Object: The utility of preoperative hemostasis screening to predict complications is uncertain.

We tested the hypothesis that the quantitative analysis of electrocardiograhic changes can predict complications in stress cardiomyopathy.

Machine-learning approaches were used to predict complications.

There is a relative paucity of data demonstrating patient factors that predict complications specifically by stage of surgery.

Body mass index and smoking are associated with complications at stage I, but do not predict complications at stage II surgery.

Machine-learning models indicated that midarm circumference, one of the GLIM models, and midarm muscle area were the most relevant criteria to predict complications.

Knowing these relationships is necessary for developing risk stratification, defining contraindications, and predicting complications and adverse outcomes.

Future investigation will focus on identification of factors predicting complications and strategies to reduce the incidence and severity.

Conclusion: Using easily obtainable information, this model showed good accuracy in predicting complications within one year after hypospadias surgery.

Objective: To analyze the performance of severity indices in predicting complications in patients in the postoperative of CABG during the ICU stay.