PREDICTING PRESSURE INJURY IN CRITICAL CARE PATIENTS: A MACHINE-LEARNING MODEL.
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...
| Publicado en: | American Journal of Critical Care Vol. 27; no. 6; pp. 461 - 469 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
American Association of Critical-Care Nurses
Nov2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132756162&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132756162 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10623264 44L jtl: American Journal of Critical Care issn: 10623264 maglogo: N pubinfo: dt: Nov2018 vid: 27 iid: 6 pid: 2559 pub: American Association of Critical-Care Nurses place: Alisa Veijo, California artinfo: ui: 132756162 132756162 132756162 10.4037/ajcc2018525 132756162 ppf: 461 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: PREDICTING PRESSURE INJURY IN CRITICAL CARE PATIENTS: A MACHINE-LEARNING MODEL. aug: au: Alderden, Jenny Pepper, Ginette Alyce Wilson, Andrew Whitney, Joanne D. Richardson, Stephanie Butcher, Ryan Jo, Yeonjung Cummins, Mollie Rebecca affil: professor, College of Nursing, University of Utah. sug: subj: Critically Ill Patients Pressure Ulcer Risk Factors Risk Assessment Methods Human Male Female Adult Middle Age Aged Utah Machine Learning Models, Theoretical Algorithms Electronic Health Records Data Mining Methods Random Sample Data Analysis Software Decision Trees Data Analysis, Statistical Methods Length of Stay Pressure Ulcer Therapy Pressure Ulcer Classification Braden Scale for Predicting Pressure Sore Risk Scales Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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