Predicting recovery at home after Ambulatory Surgery
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Otros documentos de la autoría: Viñoles, Juan; Ibáñez Gual, Maria Victoria; Ayala Gallego, Guillermo
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Título
Predicting recovery at home after Ambulatory SurgeryFecha de publicación
2011-10Editor
BioMed CentralISSN
1472-6963; 1472-6963Cita bibliográfica
Viñoles et al.: Predicting recovery at home after Ambulatory Surgery. BMC Health Services Research 2011 11:269Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://bmchealthservres.biomedcentral.com/articles/10.1186/1472-6963-11-269Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
The correct implementation of Ambulatory Surgery must be accompanied by an accurate monitoring of the
patient post-discharge state. We fit different statistical models to predict the first hours postoperative status ... [+]
The correct implementation of Ambulatory Surgery must be accompanied by an accurate monitoring of the
patient post-discharge state. We fit different statistical models to predict the first hours postoperative status of a
discharged patient. We will also be able to predict, for any discharged patient, the probability of needing a closer
follow-up, or of having a normal progress at home.
Background: The status of a discharged patient is predicted during the first 48 hours after discharge by using
variables routinely used in Ambulatory Surgery. The models fitted will provide the physician with an insight into
the post-discharge progress. These models will provide valuable information to assist in educating the patient and
their carers about what to expect after discharge as well as to improve their overall level of satisfaction.
Methods: A total of 922 patients from the Ambulatory Surgery Unit of the Dr. Peset University Hospital (Valencia,
Spain) were selected for this study. Their post-discharge status was evaluated through a phone questionnaire. We
pretend to predict four variables which were self-reported via phone interviews with the discharged patient: sleep,
pain, oral tolerance of fluid/food and bleeding status. A fifth variable called phone score will be built as the sum of
these four ordinal variables. The number of phone interviews varies between patients, depending on the evolution.
The proportional odds model was used. The predictors were age, sex, ASA status, surgical time, discharge time,
type of anaesthesia, surgical specialty and ambulatory surgical incapacity (ASI). This last variable reflects, before the
operation, the state of incapacity and severity of symptoms in the discharged patient.
Results: Age, ambulatory surgical incapacity and the surgical specialty are significant to explain the level of pain at
the first call. For the first two phone calls, ambulatory surgical incapacity is significant as a predictor for all
responses except for sleep at the first call.
Conclusions: The variable ambulatory surgical incapacity proved to be a good predictor of the patient’s status at
home. These predictions could be used to assist in educating patients and their carers about what to expect after
discharge, as well as to improve their overall level of satisfaction. [-]
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BMC Health Services Research, 2011, 11:269Derechos de acceso
info:eu-repo/semantics/openAccess
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.