Predictive modeling for the prevention of hospital-acquired pressure ulcers.

Tae Youn Kim, Norma Lang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)434-438
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2006
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)


Dive into the research topics of 'Predictive modeling for the prevention of hospital-acquired pressure ulcers.'. Together they form a unique fingerprint.

Cite this