Construction and validation of a risk prediction model for moderate-to-severe pain after pulmonary nodule surgery in the day ward
日间病房肺结节术后中重度疼痛风险预测模型构建与验证

摘要

目的 探索胸腔镜下肺结节术后中重度疼痛的危险因素，构建并验证风险预测模型，为临床干预提供依据。方法 在本院数据平台上收集2024年1月至2025年6月期间日间病房肺结节术患者80例用于模型建立和验证，另选同期80例患者作为外部验证，前者为观察组，后者为对照组，使用术后VAS评分量表作为结局变量。并通过单因素及多因素Logistic回归筛选独立危险因素，采用R语言构建列线图模型，评价其区分度（AUC）、校准度（校准曲线）及临床价值（决策曲线），并进行外部验证。结果 多因素分析显示：女性、高身体质量指数、术前1月内疼痛史是独立危险因素。列线图模型内部验证AUC为0.82，校准曲线显示预测值与实际值一致性好；外部验证AUC为0.79。结论 通过构建的日间病房肺结节术后中重度疼痛风险预测模型性能良好，可有效识别高危患者，值得推广和应用。

Abstract

Objective To explore the risk factors for moderate-to-severe pain after video-assisted thoracoscopic surgery (VATS) for pulmonary nodules, and to construct and validate a risk prediction model to provide a basis for clinical intervention.
Methods A total of 80 patients who underwent pulmonary nodule surgery in the day-surgery ward between January 2024 and June 2025 were identified from our hospital's data platform for model training and validation, an additional 80 patients from the same period were enrolled for external validation, with the former assigned to the observation group and the latter to the control group. The postoperative Visual Analogue Scale (VAS) score was used as the outcome variable. Independent risk factors were screened using univariate and multivariate Logistic regression. A nomogram model was constructed using R language. Its discrimination (AUC), calibration (calibration curve), and clinical value (decision curve analysis) were evaluated, followed by external validation.
Results Multivariate analysis identified female gender, high body mass index (BMI), and a history of pain within one month preoperatively as independent risk factors. The AUC for internal validation of the nomogram model was 0.82. The calibration curve showed good consistency between predicted and actual values. The AUC for external validation was 0.79.
Conclusion   The constructed risk prediction model for moderate-to-severe pain after pulmonary nodule surgery in the day ward demonstrates good performance, can effectively identify high-risk patients, and is worthy of promotion and application.