Predicting recovery at home after Ambulatory Surgery

<p>Abstract</p> <p>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 t...

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Main Authors: Ayala Guillermo, Ibáñez Maía V, Viñoles Juan
Format: Article
Language:English
Published: BMC 2011-10-01
Series:BMC Health Services Research
Online Access:http://www.biomedcentral.com/1472-6963/11/269
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spelling doaj-01ba854599534a4db313f56589c968d12020-11-25T00:33:28ZengBMCBMC Health Services Research1472-69632011-10-0111126910.1186/1472-6963-11-269Predicting recovery at home after Ambulatory SurgeryAyala GuillermoIbáñez Maía VViñoles Juan<p>Abstract</p> <p>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.</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p> http://www.biomedcentral.com/1472-6963/11/269
collection DOAJ
language English
format Article
sources DOAJ
author Ayala Guillermo
Ibáñez Maía V
Viñoles Juan
spellingShingle Ayala Guillermo
Ibáñez Maía V
Viñoles Juan
Predicting recovery at home after Ambulatory Surgery
BMC Health Services Research
author_facet Ayala Guillermo
Ibáñez Maía V
Viñoles Juan
author_sort Ayala Guillermo
title Predicting recovery at home after Ambulatory Surgery
title_short 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_sort predicting recovery at home after ambulatory surgery
publisher BMC
series BMC Health Services Research
issn 1472-6963
publishDate 2011-10-01
description <p>Abstract</p> <p>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.</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p>
url http://www.biomedcentral.com/1472-6963/11/269
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