
Patients in intensive care units are at higher risk for development of pressure ulcers than other patients. In order to prevent pressure ulcers from developing in intensive care patients, risk for development of pressure ulcers must be assessed accurately.To evaluate the predictive validity of the Braden scale for assessing risk for development of pressure ulcers in intensive care patients by using 4 years of data from electronic health records. Methods Data from the electronic health records of patients admitted to intensive care units between January 1, 2007, and December 31, 2010, were extracted from the data warehouse of an academic medical center. Predictive validity was measured by using sensitivity, specificity, positive predictive value, and negative predictive value. The receiver operating characteristic curve was generated, and the area under the curve was reported.A total of 7790 intensive care patients were included in the analysis. A cutoff score of 16 on the Braden scale had a sensitivity of 0.954, specificity of 0.207, positive predictive value of 0.114, and negative predictive value of 0.977. The area under the curve was 0.672 (95% CI, 0.663-0.683). The optimal cutoff for intensive care patients, determined from the receiver operating characteristic curve, was 13.The Braden scale shows insufficient predictive validity and poor accuracy in discriminating intensive care patients at risk of pressure ulcers developing. The Braden scale may not sufficiently reflect characteristics of intensive care patients. Further research is needed to determine which possibly predictive factors are specific to intensive care units in order to increase the usefulness of the Braden scale for predicting pressure ulcers in intensive care patients.
Male, Pressure Ulcer, Incidence, Length of Stay, Middle Aged, Risk Assessment, Midwestern United States, Intensive Care Units, Age Distribution, ROC Curve, Predictive Value of Tests, Electronic Health Records, Humans, Female, Sex Distribution, Ohio
Male, Pressure Ulcer, Incidence, Length of Stay, Middle Aged, Risk Assessment, Midwestern United States, Intensive Care Units, Age Distribution, ROC Curve, Predictive Value of Tests, Electronic Health Records, Humans, Female, Sex Distribution, Ohio
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