Predicting operative mortality in patients who undergo elective open thoracoabdominal aortic aneurysm repair

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dc.citation.firstpage95en_US
dc.citation.journalTitleJTCVS Openen_US
dc.citation.lastpage103en_US
dc.citation.volumeNumber22en_US
dc.contributor.authorBlackburn, Kyle W.en_US
dc.contributor.authorGreen, Susan Y.en_US
dc.contributor.authorKuncheria, Allenen_US
dc.contributor.authorLi, Mengen_US
dc.contributor.authorHassan, Adel M.en_US
dc.contributor.authorRhoades, Brittanyen_US
dc.contributor.authorWeldon, Scott A.en_US
dc.contributor.authorChatterjee, Subhasisen_US
dc.contributor.authorMoon, Marc R.en_US
dc.contributor.authorLeMaire, Scott A.en_US
dc.contributor.authorCoselli, Joseph S.en_US
dc.date.accessioned2025-01-09T20:16:55Zen_US
dc.date.available2025-01-09T20:16:55Zen_US
dc.date.issued2024en_US
dc.description.abstractBackground We have developed a model aimed at identifying preoperative predictors of operative mortality in patients who undergo elective, open thoracoabdominal aortic aneurysm (TAAA) repair. We converted this model into an intuitive nomogram to aid preoperative counseling. Methods We retrospectively analyzed data from 2884 elective, open TAAA repairs performed between 1986 and 2023 in a single practice. Using clinical and selected operative variables, we built 4 predictive models: multivariable logistic regression (MLR), random forest, support vector machine, and gradient boosting machine. Each model’s predictive effectiveness was evaluated with the C-statistic. Test C-statistics were computed using an 80:20 cross-validation scheme with 1000 iterations. Results Operative death occurred in 200 patients (6.9%). Test set C-statistics showed that the MLR model (median, 0.68; interquartile range [IQR], 0.65-0.71) outperformed the machine learning models (0.61 [IQR, 0.59-0.64] for random forest; 0.61 [IQR, 0.58-0.64] for support vector machine; 0.65 [IQR, 0.62-0.67] for gradient boosting machine). The final MLR model was based on 7 characteristics: increasing age (odds ratio [OR], 1.04/y; P < .001), cerebrovascular disease (OR, 1.54; P = .01), chronic kidney disease (OR, 1.53; P = .008), symptomatic aneurysm (OR, 1.42; P = .02), and Crawford extent I (OR, 0.66; P = .08), extent II (OR, 1.61; P = .01), and extent IV (OR, 0.41; P = .002). We converted this model into a nomogram. Conclusions Using institutional data, we evaluated several models to predict operative mortality in elective TAAA repair, using information available to surgeons preoperatively. We then converted the best predictive model, the MLR model, into an intuitive nomogram to aid patient counseling.en_US
dc.identifier.citationBlackburn, K. W., Green, S. Y., Kuncheria, A., Li, M., Hassan, A. M., Rhoades, B., Weldon, S. A., Chatterjee, S., Moon, M. R., LeMaire, S. A., & Coselli, J. S. (2024). Predicting operative mortality in patients who undergo elective open thoracoabdominal aortic aneurysm repair. JTCVS Open, 22, 95–103. https://doi.org/10.1016/j.xjon.2024.09.002en_US
dc.identifier.digital1-s2-0-S266627362400250X-mainen_US
dc.identifier.doihttps://doi.org/10.1016/j.xjon.2024.09.002en_US
dc.identifier.urihttps://hdl.handle.net/1911/118090en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subject.keywordaortic aneurysmen_US
dc.subject.keywordoperative mortalityen_US
dc.subject.keywordclinical decision rulesen_US
dc.subject.keywordprognosisen_US
dc.subject.keywordnomogramsen_US
dc.subject.keywordpatient counselingen_US
dc.subject.keywordsurgical risken_US
dc.subject.keywordoutcome assessmenten_US
dc.subject.keywordhealth careen_US
dc.titlePredicting operative mortality in patients who undergo elective open thoracoabdominal aortic aneurysm repairen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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