At present, there is no universal early warning system implemented in all Basque hospitals, but there are previous experiences, sometimes based on models generated in other health systems. In this project we intend to provide a robust model, based on the analysis of patient data from three Basque hospitals, i.e. generated in our population. A three-phase study has been designed: 1. st phase: Derivation of the predictive model by means of a reprospective cohort study in which patients hospitalised at the Galdakao-Usansolo Hospital, Donostia University Hospital and Araba University Hospital will be recruited. 2. nd phase: Creation of an alarm system based on the probability of risk of clinical deterioration and implementation of the system in the electronic medical record (EHR) of the HGU, in the form of an "Action Guide". 3. rd phase: The model will be validated by comparing the percentages of clinical deterioration by means of a quasi-experimental intervention study, comparing the results of the HGU hospital where the system will be implemented, before and after the intervention and, on the other hand, with those of Hospital Universitario Donostia (HUD) and Hospital Universitario de Araba (HUA), where normal clinical practice will be followed, with an early warning system based on vital signs in HUD and clinical criteria in HUA. Sociodemographic and clinical variables will be collected (patient's condition on arrival on the ward, main diagnosis, comorbidities, prescribed treatments and procedures performed during hospitalisation and prior to the onset of deterioration) and laboratory parameters. This information will be extracted from the osabide global data exploitation system, Oracle Business Intelligence, and the laboratory data will be extracted from the information systems of the clinical laboratories of the participating centres. Logistic regression models will be created with the dependent variable being clinical deterioration (cardiorespiratory arrest, death, admission to intensive care units) on a database of 10000 hospitalised patients. For external validation, at least 8000 admissions will be prospectively evaluated and multilevel modelling will be performed to see the influence of centre membership on the outcome variable. Confounding will be controlled for using propensity-score techniques.
Age range
18 Years
Sex
ALL
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Clinical deterioration
Timeframe: at least 24 hours after admission