journal article·2025-05-28Open access

The Relationship Between Age and the Propofol Dose for Anesthesia Induction: A Single-Center Retrospective Study Utilizing Neural Network Model Simulation

Kazuki Nishimura, Kenji Hirata, Fujita Noriaki, Akira Watabe, Yuji Morimoto

Applied Sciences· Volume 15· Issue 11

Abstract

Propofol is commonly used for anesthetic induction, but its dose-dependent hypotensive effect remains a concern. Although the required dose for loss of consciousness decreases with age, the optimal induction dose in older individuals is not well established. This retrospective study aimed to construct a neural network model to predict the propofol induction dose and to quantify the relationship between age and the propofol induction dose through model-based simulations. We analyzed data from 405 patients who underwent elective non-cardiac surgery. A multilayer perceptron model (“model N”) was developed to predict the required induction dose based on clinical parameters. Its predictive performance was compared with that of “model P”, a previously published regression equation derived from multivariable analysis, using the RMSE and MAE. Model N showed significantly better accuracy than model P (RMSE: 17.6 vs. 25.8; MAE: 13.0 vs. 16.7; p < 0.001). Simulation utilizing model N revealed that the induction dose of propofol per body weight decreased by approximately 0.1 mg/kg for every 10-year increase in age. These findings suggest that our proposed model may help determine more accurate and safer dosing strategies for elderly patients undergoing general anesthesia.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Anesthesia and Sedative Agents · Cardiac, Anesthesia and Surgical Outcomes · Hemodynamic Monitoring and Therapy

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Anesthesiology, KKR Sapporo Medical Center, Sapporo 062-0931, Japan

Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Sapporo 060-8638, Japan

Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo 060-8638, Japan

Division of Medical AI Education and Research, Faculty of Medicine, Hokkaido University, Sapporo 062-0931, Japan

Department of Anesthesiology, Hokkaido University Hospital, Sapporo 060-8638, Japan

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