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Prediction of Length of Stay Following Elective Percutaneous Coronary Intervention

There have been published risk stratification approaches to predict complications following percutaneous coronary interventions (PCI). However, a formal assessment of such approaches with respect to predicting length of stay (LOS) is lacking. Therefore, we sought to assess the performance of, an eas...

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Autores principales: Negassa, Abdissa, Monrad, E. Scott
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scholarly Research Network 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3200209/
https://www.ncbi.nlm.nih.gov/pubmed/22084771
http://dx.doi.org/10.5402/2011/714935
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author Negassa, Abdissa
Monrad, E. Scott
author_facet Negassa, Abdissa
Monrad, E. Scott
author_sort Negassa, Abdissa
collection PubMed
description There have been published risk stratification approaches to predict complications following percutaneous coronary interventions (PCI). However, a formal assessment of such approaches with respect to predicting length of stay (LOS) is lacking. Therefore, we sought to assess the performance of, an easy-to-use, tree-structured prognostic classification model in predicting LOS among patients with elective PCI. The study is based on the New York State PCI database. The model was developed on data for 1999-2000, consisting of 67,766 procedures. Validation was carried out, with respect to LOS, using data for 2001-2002, consisting of 79,545 procedures. The risk groups identified by the model exhibited a strong progressively increasing relative risk pattern of longer LOS. The predicted average LOS ranged from 3 to 9 days. The performance of this model was comparable to other published risk scores. In conclusion, the tree-structured prognostic classification is a model which can be easily applied to aid practitioners early on in their decision process regarding the need for extra resources required for the management of more complicated patients following PCI, or to justify to payors the extra costs required for the management of patients who have required extended observation and care after PCI.
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spelling pubmed-32002092011-11-14 Prediction of Length of Stay Following Elective Percutaneous Coronary Intervention Negassa, Abdissa Monrad, E. Scott ISRN Surg Research Article There have been published risk stratification approaches to predict complications following percutaneous coronary interventions (PCI). However, a formal assessment of such approaches with respect to predicting length of stay (LOS) is lacking. Therefore, we sought to assess the performance of, an easy-to-use, tree-structured prognostic classification model in predicting LOS among patients with elective PCI. The study is based on the New York State PCI database. The model was developed on data for 1999-2000, consisting of 67,766 procedures. Validation was carried out, with respect to LOS, using data for 2001-2002, consisting of 79,545 procedures. The risk groups identified by the model exhibited a strong progressively increasing relative risk pattern of longer LOS. The predicted average LOS ranged from 3 to 9 days. The performance of this model was comparable to other published risk scores. In conclusion, the tree-structured prognostic classification is a model which can be easily applied to aid practitioners early on in their decision process regarding the need for extra resources required for the management of more complicated patients following PCI, or to justify to payors the extra costs required for the management of patients who have required extended observation and care after PCI. International Scholarly Research Network 2011 2011-07-18 /pmc/articles/PMC3200209/ /pubmed/22084771 http://dx.doi.org/10.5402/2011/714935 Text en Copyright © 2011 A. Negassa and E. S. Monrad. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Negassa, Abdissa
Monrad, E. Scott
Prediction of Length of Stay Following Elective Percutaneous Coronary Intervention
title Prediction of Length of Stay Following Elective Percutaneous Coronary Intervention
title_full Prediction of Length of Stay Following Elective Percutaneous Coronary Intervention
title_fullStr Prediction of Length of Stay Following Elective Percutaneous Coronary Intervention
title_full_unstemmed Prediction of Length of Stay Following Elective Percutaneous Coronary Intervention
title_short Prediction of Length of Stay Following Elective Percutaneous Coronary Intervention
title_sort prediction of length of stay following elective percutaneous coronary intervention
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3200209/
https://www.ncbi.nlm.nih.gov/pubmed/22084771
http://dx.doi.org/10.5402/2011/714935
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