Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review
<b data-eusoft-scrollable-element="1">Background/Objetives:</b> Pressure injuries pose a significant challenge in healthcare, adversely impacting individuals’ quality of life and healthcare systems, particularly in intensive care units. The effective identification of at-risk i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Nursing Reports |
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| Online Access: | https://www.mdpi.com/2039-4403/15/4/126 |
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| author | José Alves Rita Azevedo Ana Marques Rúben Encarnação Paulo Alves |
| author_facet | José Alves Rita Azevedo Ana Marques Rúben Encarnação Paulo Alves |
| author_sort | José Alves |
| collection | DOAJ |
| description | <b data-eusoft-scrollable-element="1">Background/Objetives:</b> Pressure injuries pose a significant challenge in healthcare, adversely impacting individuals’ quality of life and healthcare systems, particularly in intensive care units. The effective identification of at-risk individuals is crucial, but traditional scales have limitations, prompting the development of new tools. Artificial intelligence offers a promising approach to identifying and preventing pressure injuries in critical care settings. This review aimed to assess the extent of the literature regarding the use of artificial intelligence technologies in the prediction of pressure injuries in critically ill patients in intensive care units to identify gaps in current knowledge and direct future research. <b data-eusoft-scrollable-element="1">Methods:</b> The review followed the Joanna Briggs Institute’s methodology for scoping reviews, and the study protocol was prospectively registered on the Open Science Framework platform. <b data-eusoft-scrollable-element="1">Results:</b> This review included 14 studies, primarily highlighting the use of machine learning models trained on electronic health records data for predicting pressure injuries. Between 6 and 86 variables were used to train these models. Only two studies reported the clinical deployment of these models, reporting results such as reduced nursing workload, decreased prevalence of hospital-acquired pressure injuries, and decreased intensive care unit length of stay. <b data-eusoft-scrollable-element="1">Conclusions:</b> Artificial intelligence technologies present themselves as a dynamic and innovative approach, with the ability to identify risk factors and predict pressure injuries effectively and promptly. This review synthesizes information about the use of these technologies and guides future directions and motivations. |
| format | Article |
| id | doaj-art-43d62d729f88427c8eedf02c02cd041b |
| institution | DOAJ |
| issn | 2039-439X 2039-4403 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nursing Reports |
| spelling | doaj-art-43d62d729f88427c8eedf02c02cd041b2025-08-20T03:13:53ZengMDPI AGNursing Reports2039-439X2039-44032025-04-0115412610.3390/nursrep15040126Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping ReviewJosé Alves0Rita Azevedo1Ana Marques2Rúben Encarnação3Paulo Alves4Center for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Católica Portuguesa, 4169-005 Porto, PortugalCenter for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Católica Portuguesa, 4169-005 Porto, PortugalCenter for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Católica Portuguesa, 4169-005 Porto, PortugalCenter for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Católica Portuguesa, 4169-005 Porto, PortugalCenter for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Católica Portuguesa, 4169-005 Porto, Portugal<b data-eusoft-scrollable-element="1">Background/Objetives:</b> Pressure injuries pose a significant challenge in healthcare, adversely impacting individuals’ quality of life and healthcare systems, particularly in intensive care units. The effective identification of at-risk individuals is crucial, but traditional scales have limitations, prompting the development of new tools. Artificial intelligence offers a promising approach to identifying and preventing pressure injuries in critical care settings. This review aimed to assess the extent of the literature regarding the use of artificial intelligence technologies in the prediction of pressure injuries in critically ill patients in intensive care units to identify gaps in current knowledge and direct future research. <b data-eusoft-scrollable-element="1">Methods:</b> The review followed the Joanna Briggs Institute’s methodology for scoping reviews, and the study protocol was prospectively registered on the Open Science Framework platform. <b data-eusoft-scrollable-element="1">Results:</b> This review included 14 studies, primarily highlighting the use of machine learning models trained on electronic health records data for predicting pressure injuries. Between 6 and 86 variables were used to train these models. Only two studies reported the clinical deployment of these models, reporting results such as reduced nursing workload, decreased prevalence of hospital-acquired pressure injuries, and decreased intensive care unit length of stay. <b data-eusoft-scrollable-element="1">Conclusions:</b> Artificial intelligence technologies present themselves as a dynamic and innovative approach, with the ability to identify risk factors and predict pressure injuries effectively and promptly. This review synthesizes information about the use of these technologies and guides future directions and motivations.https://www.mdpi.com/2039-4403/15/4/126artificial intelligencepressure injuryintensive care unitscritical carecritical care nursing |
| spellingShingle | José Alves Rita Azevedo Ana Marques Rúben Encarnação Paulo Alves Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review Nursing Reports artificial intelligence pressure injury intensive care units critical care critical care nursing |
| title | Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review |
| title_full | Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review |
| title_fullStr | Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review |
| title_full_unstemmed | Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review |
| title_short | Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review |
| title_sort | pressure injury prediction in intensive care units using artificial intelligence a scoping review |
| topic | artificial intelligence pressure injury intensive care units critical care critical care nursing |
| url | https://www.mdpi.com/2039-4403/15/4/126 |
| work_keys_str_mv | AT josealves pressureinjurypredictioninintensivecareunitsusingartificialintelligenceascopingreview AT ritaazevedo pressureinjurypredictioninintensivecareunitsusingartificialintelligenceascopingreview AT anamarques pressureinjurypredictioninintensivecareunitsusingartificialintelligenceascopingreview AT rubenencarnacao pressureinjurypredictioninintensivecareunitsusingartificialintelligenceascopingreview AT pauloalves pressureinjurypredictioninintensivecareunitsusingartificialintelligenceascopingreview |