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Readmission to Medical Intensive Care Units: Risk Factors and Prediction

PURPOSE: The objectives of this study were to find factors related to medical intensive care unit (ICU) readmission and to develop a prediction index for determining patients who are likely to be readmitted to medical ICUs. MATERIALS AND METHODS: We performed a retrospective cohort study of 343 cons...

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Autores principales: Jo, Yong Suk, Lee, Yeon Joo, Park, Jong Sun, Yoon, Ho Il, Lee, Jae Ho, Lee, Choon-Taek, Cho, Young-Jae
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Yonsei University College of Medicine 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4329370/
https://www.ncbi.nlm.nih.gov/pubmed/25684007
http://dx.doi.org/10.3349/ymj.2015.56.2.543
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author Jo, Yong Suk
Lee, Yeon Joo
Park, Jong Sun
Yoon, Ho Il
Lee, Jae Ho
Lee, Choon-Taek
Cho, Young-Jae
author_facet Jo, Yong Suk
Lee, Yeon Joo
Park, Jong Sun
Yoon, Ho Il
Lee, Jae Ho
Lee, Choon-Taek
Cho, Young-Jae
author_sort Jo, Yong Suk
collection PubMed
description PURPOSE: The objectives of this study were to find factors related to medical intensive care unit (ICU) readmission and to develop a prediction index for determining patients who are likely to be readmitted to medical ICUs. MATERIALS AND METHODS: We performed a retrospective cohort study of 343 consecutive patients who were admitted to the medical ICU of a single medical center from January 1, 2008 to December 31, 2012. We analyzed a broad range of patients' characteristics on the day of admission, extubation, and discharge from the ICU. RESULTS: Of the 343 patients discharged from the ICU alive, 33 (9.6%) were readmitted to the ICU unexpectedly. Using logistic regression analysis, the verified factors associated with increased risk of ICU readmission were male sex [odds ratio (OR) 3.17, 95% confidence interval (CI) 1.29-8.48], history of diabetes mellitus (OR 3.03, 95% CI 1.29-7.09), application of continuous renal replacement therapy during ICU stay (OR 2.78, 95% CI 0.85-9.09), white blood cell count on the day of extubation (OR 1.13, 95% CI 1.07-1.21), and heart rate just before ICU discharge (OR 1.03, 95% CI 1.01-1.06). We established a prediction index for ICU readmission using the five verified risk factors (area under the curve, 0.76, 95% CI 0.66-0.86). CONCLUSION: By using specific risk factors associated with increased readmission to the ICU, a numerical index could be established as an estimation tool to predict the risk of ICU readmission.
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spelling pubmed-43293702015-03-01 Readmission to Medical Intensive Care Units: Risk Factors and Prediction Jo, Yong Suk Lee, Yeon Joo Park, Jong Sun Yoon, Ho Il Lee, Jae Ho Lee, Choon-Taek Cho, Young-Jae Yonsei Med J Original Article PURPOSE: The objectives of this study were to find factors related to medical intensive care unit (ICU) readmission and to develop a prediction index for determining patients who are likely to be readmitted to medical ICUs. MATERIALS AND METHODS: We performed a retrospective cohort study of 343 consecutive patients who were admitted to the medical ICU of a single medical center from January 1, 2008 to December 31, 2012. We analyzed a broad range of patients' characteristics on the day of admission, extubation, and discharge from the ICU. RESULTS: Of the 343 patients discharged from the ICU alive, 33 (9.6%) were readmitted to the ICU unexpectedly. Using logistic regression analysis, the verified factors associated with increased risk of ICU readmission were male sex [odds ratio (OR) 3.17, 95% confidence interval (CI) 1.29-8.48], history of diabetes mellitus (OR 3.03, 95% CI 1.29-7.09), application of continuous renal replacement therapy during ICU stay (OR 2.78, 95% CI 0.85-9.09), white blood cell count on the day of extubation (OR 1.13, 95% CI 1.07-1.21), and heart rate just before ICU discharge (OR 1.03, 95% CI 1.01-1.06). We established a prediction index for ICU readmission using the five verified risk factors (area under the curve, 0.76, 95% CI 0.66-0.86). CONCLUSION: By using specific risk factors associated with increased readmission to the ICU, a numerical index could be established as an estimation tool to predict the risk of ICU readmission. Yonsei University College of Medicine 2015-03-01 2015-02-09 /pmc/articles/PMC4329370/ /pubmed/25684007 http://dx.doi.org/10.3349/ymj.2015.56.2.543 Text en © Copyright: Yonsei University College of Medicine 2015 http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Jo, Yong Suk
Lee, Yeon Joo
Park, Jong Sun
Yoon, Ho Il
Lee, Jae Ho
Lee, Choon-Taek
Cho, Young-Jae
Readmission to Medical Intensive Care Units: Risk Factors and Prediction
title Readmission to Medical Intensive Care Units: Risk Factors and Prediction
title_full Readmission to Medical Intensive Care Units: Risk Factors and Prediction
title_fullStr Readmission to Medical Intensive Care Units: Risk Factors and Prediction
title_full_unstemmed Readmission to Medical Intensive Care Units: Risk Factors and Prediction
title_short Readmission to Medical Intensive Care Units: Risk Factors and Prediction
title_sort readmission to medical intensive care units: risk factors and prediction
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4329370/
https://www.ncbi.nlm.nih.gov/pubmed/25684007
http://dx.doi.org/10.3349/ymj.2015.56.2.543
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