: 0.61; 95% CI: 0.38–0.98) and β blockers (OR: 0.63; 95% CI: 0.41–0.95) conferred a survival benefit. At one year after discharge, 22% (871/4029) of hospital survivors were dead. On multivariate Cox regression analysis, age and Charlson comorbidity index remained independent predictors of one-year mortality. Longer hospital stay (hazard ratio [HR] 1.01 per day; 95% CI: 1.01–1.01) and ICU admission (HR: 1.33; 95% CI: 1.03–1.73) during the hospitalization were associated with higher mortality risks. Prescription of β blockers (HR: 0.79; 95% CI: 0.67–0.93) and statins (HR: 0.66; 95% CI: 0.47–0.91) on hospital discharge were protective against one-year mortality. Even the first-ever severe COPD exacerbation signifies poor prognosis in COPD patients. Comorbidities play a crucial role in determining outcomes and should be carefully assessed. Angiotensin II receptor blockers, β blockers and statins may, in theory, have dual cardiopulmonary protective properties and probably alter prognosis of COPD patients. Nevertheless, the limitations inherent to a claims database study, such as the diagnostic accuracy of COPD and its exacerbation, should be born in mind.