Predictors Affecting In-hospital Mortality of Ruptured Abdominal Aortic Aneurysms: A Greek Multicenter Study

Antonopoulos, Constantine N.; Kakisis, John D.; Andrikopoulos, Vasilios; Dervisis, Konstantinos; Georgopoulos, Sotirios; Giannoukas, Athanasios D.; Kiskinis, Dimitrios; Machairas, Anastasios; Papavassiliou, Vasilios; Liapis, Christos D.; Antoniadis, Pavlos; Bessias, Nikolaos; Giannakopoulos, Triantafillos G.; Kaperonis, Elias; Klonaris, Chris; Saleptsis, Vasilios; Saratzis, Nikolaos; Seretis, Konstantinos; Tampakis, Charalambos

doi:10.1016/j.avsg.2013.12.028

Cited by 13 publications

(10 citation statements)

References 21 publications

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“…1 The development of a risk score that incorporates patients who underwent either EVAR or open AAA repair remains elusive. The endovascular approach may lead to improved survival in tertiary care centers where it is readily accessible, 9 although prospective data currently does not support this in the case of emergent rAAA repair. 10 …”

Section: Introductionmentioning

confidence: 99%

Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network

Wise

Hocking

Brophy

2015

Journal of Vascular Surgery

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Objective Ruptured abdominal aortic aneurysm (rAAA) carries a high mortality rate, even with prompt transfer to a medical center. An artificial neural network (ANN) is a computational model which improves predictive ability via pattern recognition, while continually adapting to new input data. The goal of this study was to effectively use ANN modeling to provide vascular surgeons a discriminant adjunct to assess the likelihood of in-hospital mortality on a pending rAAA admission using easily obtainable patient information from the field. Methods One-hundred and twenty-five of 332 total patients from a single-institution from 1998–2013 who had attempted rAAA repair were reviewed for preoperative factors associated with in-hospital mortality. One-hundred and eight patients received an open operation, and 17 patients received endovascular repair. Five variables were found significant upon multivariate analysis (P < .05), and four of these five: preoperative shock, loss of consciousness, cardiac arrest and age were modeled via multiple logistic regression and an ANN. These predictive models were compared against the Glasgow Aneurysm Score (GAS). All models were assessed by generation of receiver operating characteristic curves and Actual vs. Predicted outcomes plots, with area under the curve (AUC) and Pearson r2 value as the primary measures of discriminant ability. Results Of the 125 patients, 53 (42%) did not survive to discharge. Five preoperative factors were significant (P < .05) independent predictors of in-hospital mortality in multivariate analysis: advanced age, renal disease, loss of consciousness, cardiac arrest and shock, though renal disease was excluded from the models. The sequential accumulation of zero to four of these risk factors progressively increased overall mortality rate, from 11% to 16% to 44% to 76% to 89% (Age ≥ 70 considered a risk factor). Algorithms derived from multiple logistic regression, ANN and GAS models generated AUC values of .85 ± .04, .88 ± .04 (training-set) and .77 ± .06, and Pearson r2 values of .36, .52 and .17, respectively. The ANN model represented the most discriminant of the three. Conclusions An ANN-based predictive model may represent a simple, useful and highly discriminant adjunct to the vascular surgeon in accurately identifying those patients who may carry a high mortality risk from attempted repair of rAAA, using only easily definable preoperative variables. Though still requiring external validation, our model is available for demonstration at https://redcap.vanderbilt.edu/surveys/?s=NN97NM7DTK.

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Section: Introductionmentioning

confidence: 99%

Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network

Wise

Hocking

Brophy

2015

Journal of Vascular Surgery

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“…It was validated in two studies (C‐statistic 0·710, 95 per cent c.i. 0·66 to 0·76, H‐L P = 0·01; C‐statistic 0·780, 0·740 to 0·830). Because of the paucity and continuous nature of the variables in the model, the EPV was larger than 1.…”

Section: Resultsmentioning

confidence: 96%

Systematic review of mortality risk prediction models in the era of endovascular abdominal aortic aneurysm surgery

Lijftogt

Luijnenburg

Vahl

et al. 2017

British Journal of Surgery

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“…A recent meta-analysis of abdominal compartment syndrome after endovascular repair of rAAAs revealed that there was a significant linear correlation between hemodynamic instability and death [20]. Furthermore, in another recent study based on predictors affecting in-hospital mortality of rAAAs, it was stated that hemodynamic instability (P < 0.001) independently increased in-hospital mortality [21].…”

Section: Discussionmentioning

confidence: 97%

“…21 greater than or equal to grade 2, then the patient was considered to have a hostile anatomy (Fig. 2).…”

Section: Patients With Hostile Anat Omy (N=23) Groupmentioning

confidence: 99%

Outcomes of EVAR with the endurant stent-graft system in patients with infrarenal ruptured abdominal aortic aneurysms: Is hostile anatomy a challenging factor?

Küçükay

Karan

Simsek

et al. 2015

European Journal of Radiology

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Predictors Affecting In-hospital Mortality of Ruptured Abdominal Aortic Aneurysms: A Greek Multicenter Study

Cited by 13 publications

References 21 publications

Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network

Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network

Systematic review of mortality risk prediction models in the era of endovascular abdominal aortic aneurysm surgery

Outcomes of EVAR with the endurant stent-graft system in patients with infrarenal ruptured abdominal aortic aneurysms: Is hostile anatomy a challenging factor?

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