Predictive Modeling of Pressure Injury Risk in Patients Admitted to an Intensive Care Unit
| Autor: | José Fernández-de-Maya, María José Cabañero-Martínez, Manuel Mas-Asencio, Adrián Belso-Garzas, Francisco-Javier Ballesta-López, Mireia Ladios-Martin |
|---|---|
| Přispěvatelé: | Universidad de Alicante. Departamento de Enfermería, Calidad de Vida, Bienestar Psicológico y Salud, Person-centred Care and Health Outcomes Innovation / Atención centrada en la persona e innovación en resultados de salud (PCC-HOI) |
| Rok vydání: | 2020 |
| Předmět: |
Male
Intensive care unit patients Critical Care Nursing Logistic regression law.invention Hospitals University Machine Learning Hemoglobins 0302 clinical medicine law Risk Factors Data Mining Electronic Health Records 030212 general & internal medicine Child APACHE Aged 80 and over Pressure Ulcer education.field_of_study 030504 nursing Medical record Workload General Medicine Middle Aged Intensive care unit Predictive modeling Test (assessment) Intensive Care Units Enfermería Female 0305 other medical science Risk assessment Adult medicine.medical_specialty Adolescent Critical Care Population Sample (statistics) Risk Assessment Sensitivity and Specificity 03 medical and health sciences Young Adult medicine Humans education Aged business.industry Pressure injuries Risk factors Emergency medicine business |
| Zdroj: | American journal of critical care : an official publication, American Association of Critical-Care Nurses. 29(4) |
| ISSN: | 1937-710X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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