OBJECTIVE: To validate a previously derived risk-adjustment model for pressure ulcer development in a separate sample of nursing home residents and to determine the extent to which use of this model affects judgments of nursing home performance. DESIGN: Retrospective observational study using Minimum Data Set (MDS) data from 1998. SETTING: A large, for-profit, nursing home chain. PARTICIPANTS: Twenty-nine thousand and forty observations were made on 13,457 nursing home residents who were without a pressure ulcer on an index assessment. MEASUREMENTS: We used logistic regression in our validation sample to calculate new coefficients for the 17 previously identified predictors of pressure ulcer development. Coefficients from this new sample were compared with those previously derived. Expected rates of pressure ulcer development were determined for 108 nursing homes. Unadjusted and risk-adjusted rates of pressure ulcer development from these homes were also calculated and outlier identification using these two approaches was compared. RESULTS: Predictors of pressure ulcer development in the derivation sample generally showed similar effects in the validation sample. The model c-statistic was also unchanged at 0.73, but it was not calibrated as well in the validation sample. On applying the model to the nursing homes, expected rates of ulcer development ranged from 1.1% to 3.2% (P <.001). The observed rates ranged from 0% to 12.1% (P <.001). There were 12 outliers using unadjusted rates and 15 using adjusted performance. Ten nursing homes were identified as outliers using both approaches. CONCLUSIONS: Our MDS risk-adjustment model for pressure ulcer development performed well in this new sample. Nursing homes differ significantly in their expected rates of pressure ulcer development. Outlier identification also differs depending on whether unadjusted or risk-adjusted performance is evaluated.
OBJECTIVE: To validate a previously derived risk-adjustment model for pressure ulcer development in a separate sample of nursing home residents and to determine the extent to which use of this model affects judgments of nursing home performance. DESIGN: Retrospective observational study using Minimum Data Set (MDS) data from 1998. SETTING: A large, for-profit, nursing home chain. PARTICIPANTS: Twenty-nine thousand and forty observations were made on 13,457 nursing home residents who were without a pressure ulcer on an index assessment. MEASUREMENTS: We used logistic regression in our validation sample to calculate new coefficients for the 17 previously identified predictors of pressure ulcer development. Coefficients from this new sample were compared with those previously derived. Expected rates of pressure ulcer development were determined for 108 nursing homes. Unadjusted and risk-adjusted rates of pressure ulcer development from these homes were also calculated and outlier identification using these two approaches was compared. RESULTS: Predictors of pressure ulcer development in the derivation sample generally showed similar effects in the validation sample. The model c-statistic was also unchanged at 0.73, but it was not calibrated as well in the validation sample. On applying the model to the nursing homes, expected rates of ulcer development ranged from 1.1% to 3.2% (P <.001). The observed rates ranged from 0% to 12.1% (P <.001). There were 12 outliers using unadjusted rates and 15 using adjusted performance. Ten nursing homes were identified as outliers using both approaches. CONCLUSIONS: Our MDS risk-adjustment model for pressure ulcer development performed well in this new sample. Nursing homes differ significantly in their expected rates of pressure ulcer development. Outlier identification also differs depending on whether unadjusted or risk-adjusted performance is evaluated.
Authors: Dana B Mukamel; Laurent G Glance; Yue Li; David L Weimer; William D Spector; Jacqueline S Zinn; Laura Mosqueda Journal: Med Care Date: 2008-05 Impact factor: 2.983
Authors: Richard N Jones; John P Hirdes; Jeffrey W Poss; Maureen Kelly; Katharine Berg; Brant E Fries; John N Morris Journal: BMC Health Serv Res Date: 2010-04-15 Impact factor: 2.655
Authors: Dana B Mukamel; David L Weimer; William D Spector; Heather Ladd; Jacqueline S Zinn Journal: Health Serv Res Date: 2008-01-31 Impact factor: 3.402