Pressure injury risk assessment in intensive care: comparison of inter-rater reliability of the COMHON (Conscious level, Mobility, Haemodynamics, Oxygenation, Nutrition) Index with three scales

2015 ◽  
Vol 72 (3) ◽  
pp. 680-692 ◽  
Author(s):  
Paul Fulbrook ◽  
Alissa Anderson
Keyword(s):  
Risk Assessment ◽  
Intensive Care ◽  
Injury Risk ◽  
Rater Reliability ◽  
Pressure Injury ◽  
Conscious Level

scholarly journals Inter-Rater Reliability of a Pressure Injury Risk Assessment Scale for Home Care: A Multicenter Cross-Sectional Study

2020 ◽  
Vol Volume 13 ◽  
pp. 2031-2041
Author(s):  
Masushi Kohta ◽  
Takehiko Ohura ◽  
Kunio Tsukada ◽  
Yoshinori Nakamura ◽  
Mishiho Sukegawa ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Home Care ◽  
Injury Risk ◽  
Cross Sectional Study ◽  
Assessment Scale ◽  
Sectional Study ◽  
Cross Sectional ◽  
Rater Reliability ◽  
Pressure Injury

Objective Pressure Injury Risk Assessment Using A Wearable Pressure Sensor

2019 ◽  
Author(s):  
Sam Mansfield ◽  
Sachin Rangarajan ◽  
Katia Obraczka ◽  
Hanmin Lee ◽  
David Young ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Pressure Sensor ◽  
Injury Risk ◽  
Pressure Injury

Automated Fall and Pressure Injury Risk Assessment Systems

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Yinji Jin ◽  
Heejeong Kim ◽  
Taixian Jin ◽  
Sun-Mi Lee
Keyword(s):  
Risk Assessment ◽  
Injury Risk ◽  
Assessment Systems ◽  
Pressure Injury

scholarly journals Predictive Modeling of Pressure Injury Risk in Patients Admitted to an Intensive Care Unit

2020 ◽  
Vol 29 (4) ◽  
pp. e70-e80
Author(s):  
Mireia Ladios-Martin ◽  
José Fernández-de-Maya ◽  
Francisco-Javier Ballesta-López ◽  
Adrián Belso-Garzas ◽  
Manuel Mas-Asencio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Injury Risk ◽  
Area Under The Curve ◽  
University Hospital ◽  
Machine Learning Techniques ◽  
Real Environment ◽  
Learning Techniques ◽  
Pressure Injury

Background Pressure injuries are an important problem in hospital care. Detecting the population at risk for pressure injuries is the first step in any preventive strategy. Available tools such as the Norton and Braden scales do not take into account all of the relevant risk factors. Data mining and machine learning techniques have the potential to overcome this limitation. Objectives To build a model to detect pressure injury risk in intensive care unit patients and to put the model into production in a real environment. Methods The sample comprised adult patients admitted to an intensive care unit (N = 6694) at University Hospital of Torrevieja and University Hospital of Vinalopó. A retrospective design was used to train (n = 2508) and test (n = 1769) the model and then a prospective design was used to test the model in a real environment (n = 2417). Data mining was used to extract variables from electronic medical records and a predictive model was built with machine learning techniques. The sensitivity, specificity, area under the curve, and accuracy of the model were evaluated. Results The final model used logistic regression and incorporated 23 variables. The model had sensitivity of 0.90, specificity of 0.74, and area under the curve of 0.89 during the initial test, and thus it outperformed the Norton scale. The model performed well 1 year later in a real environment. Conclusions The model effectively predicts risk of pressure injury. This allows nurses to focus on patients at high risk for pressure injury without increasing workload.

scholarly journals A dedicated paediatric logistic organ dysfunction score - adjusted pressure injury risk assessment scale is required for tertiary paediatric ICUs

2019 ◽  
Vol 29 (3) ◽  
pp. 455-456 ◽  
Author(s):  
Natalie Crane ◽  
Natasha Pool ◽  
Ivy Chang ◽  
Sharna Rogan ◽  
Christian Stocker ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Organ Dysfunction ◽  
Injury Risk ◽  
Assessment Scale ◽  
Logistic Organ Dysfunction ◽  
Pressure Injury
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