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Univariate and multivariate logistic analyses were used to identify the risk factors for PAC.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The incidence of PAC in 661 patients who underwent hepatectomy was 54.8% (362\/661). The least absolute shrinkage and selection operator (LASSO) method was used for multivariate logistic regression analysis. The preoperative international normalized ratio (INR), intraoperative succinyl gelatin infusion and major hepatectomy were found to be independent risk factors for PAC. A nomogram for predicting the PAC after hepatectomy was constructed. The model presented a receiver operating characteristic (ROC) curve of 0.742 (95% confidence interval (CI): 0.697\u20130.786) in the training cohort. The validation set demonstrated a promising ROC of 0.711 (95% CI: 0.639\u20130.783), and the calibration curve closely approximated the true incidence. Decision curve analysis (DCA) was performed to assess the clinical usefulness of the predictive model. The risk of PAC increased when the preoperative international normalized ratio (INR) was greater than 1.025 and the volume of intraoperative succinyl gelatin infusion was greater than 1500\u00a0ml.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The PAC is closely related to the preoperative INR, intraoperative succinyl gelatin infusion and major hepatectomy. A three-factor prediction model was successfully established for predicting the PAC after hepatectomy.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12893-024-02406-2","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T11:02:19Z","timestamp":1714993339000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Abnormal coagulation after hepatectomy in patients with normal preoperative coagulation function"],"prefix":"10.1186","volume":"24","author":[{"given":"Liting","family":"Kuang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weibin","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dahui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,6]]},"reference":[{"key":"2406_CR1","doi-asserted-by":"publisher","first-page":"106648","DOI":"10.1016\/j.ijsu.2022.106648","volume":"103","author":"J Zhang","year":"2022","unstructured":"Zhang J, Yang X, Fang J, Cheng Q-C, Ding H-R, Sun Y-R, et al. 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