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A Computer Application to Predict Adverse Events in the Short-Term Evolution of Patients With Exacerbation of Chronic Obstructive Pulmonary Disease.
Arostegui, Inmaculada; Legarreta, María José; Barrio, Irantzu; Esteban, Cristobal; Garcia-Gutierrez, Susana; Aguirre, Urko; Quintana, José María.
Afiliación
  • Arostegui I; Departamento de Matemática Aplicada y Estadística e Investigación Operativa, The University of the Basque Country UPV/EHU, Leioa, Spain.
  • Legarreta MJ; Research Institute, Basque Center for Applied Mathematics, Bilbao, Spain.
  • Barrio I; Red de Investigación en Servicios Sanitarios en Enfermedades Crónicas, Galdakao, Spain.
  • Esteban C; Departamento de Matemática Aplicada y Estadística e Investigación Operativa, The University of the Basque Country UPV/EHU, Leioa, Spain.
  • Garcia-Gutierrez S; Unidad de Epidemiología Clínica, Hospital Galdakao, Galdakao, Spain.
  • Aguirre U; Departamento de Matemática Aplicada y Estadística e Investigación Operativa, The University of the Basque Country UPV/EHU, Leioa, Spain.
  • Quintana JM; Red de Investigación en Servicios Sanitarios en Enfermedades Crónicas, Galdakao, Spain.
JMIR Med Inform ; 7(2): e10773, 2019 04 17.
Article en En | MEDLINE | ID: mdl-30994471
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common chronic disease. Exacerbations of COPD (eCOPD) contribute to the worsening of the disease and the 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 this 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, Prediction of Evolution of patients with eCOPD (PrEveCOPD), was created to predict 2 outcomes related to adverse events: (1) mortality during hospital admission or within a week after an 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 Investigación en Resultados y Servicios de Salud COPD study. The app was developed for Windows and Android systems, using Visual Studio 2008 and Eclipse, respectively. RESULTS: The PrEveCOPD computer application implements the prediction models previously developed and validated for 2 relevant adverse events in the short-term evolution of patients with eCOPD. The application runs under 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 on 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 on 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, which allow for the estimation of the risk of short-term mortality and ICU or IRCU admission for patients with eCOPD. The app can be used on any computer device, including mobile phones or tablets, and it can guide the clinicians to a valid stratification of patients attending the ED with eCOPD. TRIAL REGISTRATION: ClinicalTrials.gov NCT00102401; https://clinicaltrials.gov/ct2/show/results/NCT02434536 (Archived by WebCite at http://www.webcitation.org/76iwTxYuA). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/1472-6963-11-322.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: JMIR Med Inform Año: 2019 Tipo del documento: Article País de afiliación: España Pais de publicación: Canadá

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: JMIR Med Inform Año: 2019 Tipo del documento: Article País de afiliación: España Pais de publicación: Canadá