title: [Detecting adverse drug events during the hospital stay].
abstract: BACKGROUND
  The principal objective was to determine the incidence rate of adverse drug events (ADEs) in hospitalised patients and evaluate the event prevention percentage.
  
  
  METHODS
  Multi-centre, prospective observational study lasting four months, performed in five hospitals providing different levels of care. We included all adult patients who were admitted to one of the selected centres for longer than 48 hours and who required pharmacological treatment. ADEs were identified by direct observation and the use of previously defined alarm signals. The Karch-Lasagna scale was used to determine the causality relationship, and the Schumock and Thornton questionnaire adapted by Otero was used to evaluate ADE preventability. Preventable drug-induced adverse events were classified according to the taxonomy that the Ruiz-Jarabo 2000 group defined, and coordinated by ISMP-Spain.
  
  
  RESULTS
  We included 1,550 patients, 159 of whom experienced at least one ADE (10.3 %). The preventability percentage was 51.6 %, which represented 5.3 % of the total sample. The endocrine system (34.8 %) and the cardiovascular system (20.7 %) were the most affected by preventable ADEs. Antibiotics were responsible for 16.5 % of all ADEs. 9.3 % of all preventable ADEs were triggered by use of opiates. The vast majority of preventable ADEs (36.3 %) resulted from omitting a necessary medication. Only 4.4 % of preventable ADEs are considered to be serious.
  
  
  CONCLUSIONS
  There is a high incidence rate of ADEs during patients' hospital stay (10.3 %), and half of them (51.6 %) could have been prevented. Implementation of an automatic alarm system and certain best practices for problem spots along the care circuit will help detect and avoid preventable ADEs.
authors.affiliation: Servicio de Farmacia, Hospital Clínic, Barcelona, España.
authors.forename: C
authors.initials: C
authors.lastname: Berga Culleré
authors.forename: M Q
authors.initials: MQ
authors.lastname: Gorgas Torner
authors.forename: J
authors.initials: J
authors.lastname: Altimiras Ruiz
authors.forename: M
authors.initials: M
authors.lastname: Tuset Creus
authors.forename: M
authors.initials: M
authors.lastname: Besalduch Martín
authors.forename: M
authors.initials: M
authors.lastname: Capdevila Sunyer
authors.forename: M
authors.initials: M
authors.lastname: Torres Gubert
authors.forename: M T
authors.initials: MT
authors.lastname: Casajoana Cortinas
authors.forename: E
authors.initials: E
authors.lastname: Baró Sabaté
authors.forename: J R
authors.initials: JR
authors.lastname: Fernández Solà
authors.forename: A
authors.initials: A
authors.lastname: Moron i Besolí
authors.forename: E
authors.initials: E
authors.lastname: Odena Estradé
authors.forename: J
authors.initials: J
authors.lastname: Serrais Benavente
authors.forename: M T
authors.initials: MT
authors.lastname: Vitales Farrero
authors.forename: C
authors.initials: C
authors.lastname: Codina Jané
mesh_terms: D000328:Adult; D016907:Adverse Drug Reaction Reporting Systems; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015984:Causality; D056902:Clinical Alarms; D064420:Drug-Related Side Effects and Adverse Reactions / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology* / Q000517:prevention & control; D005260:Female; D006760:Hospitalization; D006801:Humans; D015994:Incidence; D008297:Male; D008508:Medication Errors / Q000706:statistics & numerical data; D008875:Middle Aged; D004364:Pharmaceutical Preparations / Q000145:classification; D011446:Prospective Studies; D012307:Risk Factors; D013030:Spain / Q000453:epidemiology; D011795:Surveys and Questionnaires
chemical_list: D004364:Pharmaceutical Preparations