The algorithm for detecting critical conditions during anesthesia

The complexity of modern anesthesia procedures requires the development of decision support systems functioning in a smart-alarm capacity. Based on computerized anesthesia records containing hemodynamic data (heart rate, mean arterial pressure and systolic arterial pressure) and assessments made by experienced anesthesiologists reviewing printed anesthesia records, we have developed rule-based computer algorithms to detect critical conditions during surgery, such as inadequate (light) anesthesia (LA) or unstable blood pressure (lability). Our analysis indicated that a ⩾12% change in mean arterial blood pressure (MAP), compared with the median value of MAP over the preceding 10 minute interval, may be chosen as the criterion for detecting LA, with a sensitivity of 96% and a specificity of 91%. The best agreement between human and computer ratings of blood pressure lability (correlation coefficient 0.78) was achieved when we used the absolute value of the fractional change of the mean arterial pressure (|FCM|) between one 2-min epoch and the next 2-min epoch. We developed rule-based computer algorithms to defect critical conditions during surgery (light anesthesia or unstable blood pressure), based on computerized anesthesia records containing hemodynamic data (heart rate, mean arterial pressure and systolic arterial pressure)