
1. Body temperature as an additional predictor could enhance the accuracy of predicting PIH. Body temperature is a critical vital sign that reflects the patient’s overall physiological state and can influence hemodynamic stability. Perioperative hypothermia or hyperthermia can impact vascular tone and blood pressure regulation, potentially contributing to the risk of hypotension.
2. When developing predictive models for clinical purposes, it’s essential to balance explainability and predictive accuracy. Traditional statistical models, such as logistic regression, Poisson regression, and Cox Hazard regression, offer valuable interpretability by providing odds ratios (OR), relative risks (RR), and hazard ratios (HR), which quantify the impact of predictors on outcomes in a straightforward manner. These metrics are crucial for clinicians to understand and communicate the rationale behind their decisions to patients and colleagues. While machine learning models often achieve higher predictive accuracy, they typically function as “black boxes,” offering less transparency about the underlying factors influencing predictions. In my opinion, clinicians would prefer models that provide clear explanations, as this transparency is vital for informed clinical decision-making and effective patient communication. Therefore, future research should explore ways to enhance the interpretability of machine learning models, perhaps through hybrid approaches that combine the strengths of both traditional and machine learning methodologies.