Treffer: Interpretable Machine Learning Prediction Model for Predicting Mortality Risk of ICU Patients With Pressure Ulcers Based on the Braden Scale: A Clinical Study Based on MIMIC-IV.
Title:
Interpretable Machine Learning Prediction Model for Predicting Mortality Risk of ICU Patients With Pressure Ulcers Based on the Braden Scale: A Clinical Study Based on MIMIC-IV.
Authors:
Chen B; College of Nursing, Wenzhou Medical University, Wenzhou, China., Zhou J; College of Nursing, Wenzhou Medical University, Wenzhou, China., Chen S; College of Nursing, Wenzhou Medical University, Wenzhou, China., Wang F; College of Nursing, Wenzhou Medical University, Wenzhou, China., Liu P; College of Nursing, AKsu Vocational and Technical College, AKsu, China., Xu Y; College of Nursing, Wenzhou Medical University, Wenzhou, China., Huang P; College of Nursing, Wenzhou Medical University, Wenzhou, China., Cai F; College of Nursing, Wenzhou Medical University, Wenzhou, China.
Source:
Journal of clinical nursing [J Clin Nurs] 2025 Aug; Vol. 34 (8), pp. 3353-3369. Date of Electronic Publication: 2025 Jun 12.
Publication Type:
Comparative Study; Journal Article
Language:
English
Journal Info:
Publisher: Blackwell Scientific Publications Country of Publication: England NLM ID: 9207302 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1365-2702 (Electronic) Linking ISSN: 09621067 NLM ISO Abbreviation: J Clin Nurs Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford ; Boston : Blackwell Scientific Publications, c1992-
MeSH Terms:
Intensive Care Units* , Pressure Ulcer*/diagnosis , Pressure Ulcer*/mortality , Critical Care Outcomes* , Severity of Illness Index* , Predictive Learning Models*/statistics & numerical data, Retrospective Studies ; Humans ; Male ; Female ; Middle Aged ; Aged ; Aged, 80 and over ; Risk Assessment ; Logistic Models ; Databases, Factual ; Reproducibility of Results
References:
Ahtiala, M. H., R. Kivimäki, R. Laitio, and E. T. Soppi. 2020. “The Association Between Pressure Ulcer/Injury Development and Short‐Term Mortality in Critically Ill Patients: A Retrospective Cohort Study.” Wound Management & Prevention 66: 14–21.
Arihan, O., B. Wernly, M. Lichtenauer, et al. 2018. “Blood Urea Nitrogen (BUN) is Independently Associated With Mortality in Critically Ill Patients Admitted to ICU.” PLoS One 13: e0191697. https://doi.org/10.1371/journal.pone.0191697.
Bergstrom, N., B. J. Braden, A. Laguzza, and V. Holman. 1987. “The Braden Scale for Predicting Pressure Sore Risk.” Nursing Research 36: 205–210.
Cheng, H., X. Li, X. Liang, et al. 2024. “Braden Score Can Independently Predict 90‐Day Mortality in Critically Ill Patients With Dementia.” International Journal of Geriatric Psychiatry 39: e6093. https://doi.org/10.1002/gps.6093.
DecisionLinnc Core Team. “DecisionLinnc v1.1.5.8.2025.” https://www.statsape.com/.
Degenhardt, F., S. Seifert, and S. Szymczak. 2019. “Evaluation of Variable Selection Methods for Random Forests and Omics Data Sets.” Briefings in Bioinformatics 20: 492–503. https://doi.org/10.1093/bib/bbx124.
Deng, T., D. Wu, S. S. Liu, X. L. Chen, Z. W. Zhao, and L. L. Zhang. 2025. “Association of Blood Urea Nitrogen With 28‐Day Mortality in Critically Ill Patients: A Multi‐Center Retrospective Study Based on the eICU Collaborative Research Database.” PLoS One 20: e0317315. https://doi.org/10.1371/journal.pone.0317315.
