鼻整形术后鼻唇角丢失的病程及使用机器学习预测远期结局：一项概念验证研究

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中文摘要

背景/目的： 本研究的主要目的是使用术后早期测量值预测术后第12个月的绝对鼻唇角（NLA）。次要目的是描述截至第12个月的NLA纵向变化特征，并量化残余角度丢失。方法：这项单中心回顾性队列研究纳入78例接受初次开放式鼻整形术的患者，这些患者具有术前评估及术后第1、3、6和12个月完整的标准化侧面照片。第12个月的绝对NLA被指定为主要结局和预测目标。从第1、3和6个月至第12个月的NLA纵向变化及残余丢失为次要结局。NLA使用基于Python（版本3.12.13）/OpenCV的半自动图像分析系统测量。在40张图像的分层随机样本中评估观察者内和观察者间信度，由两名医生各测量两次，使用组内相关系数和Bland-Altman分析。线性和机器学习模型使用重复嵌套交叉验证进行评估。还使用相同的患者水平外层拆分评估了三个简单基准：训练折均值、持续性（NLA12 = NLA6）以及仅使用NLA6的线性回归。结果：NLA在第1个月最高，此后显著下降，但在第12个月仍高于术前水平。测量信度极好，尽管测量不确定性与平均晚期变化相当。Elastic Net预测绝对NLA12的平均绝对误差（MAE）为3.47°。它优于训练均值和持续性基准，但未优于单预测变量线性回归模型（平均绝对误差（MAE）= 3.35°；配对MAE差异，0.12°；95% CI，-0.06至0.30）。从第6个月至第12个月的患者特异性丢失无法可靠预测。结论：第6个月的NLA为绝对12个月角度提供了中等程度的预测信息。更复杂的多变量算法并未比仅基于NLA6的简单回归提供额外预测价值，而个体化晚期旋转丢失在现有变量下仍无法预测。

英文摘要

Background/Objectives: The primary objective of this study was to predict the absolute nasolabial angle (NLA) at postoperative month 12 using early postoperative measurements. Secondary objectives were to characterize longitudinal NLA changes through month 12 and quantify residual angle loss. Methods: This single-center retrospective cohort study included 78 patients who underwent primary open rhinoplasty and had complete standardized lateral photographs from the preoperative assessment and postoperative months 1, 3, 6, and 12. The absolute NLA at month 12 was designated as the primary outcome and prediction target. Longitudinal NLA changes and residual loss from months 1, 3, and 6 to month 12 were secondary outcomes. NLA was measured using a Python (version 3.12.13)/OpenCV-based semi-automatic image-analysis system. Intraobserver and interobserver reliability were evaluated in a stratified random sample of 40 images measured twice by two physicians using intraclass correlation coefficients and Bland-Altman analysis. Linear and machine-learning models were evaluated using repeated nested cross-validation. Three simple benchmarks were also assessed using identical patient-level outer splits: the training-fold mean, persistence (NLA12 = NLA6), and linear regression using NLA6 alone. Results: NLA was highest at 1 month and decreased significantly thereafter, while remaining above the preoperative level at month 12. Measurement reliability was excellent, although measurement uncertainty was comparable to the mean late change. Elastic Net predicted absolute NLA12 with an MAE of 3.47°. It outperformed the training-mean and persistence benchmarks but did not outperform the one-predictor linear regression model (Mean Absolute Error (MAE) = 3.35°; paired MAE difference, 0.12°; 95% CI, -0.06 to 0.30). Patient-specific loss from months 6 to 12 could not be predicted reliably. Conclusions: The month-6 NLA provided moderate predictive information for the absolute 12-month angle. More complex multivariable algorithms did not provide additional predictive value over a simple regression based on NLA6 alone, and individualized late rotation loss remained unpredictable with the available variables.