Publication Date
12-1-2024
Journal
JTCVS Open
DOI
10.1016/j.xjon.2024.09.002
PMID
39780810
PMCID
PMC11704537
PubMedCentral® Posted Date
9-10-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
aortic aneurysm, operative mortality, clinical decision rules, prognosis, nomograms, patient counseling, surgical risk, outcome assessment, health care
Abstract
Background
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.
Included in
Cardiology Commons, Cardiovascular Diseases Commons, Medical Sciences Commons, Mental and Social Health Commons, Otolaryngology Commons, Otorhinolaryngologic Diseases Commons, Surgery Commons
