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Observation scales to suspect dyspnea in non-communicative intensive care unit patients

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Dyspnea, like pain, is a major cause of physical suffering and emotional distress. In the intensive care unit, mechanically ventilated patients are at high risk of dyspnea [1], and increasing… Click to show full abstract

Dyspnea, like pain, is a major cause of physical suffering and emotional distress. In the intensive care unit, mechanically ventilated patients are at high risk of dyspnea [1], and increasing attention is being given to this symptom [1, 2]. Because its evaluation relies on selfreport and self-assessment [3], dyspnea carries the risk of being underestimated or even unrecognized and therefore unattended in many intensive care unit patients. This is particularly so in patients unable to communicate with their caregivers (sedation, delirium, etc.). We have recently developed and validated a specific intensive care unit version of the respiratory distress observation scale (IC-RDOS, http://www.ic-rdos.com) [4]. IC-RDOS, based on respiratory and behavioral signs, correlates strongly with ratings of dyspnea on a visual analogic scale in “communicative” patients, but this is by definition not the most pertinent target population. The present secondary analysis describes IC-RDOS in “non-communicative” intensive care unit patients, as the first step of its clinical and prognostic evaluation in this setting. A total of 120 communicative patients of the reported cohort were compared to 73 non-communicative patients (sedation, n = 49; delirium, n = 9; not understanding the questions/instructions, n = 6; or another cause, n = 9) admitted during the same 4.5month period. Clinical data were gathered during the first 24 h of the intensive care unit stay, between 8:00 a.m. and 10:00 a.m. Based on the 21 observable variables with possible clinical relevance (namely, to detect dyspnea) among the 120 communicative patients, the selection started with a principal component analysis which identified 11 explanatory variables that mostly contributed to the principal axes. These variables were entered into an iterative partial least square regression process that ultimately identified five variables, of

Keywords: care unit; dyspnea; unit patients; intensive care; care

Journal Title: Intensive Care Medicine
Year Published: 2017

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