Mieke Deschepper1, Sonia O Labeau2, Willem Waegeman3, Stijn I Blot4. 1. Strategic Policy Cell, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium. Electronic address: mieke.deschepper@uzgent.be. 2. School of Healthcare, Nurse Education Programme, HOGENT University of Applied Sciences and Arts, Keramiekstraat 80, 9000 Ghent, Belgium; Department of Internal Medicine & Pediatrics, Faculty of Medicine and Health Science, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium. Electronic address: sonia.labeau@hogent.be. 3. Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium. Electronic address: willem.waegeman@ugent.be. 4. School of Healthcare, Nurse Education Programme, HOGENT University of Applied Sciences and Arts, Keramiekstraat 80, 9000 Ghent, Belgium; Department of Internal Medicine & Pediatrics, Faculty of Medicine and Health Science, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium. Electronic address: stijn.blot@ugent.be.
Abstract
OBJECTIVE: To determine risk factors for pressure injury in distinct intensive care subpopulations according to admission type (Medical; Surgical elective; Surgery emergency; Trauma/Burns). METHODOLOGY/ DESIGN: Predictive modelling using generalised linear mixed models with backward elimination on prospectively gathered data of 13 044 adult intensive care patients. SETTINGS: 1110 intensive care units, 89 countries worldwide. MAIN OUTCOME MEASURES: Pressure injury risk factors. RESULTS: A generalised linear mixed model including admission type outperformed a model without admission type (p = 0.004). Admission type Trauma/Burns was not withheld in the model and excluded from further analyses. For the other three admission types (Medical, Surgical elective, and Surgical emergency), backward elimination resulted in distinct prediction models with 23, 17, and 16 predictors, respectively, and five common predictors only. The Area Under the Receiver Operating Curve was 0.79 for Medical admissions; and 0.88 for both the Surgical elective and Surgical emergency models. CONCLUSIONS: Risk factors for pressure injury differ according to whether intensive care patients have been admitted for medical reasons, or elective or emergency surgery. Prediction models for pressure injury should target distinct subpopulations with differing pressure injury risk profiles. Type of intensive care admission is a simple and easily retrievable parameter to distinguish between such subgroups.
OBJECTIVE: To determine risk factors for pressure injury in distinct intensive care subpopulations according to admission type (Medical; Surgical elective; Surgery emergency; Trauma/Burns). METHODOLOGY/ DESIGN: Predictive modelling using generalised linear mixed models with backward elimination on prospectively gathered data of 13 044 adult intensive care patients. SETTINGS: 1110 intensive care units, 89 countries worldwide. MAIN OUTCOME MEASURES: Pressure injury risk factors. RESULTS: A generalised linear mixed model including admission type outperformed a model without admission type (p = 0.004). Admission type Trauma/Burns was not withheld in the model and excluded from further analyses. For the other three admission types (Medical, Surgical elective, and Surgical emergency), backward elimination resulted in distinct prediction models with 23, 17, and 16 predictors, respectively, and five common predictors only. The Area Under the Receiver Operating Curve was 0.79 for Medical admissions; and 0.88 for both the Surgical elective and Surgical emergency models. CONCLUSIONS: Risk factors for pressure injury differ according to whether intensive care patients have been admitted for medical reasons, or elective or emergency surgery. Prediction models for pressure injury should target distinct subpopulations with differing pressure injury risk profiles. Type of intensive care admission is a simple and easily retrievable parameter to distinguish between such subgroups.