Hospital acquired pressure injury prediction in surgical critical care patients.


Journal

BMC medical informatics and decision making
ISSN: 1472-6947
Titre abrégé: BMC Med Inform Decis Mak
Pays: England
ID NLM: 101088682

Informations de publication

Date de publication:
06 01 2021
Historique:
received: 28 05 2020
accepted: 13 12 2020
entrez: 7 1 2021
pubmed: 8 1 2021
medline: 24 4 2021
Statut: epublish

Résumé

Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5-10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data. In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.

Sections du résumé

BACKGROUND
Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5-10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data.
METHODS
In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F
RESULTS
Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F
CONCLUSIONS
Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.

Identifiants

pubmed: 33407439
doi: 10.1186/s12911-020-01371-z
pii: 10.1186/s12911-020-01371-z
pmc: PMC7789639
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

12

Subventions

Organisme : NCATS NIH HHS
ID : UL1 TR002538
Pays : United States

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Auteurs

Jenny Alderden (J)

University of Utah College of Nursing, 10 S 2000 E, Salt Lake City, UT, 84112, USA. Jenny.Alderden@utah.edu.

Kathryn P Drake (KP)

Department of Computer Science, Boise State University, 777 W Main Street, Boise, ID, 83704, USA.

Andrew Wilson (A)

Parexel, 2520 Meridian Parkway, Durham, NC, USA.

Jonathan Dimas (J)

University of Utah College of Nursing, 10 S 2000 E, Salt Lake City, UT, 84112, USA.

Mollie R Cummins (MR)

University of Utah College of Nursing, 10 S 2000 E, Salt Lake City, UT, 84112, USA.

Tracey L Yap (TL)

Duke University School of Nursing, 307 Trent Drive, Durham, NC, 27710, USA.

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