Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 434-438 |
| Number of pages | 5 |
| Journal | AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium |
| State | Published - 2006 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Medicine(all)
Cite this
Predictive modeling for the prevention of hospital-acquired pressure ulcers. / Kim, Tae Youn; Lang, Norma.
In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2006, p. 434-438.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Predictive modeling for the prevention of hospital-acquired pressure ulcers.
AU - Kim, Tae Youn
AU - Lang, Norma
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
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M3 - Article
SP - 434
EP - 438
JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
SN - 1559-4076
ER -