1. Considering the model’s performance, what additional data or features do you think could further improve the accuracy of predicting PIH?
From my perspective, including a patient’s detailed medication history in the model can significantly improve the prediction of low blood pressure during anesthesia. Medications like antihypertensives, beta-blockers, and other drugs for heart conditions can affect how a patient’s blood pressure responds during surgery. This additional information helps create a more accurate prediction model, allowing for better preparation and management of anesthesia to prevent complications.
2. What future research directions would you suggest to address the limitations of this study and enhance the predictive model’s applicability across various surgical procedures?
In my opinion, to make the predictive model more reliable and applicable to a wider range of patients, it is necessary to include larger groups of patients in the study. Moreover, tracking patients for a longer period after surgery can provide insights into the long-term effects of low blood pressure during anesthesia. This extended follow-up can improve the model by identifying factors that impact recovery and other outcomes.