Dweekat, O. Y., S. S. Lam, and L. McGrath. 2023. “An Integrated System of Braden Scale and Random Forest Using Real‐Time Diagnoses to Predict When Hospital‐Acquired Pressure Injuries (Bedsores) Occur.” International Journal of Environmental Research and Public Health 20: 4911. https://doi.org/10.3390/ijerph20064911.
El Sharouni, M. A., M. D. Stodell, T. Ahmed, et al. 2021. “Sentinel Node Biopsy in Patients With Melanoma Improves the Accuracy of Staging When Added to Clinicopathological Features of the Primary Tumor.” Annals of Oncology 32: 375–383. https://doi.org/10.1016/j.annonc.2020.11.015.
Falcão, A. L. E., A. G. A. Barros, A. A. M. Bezerra, et al. 2019. “The Prognostic Accuracy Evaluation of SAPS 3, SOFA and APACHE II Scores for Mortality Prediction in the Surgical ICU: An External Validation Study and Decision‐Making Analysis.” Annals of Intensive Care 9: 18. https://doi.org/10.1186/s13613‐019‐0488‐9.
García‐Fernández, F. P., P. L. Pancorbo‐Hidalgo, and J. J. Agreda. 2014. “Predictive Capacity of Risk Assessment Scales and Clinical Judgment for Pressure Ulcers: A Meta‐Analysis.” Journal of Wound, Ostomy, and Continence Nursing 41: 24–34. https://doi.org/10.1097/01.WON.0000438014.90734.a2.
Giri, M., L. He, T. Hu, et al. 2022. “Blood Urea Nitrogen Is Associated With in‐Hospital Mortality in Critically Ill Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Propensity Score Matching Analysis.” Journal of Clinical Medicine 11: 6709. https://doi.org/10.3390/jcm11226709.
Goldberger, A. L., L. A. Amaral, L. Glass, et al. 2000. “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals.” Circulation 101: E215–E220.
Gould, L. J., G. Bohn, R. Bryant, et al. 2019. “Pressure Ulcer Summit 2018: An Interdisciplinary Approach to Improve Our Understanding of the Risk of Pressure‐Induced Tissue Damage.” Wound Repair and Regeneration 27: 497–508. https://doi.org/10.1111/wrr.12730.
Gupta, R., S. Kumari, A. Senapati, R. K. Ambasta, and P. Kumar. 2023. “New Era of Artificial Intelligence and Machine Learning‐Based Detection, Diagnosis, and Therapeutics in Parkinson's Disease.” Ageing Research Reviews 90: 102013. https://doi.org/10.1016/j.arr.2023.102013.
Haesler, E., ed. 2019. “European Pressure Ulcer Advisory Panel, National Pressure Injury Advisory Panel, and Pan Pacific Pressure Injury Alliance.” Prevention and Treatment of Pressure Ulcers/Injuries: Clinical Practice Guideline.
Hajhosseini, B., M. T. Longaker, and G. C. Gurtner. 2020. “Pressure Injury.” Annals of Surgery 271: 671–679. https://doi.org/10.1097/sla.0000000000003567.
Handelman, G. S., H. K. Kok, R. V. Chandra, A. H. Razavi, M. J. Lee, and H. Asadi. 2018. “eDoctor: Machine Learning and the Future of Medicine.” Journal of Internal Medicine 284: 603–619. https://doi.org/10.1111/joim.12822.
Hou, N., M. Li, L. He, et al. 2020. “Predicting 30‐Days Mortality for MIMIC‐III Patients With Sepsis‐3: A Machine Learning Approach Using XGboost.” Journal of Translational Medicine 18: 462. https://doi.org/10.1186/s12967‐020‐02620‐5.
Huang, X., X. Di, S. Lin, et al. 2025. “Artificial Intelligence‐Based Prediction of Second Stage Duration in Labor: A Multicenter Retrospective Cohort Analysis.” eClinicalMedicine 80: 103072. https://doi.org/10.1016/j.eclinm.2025.103072.
