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Based on these measurements, ADO pretreated patients had improved ventricular performance postoperatively. It also appears that ADO pretreatment results in lowered postoperative myocardial energy demand and less myocellular injury during CPB. To our knowledge, this is the first study to demonstrate that human myocardium can be hemodynamically improved with ADO pretreatment, and may be protected against IRI incurred during and following the CPB. We believe that a cardiac surgeon may now have the unique opportunity to confer myocardial protection during and after a cardiac surgical procedure.

BackgroundAdvanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics.MethodsThirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual.ResultsFactors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a “new” patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001).ConclusionsANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities.

Diabetes mellitus is associated with coronary artery disease, and diabetic patients are frequently referred for coronary bypass graft surgery. It is well known that HbA1c, which reflects long-term glycemic control, is related to diabetic morbidity and mortality. It is not known whether HbA1c is related to postoperative length of stay among patients who undergo coronary artery bypass surgery. The authors evaluated 135 patients who underwent bypass surgery at the Westchester Medical Center (Valhalla, NY). HbA1c was measured in all patients preoperatively; a value of 7% or greater was used as a threshold for uncontrolled hyperglycemia. A postoperative length of stay of 6 days or more was used as the cutoff for an extended length of stay. Linear regression was used to assess the relationship between HbA1c, adjusted for age, and length of stay in days. Logistic regression, with length of stay a binary variable <6, > or =6 days, was used to assess the accuracy of HbA1c <7%, > or =7%, adjusted for age, in predicting length of stay. An HbA1c of 7% or greater was found to be a strong predictor of a length of stay of 6 days or longer. These data suggest that HbA1c can be used as a surrogate marker for cardiac and noncardiac morbidity that prolongs hospitalization after coronary artery bypass surgery.

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