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Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients
Accurately measuring the risk of pressure injury remains the most important step for effective prevention and intervention. Time-dependent risk factors for pressure injury development in the adult intensive care unit setting are not well understood. OBJECTIVES: To develop and validate a dynamic risk...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8613355/ https://www.ncbi.nlm.nih.gov/pubmed/34841251 http://dx.doi.org/10.1097/CCE.0000000000000580 |
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author | Shui, Amy M. Kim, Phillip Aribindi, Vamsi Huang, Chiung-Yu Kim, Mi-Ok Rangarajan, Sachin Schorger, Kaelan Aldrich, J. Matthew Lee, Hanmin |
author_facet | Shui, Amy M. Kim, Phillip Aribindi, Vamsi Huang, Chiung-Yu Kim, Mi-Ok Rangarajan, Sachin Schorger, Kaelan Aldrich, J. Matthew Lee, Hanmin |
author_sort | Shui, Amy M. |
collection | PubMed |
description | Accurately measuring the risk of pressure injury remains the most important step for effective prevention and intervention. Time-dependent risk factors for pressure injury development in the adult intensive care unit setting are not well understood. OBJECTIVES: To develop and validate a dynamic risk prediction model to estimate the risk of developing a hospital-acquired pressure injury among adult ICU patients. DESIGN: ICU admission data were split into training and validation sets. With death as a competing event, both static and dynamic Fine-Gray models were developed to predict hospital-acquired pressure injury development less than 24, 72, and 168 hours postadmission. Model performance was evaluated using Wolbers’ concordance index, Brier score, net reclassification improvement, and integrated discrimination improvement. SETTING AND PARTICIPANTS: We performed a retrospective cohort study of ICU patients in a tertiary care hospital located in San Francisco, CA, from November 2013 to August 2017. MAIN OUTCOMES AND MEASURES: Data were extracted from electronic medical records of 18,019 ICU patients (age ≥ 18 yr; 21,220 encounters). Record of hospital-acquired pressure injury data was captured in our institution’s incident reporting system. The information is periodically reviewed by our wound care team. Presence of hospital-acquired pressure injury during an encounter and hospital-acquired pressure injury diagnosis date were provided. RESULTS: The dynamic model predicting hospital-acquired pressure injury more than 24 hours postadmission, including predictors age, body mass index, lactate serum, Braden scale score, and use of vasopressor and antifungal medications, had adequate discrimination ability within 6 days from time of prediction (c = 0.73). All dynamic models produced more accurate risk estimates than static models within 26 days postadmission. There were no significant differences in Brier scores between dynamic and static models. CONCLUSIONS AND RELEVANCE: A dynamic risk prediction model predicting hospital-acquired pressure injury development less than 24 hours postadmission in ICU patients for up to 7 days postadmission was developed and validated using a large dataset of clinical variables readily available in the electronic medical record. |
format | Online Article Text |
id | pubmed-8613355 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-86133552021-11-26 Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients Shui, Amy M. Kim, Phillip Aribindi, Vamsi Huang, Chiung-Yu Kim, Mi-Ok Rangarajan, Sachin Schorger, Kaelan Aldrich, J. Matthew Lee, Hanmin Crit Care Explor Original Clinical Report Accurately measuring the risk of pressure injury remains the most important step for effective prevention and intervention. Time-dependent risk factors for pressure injury development in the adult intensive care unit setting are not well understood. OBJECTIVES: To develop and validate a dynamic risk prediction model to estimate the risk of developing a hospital-acquired pressure injury among adult ICU patients. DESIGN: ICU admission data were split into training and validation sets. With death as a competing event, both static and dynamic Fine-Gray models were developed to predict hospital-acquired pressure injury development less than 24, 72, and 168 hours postadmission. Model performance was evaluated using Wolbers’ concordance index, Brier score, net reclassification improvement, and integrated discrimination improvement. SETTING AND PARTICIPANTS: We performed a retrospective cohort study of ICU patients in a tertiary care hospital located in San Francisco, CA, from November 2013 to August 2017. MAIN OUTCOMES AND MEASURES: Data were extracted from electronic medical records of 18,019 ICU patients (age ≥ 18 yr; 21,220 encounters). Record of hospital-acquired pressure injury data was captured in our institution’s incident reporting system. The information is periodically reviewed by our wound care team. Presence of hospital-acquired pressure injury during an encounter and hospital-acquired pressure injury diagnosis date were provided. RESULTS: The dynamic model predicting hospital-acquired pressure injury more than 24 hours postadmission, including predictors age, body mass index, lactate serum, Braden scale score, and use of vasopressor and antifungal medications, had adequate discrimination ability within 6 days from time of prediction (c = 0.73). All dynamic models produced more accurate risk estimates than static models within 26 days postadmission. There were no significant differences in Brier scores between dynamic and static models. CONCLUSIONS AND RELEVANCE: A dynamic risk prediction model predicting hospital-acquired pressure injury development less than 24 hours postadmission in ICU patients for up to 7 days postadmission was developed and validated using a large dataset of clinical variables readily available in the electronic medical record. Lippincott Williams & Wilkins 2021-11-11 /pmc/articles/PMC8613355/ /pubmed/34841251 http://dx.doi.org/10.1097/CCE.0000000000000580 Text en Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Original Clinical Report Shui, Amy M. Kim, Phillip Aribindi, Vamsi Huang, Chiung-Yu Kim, Mi-Ok Rangarajan, Sachin Schorger, Kaelan Aldrich, J. Matthew Lee, Hanmin Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients |
title | Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients |
title_full | Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients |
title_fullStr | Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients |
title_full_unstemmed | Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients |
title_short | Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients |
title_sort | dynamic risk prediction for hospital-acquired pressure injury in adult critical care patients |
topic | Original Clinical Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8613355/ https://www.ncbi.nlm.nih.gov/pubmed/34841251 http://dx.doi.org/10.1097/CCE.0000000000000580 |
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