Iranmanesh, S., H. Rafiei, and S. Sabzevari. 2012. “Relationship Between Braden Scale Score and Pressure Ulcer Development in Patients Admitted in Trauma Intensive Care Unit.” International Wound Journal 9: 248–252. https://doi.org/10.1111/j.1742‐481X.2011.00852.x.
Jaul, E., and R. Calderon‐Margalit. 2015. “Systemic Factors and Mortality in Elderly Patients With Pressure Ulcers.” International Wound Journal 12: 254–259. https://doi.org/10.1111/iwj.12086.
Jia, Y., H. Li, D. Li, et al. 2020. “Prognostic Value of Braden Scale in Patients With Acute Myocardial Infarction: From the Retrospective Multicenter Study for Early Evaluation of Acute Chest Pain.” Journal of Cardiovascular Nursing 35: E53–E61. https://doi.org/10.1097/jcn.0000000000000735.
Jiang, M., Y. Ma, S. Guo, et al. 2021. “Using Machine Learning Technologies in Pressure Injury Management: Systematic Review.” JMIR Medical Informatics 9: e25704. https://doi.org/10.2196/25704.
Kiyat, I., and A. Ozbas. 2024. “Comparison of the Predictive Validity of Norton and Braden Scales in Determining the Risk of Pressure Injury in Elderly Patients.” Clinical Nurse Specialist 38: 141–146. https://doi.org/10.1097/NUR.0000000000000815.
Labeau, S. O., E. Afonso, J. Benbenishty, et al. 2021. “Prevalence, Associated Factors and Outcomes of Pressure Injuries in Adult Intensive Care Unit Patients: The DecubICUs Study.” Intensive Care Medicine 47: 160–169. https://doi.org/10.1007/s00134‐020‐06234‐9.
Ladios‐Martin, M., J. Fernández‐de‐Maya, F.‐J. Ballesta‐López, A. Belso‐Garzas, M. Mas‐Asencio, and M. J. Cabañero‐Martínez. 2020. “Predictive Modeling of Pressure Injury Risk in Patients Admitted to an Intensive Care Unit.” American Journal of Critical Care 29: e70–e80. https://doi.org/10.4037/ajcc2020237.
Langemo, D. 2012. “General Principles and Approaches to Wound Prevention and Care at End of Life: An Overview.” Ostomy/Wound Management 58: 24–26.
Lima‐Serrano, M., M. I. González‐Méndez, C. Martín‐Castaño, I. Alonso‐Araujo, and J. S. Lima‐Rodríguez. 2018. “Predictive Validity and Reliability of the Braden Scale for Risk Assessment of Pressure Ulcers in an Intensive Care Unit.” Medicina Intensiva (English Edition) 42: 82–91. https://doi.org/10.1016/j.medin.2016.12.014.
McEvoy, N., P. Avsar, D. Patton, G. Curley, C. J. Kearney, and Z. Moore. 2021. “The Economic Impact of Pressure Ulcers Among Patients in Intensive Care Units. A Systematic Review.” Journal of Tissue Viability 30: 168–177. https://doi.org/10.1016/j.jtv.2020.12.004.
Mervis, J. S., and T. J. Phillips. 2019. “Pressure Ulcers: Pathophysiology, Epidemiology, Risk Factors, and Presentation.” Journal of the American Academy of Dermatology 81: 881–890. https://doi.org/10.1016/j.jaad.2018.12.069.
Mirjalili, S. R., S. Soltani, Z. H. Meybodi, et al. 2024. “Which Surrogate Insulin Resistance Indices Best Predict Coronary Artery Disease? A Machine Learning Approach.” Cardiovascular Diabetology 23: 214. https://doi.org/10.1186/s12933‐024‐02306‐y.
