Background: Dental rehabilitation under general anesthesia (GA) is often required for children who are unable to cooperate during standard dental procedures. Accurately estimating the duration of these cases is challenging, particularly when preoperative X-rays are unavailable. Efficient scheduling and optimal operating room (OR) utilization rely on precise time predictions; however, existing predictive models, including EPIC's analytics, frequently overlook patient- and case-specific factors, resulting in suboptimal OR efficiency.
Aims: This study aimed to identify preoperative, patient-specific factors that influence the duration of pediatric dental rehabilitation under GA and to develop an age-based predictive equation to improve procedure time estimation.
Methods: A retrospective review was conducted on 255 dental rehabilitation cases performed under general anesthesia (GA) between January 2022 and December 2023. Collected data included patient demographics, treatment details, availability of radiographs, and operating room (OR) time metrics. Statistical analysis was performed to assess the influence of preoperative factors on procedure duration. An age-based fitted equation was developed, and its predictive accuracy compared with that of EPIC's analytics system.
Results: Age was the strongest patient-specific predictor of procedure duration (p < 0.001, R2 = 50.73%), correlating with both dentition type and the extent of dental restoration required. The age-based fitted equation substantially outperformed EPIC's analytics, particularly in the 3-5 and 13-18 age groups, improving prediction accuracy by 42% and 114%, respectively. The fitted equation was Y = 84-4.5X + 0.6X2, where Y represents procedure time and X represents age. Other patient-specific variables, including weight, BMI, and ASA classification, demonstrated minimal influence.
Conclusions: Developing an age-specific fitted equation based on site-specific operating room (OR) data improves procedure time prediction for pediatric dental rehabilitation under GA. This model supports more precise scheduling, better resource allocation, and improved patient access to care, providing a valuable framework for efficiency in the OR.
扫码关注我们
求助内容:
应助结果提醒方式:
