Artificial Neural Networks Versus Multiple Logistic Regression to Predict 30-Day Mortality After Operations For Type A Ascending Aortic Dissection§

Abstract

Background:There are few comparative reports on the overall accuracy of neural networks (NN), assessed only versus multiple logistic regression (LR), to predict events in cardiovascular surgery studies and none has been performed among acute aortic dissection (AAD) Type A patients.Objectives:We aimed at investigating the predictive potential of 30-day mortality by a large series of risk factors in AAD Type A patients comparing the overall performance of NN versus LR.Methods:We investigated 121 plus 87 AAD Type A patients consecutively operated during 7 years in two Centres. Forced and stepwise NN and LR solutions were obtained and compared, using receiver operating characteristic area under the curve (AUC) and their 95% confidence intervals (CI) and Gini’s coefficients. Both NN and LR models were re-applied to data from the second Centre to adhere to a methodological imperative with NN.Results:Forced LR solutions provided AUC 87.9±4.1% (CI: 80.7 to 93.2%) and 85.7±5.2% (CI: 78.5 to 91.1%) in the first and second Centre, respectively. Stepwise NN solution of the first Centre had AUC 90.5±3.7% (CI: 83.8 to 95.1%). The Gini’s coefficients for LR and NN stepwise solutions of the first Centre were 0.712 and 0.816, respectively. When the LR and NN stepwise solutions were re-applied to the second Centre data, Gini’s coefficients were, respectively, 0.761 and 0.850. Few predictors were selected in common by LR and NN models: the presence of pre-operative shock, intubation and neurological symptoms, immediate post-operative presence of dialysis in continuous and the quantity of post-operative bleeding in the first 24 h. The length of extracorporeal circulation, post-operative chronic renal failure and the year of surgery were specifically detected by NN.Conclusions:Different from the International Registry of AAD, operative and immediate post-operative factors were seen as potential predictors of short-term mortality. We report a higher overall predictive accuracy with NN than with LR. However, the list of potential risk factors to predict 30-day mortality after AAD Type A by NN model is not enlarged significantly.

"Overtime Hours Effect" on Emergency Surgery of Acute Type A Aortic Dissection

“…Despite different results from previous studies, factors affecting mortality can be predicted. Macrina et al [3] evaluated 208 patients operated for ATAAD. They identified age, preoperative shock, intubation and previous cardiac surgery as predictors of mortality.…”

“…These factors include history of aortic valve replacement, migrating chest pain, hypotension and/or shock, cardiac tamponade, extremity ischemia, duration of cardiopulmonary bypass, and chronic renal failure. In investigations on the effect of surgical technique, the efficacy of antegrade, retrograde, or selective cerebral perfusion following circulatory arrest remains a controversial topic [1,3] .…”

Long-term mortality prediction after operations for type A ascending aortic dissection

“…They contributed significantly to obtain a large overall accuracy, which might be explained in part by these factors being continuous [19]. On the other hand, similar to studies investigating predictive performance of short-term mortality after coronary artery bypass surgery [9,10], neural networks had a better performance when compared to standard methods such as logistic regression [2,5]. …”

“…These were selected based on a Literature review of studies performed to assess the role of relatively short-term potential predictors [2]. Thus, year of surgery, hospital localization, age, sex and presence of clinically diagnosed high blood pressure and Marfan habitus were considered.…”

Modification in aortic arch replacement surgery

“…We modified the conventional aortic arch replacement procedure, especially for acute aortic dissection, to avoid circulation arrest and a prolonged extracorporeal circulation time, which have been identified as independent risk factors for morbidity and mortality [ 1 , 2 ].…”