Noie, A., A. C. Jackson, M. Taheri, L. Sayadi, and F. Bahramnezhad. 2024. “Determining the Frequency of Pressure Ulcers Incidence and Associated Risk Factors in Critical Care Patients: A 3‐Year Retrospective Study.” International Wound Journal 21: e70120. https://doi.org/10.1111/iwj.70120.
Radakovich, N., M. Nagy, and A. Nazha. 2020. “Machine Learning in Haematological Malignancies.” Lancet Haematology 7: e541–e550. https://doi.org/10.1016/s2352‐3026(20)30121‐6.
Raju, D., X. Su, P. A. Patrician, L. A. Loan, and M. S. McCarthy. 2015. “Exploring Factors Associated With Pressure Ulcers: A Data Mining Approach.” International Journal of Nursing Studies 52: 102–111. https://doi.org/10.1016/j.ijnurstu.2014.08.002.
Song, Y. P., H. W. Shen, J. Y. Cai, M. L. Zha, and H. L. Chen. 2019. “The Relationship Between Pressure Injury Complication and Mortality Risk of Older Patients in Follow‐Up: A Systematic Review and Meta‐Analysis.” International Wound Journal 16: 1533–1544. https://doi.org/10.1111/iwj.13243.
Stansby, G., L. Avital, K. Jones, and G. Marsden. 2014. “Prevention and Management of Pressure Ulcers in Primary and Secondary Care: Summary of NICE Guidance.” BMJ (Clinical Research Ed.) 348: g2592. https://doi.org/10.1136/bmj.g2592.
Tang, Y., X. Li, H. Cheng, et al. 2024. “Braden Score Predicts 30‐Day Mortality Risk in Patients With Ischaemic Stroke in the ICU: A Retrospective Analysis Based on the MIMIC‐IV Database.” Nursing in Critical Care 30: e13125. https://doi.org/10.1111/nicc.13125.
Torsy, T., B. Serraes, and D. Beeckman. 2024. “Pressure Ulcer Risk Assessment in the ICU: The Importance of Balancing Systemic and Body‐Site Specific Risk Factors.” Intensive & Critical Care Nursing 86: 103857. https://doi.org/10.1016/j.iccn.2024.103857.
Wernly, B., M. Lichtenauer, N. A. R. Vellinga, et al. 2018. “Blood Urea Nitrogen (BUN) Independently Predicts Mortality in Critically Ill Patients Admitted to ICU: A Multicenter Study.” Clinical Hemorheology and Microcirculation 69: 123–131. https://doi.org/10.3233/ch‐189111.
Xu, J., D. Chen, X. Deng, et al. 2022. “Development and Validation of a Machine Learning Algorithm‐Based Risk Prediction Model of Pressure Injury in the Intensive Care Unit.” International Wound Journal 19: 1637–1649. https://doi.org/10.1111/iwj.13764.
Yang, G., T. Montgomery‐Csobán, W. Ganzevoort, et al. 2025. “Consecutive Prediction of Adverse Maternal Outcomes of Preeclampsia, Using the PIERS‐ML and fullPIERS Models: A Multicountry Prospective Observational Study.” PLoS Medicine 22: e1004509. https://doi.org/10.1371/journal.pmed.1004509.
Yang, Y., H. Shen, H. Guan, et al. 2025. “Association Between Braden Scale and All‐Cause Mortality in Critically Ill Patients With Non‐Traumatic Subarachnoid Hemorrhage: Analysis of the MIMIC‐IV Database.” Neurosurgical Review 48: 345. https://doi.org/10.1007/s10143‐025‐03508‐y.
Yuan, J., J. Xiong, J. Yang, et al. 2025. “Machine Learning‐Based 28‐Day Mortality Prediction Model for Elderly Neurocritically Ill Patients.” Computer Methods and Programs in Biomedicine 260: 108589. https://doi.org/10.1016/j.cmpb.2025.108589.
