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Title: [Performance and optimisation of a trigger tool for the detection of adverse events in hospitalised adult patients].
Other Titles: Rendimiento y optimización de la herramienta trigger en la detección de eventos adversos en pacientes adultos hospitalizados.
Authors: Guzmán Ruiz, Óscar
Pérez Lázaro, Juan José
Ruiz López, Pedro
Keywords: Adverse effect;Adverse event;Efecto adverso;Error médico;Evento adverso;Medical error;Patient safety;Seguridad del paciente
metadata.dc.subject.mesh: Adult
Aged, 80 and over
Cross-Sectional Studies
Patient Safety
ROC Curve
Risk Management
Sampling Studies
Issue Date: 22-May-2017
Abstract: To characterise the performance of the triggers used in the detection of adverse events (AE) of hospitalised adult patients and to define a simplified panel of triggers to facilitate the detection of AE. Cross-sectional study of charts of patients from a service of internal medicine to detect EA through systematic review of the charts and identification of triggers (clinical event often related to AE), determining if there was AE as the context in which it appeared the trigger. Once the EA was detected, we proceeded to the characterization of the triggers that detected it. Logistic regression was applied to select the triggers with greater AE detection capability. A total of 291 charts were reviewed, with a total of 562 triggers in 103 patients, of which 163 were involved in detecting an AE. The triggers that detected the most AE were "A.1. Pressure ulcer" (9.82%), "B.5. Laxative or enema" (8.59%), "A.8. Agitation" (8.59%), "A.9. Over-sedation" (7.98%), "A.7. Haemorrhage" (6.75%) and "B.4. Antipsychotic" (6.75%). A simplified model was obtained using logistic regression, and included the variable "Number of drugs" and the triggers "Over-sedation", "Urinary catheterisation", "Readmission in 30 days", "Laxative or enema" and "Abrupt medication stop". This model showed a probability of 81% to correctly classify charts with EA or without EA (p A high number of triggers were associated with AE. The summary model is capable of detecting a large amount of AE, with a minimum of elements.
metadata.dc.identifier.doi: 10.1016/j.gaceta.2017.01.014
Appears in Collections:Producción 2020

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