# Prediction of Blood Pressure after Induction of Anesthesia Using Deep Learning: A Feasibility Study
## 4 Discussion  
We investigated whether RNN could predict future blood pressure (e.g., 3 min ahead) during anesthesia-induction period. We found that our model could predict 3-min ahead blood pressure with absolute error around 10 mmHg for each SBP, DBP, and MBP. Although this error seems to be large for helping clinicians to use our model as decision support tool in the hemodynamic management during anesthesia for now, we suggest it is feasible for RNN to predict future blood pressure using only features those obtained from various anesthesia monitors, ventilator and drug infusion pump in relatively short periods. We examine the plots of predicted blood pressure and true blood pressure. To do so, we trained the model with 90% of shuffled data, and the remaining data is used for examination. Figure 9 shows three plots of SBP prediction, where the two upper examples are relatively well predicted cases and the bottom example shows a poorly predicted case. Please note that the model gives its first prediction at the 120-th timestep because it sees the sequential data of three minutes (i.e., 60 timesteps) and predicts three minutes later (i.e., 60 timesteps). Because the SBP is sampled every minute, the plot of true SBP  
Figure 8: Bland-Altman diagrams of SBP prediction, MBP prediction, and DBP prediction, where TBP and PBP mean true blood pressures and predicted blood pressures, respectively. looks like stairs. Generally speaking, the three figures in Figure 9 show that the model well predicts the trend of future blood pressure; it captures when the SBP will arise, keep or fall. Interestingly, as shown in the second figure of Figure 9, the predicted SBP fluctuates as the true SBP even though it predicts the SBP of three minutes after. On the other hand, in the bottom figure, the predicted SBP follows the trend of true SBP but there is a steady gap between them. We believe that such gap will be reduced if we collect more data to incorporate various patterns of blood pressure. Among the hemodynamic changes occurring during surgery, hypotension is known to be frequent and has been reported to cause adverse outcomes after surgery [1]. Definition of intraoperative hypotension varies among investigators which ranges from MBP of 55 mmHg to 65 mmHg. In [33], it was revealed that MBP less than 60 mmHg for 11 to 20 min and MBP less than 55 mmHg for more than 10 min are associated with acute kidney injury. The mean absolute error of MBP predicted by our proposed model was 9 mmHg, which may not helpful to clinicians in some critical situations. For example, if the actual MBP is 58 mmHg, then MBP predicted by our model may range from 49 mmHg to 67 mmHg. Such variation of the predicted MBP might cause two opposite ways of management. If the predicted MBP is 49 mmHg, one will explore possible causes for hypotension, whereas one just observes blood pressures and do nothing if the predicted MBP is 67 mmHg. Of course, there can be another case that the predicted MBP is helpful. Assuming that actual MBP is around 75 mmHg, and predicted MBP may range from 66 mmHg to 84 mmHg. This is generally not harmful to most surgical patients. The Association for the Advancement of Medical Instrumentation (AAMI) established standards for the validation of automatic arterial pressure monitoring. It was defined  
Figure 9: Plots of predicted SBP (orange color) and true SBP (blue color), where horizontal axis represents a timestep and vertical axis is the SBP (mmHg). as acceptable if error (e.g., mean absolute error) is not greater than 5 mmHg and standard deviation of errors is not greater than 8 mmHg for SAP and DAP [34]. In this regard, as the mean absolute errors of our model for SBP and MBP were 11 mmHg and 9 mmHg, respectively, which does not meet the AAMI standards. However, there is no consensus on the accuracy of clinically acceptable blood pressure because the AAMI standards are for the approving clinical validation of new automated blood pressure devices.