Zhang, W., L. Ji, X. Wang, et al. 2021. “Nomogram Predicts Risk and Prognostic Factors for Bone Metastasis of Pancreatic Cancer: A Population‐Based Analysis.” Frontiers in Endocrinology 12: 752176. https://doi.org/10.3389/fendo.2021.752176.
Arihan, O., B. Wernly, M. Lichtenauer, et al. 2018. “Blood Urea Nitrogen (BUN) is Independently Associated With Mortality in Critically Ill Patients Admitted to ICU.” PLoS One 13: e0191697. https://doi.org/10.1371/journal.pone.0191697.
Bergstrom, N., B. J. Braden, A. Laguzza, and V. Holman. 1987. “The Braden Scale for Predicting Pressure Sore Risk.” Nursing Research 36: 205–210.
Cheng, H., X. Li, X. Liang, et al. 2024. “Braden Score Can Independently Predict 90‐Day Mortality in Critically Ill Patients With Dementia.” International Journal of Geriatric Psychiatry 39: e6093. https://doi.org/10.1002/gps.6093.
DecisionLinnc Core Team. “DecisionLinnc v1.1.5.8.2025.” https://www.statsape.com/.
Degenhardt, F., S. Seifert, and S. Szymczak. 2019. “Evaluation of Variable Selection Methods for Random Forests and Omics Data Sets.” Briefings in Bioinformatics 20: 492–503. https://doi.org/10.1093/bib/bbx124.
Deng, T., D. Wu, S. S. Liu, X. L. Chen, Z. W. Zhao, and L. L. Zhang. 2025. “Association of Blood Urea Nitrogen With 28‐Day Mortality in Critically Ill Patients: A Multi‐Center Retrospective Study Based on the eICU Collaborative Research Database.” PLoS One 20: e0317315. https://doi.org/10.1371/journal.pone.0317315.
Dweekat, O. Y., S. S. Lam, and L. McGrath. 2023. “An Integrated System of Braden Scale and Random Forest Using Real‐Time Diagnoses to Predict When Hospital‐Acquired Pressure Injuries (Bedsores) Occur.” International Journal of Environmental Research and Public Health 20: 4911. https://doi.org/10.3390/ijerph20064911.
El Sharouni, M. A., M. D. Stodell, T. Ahmed, et al. 2021. “Sentinel Node Biopsy in Patients With Melanoma Improves the Accuracy of Staging When Added to Clinicopathological Features of the Primary Tumor.” Annals of Oncology 32: 375–383. https://doi.org/10.1016/j.annonc.2020.11.015.
Falcão, A. L. E., A. G. A. Barros, A. A. M. Bezerra, et al. 2019. “The Prognostic Accuracy Evaluation of SAPS 3, SOFA and APACHE II Scores for Mortality Prediction in the Surgical ICU: An External Validation Study and Decision‐Making Analysis.” Annals of Intensive Care 9: 18. https://doi.org/10.1186/s13613‐019‐0488‐9.
García‐Fernández, F. P., P. L. Pancorbo‐Hidalgo, and J. J. Agreda. 2014. “Predictive Capacity of Risk Assessment Scales and Clinical Judgment for Pressure Ulcers: A Meta‐Analysis.” Journal of Wound, Ostomy, and Continence Nursing 41: 24–34. https://doi.org/10.1097/01.WON.0000438014.90734.a2.
Giri, M., L. He, T. Hu, et al. 2022. “Blood Urea Nitrogen Is Associated With in‐Hospital Mortality in Critically Ill Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Propensity Score Matching Analysis.” Journal of Clinical Medicine 11: 6709. https://doi.org/10.3390/jcm11226709.
Goldberger, A. L., L. A. Amaral, L. Glass, et al. 2000. “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals.” Circulation 101: E215–E220.
