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      Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model

      research-article
      , PhD, APRN, CCRN, CCNS, , PhD, RN, , PhD, MSTAT, , PhD, RN, CWCN, , PhD, RN, , MS, , MSTAT, doctoral (PhD) student in population health science, , PhD, RN
      American journal of critical care : an official publication, American Association of Critical-Care Nurses

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          Abstract

          Background

          Hospital-acquired pressure injuries are a serious problem among critical care patients. Some can be prevented by using measures such as specialty beds, which are not feasible for every patient because of costs. However, decisions about which patient would benefit most from a specialty bed are difficult because results of existing tools to determine risk for pressure injury indicate that most critical care patients are at high risk.

          Objective

          To develop a model for predicting development of pressure injuries among surgical critical care patients.

          Methods

          Data from electronic health records were divided into training (67%) and testing (33%) data sets, and a model was developed by using a random forest algorithm via the R package “randomforest.”

          Results

          Among a sample of 6376 patients, hospital-acquired pressure injuries of stage 1 or greater (outcome variable 1) developed in 516 patients (8.1%) and injuries of stage 2 or greater (outcome variable 2) developed in 257 (4.0%). Random forest models were developed to predict stage 1 and greater and stage 2 and greater injuries by using the testing set to evaluate classifier performance. The area under the receiver operating characteristic curve for both models was 0.79.

          Conclusion

          This machine-learning approach differs from other available models because it does not require clinicians to input information into a tool (eg, the Braden Scale). Rather, it uses information readily available in electronic health records. Next steps include testing in an independent sample and then calibration to optimize specificity. ( American Journal of Critical Care. 2018; 27:461–468)

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          Author and article information

          Contributors
          Role: assistant professorRole: adjunct assistant professor
          Role: professor
          Role: clinical assistant professor
          Role: professor
          Role: professor
          Role: senior data architect
          Role: professor
          Journal
          9211547
          2500
          Am J Crit Care
          Am. J. Crit. Care
          American journal of critical care : an official publication, American Association of Critical-Care Nurses
          1062-3264
          1937-710X
          10 November 2018
          November 2018
          21 November 2018
          : 27
          : 6
          : 461-468
          Affiliations
          School of Nursing, Boise State University, Boise, Idaho,
          College of Nursing, University of Utah, Salt Lake City, Utah.
          College of Nursing, University of Utah.
          College of Nursing, University of Utah.
          College of Nursing, University of Washington, Seattle, Washington.
          Rocky Mountain University of the Health Professions, Provo, Utah.
          Biomedical Informatics Team, Center for Clinical and Translational Science, University of Utah.
          College of Nursing, University of Utah.
          College of Nursing, University of Utah.
          Author notes
          Corresponding author: Jenny Alderden, PhD, APRN, CCRN, CCNS, Boise State University School of Nursing, 1910 University Dr, Boise, ID 83725 ( jennyalderden@ 123456boisestate.edu ).
          Article
          PMC6247790 PMC6247790 6247790 nihpa996166
          10.4037/ajcc2018525
          6247790
          30385537
          3621ccc4-8c51-43c5-938a-6eb716d288fa
          Categories
          Article

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