Predictive modeling for the prevention of hospital-acquired pressure ulcers.
A one-to-one case control study was conducted on a pre-existing dataset to examine a predictive model with a set of risk factors for pressure ulcer development in acute care settings. Various techniques were used to select the most relevant predictors from ten subsets of a pre-existing dataset. The predictors identified were further examined using ten additional subsets by measuring sensitivities, specificities, positive/negative predictive values, and the areas under the ROC (receiver operating characteristic) curves. The best components for identifying at-risk patients consisted of three Braden subscales and five risk factors routinely collected through electronic health records. Entering these eight predictors into the logistic regression model yielded a sensitivity of 92%, a specificity of 67%, and an area under the ROC curve of 89%. Further evaluation, however, is needed to explore the validity of the model.
Kim
Tae Youn
TY
University of Pennsylvania, School of Nursing, Philadelphia, PA, USA.
Lang
Norma
N
D016022:Case-Control Studies; D006801:Humans; D016015:Logistic Models; D003668:Pressure Ulcer / Q000209:etiology / Q000517:prevention & control*; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity