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Identification of patients with cancer with a high risk to develop delirium

Delirium deteriorates the quality of life in patients with cancer, but is frequently underdiagnosed and not adequately treated. In this study, we evaluated the occurrence of delirium and its risk factors in patients admitted to the hospital for treatment or palliative care in order to develop a pred...

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Autores principales: Neefjes, Elisabeth C. W., van der Vorst, Maurice J. D. L., Verdegaal, Bertha A. T. T., Beekman, Aartjan T. F., Berkhof, Johannes, Verheul, Henk M. W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5548884/
https://www.ncbi.nlm.nih.gov/pubmed/28688161
http://dx.doi.org/10.1002/cam4.1106
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author Neefjes, Elisabeth C. W.
van der Vorst, Maurice J. D. L.
Verdegaal, Bertha A. T. T.
Beekman, Aartjan T. F.
Berkhof, Johannes
Verheul, Henk M. W.
author_facet Neefjes, Elisabeth C. W.
van der Vorst, Maurice J. D. L.
Verdegaal, Bertha A. T. T.
Beekman, Aartjan T. F.
Berkhof, Johannes
Verheul, Henk M. W.
author_sort Neefjes, Elisabeth C. W.
collection PubMed
description Delirium deteriorates the quality of life in patients with cancer, but is frequently underdiagnosed and not adequately treated. In this study, we evaluated the occurrence of delirium and its risk factors in patients admitted to the hospital for treatment or palliative care in order to develop a prediction model to identify patients at high risk for delirium. In a period of 1.5 years, we evaluated the risk of developing delirium in 574 consecutively admitted patients with cancer to our academic oncology department with the Delirium Observation Screening Scale. Risk factors for delirium were extracted from the patient's chart. A delirium prediction algorithm was constructed using tree analysis, and validated with fivefold cross‐validation. A total of 574 patients with cancer were acutely (42%) or electively (58%) admitted 1733 times. The incidence rate of delirium was 3.5 per 100 admittances. Tree analysis revealed that the predisposing factors of an unscheduled admittance and a metabolic imbalance accurately predicted the development of delirium. In this group the incidence rate of delirium was 33 per 100 patients (1:3). The AUC of the model was 0.81, and 0.65 after fivefold cross‐validation. We identified that especially patients undergoing an unscheduled admittance with a metabolic imbalance do have a clinically relevant high risk to develop a delirium. Based on these factors, we propose to evaluate preventive treatment of these patients when admitted to the hospital in order to improve their quality of life.
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spelling pubmed-55488842017-08-09 Identification of patients with cancer with a high risk to develop delirium Neefjes, Elisabeth C. W. van der Vorst, Maurice J. D. L. Verdegaal, Bertha A. T. T. Beekman, Aartjan T. F. Berkhof, Johannes Verheul, Henk M. W. Cancer Med Clinical Cancer Research Delirium deteriorates the quality of life in patients with cancer, but is frequently underdiagnosed and not adequately treated. In this study, we evaluated the occurrence of delirium and its risk factors in patients admitted to the hospital for treatment or palliative care in order to develop a prediction model to identify patients at high risk for delirium. In a period of 1.5 years, we evaluated the risk of developing delirium in 574 consecutively admitted patients with cancer to our academic oncology department with the Delirium Observation Screening Scale. Risk factors for delirium were extracted from the patient's chart. A delirium prediction algorithm was constructed using tree analysis, and validated with fivefold cross‐validation. A total of 574 patients with cancer were acutely (42%) or electively (58%) admitted 1733 times. The incidence rate of delirium was 3.5 per 100 admittances. Tree analysis revealed that the predisposing factors of an unscheduled admittance and a metabolic imbalance accurately predicted the development of delirium. In this group the incidence rate of delirium was 33 per 100 patients (1:3). The AUC of the model was 0.81, and 0.65 after fivefold cross‐validation. We identified that especially patients undergoing an unscheduled admittance with a metabolic imbalance do have a clinically relevant high risk to develop a delirium. Based on these factors, we propose to evaluate preventive treatment of these patients when admitted to the hospital in order to improve their quality of life. John Wiley and Sons Inc. 2017-07-07 /pmc/articles/PMC5548884/ /pubmed/28688161 http://dx.doi.org/10.1002/cam4.1106 Text en © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Cancer Research
Neefjes, Elisabeth C. W.
van der Vorst, Maurice J. D. L.
Verdegaal, Bertha A. T. T.
Beekman, Aartjan T. F.
Berkhof, Johannes
Verheul, Henk M. W.
Identification of patients with cancer with a high risk to develop delirium
title Identification of patients with cancer with a high risk to develop delirium
title_full Identification of patients with cancer with a high risk to develop delirium
title_fullStr Identification of patients with cancer with a high risk to develop delirium
title_full_unstemmed Identification of patients with cancer with a high risk to develop delirium
title_short Identification of patients with cancer with a high risk to develop delirium
title_sort identification of patients with cancer with a high risk to develop delirium
topic Clinical Cancer Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5548884/
https://www.ncbi.nlm.nih.gov/pubmed/28688161
http://dx.doi.org/10.1002/cam4.1106
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