{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T20:02:46Z","timestamp":1766088166354,"version":"3.40.5"},"reference-count":38,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T00:00:00Z","timestamp":1702425600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Head &amp; Neck"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Tracheal necrosis is a potentially severe complication of total pharyngolarynjectomy (TPL), sometimes combined with total esophagectomy. The risk factors for tracheal necrosis after TPL without total esophagectomy remain unknown.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We retrospectively reviewed data of 395 patients who underwent TPL without total esophagectomy. Relevant factors associated with tracheal necrosis were evaluated using random forest machine learning and traditional multivariable logistic regression models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Tracheal necrosis occurred in 25 (6.3%) patients. Both the models identified almost the same factors relevant to tracheal necrosis. History of radiotherapy was the most important predicting and significant risk factor in both models. Paratracheal lymph node dissection and total thyroidectomy with TPL were also relevant. Random forest model was able to predict tracheal necrosis with an accuracy of 0.927.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Random forest is useful in predicting tracheal necrosis. Countermeasures should be considered when creating a tracheostoma, particularly in patients with identified risk factors.<\/jats:p><\/jats:sec>","DOI":"10.1002\/hed.27598","type":"journal-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T11:22:14Z","timestamp":1702466534000},"page":"408-416","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine learning approach to predict tracheal necrosis after total pharyngolaryngectomy"],"prefix":"10.1002","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6581-4791","authenticated-orcid":false,"given":"Takeaki","family":"Hidaka","sequence":"first","affiliation":[{"name":"Department of Plastic and Reconstructive Surgery National Cancer Center Hospital East  Kashiwa Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shimpei","family":"Miyamoto","sequence":"additional","affiliation":[{"name":"Department of Plastic, 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