Gould, L. J., G. Bohn, R. Bryant, et al. 2019. “Pressure Ulcer Summit 2018: An Interdisciplinary Approach to Improve Our Understanding of the Risk of Pressure‐Induced Tissue Damage.” Wound Repair and Regeneration 27: 497–508. https://doi.org/10.1111/wrr.12730.
Gupta, R., S. Kumari, A. Senapati, R. K. Ambasta, and P. Kumar. 2023. “New Era of Artificial Intelligence and Machine Learning‐Based Detection, Diagnosis, and Therapeutics in Parkinson's Disease.” Ageing Research Reviews 90: 102013. https://doi.org/10.1016/j.arr.2023.102013.
Haesler, E., ed. 2019. “European Pressure Ulcer Advisory Panel, National Pressure Injury Advisory Panel, and Pan Pacific Pressure Injury Alliance.” Prevention and Treatment of Pressure Ulcers/Injuries: Clinical Practice Guideline.
Hajhosseini, B., M. T. Longaker, and G. C. Gurtner. 2020. “Pressure Injury.” Annals of Surgery 271: 671–679. https://doi.org/10.1097/sla.0000000000003567.
Handelman, G. S., H. K. Kok, R. V. Chandra, A. H. Razavi, M. J. Lee, and H. Asadi. 2018. “eDoctor: Machine Learning and the Future of Medicine.” Journal of Internal Medicine 284: 603–619. https://doi.org/10.1111/joim.12822.
Hou, N., M. Li, L. He, et al. 2020. “Predicting 30‐Days Mortality for MIMIC‐III Patients With Sepsis‐3: A Machine Learning Approach Using XGboost.” Journal of Translational Medicine 18: 462. https://doi.org/10.1186/s12967‐020‐02620‐5.
Huang, X., X. Di, S. Lin, et al. 2025. “Artificial Intelligence‐Based Prediction of Second Stage Duration in Labor: A Multicenter Retrospective Cohort Analysis.” eClinicalMedicine 80: 103072. https://doi.org/10.1016/j.eclinm.2025.103072.
Iranmanesh, S., H. Rafiei, and S. Sabzevari. 2012. “Relationship Between Braden Scale Score and Pressure Ulcer Development in Patients Admitted in Trauma Intensive Care Unit.” International Wound Journal 9: 248–252. https://doi.org/10.1111/j.1742‐481X.2011.00852.x.
Jaul, E., and R. Calderon‐Margalit. 2015. “Systemic Factors and Mortality in Elderly Patients With Pressure Ulcers.” International Wound Journal 12: 254–259. https://doi.org/10.1111/iwj.12086.
Jia, Y., H. Li, D. Li, et al. 2020. “Prognostic Value of Braden Scale in Patients With Acute Myocardial Infarction: From the Retrospective Multicenter Study for Early Evaluation of Acute Chest Pain.” Journal of Cardiovascular Nursing 35: E53–E61. https://doi.org/10.1097/jcn.0000000000000735.
Jiang, M., Y. Ma, S. Guo, et al. 2021. “Using Machine Learning Technologies in Pressure Injury Management: Systematic Review.” JMIR Medical Informatics 9: e25704. https://doi.org/10.2196/25704.
Kiyat, I., and A. Ozbas. 2024. “Comparison of the Predictive Validity of Norton and Braden Scales in Determining the Risk of Pressure Injury in Elderly Patients.” Clinical Nurse Specialist 38: 141–146. https://doi.org/10.1097/NUR.0000000000000815.
Labeau, S. O., E. Afonso, J. Benbenishty, et al. 2021. “Prevalence, Associated Factors and Outcomes of Pressure Injuries in Adult Intensive Care Unit Patients: The DecubICUs Study.” Intensive Care Medicine 47: 160–169. https://doi.org/10.1007/s00134‐020‐06234‐9.
