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Predicting recovery at home after Ambulatory Surgery

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...

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Autores principales: Viñoles, Juan, Ibáñez, Maía V, Ayala, Guillermo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3219581/
https://www.ncbi.nlm.nih.gov/pubmed/21995311
http://dx.doi.org/10.1186/1472-6963-11-269
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author Viñoles, Juan
Ibáñez, Maía V
Ayala, Guillermo
author_facet Viñoles, Juan
Ibáñez, Maía V
Ayala, Guillermo
author_sort Viñoles, Juan
collection PubMed
description 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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spelling pubmed-32195812011-11-18 Predicting recovery at home after Ambulatory Surgery Viñoles, Juan Ibáñez, Maía V Ayala, Guillermo BMC Health Serv Res Research Article 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. BioMed Central 2011-10-13 /pmc/articles/PMC3219581/ /pubmed/21995311 http://dx.doi.org/10.1186/1472-6963-11-269 Text en Copyright ©2011 Viñoles et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 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.
spellingShingle Research Article
Viñoles, Juan
Ibáñez, Maía V
Ayala, Guillermo
Predicting recovery at home after Ambulatory Surgery
title Predicting recovery at home after Ambulatory Surgery
title_full Predicting recovery at home after Ambulatory Surgery
title_fullStr Predicting recovery at home after Ambulatory Surgery
title_full_unstemmed Predicting recovery at home after Ambulatory Surgery
title_short Predicting recovery at home after Ambulatory Surgery
title_sort predicting recovery at home after ambulatory surgery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3219581/
https://www.ncbi.nlm.nih.gov/pubmed/21995311
http://dx.doi.org/10.1186/1472-6963-11-269
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