Predictive score for mortality in patients with COPD exacerbations attending hospital emergency departments

Background: Limited information is available about predictors of short-term outcomes in patients with exacerbation of chronic obstructive pulmonary disease (eCOPD) attending an emergency department (ED). Such information could help stratify these patients and guide medical decision-making. The aim of this study was to develop a clinical prediction rule for short-term mortality during hospital admission or within a week after the index ED visit. Methods: This was a prospective cohort study of patients with eCOPD attending the EDs of 16 participating hospitals. Recruitment started in June 2008 and ended in September 2010. Information on possible predictor variables was recorded during the time the patient was evaluated in the ED, at the time a decision was made to admit the patient to the hospital or discharge home, and during follow-up. Main short-term outcomes were death during hospital admission or within 1 week of discharge to home from the ED, as well as at death within 1 month of the index ED visit. Multivariate logistic regression models were developed in a derivation sample and validated in a validation sample. The score was compared with other published prediction rules for patients with stable COPD. Results: In total, 2,487 patients were included in the study. Predictors of death during hospital admission, or within 1 week of discharge to home from the ED were patient age, baseline dyspnea, previous need for long-term home oxygen therapy or non-invasive mechanical ventilation, altered mental status, and use of inspiratory accessory muscles or paradoxical breathing upon ED arrival (area under the curve (AUC) = 0.85). Addition of arterial blood gas parameters (oxygen and carbon dioxide partial pressures (PO2 and PCO2)) and pH) did not improve the model. The same variables were predictors of death at 1 month (AUC = 0.85). Compared with other commonly used tools for predicting the severity of COPD in stable patients, our rule was significantly better. Conclusions: Five clinical predictors easily available in the ED, and also in the primary care setting, can be used to create a simple and easily obtained score that allows clinicians to stratify patients with eCOPD upon ED arrival and guide the medical decision-making process.

Social, economic, and health impact of the respiratory syncytial virus: a systematic search

Background: Bronchiolitis caused by the respiratory syncytial virus (RSV) and its related complications are common in infants born prematurely, with severe congenital heart disease, or bronchopulmonary dysplasia, as well as in immunosuppressed infants. There is a rich literature on the different aspects of RSV infection with a focus, for the most part, on specific risk populations. However, there is a need for a systematic global analysis of the impact of RSV infection in terms of use of resources and health impact on both children and adults. With this aim, we performed a systematic search of scientific evidence on the social, economic, and health impact of RSV infection. Methods: A systematic search of the following databases was performed: MEDLINE, EMBASE, Spanish Medical Index, MEDES-MEDicina in Spanish, Cochrane Plus Library, and Google without time limits. We selected 421 abstracts based on the 6,598 articles identified. From these abstracts, 4 RSV experts selected the most relevant articles. They selected 65 articles. After reading the full articles, 23 of their references were also selected. Finally, one more article found through a literature information alert system was included. Results: The information collected was summarized and organized into the following topics: 1. Impact on health (infections and respiratory complications, mid-to long-term lung function decline, recurrent wheezing, asthma, other complications such as otitis and rhino-conjunctivitis, and mortality; 2. Impact on resources (visits to primary care and specialists offices, emergency room visits, hospital admissions, ICU admissions, diagnostic tests, and treatments); 3. Impact on costs (direct and indirect costs); 4. Impact on quality of life; and 5. Strategies to reduce the impact (interventions on social and hygienic factors and prophylactic treatments). Conclusions: We concluded that 1. The health impact of RSV infection is relevant and goes beyond the acute episode phase; 2. The health impact of RSV infection on children is much better documented than the impact on adults; 3. Further research is needed on mid-and long-term impact of RSV infection on the adult population, especially those at high-risk; 4. There is a need for interventions aimed at reducing the impact of RSV infection by targeting health education, information, and prophylaxis in high-risk populations.

Análisis de la interferencia inducida en el electrocardiograma y señal de impedancia por compresiones torácicas sobre un maniquí.

[EN]This paper presents a project within that research field. The project consists on the development of an experimental environment comprised by a sensorized practice manikin and a management software system. Manikin model allows the simulation of cardiac arrest episodes on laboratory settings. The management software system adds the capacity to compute and analyze the characteristics of the artifact induced on the electrocardiogram and the thoracic impedance signals by chest compressions during cardiopulmonary resuscitation due to variations of the electrode-skin interface. The main reason for choosing this kind of model is the impossibility to use real people because of the risk of thoracic injuries during chest compression. Moreover, this platform could be used for training in reanimation techniques for real situations. Even laypeople with minimal training can perform cardiopulmonary resuscitation. This can reduce the response time to an emergency while the healthcare personnel arrives, which is key to improve outcomes, since with every minute the chances of survival decrease approximately 10%. It is not necessary to have medical knowledge to perform cardiopulmonary resuscitation, which could increase chances of survival for a patient with an early reanimation since In this context, this paper details the technique solution for the manikin sensorisation to acquire the electrocardiogram, the impedance signal measured between the defibrillation pads placed on the patient’s chest, the compression depth, the compression force and the acceleration experienced by the chest in the three orthogonal axes. Moreover, it is possible to inject a previously recorded electrocardiogram signal.

MW, Medical Watches

My project is a business plan about the set up of a company and the development of a new and innovative product aimed for the elders. I decide do this project when I discover that one of the more important needs that have the elders is to remember the medicines that they have to take. I thought that a good way could be through a smart watch. My watch have an only function, is a cheap device, easy to use, easy to understand and easy to set up, because the elders usually do not know to use complex electronics devices. There are other similar smart watches and other devices but do not have the necessary characteristics to be a good reminder for elders. My watch is centred to improve the life of the elders, but my product could also be useful for ill people who have to take many medicines during the day. After realizing this business plan, I have proved that my company is viable in the environment and profitable in the market.

Towards Application of One-Class Classification Methods to Medical Data

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques-Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description-using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.