Ladios‐Martin, M., J. Fernández‐de‐Maya, F.‐J. Ballesta‐López, A. Belso‐Garzas, M. Mas‐Asencio, and M. J. Cabañero‐Martínez. 2020. “Predictive Modeling of Pressure Injury Risk in Patients Admitted to an Intensive Care Unit.” American Journal of Critical Care 29: e70–e80. https://doi.org/10.4037/ajcc2020237.
Langemo, D. 2012. “General Principles and Approaches to Wound Prevention and Care at End of Life: An Overview.” Ostomy/Wound Management 58: 24–26.
Lima‐Serrano, M., M. I. González‐Méndez, C. Martín‐Castaño, I. Alonso‐Araujo, and J. S. Lima‐Rodríguez. 2018. “Predictive Validity and Reliability of the Braden Scale for Risk Assessment of Pressure Ulcers in an Intensive Care Unit.” Medicina Intensiva (English Edition) 42: 82–91. https://doi.org/10.1016/j.medin.2016.12.014.
McEvoy, N., P. Avsar, D. Patton, G. Curley, C. J. Kearney, and Z. Moore. 2021. “The Economic Impact of Pressure Ulcers Among Patients in Intensive Care Units. A Systematic Review.” Journal of Tissue Viability 30: 168–177. https://doi.org/10.1016/j.jtv.2020.12.004.
Mervis, J. S., and T. J. Phillips. 2019. “Pressure Ulcers: Pathophysiology, Epidemiology, Risk Factors, and Presentation.” Journal of the American Academy of Dermatology 81: 881–890. https://doi.org/10.1016/j.jaad.2018.12.069.
Mirjalili, S. R., S. Soltani, Z. H. Meybodi, et al. 2024. “Which Surrogate Insulin Resistance Indices Best Predict Coronary Artery Disease? A Machine Learning Approach.” Cardiovascular Diabetology 23: 214. https://doi.org/10.1186/s12933‐024‐02306‐y.
Noie, A., A. C. Jackson, M. Taheri, L. Sayadi, and F. Bahramnezhad. 2024. “Determining the Frequency of Pressure Ulcers Incidence and Associated Risk Factors in Critical Care Patients: A 3‐Year Retrospective Study.” International Wound Journal 21: e70120. https://doi.org/10.1111/iwj.70120.
Radakovich, N., M. Nagy, and A. Nazha. 2020. “Machine Learning in Haematological Malignancies.” Lancet Haematology 7: e541–e550. https://doi.org/10.1016/s2352‐3026(20)30121‐6.
Raju, D., X. Su, P. A. Patrician, L. A. Loan, and M. S. McCarthy. 2015. “Exploring Factors Associated With Pressure Ulcers: A Data Mining Approach.” International Journal of Nursing Studies 52: 102–111. https://doi.org/10.1016/j.ijnurstu.2014.08.002.
Song, Y. P., H. W. Shen, J. Y. Cai, M. L. Zha, and H. L. Chen. 2019. “The Relationship Between Pressure Injury Complication and Mortality Risk of Older Patients in Follow‐Up: A Systematic Review and Meta‐Analysis.” International Wound Journal 16: 1533–1544. https://doi.org/10.1111/iwj.13243.
Stansby, G., L. Avital, K. Jones, and G. Marsden. 2014. “Prevention and Management of Pressure Ulcers in Primary and Secondary Care: Summary of NICE Guidance.” BMJ (Clinical Research Ed.) 348: g2592. https://doi.org/10.1136/bmj.g2592.
Tang, Y., X. Li, H. Cheng, et al. 2024. “Braden Score Predicts 30‐Day Mortality Risk in Patients With Ischaemic Stroke in the ICU: A Retrospective Analysis Based on the MIMIC‐IV Database.” Nursing in Critical Care 30: e13125. https://doi.org/10.1111/nicc.13125.
Torsy, T., B. Serraes, and D. Beeckman. 2024. “Pressure Ulcer Risk Assessment in the ICU: The Importance of Balancing Systemic and Body‐Site Specific Risk Factors.” Intensive & Critical Care Nursing 86: 103857. https://doi.org/10.1016/j.iccn.2024.103857.
