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.

Chronic obstructive pulmonary disease (COPD) is the leading cause of morbidity and mortality worldwide. There is evidence to support a connection between COPD and diabetes mellitus (DM), another common medical disorder. However, additional research is required to improve our knowledge of these relationships and their possible implications. In this study, we investigated the impact of DM on patient outcomes through the clinical course of COPD. We conducted a cohort study in patients from the Taiwan Longitudinal Health Insurance Database between 2000 and 2013. Patients with COPD were identified and assessed for pre-existing and incident DM. A Cox proportional hazards model was built to identify factors associated with incident DM and to explore the prognostic effects of DM on COPD patients. A propensity score method was used to match COPD patients with incident DM to controls without incident DM. Pre-existing DM was present in 332 (16%) of 2015 COPD patients who had a significantly higher hazard ratio (HR) [1.244, 95% confidence interval (CI) 1.010–1.532] for mortality than that of the COPD patients without pre-existing DM. During the 10-year follow-up period, 304 (19%) of 1568 COPD patients developed incident DM; comorbid hypertension (HR, 1.810; 95% CI, 1.363–2.403), cerebrovascular disease (HR, 1.517; 95% CI, 1.146–2.008) and coronary artery disease (HR, 1.408; 95% CI 1.089–1.820) were significant factors associated with incident DM. Survival was worse for the COPD patients with incident DM than for the matched controls without incident DM (Log-rank, p = 0.027). DM, either pre-existing or incident, was associated with worse outcomes in COPD patients. Targeted surveillance and management of DM may be important in clinical care of the COPD population.

Chronic obstructive pulmonary disease (COPD) is the leading cause of death worldwide and poses a substantial economic and social burden. Telemonitoring has been proposed as a solution to this growing problem, but its impact on patient outcome is equivocal. This randomized controlled trial aimed to investigate effectiveness of telemonitoring in improving COPD patient outcome. In total, 106 subjects were randomly assigned to the telemonitoring (n = 53) or usual care (n = 53) group. During the two months following discharge, telemonitoring group patients had to report their symptoms daily using an electronic diary. The primary outcome measure was time to first re-admission for COPD exacerbation within six months of discharge. During the follow-up period, time to first re-admission for COPD exacerbation was significantly increased in the telemonitoring group than in the usual care group (p = 0.026). Telemonitoring was also associated with a reduced number of all-cause re-admissions (0.23 vs. 0.68/patient; p = 0.002) and emergency room visits (0.36 vs. 0.91/patient; p = 0.006). In conclusion, telemonitoring intervention was associated with improved outcomes among COPD patients admitted for exacerbation in a country characterized by a small territory and high accessibility to medical services. The findings are encouraging and add further support to implementation of telemonitoring as part of COPD care.

Among patients with chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM) is a common comorbidity and is probably associated with increased systemic inflammation and worse prognosis. Metformin, with its pleiotropic anti-inflammatory and antioxidant actions, may offer theoretical benefits in COPD patients with DM. Thus, this study aimed to investigate the effects of DM and metformin use on mortality in the clinical trajectory of COPD. This was a retrospective cohort study comprising patients with spirometry-confirmed COPD and an age of ≥40 years from 2008 to 2014. The primary outcome of interest was all-cause mortality. We evaluated the effects of DM on mortality through the clinical course of COPD and we also assessed the impact of metformin use on survival of the COPD population. Among 4231 COPD patients, 556 (13%) had DM, and these patients had 1.62 times higher hazards of 2-year mortality than those without DM (95% confidence interval [CI], 1.15–2.28) after adjusting for age, gender, COPD stage, comorbidities and prior COPD hospitalization. Over a 2-year period, metformin users had a significantly lower risk of death (hazard ratio, 0.46; 95% CI, 0.23–0.92) compared with non-metformin users in patients with coexistent COPD and DM. Moreover, metformin users had similar survival to COPD patients without DM. This study shows that DM is associated with an increased risk of death in COPD patients and metformin use seems to mitigate the hazard. Our findings suggest a potential role of metformin in the management of DM in COPD.

A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection.

To increase the efficiency in the emergency room, the goal of this research is to implement a mobile-based indoor positioning system using mobile applications (APP) with the iBeacon solution based on the Bluetooth Low Energy (BLE) technology. We use the Received Signal Strength (RSS) based localization method to estimate the patients' locations. Our positioning algorithm achieves 97.22% (95% Confidence Interval = 95.90% - 98.55%) accuracy of classification. As the result, our mechanism is reliable enough to satisfy the need for medical staff to track the locations of their patients.