Individualized Dynamic Prediction Model for Patient-Reported Voice Quality in Early-Stage Glottic Cancer

Research output: Contribution to journalArticleAcademicpeer-review

22 Downloads (Pure)

Abstract

Objective: 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. Study Design: Longitudinal cohort study. Setting: Tertiary cancer center. Methods: 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. Results: 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. Conclusion: 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.

Original languageEnglish
Pages (from-to)169-178
Number of pages10
JournalOtolaryngology - Head and Neck Surgery (United States)
Volume170
Issue number1
Early online date13 Aug 2023
DOIs
Publication statusPublished - Jan 2024

Bibliographical note

Publisher Copyright:
© 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.

Fingerprint

Dive into the research topics of 'Individualized Dynamic Prediction Model for Patient-Reported Voice Quality in Early-Stage Glottic Cancer'. Together they form a unique fingerprint.

Cite this