Wernly, B., M. Lichtenauer, N. A. R. Vellinga, et al. 2018. “Blood Urea Nitrogen (BUN) Independently Predicts Mortality in Critically Ill Patients Admitted to ICU: A Multicenter Study.” Clinical Hemorheology and Microcirculation 69: 123–131. https://doi.org/10.3233/ch‐189111.
Xu, J., D. Chen, X. Deng, et al. 2022. “Development and Validation of a Machine Learning Algorithm‐Based Risk Prediction Model of Pressure Injury in the Intensive Care Unit.” International Wound Journal 19: 1637–1649. https://doi.org/10.1111/iwj.13764.
Yang, G., T. Montgomery‐Csobán, W. Ganzevoort, et al. 2025. “Consecutive Prediction of Adverse Maternal Outcomes of Preeclampsia, Using the PIERS‐ML and fullPIERS Models: A Multicountry Prospective Observational Study.” PLoS Medicine 22: e1004509. https://doi.org/10.1371/journal.pmed.1004509.
Yang, Y., H. Shen, H. Guan, et al. 2025. “Association Between Braden Scale and All‐Cause Mortality in Critically Ill Patients With Non‐Traumatic Subarachnoid Hemorrhage: Analysis of the MIMIC‐IV Database.” Neurosurgical Review 48: 345. https://doi.org/10.1007/s10143‐025‐03508‐y.
Yuan, J., J. Xiong, J. Yang, et al. 2025. “Machine Learning‐Based 28‐Day Mortality Prediction Model for Elderly Neurocritically Ill Patients.” Computer Methods and Programs in Biomedicine 260: 108589. https://doi.org/10.1016/j.cmpb.2025.108589.
Zhang, W., L. Ji, X. Wang, et al. 2021. “Nomogram Predicts Risk and Prognostic Factors for Bone Metastasis of Pancreatic Cancer: A Population‐Based Analysis.” Frontiers in Endocrinology 12: 752176. https://doi.org/10.3389/fendo.2021.752176.
Grant Information:
Wenzhou Municipal Science and Technology Bureau
Contributed Indexing:
Keywords: Braden Scale; MIMIC; death risk; machine learning; pressure ulcer
Entry Date(s):
Date Created: 20250612 Date Completed: 20250819 Latest Revision: 20250819
Update Code:
20250827
DOI:
10.1111/jocn.17860
PMID:
40506399
Database:
MEDLINE
Weitere Informationen
Aims: This study was to create an interpretable machine learning model to predict the risk of mortality within 90 days for ICU patients suffering from pressure ulcers.
Design: We retrospectively analysed 1774 ICU pressure ulcer patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database.
Methods: We used the LASSO regression and the Boruta algorithm for feature selection. The dataset was split into training and test sets at a 7:3 ratio for constructing machine learning models. We employed logistic regression and nine other machine learning algorithms to build the prediction model. Restricted cubic spline (RCS) was used to analyse the linear relationship between the Braden score and the outcome, whereas the SHAP (Shapley additive explanations) method was applied to visualise the model's characteristics.
Results: This study compared the predictive ability of the Braden Scale with other scoring systems (SOFA, APSIII, Charlson, SAPSII). The results showed that the Braden Scale model had the highest performance, and SHAP analysis indicated that the Braden Scale is an important influencing factor for the risk of 90-day mortality in the ICU. The restricted cubic spline curve demonstrated a significant negative correlation between the Braden Scale and mortality. Subgroup analysis showed no significant interaction effects among subgroups except for age.
Conclusions: The machine learning-enhanced Braden Scale has been developed to forecast the 90-day mortality risk for ICU patients suffering from pressure ulcers, and its efficacy as a clinically reliable tool has been substantiated.
Patient or Public Contribution: Patients or public members were not directly involved in this study.
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