Pressure injury prediction in intensive care units using artificial intelligence
scoping review protocol
DOI:
https://doi.org/10.48492/servir0212.36731Keywords:
Artificial Intelligence, Pressure Ulcer, Intensive Care Units, Critical Care, Critical Care NursingAbstract
Introduction: Pressure injuries are common adverse events in intensive care units, impacting individuals’ quality of life and increasing healthcare costs. Traditional risk assessment scales have significant limitations in the context of critically ill patients. Artificial intelligence has emerged as a promising approach for early risk identification, offering greater sensitivity and the ability to integrate complex clinical data.
Objective: To identify and map the available scientific evidence on the use of artificial intelligence for predicting pressure injuries in critically ill adult patients admitted to intensive care units.
Methods: A scoping review will be conducted according to the Joanna Briggs Institute methodology and the PRISMA-ScR checklist. The search will include scientific databases and grey literature sources, with no restrictions on language or publication date. Studies addressing the use of artificial intelligence to predict pressure injuries in intensive care settings will be included.
Results: Data will be presented descriptively and narratively, using summary tables to highlight types of artificial intelligence, predictive variables, model performance, and clinical implications.
Conclusions: This review will systematize current knowledge, identify research gaps, and support the integration of artificial intelligence-based solutions in nursing practice within intensive care contexts.
Downloads
References
Al Mutairi, K. B., & Hendrie, D. (2018). Global incidence and prevalence of pressure injuries in public hospitals: A systematic review. Wound Medicine, 22, 23–31. https://doi.org/10.1016/j.wndm.2018.05.004
Alderden, J., Kennerly, S. M., Wilson, A., Dimas, J., McFarland, C., Yap, D. Y., Zhao, L., & Yap, T. L. (2022). Explainable Artificial Intelligence for Predicting Hospital-Acquired Pressure Injuries in COVID-19-Positive Critical Care Patients. Computers, Informatics, Nursing : CIN, 40(10), 659–665. https://doi.org/10.1097/CIN.0000000000000943
Barghouthi, E. D., Owda, A. Y., Asia, M., & Owda, M. (2023). Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms. Diagnostics, 13(17), 2739. https://doi.org/10.3390/diagnostics13172739
Çayirtepe, Z., & Şenel, A. C. (2022). Risk Management In Intensive Care Units With Artificial Intelligence Technologies: Systematic Review of Prediction Models Using Electronic Health Records. Journal of Basic and Clinical Health Sciences, 6(3), 958–976. https://doi.org/10.30621/jbachs.993798
Chaboyer, W. P., Thalib, L., Harbeck, E. L., Coyer, F. M., Blot, S., Bull, C. F., Nogueira, P. C., & Lin, F. F. (2018). Incidence and Prevalence of Pressure Injuries in Adult Intensive Care Patients: A Systematic Review and Meta-Analysis. Critical Care Medicine, 46(11), e1074–e1081. https://doi.org/10.1097/CCM.0000000000003366
De Micco, F., Di Palma, G., Ferorelli, D., De Benedictis, A., Tomassini, L., Tambone, V., Cingolani, M., & Scendoni, R. (2025). Artificial intelligence in healthcare: Transforming patient safety with intelligent systems—A systematic review. Frontiers in Medicine, 11. https://doi.org/10.3389/fmed.2024.1522554
Fulbrook, P., Lovegrove, J., Hay, K., & Coyer, F. (2023). State-wide prevalence of pressure injury in intensive care versus acute general patients: A five-year analysis. Journal of Clinical Nursing, 32(15–16), 4947–4961. https://doi.org/10.1111/jocn.16687
Gefen, A., Alves, P., Ciprandi, G., Coyer, F., Milne, C. T., Ousey, K., Ohura, N., Waters, N., & Worsley, P. (2020). Device-related pressure ulcers: SECURE prevention. Journal of Wound Care, 29(Sup2a), S1–S52. https://doi.org/10.12968/jowc.2020.29.Sup2a.S1
Haesler, E. (Ed.). (2019). Prevention and treatment of pressure ulcers/injuries: Clinical practice guideline: the international guideline (3. edition). Epuap, European Pressure Ulcer Advisory Panel.
