Predicting operative mortality in patients who undergo elective open thoracoabdominal aortic aneurysm repairCentral MessagePerspective
Kyle W. Blackburn, BS,
Susan Y. Green, MPH,
Allen Kuncheria, BA,
Meng Li, PhD,
Adel M. Hassan, BA,
Brittany Rhoades, PhD,
Scott A. Weldon, MA,
Subhasis Chatterjee, MD,
Marc R. Moon, MD,
Scott A. LeMaire, MD,
Joseph S. Coselli, MD
Affiliations
Kyle W. Blackburn, BS
Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex
Susan Y. Green, MPH
Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Office of Surgical Research, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex
Allen Kuncheria, BA
Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex
Meng Li, PhD
Department of Statistics, Rice University, Houston, Tex
Adel M. Hassan, BA
Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex
Brittany Rhoades, PhD
Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex
Scott A. Weldon, MA
Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Office of Surgical Research, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex
Subhasis Chatterjee, MD
Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, The Texas Heart Institute, Houston, Tex; Department of Cardiovascular Surgery, CHI St Luke’s Health–Baylor St Luke’s Medical Center, Houston, Tex; Cardiovascular Research Institute, Baylor College of Medicine, Houston, Tex
Marc R. Moon, MD
Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, The Texas Heart Institute, Houston, Tex; Department of Cardiovascular Surgery, CHI St Luke’s Health–Baylor St Luke’s Medical Center, Houston, Tex; Cardiovascular Research Institute, Baylor College of Medicine, Houston, Tex
Scott A. LeMaire, MD
Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Research Institute and Heart & Vascular Institute, Geisinger, Danville, Pa
Joseph S. Coselli, MD
Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, The Texas Heart Institute, Houston, Tex; Department of Cardiovascular Surgery, CHI St Luke’s Health–Baylor St Luke’s Medical Center, Houston, Tex; Cardiovascular Research Institute, Baylor College of Medicine, Houston, Tex; Address for reprints: Joseph S. Coselli, MD, Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, One Baylor Plaza, BCM 390, Houston, TX 77030.
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.
