title: Predictive validity of the Braden scale for patients in intensive care units.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Sookyung Hyun is an assistant professor in the College of Nursing and the Department of Biomedical Informatics at The Ohio State University, Columbus, Ohio. Brenda Vermillion is director of nursing education in the Department of Health Services Nursing Education, Wexner Medical Center and clinical assistant professor at the College of Nursing at The Ohio State University. Cheryl Newton is a clinical nurse specialist in the Department of Critical Care Nursing, Wexner Medical Center at The Ohio State University. Monica Fall is a dietitian in the Department of Nutrition Services, Wexner Medical Center at The Ohio State University. Xiaobai Li is a bio-statistician in the Center for Biostatistics, Wexner Medical Center at The Ohio State University. Pacharmon Kaewprag is a doctoral candidate in the Department of Computer Science and Engineering, The Ohio State University. Susan Moffatt-Bruce is an associate professor in the Department of Surgery, Wexner Medical Center at The Ohio State University. Elizabeth R. Lenz is a professor emeritus in the College of Nursing at The Ohio State University.
authors.forename: Sookyung
authors.initials: S
authors.lastname: Hyun
authors.forename: Brenda
authors.initials: B
authors.lastname: Vermillion
authors.forename: Cheryl
authors.initials: C
authors.lastname: Newton
authors.forename: Monica
authors.initials: M
authors.lastname: Fall
authors.forename: Xiaobai
authors.initials: X
authors.lastname: Li
authors.forename: Pacharmon
authors.initials: P
authors.lastname: Kaewprag
authors.forename: Susan
authors.initials: S
authors.lastname: Moffatt-Bruce
authors.forename: Elizabeth R
authors.initials: ER
authors.lastname: Lenz
mesh_terms: D017677:Age Distribution; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D015994:Incidence; D007362:Intensive Care Units / Q000592:standards* / Q000706:statistics & numerical data; D007902:Length of Stay; D008297:Male; D008875:Middle Aged; D015146:Midwestern United States / Q000453:epidemiology; D009820:Ohio / Q000453:epidemiology; D011237:Predictive Value of Tests; D003668:Pressure Ulcer / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control*; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods; D017678:Sex Distribution