虚拟现实技术在小儿外科围手术期健康教育中的应用：知晓率影响因素预测模型的 构建与验证

Application of Virtual Reality Technology in Perioperative Health Education for Pediatric Surgery: Development and Validation of a Predictive Model for Factors Influencing Awareness Rates

摘要: 目的：分析行VR的术前宣教后影响健康知晓率的因素，并建立预测模型。方法：2024~2025年我院进行外科手术治疗的500例患者，并随机将80%的病人纳入建模组(400例)，20%的病人纳入验证组(100例)。进行单因素及多因素Logistic回归分析时，我们使用了SPSS25.0软件来鉴定潜在的影响因素。使用R 4.2.1软件构建了Nomogram预测模型，通过绘制受试者操作特性(ROC)曲线并计算其曲线下面积(AUC)，评估了模型的区分能力。同时，通过绘制校准曲线，我们进一步评价了模型的校准精度。结果：年龄、所在地区、文化程度、家长素养、诊断在影响健康知晓率存在差异性，Nomogram预测模型的AUC = 0.81% (95% CI: 75.8~86.0)，预测具有一定的预测能力。结论：VR技术干预效果受到患者年龄、地区、文化程度、家长素养以及诊断等多种因素的共同影响，且预测模型具有良好的效果。

Abstract: Objective: To analyze factors influencing health knowledge rates after preoperative education for VR procedures and establish a predictive model. Methods: From 2024 to 2025, 500 patients undergoing surgical treatment at our hospital were enrolled. Eighty percent (400 patients) were randomly assigned to the modeling group, and 20% (100 patients) to the validation group. Univariate and multivariate logistic regression analyses were performed using SPSS 25.0 software to identify potential influencing factors. A nomogram prediction model was constructed using R 4.2.1 software. The model’s discriminatory ability was evaluated by plotting receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Calibration accuracy was further assessed by plotting calibration curves. Results: Age, region, educational attainment, parental literacy, and diagnosis significantly influenced health knowledge rates. The nomogram prediction model demonstrated an AUC of 0.81 (95% CI: 0.758~0.860), indicating moderate predictive capability. Conclusion: The effectiveness of VR technology interventions is influenced by multiple factors including patient age, region, educational level, parental literacy, and diagnosis. The predictive model developed in this study exhibits satisfactory performance.