{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:55:44Z","timestamp":1781844944435,"version":"3.54.5"},"reference-count":63,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019783","name":"National Agency for Research and Development","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100019783","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Scholarship program, Subdirectorate of Human Capital\u2014National Doctorate 2024","award":["21240875"],"award-info":[{"award-number":["21240875"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Pressure injury (PI) prevention relies heavily on the application of the Braden Scale, yet its predictive performance is limited mainly due to subjective measurements and fixed cut-off points, yielding potential errors in terms of false-positive alarm rates and workforce misuse. Machine learning (ML) has emerged as a promising approach to improve discrimination, but fair comparisons with traditional tools remain scarce in clinical settings. Methods: Using data from 446 hospitalized patients in a tertiary hospital in Chile, we compared five classic ML models for classification against the Braden Scale. ML models were trained exclusively on routinely collected clinical and nursing variables, excluding Braden inputs. A matched operating-point framework was applied, aligning ML decision thresholds with Braden cutoffs based on equivalent recall or specificity. Results: The XGB model showed the highest overall discrimination performance (AUC = 0.835). When matched to Braden recall, XGB achieved about 17% gains in specificity, substantially reducing false positives. When matched on specificity, recall improvements ranged from 13% to 25%. These gains were consistent across clinically relevant thresholds considered during the comparison. Conclusions: ML models, particularly XGB, outperform the Braden Scale under equivalent clinical operating conditions. Rather than replacing Braden, ML has emerged as a promising approach that preserves patient safety while improving precision and resource allocation.<\/jats:p>","DOI":"10.3390\/jcm15124683","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T05:56:31Z","timestamp":1781675791000},"page":"4683","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pressure Injury Risk Assessment in Nursing Practice: A Head-to-Head Comparison of the Braden Scale and Machine Learning Models"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5346-3841","authenticated-orcid":false,"given":"Fredy","family":"Barriga-Gallegos","sequence":"first","affiliation":[{"name":"Institute for Health Care Research, Faculty of Nursing, Universidad Andr\u00e9s Bello, Santiago 8370146, Chile"},{"name":"Transportation and Logistics Center, Department of Engineering Sciences, Universidad Andr\u00e9s Bello, Santiago 7500735, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7626-8334","authenticated-orcid":false,"given":"Gonzalo","family":"R\u00edos-V\u00e1squez","sequence":"additional","affiliation":[{"name":"School of Industrial Engineering, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Valpara\u00edso 2340025, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6998-6408","authenticated-orcid":false,"given":"Paulo","family":"Figueroa-Torrez","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias Industriales, Medio Ambiente y Energ\u00eda, Universidad Cat\u00f3lica Boliviana San Pablo, Col\u00f3n 734, Tarija, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2564-8770","authenticated-orcid":false,"given":"Hanns","family":"de la Fuente-Mella","sequence":"additional","affiliation":[{"name":"Instituto de Estad\u00edstica, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Valpara\u00edso 2340031, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine Almarza","family":"Garrido","sequence":"additional","affiliation":[{"name":"Subdirection of Care Management, F\u00e9lix Bulnes Clinical Hospital, Santiago 9110056, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6885-5801","authenticated-orcid":false,"given":"Naldy","family":"Febr\u00e9 Vergara","sequence":"additional","affiliation":[{"name":"Institute for Health Care Research, Faculty of Nursing, Universidad Andr\u00e9s Bello, Santiago 8370146, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1097\/WON.0000000000000281","article-title":"Revised National Pressure Ulcer Advisory Panel Pressure Injury Staging System: Revised Pressure Injury Staging System","volume":"43","author":"Edsberg","year":"2016","journal-title":"J. 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