早期声带癌（Early-stage glottic cancer，简称ESGC）属于头颈恶性肿瘤。除了疾病控制外，保护和改善语音质量至关重要。为了能够进行预期管理和知情决策，患者应接受个性化的咨询，获得关于预期语音质量的信息。本研究旨在开发一种适用于患者报告的语音质量的个性化动态预测模型。该模型应能够从接诊到随访结束的每个时间点提供个性化的预测。采用纵向队列研究设计。采用三级癌症中心。本研究纳入了299例接受ESGC治疗的患者。通过前瞻性的方式获得的声音障碍指数。采用混合模型和联合模型的框架。所选的预后因素包括治疗方式、年龄、性别、合并症、活动状态评分、吸烟、T分期以及前联合部受累情况。通过内部交叉验证程序和箱形图展示绝对误差来评估这些模型的整体性能。该队列的平均年龄为67岁，81.3%为男性。患者接受了经口二氧化碳激光显微手术治疗（57.8%）、单侧声带放疗（24.5%）或局部放疗（17.5%）。平均随访时间为43.4个月（标准偏差21.5）。在预测过程中增加更多的测量数据可以提高预测性能，而增加更多的临床和人口学变量并不能提供更好的预测结果。模型之间的预测性能差异较小。我们开发了一种动态个性化预测模型，用于患者报告的语音质量。该模型有潜力使患者和专业人士能够做出明智决策，并实现量身定制的咨询。本文发表于2023年，《耳鼻喉头颈外科学》由Wiley Periodicals LLC代表美国耳鼻喉头颈外科学会基金会发表。

Early-stage glottic cancer (ESGC) is a malignancy of the head and neck. Besides disease control, preservation and improvement of voice quality are essential. To enable expectation management and well-informed decision-making, patients should be sufficiently counseled with individualized information on expected voice quality. This study aims to develop an individualized dynamic prediction model for patient-reported voice quality. This model should be able to provide individualized predictions at every time point from intake to the end of follow-up.Longitudinal cohort study.Tertiary cancer center.Patients treated for ESGC were included in this study (N = 294). The Voice Handicap Index was obtained prospectively. The framework of mixed and joint models was used. The prognostic factors used are treatment, age, gender, comorbidity, performance score, smoking, T-stage, and involvement of the anterior commissure. The overall performance of these models was assessed during an internal cross-validation procedure and presentation of absolute errors using box plots.The mean age in this cohort was 67 years and 81.3% are male. Patients were treated with transoral CO2 laser microsurgery (57.8%), single vocal cord irradiation up to (24.5), or local radiotherapy (17.5%). The mean follow-up was 43.4 months (SD 21.5). Including more measurements during prediction improves predictive performance. Including more clinical and demographic variables did not provide better predictions. Little differences in predictive performance between models were found.We developed a dynamic individualized prediction model for patient-reported voice quality. This model has the potential to empower patients and professionals in making well-informed decisions and enables tailor-made counseling.© 2023 The Authors. Otolaryngology-Head and Neck Surgery published by Wiley Periodicals LLC on behalf of American Academy of Otolaryngology-Head and Neck Surgery Foundation.