JMIR Publications

ABSTRACT

Background:

Chronic obstructive Pulmonary Disease (COPD) is a common chronic disease. Exacerbations of COPD (eCOPD) contribute to worsening of the disease and patient´s evolution. There are some clinical prediction rules that may help to stratify patients with eCOPD by their risk of poor evolution or adverse events. The translation of these clinical prediction rules into computer applications would allow their implementation in clinical practice.

Objective:

The goal of the study was to create a computer application to predict various outcomes related to adverse events of short-term evolution in eCOPD patients attending an emergency department (ED) based on valid and reliable clinical prediction rules.

Methods:

A computer application, PrEveCOPD (Prediction of Evolution of patients with eCOPD), was created for prediction of two outcomes related to adverse events: 1) mortality during hospital admission or within a week after the ED visit; and 2) admission to an intensive care unit (ICU) or an intermediate respiratory care unit (IRCU) during the eCOPD episode. The algorithms included in the computer tool were based on clinical prediction rules previously developed and validated within the IRYSS-COPD Study. The app was developed for Windows and Android systems, using Visual Studio 2008 and Eclipse, respectively.

Results:

The computer application PrEveCOPD implements the prediction models previously developed and validated for two relevant adverse events in the short-term evolution of patients with eCOPD. The application runs into Windows and Android systems and it can be used locally or remotely as a web application. Full description of the clinical prediction rules, as well as the original references, is included in the screen. Input of the predictive variables is controlled for out of range and missing values. Language can be switched between English and Spanish. The application is available for downloading and installing in a computer, as a mobile app or to be used remotely via internet.

Conclusions:

The PrEveCOPD app shows how clinical prediction rules can be summarized into simple and easy to use tools that allow the estimation of the risk of short-term mortality and ICU or IRCU admission for patients with eCOPD. The app can be used in any computer device, including smartphones or tablets, and it can guide the clinicians to a valid stratification of patients attending the ED with eCOPD.