{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T23:56:37Z","timestamp":1787961397397,"version":"build-2784847793"},"reference-count":26,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"content-version":"vor","delay-in-days":20,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Wound Journal"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>To construct and validate the risk assessment tool of medical device\u2010related pressure injury (MDRPI) for Pulmonary and Critical Care Medicine (PCCM), help clinical medical staff to quickly and effectively screen high\u2010risk groups and provide a reference for the development of targeted early intervention measures. The department of PCCM mainly treats elderly patients and patients with chronic diseases of the respiratory system, and frequently uses oxygen therapy devices, monitors and treatment pipelines. It is a high\u2010risk department for MDRPI. Once MDRPI occurs, it is not easy to heal and may lead to various complications and affect the disease prognosis. At present, there is no specialised assessment tool for PCCM patients. A multi\u2010centre prospective study. We collected data from 932 PCCM patients who used medical devices in three Grade III Class A Comprehensive Hospitals from November 2022 to October 2023. Of those, 652 cases were assigned to the modelling and 280 to the verification groups. Logistic regression was used to construct the model. The AUC was used to test the predictive effect of the model. The risk assessment tool was constructed with the OR (odds ratio) value obtained by binary Logistic multi\u2010variate regression analysis. Verification groups were used for validated the risk assessment tool. The factors entered into the prediction model were use of nasal catheter, high flow oxygen therapy, non\u2010invasive ventilation, invasive ventilation, having chronic respiratory disease, using hormonal drugs, sedative drugs and abnormal skin condition. The prediction model was transformed into a risk assessment tool, and the OR values were rounded to form the MDRPI risk assessment tool with the values ranging from 0 to 66 points. The area under the ROC curve (AUC) is 0.861, and the maximum value of Youden index (YI) is 0.606, corresponding to a sensitivity of 80.6%, specificity of 80.0% and a cut\u2010off value of 17, divided patients into low risk\u2009\u2264\u200917 and high risk\u2009&gt;\u200917. The risk assessment tool applied to the clinic, and the accuracy was 92.75%. The risk assessment tool can provide clinical guidance and predict the risk of MDRPI for PCCM patients. Clinical nurses in PCCM can use the risk assessment tool to assess the risk of MDRPI occurrence and provide a reference for preventive measures.<\/jats:p>","DOI":"10.1111\/iwj.70335","type":"journal-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:21:15Z","timestamp":1745281275000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Construction of Medical Device\u2010Related Pressure Injury Risk Assessment Tool for Pulmonary and Critical Care Medicine: A Multi\u2010Centre Prospective Study"],"prefix":"10.1111","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7323-6020","authenticated-orcid":false,"given":"Yan\u2010Qiao","family":"Bao","sequence":"first","affiliation":[{"name":"The First College of Clinical Medical Science China Three Gorges University  Yichang China"},{"name":"Yichang Central People's Hospital  Yichang 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