Kayambankadzanja, R. K., Schell, C. O., Gerdin Wärnberg, M., Tamras, T., Mollazadegan, H., Holmberg, M., Alvesson, H. M., & Baker, T. (2022). Towards definitions of critical illness and critical care using concept analysis. BMJ Open, 12(9), e060972. https://doi.org/10.1136/bmjopen-2022-060972
Labeau, S. O., Afonso, E., Benbenishty, J., Blackwood, B., Boulanger, C., Brett, S. J., Calvino-Gunther, S., Chaboyer, W., Coyer, F., Deschepper, M., François, G., Honore, P. M., Jankovic, R., Khanna, A. K., Llaurado-Serra, M., Lin, F., Rose, L., Rubulotta, F., Saager, L., … Erdogan, E. (2021). Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: The DecubICUs study. Intensive Care Medicine, 47(2), 160–169. https://doi.org/10.1007/s00134-020-06234-9
Ladios-Martin, M., Fernández-de-Maya, J., Ballesta-López, F.-J., Belso-Garzas, A., Mas-Asencio, M., & Cabañero-Martínez, M. J. (2020). Predictive Modeling of Pressure Injury Risk in Patients Admitted to an Intensive Care Unit. American Journal of Critical Care : An Official Publication, American Association of Critical-Care Nurses, 29(4), e70–e80. https://doi.org/10.4037/ajcc2020237
Luo, Y., Yin, L., Bai, W., & Mao, K. (2020). An Appraisal of Incremental Learning Methods. Entropy, 22(11), 1190. https://doi.org/10.3390/e22111190
Marshall, J. C., Bosco, L., Adhikari, N. K., Connolly, B., Diaz, J. V., Dorman, T., Fowler, R. A., Meyfroidt, G., Nakagawa, S., Pelosi, P., Vincent, J.-L., Vollman, K., & Zimmerman, J. (2017). What is an intensive care unit? A report of the task force of the World Federation of Societies of Intensive and Critical Care Medicine. Journal of Critical Care, 37, 270–276. https://doi.org/10.1016/j.jcrc.2016.07.015
Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan—A web and mobile app for systematic reviews. Systematic Reviews, 5(1). https://doi.org/10.1186/s13643-016-0384-4
Peters, M. D. J., Godfrey, C., McInerney, P., Khalil, H., Larsen, P., Marnie, C., Pollock, D., Tricco, A. C., & Munn, Z. (2022). Best practice guidance and reporting items for the development of scoping review protocols. JBI Evidence Synthesis, 20(4), 953. https://doi.org/10.11124/JBIES-21-00242
Peters, M. D. J., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis, 18(10), 2119–2126. https://doi.org/10.11124/JBIES-20-00167
Picoito, R. J. de B. R., Lapuente, S. M. M. P. da C., Ramos, A. C. P., Rabiais, I. C. M., Deodato, S. J., & Nunes, E. M. G. T. (2023). Risk assessment instruments for pressure ulcer in adults in critical situation: A scoping review. Revista Latino-Americana de Enfermagem, 31(e3983). https://doi.org/10.1590/1518-8345.6659.3983
Raju, D., Su, X., Patrician, P. A., Loan, L. A., & McCarthy, M. S. (2015). Exploring factors associated with pressure ulcers: A data mining approach. International Journal of Nursing Studies, 52(1), 102–111. https://doi.org/10.1016/j.ijnurstu.2014.08.002
Roussou, E., Fasoi, G., Stavropoulou, A., Kelesi, M., Vasilopoulos, G., Gerogianni, G., & Alikari, V. (2023). Quality of life of patients with pressure ulcers: A systematic review. Medicine and Pharmacy Reports, 96(2), 123–130. https://doi.org/10.15386/mpr-2531
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850
Wei, M., Wu, L., Chen, Y., Fu, Q., Chen, W., & Yang, D. (2020). Predictive Validity of the Braden Scale for Pressure Ulcer Risk in Critical Care: A Meta-Analysis. Nursing in Critical Care, 25(3), 165–170. https://doi.org/10.1111/nicc.12500
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., … Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4), 100179. https://doi.org/10.1016/j.xinn.2021.100179
Zheng, L., Xue, Y., Yuan, Z., & Xing, X. (2025). Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit. Scientific Reports, 15(1), 9878. https://doi.org/10.1038/s41598-025-92848-2
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 José Alves, Rita Azevedo, Ana Marques, Paulo Alves
This work is licensed under a Creative Commons Attribution 4.0 International License.
In order to promote the free circulation of knowledge, Servir is open access journal. All its content is available and protected under the Creative Commons license (CC BY 4.0).
The journal allows self-archiving in institutional repositories of all versions, which may become immediately available
