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Patients were randomly split into training (70%) and validation (30%) cohorts. AKI was defined per RIFLE criteria. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection from demographics, medical history, imaging, surgical data, and inflammatory ratios (including preoperative, postoperative, and \u0394 values). Multivariable logistic regression built the final model, evaluated by discrimination (C-statistic), calibration (plots, Hosmer-Lemeshow test), and clinical utility (decision curve analysis).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The incidence of postoperative AKI was 17.2%. The final model incorporated four independent predictors: \u0394NLR (Odds Ratio [OR]: 2.23), preoperative platelet-to-fibrinogen ratio (PFR) (OR: 1.95), open surgery (OR: 5.37), and drinking history (OR: 1.72). The model demonstrated good and consistent discrimination, with a C-statistic of 0.751 (95% CI: 0.708\u20130.794,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) in the training cohort and 0.732 (95% CI: 0.673\u20130.791,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) in the validation cohort. Calibration curves showed excellent agreement between predicted and observed probabilities (Hosmer-Lemeshow test\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.172). Decision curve analysis confirmed significant clinical net benefit across a clinically relevant range of risk thresholds (approximately 5% to 80%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed a robust predictive model for AKI after AD surgery, highlighting the critical value of dynamic inflammation monitoring via \u0394NLR. This practical tool facilitates early identification of high-risk patients, potentially enabling timely preventive strategies to improve postoperative outcomes. External validation is warranted to confirm generalizability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial number<\/jats:title>\n                    <jats:p>Not applicable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-026-03443-y","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T09:38:55Z","timestamp":1774431535000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning prediction of postoperative acute kidney injury in aortic dissection patients using dynamic inflammatory markers and clinical features"],"prefix":"10.1186","volume":"26","author":[{"given":"Yansong","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuewu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chanyu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanbiao","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caiying","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiying","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junting","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"issue":"8","key":"3443_CR1","doi-asserted-by":"publisher","first-page":"2624","DOI":"10.1007\/s00259-020-05186-y","volume":"48","author":"X Li","year":"2021","unstructured":"Li X, Montazeri SA, Paz-Fumagalli R, Padula CA, Wang W, Mody K, Roberts LR, Patel T, Krishnan S, Toskich B. 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